"ManagedBridge"
String -> Object map of result elements.
Result data can
be retrieved from this map instead of using the specific
properties in IRecognitionResult implementing classes.
In the instances when specific property is not implemented
the only way of retrieving the data is via this map.
Indicates if the result is empty
Indicates if the result is valid
true if returned data is uncertain.
Only applicable if used with PDF417RecognizerSettings.UncertainScanMode set to true.
Raw barcode data.
String representation of data inside barcode.
Optional on compact encoding.
Document discriminator.
Optional on AAMVA version 01.
A number or alphanumeric string used by some jurisdictions to identify a "customer" across multiple data bases.
Optional on AAMVA version 01.
Non-Resident Indicator. "Y". Used by some jurisdictions to indicate holder of the document is a non-resident.
Optional on AAMVA version 01.
Medical Indicator/Codes.
STATE SPECIFIC. Freeform; Standard "TBD"
Optional on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
Date on which the hazardous material endorsement granted by the document is
no longer valid. (MMDDCCYY format)
Optional on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
DHS required field that indicates compliance: "M" = materially compliant;
"F" = fully compliant; and, "N" = non-compliant.
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
A string of letters and/or numbers that identifies when, where, and by whom a driver
license/ID card was made.
If audit information is not used on the card or the MRT, it
must be included in the driver record.
Optional on AAMVA version 01.
Number of duplicate cards issued for a license or ID if any.
Optional on AAMVA version 01.
Driver Permit Issue Date. MMDDCCYY format. Date permit was issued.
Optional on AAMVA version 01.
Type of permit.
Optional on AAMVA version 01.
Driver Permit Expiration Date. MMDDCCYY format. Date permit expires.
Optional on AAMVA version 01.
Issue Timestamp. A string used by some jurisdictions to validate the document against their data base.
Optional on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
DHS required field that indicates that the cardholder has temporary lawful status = "1".
Mandatory on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Number must uniquely identify a particular document issued to that customer
from others that may have been issued in the past. This number may serve multiple
purposes of document discrimination, audit information number, and/or inventory control.
Optional on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
DHS required field that indicates date of the most recent version change or
modification to the visible format of the DL/ID (MMDDCCYY format)
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08
A string of letters and/or numbers that is affixed to the raw materials (card stock,
laminate, etc.) used in producing driver licenses and ID cards. (DHS recommended field)
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Text describing the jurisdiction-specific restriction code(s) that curtail driving privileges.
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Text that explains the jurisdiction-specific code(s) that indicates additional
driving privileges granted to the cardholder beyond the vehicle class.
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Text that explains the jurisdiction-specific code(s) for classifications
of vehicles cardholder is authorized to drive.
Optional on compact encodings.
Biometric data block
Optional on compact encodings.
Biometric data block length
Optional on compact encodings.
BDB format type
Optional on compact encodings.
BDB format owner
Optional on compact encoding.
Portrait image
Optional on compact encoding.
Type of image
Optional on compact encoding.
Portrait image timestamp
Optional on AAMVA 01. (MMDDCCYY format)
ALTERNATIVE DATES(S) given as date of birth.
Optional on AAMVA 07, 08
Field that indicates that the cardholder is a veteran = "1"
Optional on AAMVA 06, 07, 08
Field that indicates that the cardholder is an organ donor = "1".
Optional on AAMVA 01, 03, 04, 05, 06, 07, 08 and Compact Encoding
Other suffix by which cardholder is known.
The Suffix Code Portion, if submitted, can contain only the Suffix Codes shown in the following table (e.g., Andrew Johnson, III = JOHNSON@ANDREW@@3RD):
Suffix Meaning or Synonym
JR Junior
SR Senior or Esquire 1ST First
2ND Second
3RD Third
4TH Fourth
5TH Fifth
6TH Sixth
7TH Seventh
8TH Eighth
9TH Ninth
Optional on AAMVA 01
ALTERNATIVE PREFIX to Driver Name. Freeform as defined by issuing jurisdiction.
Optional on AAMVA 01, 03, 04, 05, 06, 07, 08 and Compact Encoding
Other given name by which cardholder is known
Optional on AAMVA 01, 03, 04, 05, 06, 07, 08 and Compact Encoding
Other family name by which cardholder is known.
Optional on AAMVA version 01, 02
Other name by which cardholder is known.
ALTERNATIVE NAME(S) of the individual holding
the Driver License or ID. FORMAT same as defined in ANSI D20 Data Dictionary.
(Lastname@Firstname@MI@ suffix if any.)
(Machine, Mag Stripe uses ‘$’ and Bar Code uses ‘,’ in place of ‘@’)
Firstname, Middle Initial, Lastname (Human)
The Name field contains four portions, separated with the "@" delimiter: Last Name (required)
@ (required)
First Name (required)
@ (required if other name portions follow, otherwise optional)
Middle Name(s) (optional)
@ (required if other name portions follow, otherwise optional)
Suffix Code (optional)
@ (optional)
Optional on AAMVA version 01.
Driver "AKA" Social Security Number. FORMAT SAME AS DRIVER SOC SEC NUM. ALTERNATIVE NUMBERS(S) used as SS NUM.
Optional on AAMVA version 01.
The number assigned to an individual by the Social Security Administration.
Optional on AAMVA 05, 06, 07, 08
Date on which the cardholder turns 21 years old. (MMDDCCYY format)
Optional on AAMVA 05, 06, 07, 08
Date on which the cardholder turns 19 years old. (MMDDCCYY format)
Optional on AAMVA 05, 06, 07, 08
Date on which the cardholder turns 18 years old. (MMDDCCYY format)
Optional on AAMVA 01
Mandatory on Compact encoding
HEIGHT in CENTIMETERS
Optional on AAMVA 01
Height (FT/IN)
FEET (1st char); Inches (2nd and 3rd char).
Ex. 509 = 5 ft., 9 in.
Optional on AAMVA version 01.
Driver Residence Postal Code.
Optional on AAMVA version 01.
Driver Residence Jurisdiction Code.
Optional on AAMVA version 01.
Driver Residence City
Optional on AAMVA version 01.
Driver Residence Street Address 2.
Optional on AAMVA version 01.
Driver Residence Street Address 1.
Mandatory on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Country in which DL/ID is issued. U.S. = USA, Canada = CAN.
Optional on AAMVA 01
PREFIX to Driver Name. Freeform as defined by issuing jurisdiction.
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Codes for race or ethnicity of the cardholder, as defined in ANSI D20.
Race:
Code Description
AI Alaskan or American Indian (Having Origins in Any of The Original Peoples of
North America, and Maintaining Cultural Identification Through Tribal
Affiliation of Community Recognition)
AP Asian or Pacific Islander (Having Origins in Any of the Original Peoples of
the Far East, Southeast Asia, or Pacific Islands. This Includes China, India,
Japan, Korea, the Philippines Islands, and Samoa)
BK Black (Having Origins in Any of the Black Racial Groups of Africa)
W White (Having Origins in Any of The Original Peoples of Europe, North Africa,
or the Middle East)
Ethnicity:
Code Description
H Hispanic Origin (A Person of Mexican, Puerto Rican, Cuban, Central or South
American or Other Spanish Culture or Origin, Regardless of Race)
O Not of Hispanic Origin (Any Person Other Than Hispanic)
U Unknown
Optional on AAMVA 01, 04, 05, 06, 07, 08 and Compact Encoding
Cardholder weight in kilograms Ex. 84 kg = "084"
Optional on AAMVA 01, 04, 05, 06, 07, 08
Cardholder weight in pounds Ex. 185 lb = "185"
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08
Indicates the approximate weight range of the cardholder:
0 = up to 31 kg (up to 70 lbs)
1 = 32 – 45 kg (71 – 100 lbs)
2 = 46 - 59 kg (101 – 130 lbs)
3 = 60 - 70 kg (131 – 160 lbs)
4 = 71 - 86 kg (161 – 190 lbs)
5 = 87 - 100 kg (191 – 220 lbs)
6 = 101 - 113 kg (221 – 250 lbs)
7 = 114 - 127 kg (251 – 280 lbs)
8 = 128 – 145 kg (281 – 320 lbs)
9 = 146+ kg (321+ lbs)
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Country and municipality and/or state/province
Mandatory on AMMVA Magnetic Stripe Encoding
Security version beeing used.
Mandatory on AAMVA versions 02 and 03.
Federally established codes for vehicle categories, endorsements, and restrictions
that are generally applicable to commercial motor vehicles. If the vehicle is not a
commercial vehicle, "NONE" is to be entered.
Mandatory on AAMVA 04, 05, 06, 07, 08
A code that indicates whether a field has been truncated (T), has not been
truncated (N), or – unknown whether truncated (U).
Mandatory on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
A code that indicates whether a field has been truncated (T), has not been
truncated (N), or – unknown whether truncated (U).
Mandatory on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
A code that indicates whether a field has been truncated (T), has not been
truncated (N), or – unknown whether truncated (U).
Mandatory on compact encoding.
Cardholder address.
Mandatory on AAMVA 01
NAME of the individual holding the Driver License or ID as defined in
ANSI D20 Data Dictionary. (Lastname@Firstname@MI@ suffix if any)
(Machine, Mag Stripe uses ‘$’ and Bar Code uses ‘,’ in place of ‘@’)
Firstname, Middle Initial, Lastname (Human)
The Name field contains four portions, separated with the "@" delimiter: Last Name (required)
@ (required)
First Name (required)
@ (required if other name portions follow, otherwise optional)
Middle Name(s) (optional)
@ (required if other name portions follow, otherwise optional)
Suffix Code (optional)
@ (optional)
Mandatory on AAMVA 02, 03, 04, 05, 06, 07, 08
Height of cardholder.
Inches (in): number of inches followed by " in"
example. 6'1'' = "073 in"
Centimeters (cm): number of centimeters followed by " cm"
example. 181 centimeters="181 cm"
Mandatory on AAMVA 04, 05, 06, 07, 08
Optional on 01.
Middle name(s) of the cardholder.
In the case of multiple middle names they
shall be separated by a comma ",".
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07 and 08
Standard restriction code(s) for cardholder.
See codes in D20. This data element is a placeholder
for future efforts to standardize restriction codes.
Code Description
B Corrective Lenses
C Mechanical Devices (Special Brakes, Hand Controls, or Other Adaptive Devices)
D Prosthetic Aid
E Automatic Transmission
F Outside Mirror
G Limit to Daylight Only
H Limit to Employment
I Limited Other
J Other
K CDL Intrastate Only
L Vehicles without air brakes
M Except Class A bus
N Except Class A and Class B bus
O Except Tractor-Trailer
V Medical Variance Documentation Required
W Farm Waiver
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07 and 08
Standard endorsement code(s) for cardholder.
See codes in D20. This data element is a
placeholder for future efforts to standardize endorsement codes.
Code Description
H Hazardous Material - This endorsement is required for the operation of any vehicle
transporting hazardous materials requiring placarding, as defined by U.S.
Department of Transportation regulations.
L Motorcycles – Including Mopeds/Motorized Bicycles.
N Tank - This endorsement is required for the operation of any vehicle transporting,
as its primary cargo, any liquid or gaseous material within a tank attached to the vehicle.
O Other Jurisdiction Specific Endorsement(s) - This code indicates one or more
additional jurisdiction assigned endorsements.
P Passenger - This endorsement is required for the operation of any vehicle used for
transportation of sixteen or more occupants, including the driver.
S School Bus - This endorsement is required for the operation of a school bus. School bus means a
CMV used to transport pre-primary, primary, or secondary school students from home to school,
from school to home, or to and from school sponsored events. School bus does not include a
bus used as common carrier (49 CRF 383.5).
T Doubles/Triples - This endorsement is required for the operation of any vehicle that would be
referred to as a double or triple.
X Combined Tank/HAZ-MAT - This endorsement may be issued to any driver who qualifies for
both the N and H endorsements.
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Standard vehicle classification code(s) for cardholder.
This data element is a
placeholder for future efforts to standardize vehicle classifications.
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Mandatory on Compact Encoding
Jurisdictions may define a subfile to contain jurisdiction-specific information.
These subfiles are designated with the first character of “Z” and the second
character is the first letter of the jurisdiction's name. For example, "ZC" would
be the designator for a California or Colorado jurisdiction-defined subfile; "ZQ"
would be the designator for a Quebec jurisdiction-defined subfile. In the case of
a jurisdiction-defined subfile that has a first letter that could be more than
one jurisdiction (e.g. California, Colorado, Connecticut) then other data, like
the IIN or address, must be examined to determine the jurisdiction.
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On Compact encoding, use kFullAddress
Second line of street portion of the cardholder address.
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Bald, black, blonde, brown, gray, red/auburn, sandy, white, unknown.
If the issuing
jurisdiction wishes to abbreviate colors, the three-character codes provided in ANSI D20 must be
used.
Code Description
BAL Bald
BLK Black
BLN Blond
BRO Brown
GRY Grey
RED Red/Auburn
SDY Sandy
WHI White
UNK Unknown
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
The number assigned or calculated by the issuing authority.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Jurisdiction-specific codes that represent additional privileges
granted to the cardholder beyond the vehicle class (such as transportation of
passengers, hazardous materials, operation of motorcycles, etc.).
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Jurisdiction-specific codes that represent restrictions to driving
privileges (such as airbrakes, automatic transmission, daylight only, etc.).
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Jurisdiction-specific vehicle class / group code
designating the type
of vehicle the cardholder has privilege to drive.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Jurisdiction Version Number
This is a decimal value between 00 and 99 that
specifies the jurisdiction version level of the PDF417 bar code format.
Notwithstanding iterations of this standard, jurisdictions implement incremental
changes to their bar codes, including new jurisdiction-specific data, compression
algorithms for digitized images, digital signatures, or new truncation
conventions used for names and addresses. Each change to the bar code format
within each AAMVA version (above) must be noted, beginning with Jurisdiction
Version 00.
Optional on AAMVA Magnetic Stripe Encoding
Field that indicates that the driving and identification privileges granted by the
document are nonexpiring = "1".
Optional on AAMVA Magnetic Stripe Encoding
Date on which the driving and identification privileges granted by the document are
no longer valid. (MMYY format)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Date on which the driving and identification privileges granted by the document are
no longer valid. (MMDDCCYY format)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Date on which the document was issued. (MMDDCCYY format)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Optional on Compact encoding
This number uniquely identifies the issuing jurisdiction and can
be obtained by contacting the ISO Issuing Authority (AAMVA)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On compact encoding, use kFullAddress.
Postal code portion of the cardholder address in the U.S. and Canada. If the
trailing portion of the postal code in the U.S. is not known, zeros will be used
to fill the trailing set of numbers up to nine (9) digits.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On compact encoding, use kFullAddress.
State portion of the cardholder address.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On compact encoding, use kFullAddress.
City portion of the cardholder address.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On compact encoding, use kFullAddress.
Street portion of the cardholder address.
The place where the registered driver of a vehicle (individual or corporation) may be contacted such as a house number, street address etc.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact encoding
Color of cardholder's eyes.
(ANSI D-20 codes)
Code Description
BLK Black
BLU Blue
BRO Brown
GRY Gray
GRN Green
HAZ Hazel
MAR Maroon
PNK Pink
DIC Dichromatic
UNK Unknown
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact encoding
Gender of the cardholder. 1 = male, 2 = female.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact encoding
Date on which the cardholder was born. (MMDDCCYY format)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact encoding
First name of the cardholder.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 AND compact encoding.
Family name of the cardholder.
(Family name is sometimes also called "last name" or "surname.")
Collect full name for record, print as many characters as possible on portrait side of DL/ID.
Mandatory on all AAMVA driver's license versions.
AAMVA Version Number.
This is a decimal value between 00 and 99 that
specifies the version level of the PDF417 bar code format. Version "0" and "00"
is reserved for bar codes printed to the specification of the American Association
of Motor Vehicle Administrators (AAMVA) prior to the adoption of the AAMVA DL/ID-2000
standard. All bar codes compliant with the AAMVA DL/ID-2000 standard are designated
Version "01." All barcodes compliant with AAMVA Card Design Specification version
1.0, dated 09-2003 shall be designated Version "02." All barcodes compliant with
AAMVA Card Design Specification version 2.0, dated 03-2005 shall be designated
Version "03." All barcodes compliant with AAMVA Card Design Standard version 1.0,
dated 07-2009 shall be designated Version "04." All barcodes compliant with AAMVA
Card Design Standard version 1.0, dated 07-2010 shall be designated Version "05."
All barcodes compliant with AAMVA Card Design Standard version 1.0, dated 07- 2011
shall be designated Version "06". All barcodes compliant with AAMVA Card Design
Standard version 1.0, dated 06-2012 shall be designated Version "07". All barcodes
compliant with this current AAMVA standard shall be designated "08". Should a need
arise requiring major revision to the format, this field provides the means to
accommodate additional revision. "Compact" when Compact encoding is used.
US driver's license recognition result
Enables detecting the scale of the image before trying to read code29 and code 128 barcodes
@param autoScale
Enables to spent more time trying to read code39 and code128 barcodes
@param tryHarder
Enables decoding of barcodes without quiet zone round the barcode
(e.g. text concatenated with barcode blocks)
@param allowNullQuietZone
Enables decoding of barcodes where multiple rows are missing from the end of the barcode
(e.g. when more lines at the end of barcode are not printed, and there is not enough
error correction codewords left to compensate for missing rows)
@param useUncertainDecoding
Resets the best results in the whole chain
and clears history
* Method fills the recognition result object with
* payment data recognized from given image
* Returns true if scanning was successful, false otherwise
Optional on compact encoding.
Document discriminator.
Optional on AAMVA version 01.
A number or alphanumeric string used by some jurisdictions to identify a "customer" across multiple data bases.
Optional on AAMVA version 01.
Non-Resident Indicator. "Y". Used by some jurisdictions to indicate holder of the document is a non-resident.
Optional on AAMVA version 01.
Medical Indicator/Codes.
STATE SPECIFIC. Freeform; Standard "TBD"
Optional on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
Date on which the hazardous material endorsement granted by the document is
no longer valid. (MMDDCCYY format)
Optional on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
DHS required field that indicates compliance: "M" = materially compliant;
"F" = fully compliant; and, "N" = non-compliant.
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
A string of letters and/or numbers that identifies when, where, and by whom a driver
license/ID card was made. If audit information is not used on the card or the MRT, it
must be included in the driver record.
Optional on AAMVA version 01.
Number of duplicate cards issued for a license or ID if any.
Optional on AAMVA version 01.
Driver Permit Issue Date. MMDDCCYY format. Date permit was issued.
Optional on AAMVA version 01.
Type of permit.
Optional on AAMVA version 01.
Driver Permit Expiration Date. MMDDCCYY format. Date permit expires.
Optional on AAMVA version 01.
Issue Timestamp. A string used by some jurisdictions to validate the document against their data base.
Optional on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
DHS required field that indicates that the cardholder has temporary lawful status = "1".
Mandatory on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Number must uniquely identify a particular document issued to that customer
from others that may have been issued in the past. This number may serve multiple
purposes of document discrimination, audit information number, and/or inventory control.
Optional on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
DHS required field that indicates date of the most recent version change or
modification to the visible format of the DL/ID (MMDDCCYY format)
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08
A string of letters and/or numbers that is affixed to the raw materials (card stock,
laminate, etc.) used in producing driver licenses and ID cards. (DHS recommended field)
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Text describing the jurisdiction-specific restriction code(s) that curtail driving privileges.
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Text that explains the jurisdiction-specific code(s) that indicates additional
driving privileges granted to the cardholder beyond the vehicle class.
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Text that explains the jurisdiction-specific code(s) for classifications
of vehicles cardholder is authorized to drive.
Optional on compact encodings.
Biometric data block
Optional on compact encodings.
Biometric data block length
Optional on compact encodings.
BDB format type
Optional on compact encodings.
BDB format owner
Optional on compact encoding.
Portrait image
Optional on compact encoding.
Type of image
Optional on compact encoding.
Portrait image timestamp
Optional on AAMVA 01. (MMDDCCYY format)
ALTERNATIVE DATES(S) given as date of birth.
Optional on AAMVA 07, 08
Field that indicates that the cardholder is a veteran = "1"
Optional on AAMVA 06, 07, 08
Field that indicates that the cardholder is an organ donor = "1".
Optional on AAMVA 01, 03, 04, 05, 06, 07, 08 and Compact Encoding
Other suffix by which cardholder is known.
The Suffix Code Portion, if submitted, can contain only the Suffix Codes shown in the following table (e.g., Andrew Johnson, III = JOHNSON@ANDREW@@3RD):
Suffix Meaning or Synonym
JR Junior
SR Senior or Esquire 1ST First
2ND Second
3RD Third
4TH Fourth
5TH Fifth
6TH Sixth
7TH Seventh
8TH Eighth
9TH Ninth
Optional on AAMVA 01
ALTERNATIVE PREFIX to Driver Name. Freeform as defined by issuing jurisdiction.
Optional on AAMVA 01, 03, 04, 05, 06, 07, 08 and Compact Encoding
Other given name by which cardholder is known
Optional on AAMVA 01
ALTERNATIVE MIDDLE NAME(s) or INITIALS of the individual holding the Driver License or ID.
Hyphenated names acceptable, spaces between names acceptable, but no other
use of special symbols
Optional on AAMVA 01, 03, 04, 05, 06, 07, 08 and Compact Encoding
Other family name by which cardholder is known.
Optional on AAMVA version 01, 02
Other name by which cardholder is known. ALTERNATIVE NAME(S) of the individual holding
the Driver License or ID. FORMAT same as defined in ANSI D20 Data Dictionary.
(Lastname@Firstname@MI@ suffix if any.)
(Machine, Mag Stripe uses â€$’ and Bar Code uses â€,’ in place of â€@’)
Firstname, Middle Initial, Lastname (Human)
The Name field contains four portions, separated with the "@" delimiter: Last Name (required)
@ (required)
First Name (required)
@ (required if other name portions follow, otherwise optional)
Middle Name(s) (optional)
@ (required if other name portions follow, otherwise optional)
Suffix Code (optional)
@ (optional)
Optional on AAMVA version 01.
Driver "AKA" Social Security Number. FORMAT SAME AS DRIVER SOC SEC NUM. ALTERNATIVE NUMBERS(S) used as SS NUM.
Optional on AAMVA version 01.
The number assigned to an individual by the Social Security Administration.
Optional on AAMVA 05, 06, 07, 08
Date on which the cardholder turns 21 years old. (MMDDCCYY format)
Optional on AAMVA 05, 06, 07, 08
Date on which the cardholder turns 19 years old. (MMDDCCYY format)
Optional on AAMVA 05, 06, 07, 08
Date on which the cardholder turns 18 years old. (MMDDCCYY format)
Optional on AAMVA 01
Mandatory on Compact encoding
HEIGHT in CENTIMETERS
Optional on AAMVA 01
Height (FT/IN)
FEET (1st char); Inches (2nd and 3rd char).
Ex. 509 = 5 ft., 9 in.
Optional on AAMVA version 01.
Driver Residence Postal Code.
Optional on AAMVA version 01.
Driver Residence Jurisdiction Code.
Optional on AAMVA version 01.
Driver Residence City
Optional on AAMVA version 01.
Driver Residence Street Address 2.
Optional on AAMVA version 01.
Driver Residence Street Address 1.
Mandatory on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Country in which DL/ID is issued. U.S. = USA, Canada = CAN.
Optional on AAMVA 01
PREFIX to Driver Name. Freeform as defined by issuing jurisdiction.
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Codes for race or ethnicity of the cardholder, as defined in ANSI D20.
Race:
Code Description
AI Alaskan or American Indian (Having Origins in Any of The Original Peoples of
North America, and Maintaining Cultural Identification Through Tribal
Affiliation of Community Recognition)
AP Asian or Pacific Islander (Having Origins in Any of the Original Peoples of
the Far East, Southeast Asia, or Pacific Islands. This Includes China, India,
Japan, Korea, the Philippines Islands, and Samoa)
BK Black (Having Origins in Any of the Black Racial Groups of Africa)
W White (Having Origins in Any of The Original Peoples of Europe, North Africa,
or the Middle East)
Ethnicity:
Code Description
H Hispanic Origin (A Person of Mexican, Puerto Rican, Cuban, Central or South
American or Other Spanish Culture or Origin, Regardless of Race)
O Not of Hispanic Origin (Any Person Other Than Hispanic) U Unknown
Optional on AAMVA 01, 04, 05, 06, 07, 08 and Compact Encoding
Cardholder weight in kilograms Ex. 84 kg = "084"
Optional on AAMVA 01, 04, 05, 06, 07, 08
Cardholder weight in pounds Ex. 185 lb = "185"
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08
Indicates the approximate weight range of the cardholder:
0 = up to 31 kg (up to 70 lbs)
1 = 32 – 45 kg (71 – 100 lbs)
2 = 46 - 59 kg (101 – 130 lbs)
3 = 60 - 70 kg (131 – 160 lbs)
4 = 71 - 86 kg (161 – 190 lbs)
5 = 87 - 100 kg (191 – 220 lbs)
6 = 101 - 113 kg (221 – 250 lbs)
7 = 114 - 127 kg (251 – 280 lbs)
8 = 128 – 145 kg (281 – 320 lbs)
9 = 146+ kg (321+ lbs)
Optional on AAMVA 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Country and municipality and/or state/province
Mandatory on AMMVA Magnetic Stripe Encoding
Security version beeing used.
Mandatory on AAMVA versions 02 and 03.
Federally established codes for vehicle categories, endorsements, and restrictions
that are generally applicable to commercial motor vehicles. If the vehicle is not a
commercial vehicle, "NONE" is to be entered.
Mandatory on AAMVA 04, 05, 06, 07, 08
A code that indicates whether a field has been truncated (T), has not been
truncated (N), or – unknown whether truncated (U).
Mandatory on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
A code that indicates whether a field has been truncated (T), has not been
truncated (N), or – unknown whether truncated (U).
Mandatory on AAMVA 04, 05, 06, 07, 08 and Compact Encoding
A code that indicates whether a field has been truncated (T), has not been
truncated (N), or – unknown whether truncated (U).
Mandatory on compact encoding.
Cardholder address.
Mandatory on AAMVA 01
NAME of the individual holding the Driver License or ID as defined in
ANSI D20 Data Dictionary. (Lastname@Firstname@MI@ suffix if any)
(Machine, Mag Stripe uses â€$’ and Bar Code uses â€,’ in place of â€@’)
Firstname, Middle Initial, Lastname (Human)
The Name field contains four portions, separated with the "@" delimiter: Last Name (required)
@ (required)
First Name (required)
@ (required if other name portions follow, otherwise optional)
Middle Name(s) (optional)
@ (required if other name portions follow, otherwise optional)
Suffix Code (optional)
@ (optional)
Mandatory on AAMVA 02, 03, 04, 05, 06, 07, 08
Height of cardholder.
Inches (in): number of inches followed by " in"
example. 6'1'' = "073 in"
Centimeters (cm): number of centimeters followed by " cm"
example. 181 centimeters="181 cm"
Mandatory on AAMVA 04, 05, 06, 07, 08
Optional on 01.
Middle name(s) of the cardholder. In the case of multiple middle names they
shall be separated by a comma ",".
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07 and 08
Standard restriction code(s) for cardholder. See codes in D20. This data element is a placeholder
for future efforts to standardize restriction codes.
Code Description
B Corrective Lenses
C Mechanical Devices (Special Brakes, Hand Controls, or Other Adaptive Devices)
D Prosthetic Aid
E Automatic Transmission
F Outside Mirror
G Limit to Daylight Only
H Limit to Employment
I Limited Other
J Other
K CDL Intrastate Only
L Vehicles without air brakes
M Except Class A bus
N Except Class A and Class B bus
O Except Tractor-Trailer
V Medical Variance Documentation Required
W Farm Waiver
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07 and 08
Standard endorsement code(s) for cardholder. See codes in D20. This data element is a
placeholder for future efforts to standardize endorsement codes.
Code Description
H Hazardous Material - This endorsement is required for the operation of any vehicle
transporting hazardous materials requiring placarding, as defined by U.S.
Department of Transportation regulations.
L Motorcycles – Including Mopeds/Motorized Bicycles.
N Tank - This endorsement is required for the operation of any vehicle transporting,
as its primary cargo, any liquid or gaseous material within a tank attached to the vehicle.
O Other Jurisdiction Specific Endorsement(s) - This code indicates one or more
additional jurisdiction assigned endorsements.
P Passenger - This endorsement is required for the operation of any vehicle used for
transportation of sixteen or more occupants, including the driver.
S School Bus - This endorsement is required for the operation of a school bus. School bus means a
CMV used to transport pre-primary, primary, or secondary school students from home to school,
from school to home, or to and from school sponsored events. School bus does not include a
bus used as common carrier (49 CRF 383.5).
T Doubles/Triples - This endorsement is required for the operation of any vehicle that would be
referred to as a double or triple.
X Combined Tank/HAZ-MAT - This endorsement may be issued to any driver who qualifies for
both the N and H endorsements.
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Standard vehicle classification code(s) for cardholder. This data element is a
placeholder for future efforts to standardize vehicle classifications.
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Mandatory on Compact Encoding
Jurisdictions may define a subfile to contain jurisdiction-specific information.
These subfiles are designated with the first character of “Z” and the second
character is the first letter of the jurisdiction's name. For example, "ZC" would
be the designator for a California or Colorado jurisdiction-defined subfile; "ZQ"
would be the designator for a Quebec jurisdiction-defined subfile. In the case of
a jurisdiction-defined subfile that has a first letter that could be more than
one jurisdiction (e.g. California, Colorado, Connecticut) then other data, like
the IIN or address, must be examined to determine the jurisdiction.
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On Compact encoding, use kFullAddress
Second line of street portion of the cardholder address.
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Name Suffix (If jurisdiction participates in systems requiring name suffix (PDPS, CDLIS, etc.), the suffix must be collected and displayed on the DL/ID and in the MRT). Collect full name for record, print as many characters as possible on portrait side of DL/ID.
- JR (Junior)
- SR (Senior)
- 1ST or I (First)
- 2ND or II (Second)
- 3RD or III (Third)
- 4TH or IV (Fourth)
- 5TH or V (Fifth)
- 6TH or VI (Sixth)
- 7TH or VII (Seventh)
- 8TH or VIII (Eighth)
- 9TH or IX (Ninth)
Optional on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Bald, black, blonde, brown, gray, red/auburn, sandy, white, unknown. If the issuing
jurisdiction wishes to abbreviate colors, the three-character codes provided in ANSI D20 must be
used.
Code Description
BAL Bald
BLK Black
BLN Blond
BRO Brown
GRY Grey
RED Red/Auburn
SDY Sandy
WHI White
UNK Unknown
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
The number assigned or calculated by the issuing authority.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Jurisdiction-specific codes that represent additional privileges
granted to the cardholder beyond the vehicle class (such as transportation of
passengers, hazardous materials, operation of motorcycles, etc.).
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Jurisdiction-specific codes that represent restrictions to driving
privileges (such as airbrakes, automatic transmission, daylight only, etc.).
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Jurisdiction-specific vehicle class / group code, designating the type
of vehicle the cardholder has privilege to drive.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Jurisdiction Version Number: This is a decimal value between 00 and 99 that
specifies the jurisdiction version level of the PDF417 bar code format.
Notwithstanding iterations of this standard, jurisdictions implement incremental
changes to their bar codes, including new jurisdiction-specific data, compression
algorithms for digitized images, digital signatures, or new truncation
conventions used for names and addresses. Each change to the bar code format
within each AAMVA version (above) must be noted, beginning with Jurisdiction
Version 00.
Optional on AAMVA Magnetic Stripe Encoding
Field that indicates that the driving and identification privileges granted by the
document are nonexpiring = "1".
Optional on AAMVA Magnetic Stripe Encoding
Date on which the driving and identification privileges granted by the document are
no longer valid. (MMYY format)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Optional on AAMVA Magnetic Stripe Encoding
Date on which the driving and identification privileges granted by the document are
no longer valid. (MMDDCCYY format)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact Encoding
Date on which the document was issued. (MMDDCCYY format)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
Optional on Compact encoding
This number uniquely identifies the issuing jurisdiction and can
be obtained by contacting the ISO Issuing Authority (AAMVA)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On compact encoding, use kFullAddress.
Postal code portion of the cardholder address in the U.S. and Canada. If the
trailing portion of the postal code in the U.S. is not known, zeros will be used
to fill the trailing set of numbers up to nine (9) digits.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On compact encoding, use kFullAddress.
State portion of the cardholder address.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On compact encoding, use kFullAddress.
City portion of the cardholder address.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08
On compact encoding, use kFullAddress.
Street portion of the cardholder address.
The place where the registered driver of a vehicle (individual or corporation) may be contacted such as a house number, street address etc.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact encoding
Color of cardholder's eyes. (ANSI D-20 codes)
Code Description
BLK Black
BLU Blue
BRO Brown
GRY Gray
GRN Green
HAZ Hazel
MAR Maroon
PNK Pink
DIC Dichromatic
UNK Unknown
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact encoding
Gender of the cardholder. 1 = male, 2 = female.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact encoding
Date on which the cardholder was born. (MMDDCCYY format)
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 and Compact encoding
First name of the cardholder.
Mandatory on AAMVA 01, 02, 03, 04, 05, 06, 07, 08 AND compact encoding.
Family name of the cardholder. (Family name is sometimes also called "last name" or "surname.")
Collect full name for record, print as many characters as possible on portrait side of DL/ID.
Mandatory on all driver's licenses.
AAMVA Version Number: This is a decimal value between 00 and 99 that
specifies the version level of the PDF417 bar code format. Version "0" and "00"
is reserved for bar codes printed to the specification of the American Association
of Motor Vehicle Administrators (AAMVA) prior to the adoption of the AAMVA DL/ID-2000
standard. All bar codes compliant with the AAMVA DL/ID-2000 standard are designated
Version "01." All barcodes compliant with AAMVA Card Design Specification version
1.0, dated 09-2003 shall be designated Version "02." All barcodes compliant with
AAMVA Card Design Specification version 2.0, dated 03-2005 shall be designated
Version "03." All barcodes compliant with AAMVA Card Design Standard version 1.0,
dated 07-2009 shall be designated Version "04." All barcodes compliant with AAMVA
Card Design Standard version 1.0, dated 07-2010 shall be designated Version "05."
All barcodes compliant with AAMVA Card Design Standard version 1.0, dated 07- 2011
shall be designated Version "06". All barcodes compliant with AAMVA Card Design
Standard version 1.0, dated 06-2012 shall be designated Version "07". All barcodes
compliant with this current AAMVA standard shall be designated "08". Should a need
arise requiring major revision to the format, this field provides the means to
accommodate additional revision.
Mandatory on all driver's licenses. All barcodes which are using 3-track magnetic
stripe encoding used in the interest of smoothing a transition from legacy documents
shall be designated as "Magnetic". All barcodes which are using compact encoding
compliant with ISO/IEC 18013-2 shall be designated as "Compact". All barcodes (majority)
compliant with Mandatory PDF417 Bar Code of the American Association of Motor Vehicle
Administrators (AAMVA) Card Design Standard from AAMVA DL/ID-2000 standard to DL/ID-2013
shall be designated as "AAMVA".
@brief formats the datetime object to string
@param dateTime
@return
@brief parse
@param str
@param dateTime
@return
Virtual destructor
@brief DateTimeFormatter
@param format
Format
@brief Class responsible for parsing date time objects from string
Currently supports following values:
DD day in the month, two digit
MM month, two digits
YYYY year, four digits
All combinations are allowed, e.g
DDMMYYYY
YYYYMMDD
etc.
@brief getCentury
@return century in which the date is
@brief getYear
@return Year of the date
@brief getMonth
@return Month in year from 1 to 12
@brief getDay
@return day in month
Virtual destructor
@brief creates a day with a given day, month and year
@param day
@param month
@param year
@param date, returned by reference
@return true if succeded, false otherwise.
@brief creates a day with a current day, month and year
@param date, current date, returned by reference
Creates Now object
Designated constructor.
Private. Use factory method which returns a status if creation failed.
Exact time
@brief time_
\file
Date.hpp
Created on: May 22, 2014
Author: cerovec
Copyright (c)20114 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
@brief Creates test set
@brief Generates sample from gaussian mixture distribution
@brief Generates _sample_ from multivariate normal distribution
@param mean an average row vector
@param cov symmetric covariation matrix
@param nsamples returned samples count
@param samples returned samples array
@brief Creates empty model.
Creates Logistic Regression model with parameters given.
@brief This function returns the trained paramters arranged across rows.
For a two class classifcation problem, it returns a row matrix. It returns learnt paramters of
the Logistic Regression as a matrix of type CV_32F.
@brief Predicts responses for input samples and returns a float type.
@param samples The input data for the prediction algorithm. Matrix [m x n], where each row
contains variables (features) of one object being classified. Should have data type CV_32F.
@param results Predicted labels as a column matrix of type CV_32S.
@param flags Not used.
@copybrief getTermCriteria @see getTermCriteria
Termination criteria of the algorithm.
@see setTermCriteria
@copybrief getMiniBatchSize @see getMiniBatchSize
Specifies the number of training samples taken in each step of Mini-Batch Gradient
Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It
has to take values less than the total number of training samples.
@see setMiniBatchSize
@copybrief getTrainMethod @see getTrainMethod
Kind of training method used. See LogisticRegression::Methods.
@see setTrainMethod
@copybrief getRegularization @see getRegularization
Kind of regularization to be applied. See LogisticRegression::RegKinds.
@see setRegularization
@copybrief getIterations @see getIterations
Number of iterations.
@see setIterations
@copybrief getLearningRate @see getLearningRate
Learning rate.
@see setLearningRate
@brief Implements Logistic Regression classifier.
@sa @ref ml_intro_lr
Do not normalize the output vectors. If the flag is not set, the training algorithm
normalizes each output feature independently, by transforming it to the certain range
depending on the used activation function.
Do not normalize the input vectors. If this flag is not set, the training algorithm
normalizes each input feature independently, shifting its mean value to 0 and making the
standard deviation equal to 1. If the network is assumed to be updated frequently, the new
training data could be much different from original one. In this case, you should take care
of proper normalization.
Update the network weights, rather than compute them from scratch. In the latter case
the weights are initialized using the Nguyen-Widrow algorithm.
Train options
Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$
Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}\f$
@note
If you are using the default sigmoid activation function with the default parameter values
fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x), so the output
will range from [-1.7159, 1.7159], instead of [0,1].
Identity function: \f$f(x)=x\f$
possible activation functions
@copybrief getRpropDWMax @see getRpropDWMax
RPROP: Update-values upper limit \f$\Delta_{max}\f$.
It must be \>1. Default value is 50.
@see setRpropDWMax
@copybrief getRpropDWMin @see getRpropDWMin
RPROP: Update-values lower limit \f$\Delta_{min}\f$.
It must be positive. Default value is FLT_EPSILON.
@see setRpropDWMin
@copybrief getRpropDWMinus @see getRpropDWMinus
@copybrief getRpropDWPlus @see getRpropDWPlus
RPROP: Increase factor \f$\eta^+\f$.
It must be \>1. Default value is 1.2.
@see setRpropDWPlus
@copybrief getRpropDW0 @see getRpropDW0
RPROP: Initial value \f$\Delta_0\f$ of update-values \f$\Delta_{ij}\f$.
Default value is 0.1.
@see setRpropDW0
@copybrief getBackpropMomentumScale @see getBackpropMomentumScale
BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations).
This parameter provides some inertia to smooth the random fluctuations of the weights. It can
vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough.
Default value is 0.1.
@see setBackpropMomentumScale
@copybrief getBackpropWeightScale @see getBackpropWeightScale
BPROP: Strength of the weight gradient term.
The recommended value is about 0.1. Default value is 0.1.
@see setBackpropWeightScale
@copybrief getTermCriteria @see getTermCriteria
Termination criteria of the training algorithm.
You can specify the maximum number of iterations (maxCount) and/or how much the error could
change between the iterations to make the algorithm continue (epsilon). Default value is
TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).
@see setTermCriteria
Integer vector specifying the number of neurons in each layer including the input and output layers.
The very first element specifies the number of elements in the input layer.
The last element - number of elements in the output layer.
@sa setLayerSizes
Integer vector specifying the number of neurons in each layer including the input and output layers.
The very first element specifies the number of elements in the input layer.
The last element - number of elements in the output layer. Default value is empty Mat.
@sa getLayerSizes
Initialize the activation function for each neuron.
Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
@param type The type of activation function. See ANN_MLP::ActivationFunctions.
@param param1 The first parameter of the activation function, \f$\alpha\f$. Default value is 0.
@param param2 The second parameter of the activation function, \f$\beta\f$. Default value is 0.
Returns current training method
Sets training method and common parameters.
@param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
@param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP
@param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP.
Available training methods
@brief Artificial Neural Networks - Multi-Layer Perceptrons.
Unlike many other models in ML that are constructed and trained at once, in the MLP model these
steps are separated. First, a network with the specified topology is created using the non-default
constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is
trained using a set of input and output vectors. The training procedure can be repeated more than
once, that is, the weights can be adjusted based on the new training data.
Additional flags for StatModel::train are available: ANN_MLP::TrainFlags.
@sa @ref ml_intro_ann
Boosting type.
Gentle AdaBoost and Real AdaBoost are often the preferable choices.
@copybrief getWeightTrimRate @see getWeightTrimRate
A threshold between 0 and 1 used to save computational time.
Samples with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next*
iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.
@see setWeightTrimRate
@copybrief getWeakCount @see getWeakCount
The number of weak classifiers.
Default value is 100.
@see setWeakCount
@copybrief getBoostType @see getBoostType
Type of the boosting algorithm.
See Boost::Types. Default value is Boost::REAL.
@see setBoostType
@brief Boosted tree classifier derived from DTrees
@sa @ref ml_intro_boost
Creates the empty model.
Use StatModel::train to train the model, StatModel::train to create and train the model,
Algorithm::load to load the pre-trained model.
Returns the variable importance array.
The method returns the variable importance vector, computed at the training stage when
CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is
returned.
@copybrief getTermCriteria @see getTermCriteria
The termination criteria that specifies when the training algorithm stops.
Either when the specified number of trees is trained and added to the ensemble or when
sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the
better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes
pass a certain number of trees. Also to keep in mind, the number of tree increases the
prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS +
TermCriteria::EPS, 50, 0.1)
@see setTermCriteria
@copybrief getActiveVarCount @see getActiveVarCount
The size of the randomly selected subset of features at each tree node and that are used
to find the best split(s).
If you set it to 0 then the size will be set to the square root of the total number of
features. Default value is 0.
@see setActiveVarCount
@copybrief getCalculateVarImportance @see getCalculateVarImportance
If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance.
Default value is false.
@see setCalculateVarImportance
@brief The class implements the random forest predictor.
@sa @ref ml_intro_rtrees
@brief Returns all the bitsets for categorical splits
Split::subsetOfs is an offset in the returned vector
@brief Returns all the splits
all the split indices are indices in the returned vector
@brief Returns all the nodes
all the node indices are indices in the returned vector
@brief Returns indices of root nodes
@brief The class represents split in a decision tree.
@brief The class represents a decision tree node.
@copybrief getPriors @see getPriors
@brief The array of a priori class probabilities, sorted by the class label value.
The parameter can be used to tune the decision tree preferences toward a certain class. For
example, if you want to detect some rare anomaly occurrence, the training base will likely
contain much more normal cases than anomalies, so a very good classification performance
will be achieved just by considering every case as normal. To avoid this, the priors can be
specified, where the anomaly probability is artificially increased (up to 0.5 or even
greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is
adjusted properly.
You can also think about this parameter as weights of prediction categories which determine
relative weights that you give to misclassification. That is, if the weight of the first
category is 1 and the weight of the second category is 10, then each mistake in predicting
the second category is equivalent to making 10 mistakes in predicting the first category.
Default value is empty Mat.
@see setPriors
@copybrief getRegressionAccuracy @see getRegressionAccuracy
Termination criteria for regression trees.
If all absolute differences between an estimated value in a node and values of train samples
in this node are less than this parameter then the node will not be split further. Default
value is 0.01f
@see setRegressionAccuracy
@copybrief getTruncatePrunedTree @see getTruncatePrunedTree
If true then pruned branches are physically removed from the tree.
Otherwise they are retained and it is possible to get results from the original unpruned (or
pruned less aggressively) tree. Default value is true.
@see setTruncatePrunedTree
@copybrief getUse1SERule @see getUse1SERule
If true then a pruning will be harsher.
This will make a tree more compact and more resistant to the training data noise but a bit less
accurate. Default value is true.
@see setUse1SERule
@copybrief getUseSurrogates @see getUseSurrogates
If true then surrogate splits will be built.
These splits allow to work with missing data and compute variable importance correctly.
Default value is false.
@note currently it's not implemented.
@see setUseSurrogates
@copybrief getCVFolds @see getCVFolds
If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold
cross-validation procedure where K is equal to CVFolds.
Default value is 10.
@see setCVFolds
@copybrief getMinSampleCount @see getMinSampleCount
If the number of samples in a node is less than this parameter then the node will not be split.
Default value is 10.
@see setMinSampleCount
@copybrief getMaxDepth @see getMaxDepth
The maximum possible depth of the tree.
That is the training algorithms attempts to split a node while its depth is less than maxDepth.
The root node has zero depth. The actual depth may be smaller if the other termination criteria
are met (see the outline of the training procedure @ref ml_intro_trees "here"), and/or if the
tree is pruned. Default value is INT_MAX.
@see setMaxDepth
@copybrief getMaxCategories @see getMaxCategories
Predict options
@brief The class represents a single decision tree or a collection of decision trees.
The current public interface of the class allows user to train only a single decision tree, however
the class is capable of storing multiple decision trees and using them for prediction (by summing
responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost)
use this capability to implement decision tree ensembles.
@sa @ref ml_intro_trees
@brief Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Maximization step. You need to provide initial probabilities
\f$p_{i,k}\f$ to use this option.
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.
@param probs0
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
@param labels The optional output "class label" for each sample:
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
CV_64FC1 type.
@brief Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of
mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices
\f$S_k\f$ of mixture components.
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.
@param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of
\f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be
converted to the inner matrix of such type for the further computing.
@param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of
covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices
do not have CV_64F type they will be converted to the inner matrices of such type for the
further computing.
@param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel
floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
@param labels The optional output "class label" for each sample:
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
CV_64FC1 type.
@brief Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. Initial values of the model parameters will be
estimated by the k-means algorithm.
Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
responses (class labels or function values) as input. Instead, it computes the *Maximum
Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in
covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each
sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most
probable mixture component for each sample).
The trained model can be used further for prediction, just like any other classifier. The
trained model is similar to the NormalBayesClassifier.
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
@param labels The optional output "class label" for each sample:
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
CV_64FC1 type.
@brief Returns a likelihood logarithm value and an index of the most probable mixture component
for the given sample.
@param sample A sample for classification. It should be a one-channel matrix of
\f$1 \times dims\f$ or \f$dims \times 1\f$ size.
@param probs Optional output matrix that contains posterior probabilities of each component
given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type.
The method returns a two-element double vector. Zero element is a likelihood logarithm value for
the sample. First element is an index of the most probable mixture component for the given
sample.
@brief Returns covariation matrices
Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
each matrix is a square floating-point matrix NxN, where N is the space dimensionality.
@brief Returns the cluster centers (means of the Gaussian mixture)
Returns matrix with the number of rows equal to the number of mixtures and number of columns
equal to the space dimensionality.
@brief Returns weights of the mixtures
Returns vector with the number of elements equal to the number of mixtures.
@copybrief getTermCriteria @see getTermCriteria
The termination criteria of the %EM algorithm.
The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of
M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default
maximum number of iterations is EM::DEFAULT_MAX_ITERS=100.
@see setTermCriteria
@copybrief getCovarianceMatrixType @see getCovarianceMatrixType
Constraint on covariance matrices which defines type of matrices.
See EM::Types.
@see setCovarianceMatrixType
@copybrief getClustersNumber @see getClustersNumber
The number of mixture components in the Gaussian mixture model.
Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could
determine the optimal number of mixtures within a specified value range, but that is not the
case in ML yet.
@see setClustersNumber
A symmetric positively defined matrix. The number of free
parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless
there is pretty accurate initial estimation of the parameters and/or a huge number of
training samples.
A diagonal matrix with positive diagonal elements. The number of
free parameters is d for each matrix. This is most commonly used option yielding good
estimation results.
A scaled identity matrix \f$\mu_k * I\f$. There is the only
parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases,
when the constraint is relevant, or as a first step in the optimization (for example in case
when the data is preprocessed with PCA). The results of such preliminary estimation may be
passed again to the optimization procedure, this time with
covMatType=EM::COV_MAT_DIAGONAL.
@brief The class implements the Expectation Maximization algorithm.
@sa @ref ml_intro_em
Creates empty model.
Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
find the best parameters for your problem, it can be done with SVM::trainAuto.
@brief Generates a grid for %SVM parameters.
@param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
generated for the parameter with this ID.
The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be
passed to the function SVM::trainAuto.
@brief Retrieves the decision function
@param i the index of the decision function. If the problem solved is regression, 1-class or
2-class classification, then there will be just one decision function and the index should
always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$
decision functions.
@param alpha the optional output vector for weights, corresponding to different support vectors.
In the case of linear %SVM all the alpha's will be 1's.
@param svidx the optional output vector of indices of support vectors within the matrix of
support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear
%SVM each decision function consists of a single "compressed" support vector.
The method returns rho parameter of the decision function, a scalar subtracted from the weighted
sum of kernel responses.
@brief Retrieves all the support vectors
The method returns all the support vector as floating-point matrix, where support vectors are
stored as matrix rows.
@brief Trains an %SVM with optimal parameters.
@param data the training data that can be constructed using TrainData::create or
TrainData::loadFromCSV.
@param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is
executed kFold times.
@param Cgrid grid for C
@param gammaGrid grid for gamma
@param pGrid grid for p
@param nuGrid grid for nu
@param coeffGrid grid for coeff
@param degreeGrid grid for degree
@param balanced If true and the problem is 2-class classification then the method creates more
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
If there is no need to optimize a parameter, the corresponding grid step should be set to any
value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step
= 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value
`Gamma` is taken for gamma.
And, finally, if the optimization in a parameter is required but the corresponding grid is
unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for
gamma, call `SVM::getDefaultGrid(SVM::GAMMA)`.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.
Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$.
Exponential Chi2 kernel, similar to the RBF kernel:
\f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$.
Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$.
Radial basis function (RBF), a good choice in most cases.
\f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$.
Polynomial kernel:
\f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$.
Linear kernel. No mapping is done, linear discrimination (or regression) is
done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$.
Returned by SVM::getKernelType in case when custom kernel has been set
\f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p.
See @cite LibSVM for details.
\f$\epsilon\f$-Support Vector Regression. The distance between feature vectors
from the training set and the fitting hyper-plane must be less than p. For outliers the
penalty multiplier C is used.
Distribution Estimation (One-class %SVM). All the training data are from
the same class, %SVM builds a boundary that separates the class from the rest of the feature
space.
\f$\nu\f$-Support Vector Classification. n-class classification with possible
imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother
the decision boundary) is used instead of C.
C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows
imperfect separation of classes with penalty multiplier C for outliers.
Initialize with custom kernel.
See SVM::Kernel class for implementation details
Initialize with one of predefined kernels.
See SVM::KernelTypes.
Type of a %SVM kernel.
See SVM::KernelTypes. Default value is SVM::RBF.
@copybrief getTermCriteria @see getTermCriteria
Termination criteria of the iterative %SVM training procedure which solves a partial
case of constrained quadratic optimization problem.
You can specify tolerance and/or the maximum number of iterations. Default value is
`TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`;
@see setTermCriteria
@copybrief getClassWeights @see getClassWeights
Optional weights in the SVM::C_SVC problem, assigned to particular classes.
They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus
these weights affect the misclassification penalty for different classes. The larger weight,
the larger penalty on misclassification of data from the corresponding class. Default value is
empty Mat.
@see setClassWeights
@copybrief getP @see getP
Parameter \f$\epsilon\f$ of a %SVM optimization problem.
For SVM::EPS_SVR. Default value is 0.
@see setP
@copybrief getNu @see getNu
Parameter \f$\nu\f$ of a %SVM optimization problem.
For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.
@see setNu
@copybrief getC @see getC
Parameter _C_ of a %SVM optimization problem.
For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.
@see setC
@copybrief getDegree @see getDegree
Parameter _degree_ of a kernel function.
For SVM::POLY. Default value is 0.
@see setDegree
@copybrief getCoef0 @see getCoef0
Parameter _coef0_ of a kernel function.
For SVM::POLY or SVM::SIGMOID. Default value is 0.
@see setCoef0
@copybrief getGamma @see getGamma
Parameter \f$\gamma\f$ of a kernel function.
For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.
@see setGamma
@copybrief getType @see getType
Type of a %SVM formulation.
See SVM::Types. Default value is SVM::C_SVC.
@see setType
@brief Support Vector Machines.
@sa @ref ml_intro_svm
@brief Creates the empty model
The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
@brief Implementations of KNearest algorithm
@copybrief getAlgorithmType @see getAlgorithmType
%Algorithm type, one of KNearest::Types.
@see setAlgorithmType
@copybrief getEmax @see getEmax
Parameter for KDTree implementation.
@see setEmax
@copybrief getIsClassifier @see getIsClassifier
Whether classification or regression model should be trained.
@see setIsClassifier
@copybrief getDefaultK @see getDefaultK
Default number of neighbors to use in predict method.
@see setDefaultK
@brief The class implements K-Nearest Neighbors model
@sa @ref ml_intro_knn
Creates empty model
Use StatModel::train to train the model after creation.
@brief Predicts the response for sample(s).
The method estimates the most probable classes for input vectors. Input vectors (one or more)
are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one
output vector outputs. The predicted class for a single input vector is returned by the method.
The vector outputProbs contains the output probabilities corresponding to each element of
result.
@brief Bayes classifier for normally distributed data.
@sa @ref ml_intro_bayes
@brief Predicts response(s) for the provided sample(s)
@param samples The input samples, floating-point matrix
@param results The optional output matrix of results.
@param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags.
@brief Computes error on the training or test dataset
@param data the training data
@param test if true, the error is computed over the test subset of the data, otherwise it's
computed over the training subset of the data. Please note that if you loaded a completely
different dataset to evaluate already trained classifier, you will probably want not to set
the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so
that the error is computed for the whole new set. Yes, this sounds a bit confusing.
@param resp the optional output responses.
The method uses StatModel::predict to compute the error. For regression models the error is
computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
@brief Trains the statistical model
@param samples training samples
@param layout See ml::SampleTypes.
@param responses vector of responses associated with the training samples.
@brief Trains the statistical model
@param trainData training data that can be loaded from file using TrainData::loadFromCSV or
created with TrainData::create.
@param flags optional flags, depending on the model. Some of the models can be updated with the
new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
@brief Returns true if the model is classifier
@brief Returns true if the model is trained
@brief Returns the number of variables in training samples
Predict options
@brief Base class for statistical models in OpenCV ML.
@brief Reads the dataset from a .csv file and returns the ready-to-use training data.
@param filename The input file name
@param headerLineCount The number of lines in the beginning to skip; besides the header, the
function also skips empty lines and lines staring with `#`
@param responseStartIdx Index of the first output variable. If -1, the function considers the
last variable as the response
@param responseEndIdx Index of the last output variable + 1. If -1, then there is single
response variable at responseStartIdx.
@param varTypeSpec The optional text string that specifies the variables' types. It has the
format `ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]`. That is, variables from `n1 to n2`
(inclusive range), `n3`, `n4 to n5` ... are considered ordered and `n6`, `n7 to n8` ... are
considered as categorical. The range `[n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8]`
should cover all the variables. If varTypeSpec is not specified, then algorithm uses the
following rules:
- all input variables are considered ordered by default. If some column contains has non-
numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding
variable is considered categorical.
- if there are several output variables, they are all considered as ordered. Error is
reported when non-numerical values are used.
- if there is a single output variable, then if its values are non-numerical or are all
integers, then it's considered categorical. Otherwise, it's considered ordered.
@param delimiter The character used to separate values in each line.
@param missch The character used to specify missing measurements. It should not be a digit.
Although it's a non-numerical value, it surely does not affect the decision of whether the
variable ordered or categorical.
@brief Splits the training data into the training and test parts
The function selects a subset of specified relative size and then returns it as the training
set. If the function is not called, all the data is used for training. Please, note that for
each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test
subset can be retrieved and processed as well.
@sa TrainData::setTrainTestSplit
@brief Splits the training data into the training and test parts
@sa TrainData::setTrainTestSplitRatio
@brief Returns the vector of class labels
The function returns vector of unique labels occurred in the responses.
@brief Returns the vector of responses
The function returns ordered or the original categorical responses. Usually it's used in
regression algorithms.
@brief Returns matrix of train samples
@param layout The requested layout. If it's different from the initial one, the matrix is
transposed. See ml::SampleTypes.
@param compressSamples if true, the function returns only the training samples (specified by
sampleIdx)
@param compressVars if true, the function returns the shorter training samples, containing only
the active variables.
In current implementation the function tries to avoid physical data copying and returns the
matrix stored inside TrainData (unless the transposition or compression is needed).
@brief Constructor with parameters
@brief Default constructor
@brief The structure represents the logarithmic grid range of statmodel parameters.
It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate
being computed by cross-validation.
@brief Sample types
@brief %Error types
@brief Variable types
@brief Loads parameters of the specified window.
@param windowName Name of the window.
The function loadWindowParameters loads size, location, flags, trackbars value, zoom and panning
location of the window window_name .
@brief Saves parameters of the specified window.
@param windowName Name of the window.
The function saveWindowParameters saves size, location, flags, trackbars value, zoom and panning
location of the window window_name .
@brief Displays a text on the window statusbar during the specified period of time.
@param winname Name of the window.
@param text Text to write on the window statusbar.
@param delayms Duration (in milliseconds) to display the text. If this function is called before
the previous text timed out, the timer is restarted and the text is updated. If this value is
zero, the text never disappears.
The function displayOverlay displays useful information/tips on top of the window for a certain
amount of time *delayms* . This information is displayed on the window statusbar (the window must be
created with the CV_GUI_EXPANDED flags).
@brief Displays a text on a window image as an overlay for a specified duration.
@param winname Name of the window.
@param text Overlay text to write on a window image.
@param delayms The period (in milliseconds), during which the overlay text is displayed. If this
function is called before the previous overlay text timed out, the timer is restarted and the text
is updated. If this value is zero, the text never disappears.
The function displayOverlay displays useful information/tips on top of the window for a certain
amount of time *delayms*. The function does not modify the image, displayed in the window, that is,
after the specified delay the original content of the window is restored.
@brief Creates the font to draw a text on an image.
@param img 8-bit 3-channel image where the text should be drawn.
@param text Text to write on an image.
@param org Point(x,y) where the text should start on an image.
@param font Font to use to draw a text.
The function addText draws *text* on an image *img* using a specific font *font* (see example fontQt
)
@brief Creates the font to draw a text on an image.
@param nameFont Name of the font. The name should match the name of a system font (such as
*Times*). If the font is not found, a default one is used.
@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the
font is set to a system-dependent default value. Generally, this is 12 points.
@param color Color of the font in BGRA where A = 255 is fully transparent. Use the macro CV _ RGB
for simplicity.
@param weight Font weight. The following operation flags are available:
- **CV_FONT_LIGHT** Weight of 25
- **CV_FONT_NORMAL** Weight of 50
- **CV_FONT_DEMIBOLD** Weight of 63
- **CV_FONT_BOLD** Weight of 75
- **CV_FONT_BLACK** Weight of 87
You can also specify a positive integer for better control.
@param style Font style. The following operation flags are available:
- **CV_STYLE_NORMAL** Normal font
- **CV_STYLE_ITALIC** Italic font
- **CV_STYLE_OBLIQUE** Oblique font
@param spacing Spacing between characters. It can be negative or positive.
The function fontQt creates a CvFont object. This CvFont is not compatible with putText .
A basic usage of this function is the following: :
@code
CvFont font = fontQt(''Times'');
addText( img1, ``Hello World !'', Point(50,50), font);
@endcode
@brief Force window to redraw its context and call draw callback ( setOpenGlDrawCallback ).
@param winname Window name
@brief Sets the specified window as current OpenGL context.
@param winname Window name
@brief Sets the trackbar maximum position.
@param trackbarname Name of the trackbar.
@param winname Name of the window that is the parent of trackbar.
@param maxval New maximum position.
The function sets the maximum position of the specified trackbar in the specified window.
@note
**[Qt Backend Only]** winname can be empty (or NULL) if the trackbar is attached to the control
panel.
@brief Sets the trackbar position.
@param trackbarname Name of the trackbar.
@param winname Name of the window that is the parent of trackbar.
@param pos New position.
The function sets the position of the specified trackbar in the specified window.
@note
**[Qt Backend Only]** winname can be empty (or NULL) if the trackbar is attached to the control
panel.
@brief Returns the trackbar position.
@param trackbarname Name of the trackbar.
@param winname Name of the window that is the parent of the trackbar.
The function returns the current position of the specified trackbar.
@note
**[Qt Backend Only]** winname can be empty (or NULL) if the trackbar is attached to the control
panel.
@brief Creates a trackbar and attaches it to the specified window.
@param trackbarname Name of the created trackbar.
@param winname Name of the window that will be used as a parent of the created trackbar.
@param value Optional pointer to an integer variable whose value reflects the position of the
slider. Upon creation, the slider position is defined by this variable.
@param count Maximal position of the slider. The minimal position is always 0.
@param onChange Pointer to the function to be called every time the slider changes position. This
function should be prototyped as void Foo(int,void\*); , where the first parameter is the trackbar
position and the second parameter is the user data (see the next parameter). If the callback is
the NULL pointer, no callbacks are called, but only value is updated.
@param userdata User data that is passed as is to the callback. It can be used to handle trackbar
events without using global variables.
The function createTrackbar creates a trackbar (a slider or range control) with the specified name
and range, assigns a variable value to be a position synchronized with the trackbar and specifies
the callback function onChange to be called on the trackbar position change. The created trackbar is
displayed in the specified window winname.
@note
**[Qt Backend Only]** winname can be empty (or NULL) if the trackbar should be attached to the
control panel.
Clicking the label of each trackbar enables editing the trackbar values manually.
@note
- An example of using the trackbar functionality can be found at
opencv_source_code/samples/cpp/connected_components.cpp
@brief Gets the mouse-wheel motion delta, when handling mouse-wheel events EVENT_MOUSEWHEEL and
EVENT_MOUSEHWHEEL.
@param flags The mouse callback flags parameter.
For regular mice with a scroll-wheel, delta will be a multiple of 120. The value 120 corresponds to
a one notch rotation of the wheel or the threshold for action to be taken and one such action should
occur for each delta. Some high-precision mice with higher-resolution freely-rotating wheels may
generate smaller values.
For EVENT_MOUSEWHEEL positive and negative values mean forward and backward scrolling,
respectively. For EVENT_MOUSEHWHEEL, where available, positive and negative values mean right and
left scrolling, respectively.
With the C API, the macro CV_GET_WHEEL_DELTA(flags) can be used alternatively.
@note
Mouse-wheel events are currently supported only on Windows.
@brief Provides parameters of a window.
@param winname Name of the window.
@param prop_id Window property to retrieve. The following operation flags are available:
- **CV_WND_PROP_FULLSCREEN** Change if the window is fullscreen ( CV_WINDOW_NORMAL or
CV_WINDOW_FULLSCREEN ).
- **CV_WND_PROP_AUTOSIZE** Change if the window is resizable (CV_WINDOW_NORMAL or
CV_WINDOW_AUTOSIZE ).
- **CV_WND_PROP_ASPECTRATIO** Change if the aspect ratio of the image is preserved
(CV_WINDOW_FREERATIO or CV_WINDOW_KEEPRATIO ).
See setWindowProperty to know the meaning of the returned values.
The function getWindowProperty returns properties of a window.
@brief Updates window title
@brief Changes parameters of a window dynamically.
@param winname Name of the window.
@param prop_id Window property to edit. The following operation flags are available:
- **CV_WND_PROP_FULLSCREEN** Change if the window is fullscreen ( CV_WINDOW_NORMAL or
CV_WINDOW_FULLSCREEN ).
- **CV_WND_PROP_AUTOSIZE** Change if the window is resizable (CV_WINDOW_NORMAL or
CV_WINDOW_AUTOSIZE ).
- **CV_WND_PROP_ASPECTRATIO** Change if the aspect ratio of the image is preserved (
CV_WINDOW_FREERATIO or CV_WINDOW_KEEPRATIO ).
@param prop_value New value of the window property. The following operation flags are available:
- **CV_WINDOW_NORMAL** Change the window to normal size or make the window resizable.
- **CV_WINDOW_AUTOSIZE** Constrain the size by the displayed image. The window is not
resizable.
- **CV_WINDOW_FULLSCREEN** Change the window to fullscreen.
- **CV_WINDOW_FREERATIO** Make the window resizable without any ratio constraints.
- **CV_WINDOW_KEEPRATIO** Make the window resizable, but preserve the proportions of the
displayed image.
The function setWindowProperty enables changing properties of a window.
@brief Moves window to the specified position
@param winname Window name
@param x The new x-coordinate of the window
@param y The new y-coordinate of the window
@brief Resizes window to the specified size
@param winname Window name
@param width The new window width
@param height The new window height
@note
- The specified window size is for the image area. Toolbars are not counted.
- Only windows created without CV_WINDOW_AUTOSIZE flag can be resized.
@brief Displays an image in the specified window.
@param winname Name of the window.
@param mat Image to be shown.
The function imshow displays an image in the specified window. If the window was created with the
CV_WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution.
Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth:
- If the image is 8-bit unsigned, it is displayed as is.
- If the image is 16-bit unsigned or 32-bit integer, the pixels are divided by 256. That is, the
value range [0,255\*256] is mapped to [0,255].
- If the image is 32-bit floating-point, the pixel values are multiplied by 255. That is, the
value range [0,1] is mapped to [0,255].
If window was created with OpenGL support, imshow also support ogl::Buffer , ogl::Texture2D and
cuda::GpuMat as input.
If the window was not created before this function, it is assumed creating a window with CV_WINDOW_AUTOSIZE.
If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow.
@note This function should be followed by waitKey function which displays the image for specified
milliseconds. Otherwise, it won't display the image. For example, waitKey(0) will display the window
infinitely until any keypress (it is suitable for image display). waitKey(25) will display a frame
for 25 ms, after which display will be automatically closed. (If you put it in a loop to read
videos, it will display the video frame-by-frame)
@note
[Windows Backend Only] Pressing Ctrl+C will copy the image to the clipboard.
@brief Waits for a pressed key.
@param delay Delay in milliseconds. 0 is the special value that means "forever".
The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay
milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the
function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is
running on your computer at that time. It returns the code of the pressed key or -1 if no key was
pressed before the specified time had elapsed.
@note
This function is the only method in HighGUI that can fetch and handle events, so it needs to be
called periodically for normal event processing unless HighGUI is used within an environment that
takes care of event processing.
@note
The function only works if there is at least one HighGUI window created and the window is active.
If there are several HighGUI windows, any of them can be active.
@brief Destroys all of the HighGUI windows.
The function destroyAllWindows destroys all of the opened HighGUI windows.
@brief Destroys a window.
@param winname Name of the window to be destroyed.
The function destroyWindow destroys the window with the given name.
@brief Creates a window.
@param winname Name of the window in the window caption that may be used as a window identifier.
@param flags Flags of the window. The supported flags are:
> - **WINDOW_NORMAL** If this is set, the user can resize the window (no constraint).
> - **WINDOW_AUTOSIZE** If this is set, the window size is automatically adjusted to fit the
> displayed image (see imshow ), and you cannot change the window size manually.
> - **WINDOW_OPENGL** If this is set, the window will be created with OpenGL support.
The function namedWindow creates a window that can be used as a placeholder for images and
trackbars. Created windows are referred to by their names.
If a window with the same name already exists, the function does nothing.
You can call destroyWindow or destroyAllWindows to close the window and de-allocate any associated
memory usage. For a simple program, you do not really have to call these functions because all the
resources and windows of the application are closed automatically by the operating system upon exit.
@note
Qt backend supports additional flags:
- **CV_WINDOW_NORMAL or CV_WINDOW_AUTOSIZE:** CV_WINDOW_NORMAL enables you to resize the
window, whereas CV_WINDOW_AUTOSIZE adjusts automatically the window size to fit the
displayed image (see imshow ), and you cannot change the window size manually.
- **CV_WINDOW_FREERATIO or CV_WINDOW_KEEPRATIO:** CV_WINDOW_FREERATIO adjusts the image
with no respect to its ratio, whereas CV_WINDOW_KEEPRATIO keeps the image ratio.
- **CV_GUI_NORMAL or CV_GUI_EXPANDED:** CV_GUI_NORMAL is the old way to draw the window
without statusbar and toolbar, whereas CV_GUI_EXPANDED is a new enhanced GUI.
By default, flags == CV_WINDOW_AUTOSIZE | CV_WINDOW_KEEPRATIO | CV_GUI_EXPANDED
@brief Concatenates 4 chars to a fourcc code
This static method constructs the fourcc code of the codec to be used in the constructor
VideoWriter::VideoWriter or VideoWriter::open.
@brief Returns the specified VideoWriter property
@param propId Property identifier. It can be one of the following:
- **VIDEOWRITER_PROP_QUALITY** Current quality of the encoded videostream.
- **VIDEOWRITER_PROP_FRAMEBYTES** (Read-only) Size of just encoded video frame; note that the encoding order may be different from representation order.
@note When querying a property that is not supported by the backend used by the VideoWriter
class, value 0 is returned.
@brief Sets a property in the VideoWriter.
@param propId Property identifier. It can be one of the following:
- **VIDEOWRITER_PROP_QUALITY** Quality (0..100%) of the videostream encoded. Can be adjusted dynamically in some codecs.
@param value Value of the property.
@brief Writes the next video frame
@param image The written frame
The functions/methods write the specified image to video file. It must have the same size as has
been specified when opening the video writer.
@brief Closes the video writer.
The methods are automatically called by subsequent VideoWriter::open and by the VideoWriter
destructor.
@brief Returns true if video writer has been successfully initialized.
@brief Initializes or reinitializes video writer.
The method opens video writer. Parameters are the same as in the constructor
VideoWriter::VideoWriter.
@overload
@param filename Name of the output video file.
@param fourcc 4-character code of codec used to compress the frames. For example,
VideoWriter::fourcc('P','I','M','1') is a MPEG-1 codec, VideoWriter::fourcc('M','J','P','G') is a
motion-jpeg codec etc. List of codes can be obtained at [Video Codecs by
FOURCC](http://www.fourcc.org/codecs.php) page.
@param fps Framerate of the created video stream.
@param frameSize Size of the video frames.
@param isColor If it is not zero, the encoder will expect and encode color frames, otherwise it
will work with grayscale frames (the flag is currently supported on Windows only).
@brief VideoWriter constructors
The constructors/functions initialize video writers. On Linux FFMPEG is used to write videos; on
Windows FFMPEG or VFW is used; on MacOSX QTKit is used.
@brief Video writer class.
@brief Returns the specified VideoCapture property
@param propId Property identifier. It can be one of the following:
- **CAP_PROP_POS_MSEC** Current position of the video file in milliseconds or video
capture timestamp.
- **CAP_PROP_POS_FRAMES** 0-based index of the frame to be decoded/captured next.
- **CAP_PROP_POS_AVI_RATIO** Relative position of the video file: 0 - start of the
film, 1 - end of the film.
- **CAP_PROP_FRAME_WIDTH** Width of the frames in the video stream.
- **CAP_PROP_FRAME_HEIGHT** Height of the frames in the video stream.
- **CAP_PROP_FPS** Frame rate.
- **CAP_PROP_FOURCC** 4-character code of codec.
- **CAP_PROP_FRAME_COUNT** Number of frames in the video file.
- **CAP_PROP_FORMAT** Format of the Mat objects returned by retrieve() .
- **CAP_PROP_MODE** Backend-specific value indicating the current capture mode.
- **CAP_PROP_BRIGHTNESS** Brightness of the image (only for cameras).
- **CAP_PROP_CONTRAST** Contrast of the image (only for cameras).
- **CAP_PROP_SATURATION** Saturation of the image (only for cameras).
- **CAP_PROP_HUE** Hue of the image (only for cameras).
- **CAP_PROP_GAIN** Gain of the image (only for cameras).
- **CAP_PROP_EXPOSURE** Exposure (only for cameras).
- **CAP_PROP_CONVERT_RGB** Boolean flags indicating whether images should be converted
to RGB.
- **CAP_PROP_WHITE_BALANCE** Currently not supported
- **CAP_PROP_RECTIFICATION** Rectification flag for stereo cameras (note: only supported
by DC1394 v 2.x backend currently)
@note When querying a property that is not supported by the backend used by the VideoCapture
class, value 0 is returned.
@brief Sets a property in the VideoCapture.
@param propId Property identifier. It can be one of the following:
- **CAP_PROP_POS_MSEC** Current position of the video file in milliseconds.
- **CAP_PROP_POS_FRAMES** 0-based index of the frame to be decoded/captured next.
- **CAP_PROP_POS_AVI_RATIO** Relative position of the video file: 0 - start of the
film, 1 - end of the film.
- **CAP_PROP_FRAME_WIDTH** Width of the frames in the video stream.
- **CAP_PROP_FRAME_HEIGHT** Height of the frames in the video stream.
- **CAP_PROP_FPS** Frame rate.
- **CAP_PROP_FOURCC** 4-character code of codec.
- **CAP_PROP_FRAME_COUNT** Number of frames in the video file.
- **CAP_PROP_FORMAT** Format of the Mat objects returned by retrieve() .
- **CAP_PROP_MODE** Backend-specific value indicating the current capture mode.
- **CAP_PROP_BRIGHTNESS** Brightness of the image (only for cameras).
- **CAP_PROP_CONTRAST** Contrast of the image (only for cameras).
- **CAP_PROP_SATURATION** Saturation of the image (only for cameras).
- **CAP_PROP_HUE** Hue of the image (only for cameras).
- **CAP_PROP_GAIN** Gain of the image (only for cameras).
- **CAP_PROP_EXPOSURE** Exposure (only for cameras).
- **CAP_PROP_CONVERT_RGB** Boolean flags indicating whether images should be converted
to RGB.
- **CAP_PROP_WHITE_BALANCE** Currently unsupported
- **CAP_PROP_RECTIFICATION** Rectification flag for stereo cameras (note: only supported
by DC1394 v 2.x backend currently)
@param value Value of the property.
@brief Grabs, decodes and returns the next video frame.
The methods/functions combine VideoCapture::grab and VideoCapture::retrieve in one call. This is the
most convenient method for reading video files or capturing data from decode and return the just
grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more
frames in video file), the methods return false and the functions return NULL pointer.
@note OpenCV 1.x functions cvRetrieveFrame and cv.RetrieveFrame return image stored inside the video
capturing structure. It is not allowed to modify or release the image! You can copy the frame using
:ocvcvCloneImage and then do whatever you want with the copy.
@brief Decodes and returns the grabbed video frame.
The methods/functions decode and return the just grabbed frame. If no frames has been grabbed
(camera has been disconnected, or there are no more frames in video file), the methods return false
and the functions return NULL pointer.
@note OpenCV 1.x functions cvRetrieveFrame and cv.RetrieveFrame return image stored inside the video
capturing structure. It is not allowed to modify or release the image! You can copy the frame using
:ocvcvCloneImage and then do whatever you want with the copy.
@brief Closes video file or capturing device.
The methods are automatically called by subsequent VideoCapture::open and by VideoCapture
destructor.
The C function also deallocates memory and clears \*capture pointer.
@brief Returns true if video capturing has been initialized already.
If the previous call to VideoCapture constructor or VideoCapture::open succeeded, the method returns
true.
@overload
@param device id of the opened video capturing device (i.e. a camera index).
@brief Open video file or a capturing device for video capturing
@param filename name of the opened video file (eg. video.avi) or image sequence (eg.
img_%02d.jpg, which will read samples like img_00.jpg, img_01.jpg, img_02.jpg, ...)
The methods first call VideoCapture::release to close the already opened file or camera.
@overload
@param device id of the opened video capturing device (i.e. a camera index). If there is a single
camera connected, just pass 0.
@overload
@param filename name of the opened video file (eg. video.avi) or image sequence (eg.
img_%02d.jpg, which will read samples like img_00.jpg, img_01.jpg, img_02.jpg, ...)
@brief Class for video capturing from video files, image sequences or cameras. The class provides C++ API
for capturing video from cameras or for reading video files and image sequences. Here is how the
class can be used: :
@code
#include "opencv2/opencv.hpp"
using namespace cv;
int main(int, char**)
{
VideoCapture cap(0); // open the default camera
if(!cap.isOpened()) // check if we succeeded
return -1;
Mat edges;
namedWindow("edges",1);
for(;;)
{
Mat frame;
cap >> frame; // get a new frame from camera
cvtColor(frame, edges, COLOR_BGR2GRAY);
GaussianBlur(edges, edges, Size(7,7), 1.5, 1.5);
Canny(edges, edges, 0, 30, 3);
imshow("edges", edges);
if(waitKey(30) >= 0) break;
}
// the camera will be deinitialized automatically in VideoCapture destructor
return 0;
}
@endcode
@note In C API the black-box structure CvCapture is used instead of VideoCapture.
@note
- A basic sample on using the VideoCapture interface can be found at
opencv_source_code/samples/cpp/starter_video.cpp
- Another basic video processing sample can be found at
opencv_source_code/samples/cpp/video_dmtx.cpp
- (Python) A basic sample on using the VideoCapture interface can be found at
opencv_source_code/samples/python2/video.py
- (Python) Another basic video processing sample can be found at
opencv_source_code/samples/python2/video_dmtx.py
- (Python) A multi threaded video processing sample can be found at
opencv_source_code/samples/python2/video_threaded.py
@defgroup videoio Media I/O
@{
@defgroup videoio_c C API
@defgroup videoio_ios iOS glue
@defgroup videoio_winrt WinRT glue
@}
@addtogroup videoio_c
@{
@brief Encodes an image into a memory buffer.
@param ext File extension that defines the output format.
@param img Image to be written.
@param buf Output buffer resized to fit the compressed image.
@param params Format-specific parameters. See imwrite and @ref cv::ImwriteFlags.
The function compresses the image and stores it in the memory buffer that is resized to fit the
result. See imwrite for the list of supported formats and flags description.
@note cvEncodeImage returns single-row matrix of type CV_8UC1 that contains encoded image as array
of bytes.
@brief Reads an image from a buffer in memory.
@param buf Input array or vector of bytes.
@param flags The same flags as in imread, see @ref cv::ImreadModes.
@param dst The optional output placeholder for the decoded matrix. It can save the image
reallocations when the function is called repeatedly for images of the same size.
The function reads an image from the specified buffer in the memory. If the buffer is too short or
contains invalid data, the empty matrix/image is returned.
See imread for the list of supported formats and flags description.
@note In the case of color images, the decoded images will have the channels stored in B G R order.
@overload
@brief Loads a multi-page image from a file. (see imread for details.)
@param filename Name of file to be loaded.
@param flags Flag that can take values of @ref cv::ImreadModes, default with IMREAD_ANYCOLOR.
@param mats A vector of Mat objects holding each page, if more than one.
@brief Loads an image from a file.
@anchor imread
@param filename Name of file to be loaded.
@param flags Flag that can take values of @ref cv::ImreadModes
The function imread loads an image from the specified file and returns it. If the image cannot be
read (because of missing file, improper permissions, unsupported or invalid format), the function
returns an empty matrix ( Mat::data==NULL ). Currently, the following file formats are supported:
- Windows bitmaps - \*.bmp, \*.dib (always supported)
- JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Notes* section)
- JPEG 2000 files - \*.jp2 (see the *Notes* section)
- Portable Network Graphics - \*.png (see the *Notes* section)
- WebP - \*.webp (see the *Notes* section)
- Portable image format - \*.pbm, \*.pgm, \*.ppm (always supported)
- Sun rasters - \*.sr, \*.ras (always supported)
- TIFF files - \*.tiff, \*.tif (see the *Notes* section)
@note
- The function determines the type of an image by the content, not by the file extension.
- On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg,
libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs,
and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware
that currently these native image loaders give images with different pixel values because of
the color management embedded into MacOSX.
- On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for
codecs supplied with an OS image. Install the relevant packages (do not forget the development
files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn
on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake.
@note In the case of color images, the decoded images will have the channels stored in B G R order.
@addtogroup calib3d_c
@{
@brief Performs stereo calibration
@param objectPoints Vector of vectors of the calibration pattern points.
@param imagePoints1 Vector of vectors of the projections of the calibration pattern points,
observed by the first camera.
@param imagePoints2 Vector of vectors of the projections of the calibration pattern points,
observed by the second camera.
@param K1 Input/output first camera matrix:
\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If
any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CV_CALIB_FIX_INTRINSIC are specified,
some or all of the matrix components must be initialized.
@param D1 Input/output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$ of 4 elements.
@param K2 Input/output second camera matrix. The parameter is similar to K1 .
@param D2 Input/output lens distortion coefficients for the second camera. The parameter is
similar to D1 .
@param imageSize Size of the image used only to initialize intrinsic camera matrix.
@param R Output rotation matrix between the 1st and the 2nd camera coordinate systems.
@param T Output translation vector between the coordinate systems of the cameras.
@param flags Different flags that may be zero or a combination of the following values:
- **fisheye::CV_CALIB_FIX_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices
are estimated.
- **fisheye::CALIB_USE_INTRINSIC_GUESS** K1, K2 contains valid initial values of
fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
center (imageSize is used), and focal distances are computed in a least-squares fashion.
- **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration
of intrinsic optimization.
- **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number.
- **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero.
- **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay
zero.
@param criteria Termination criteria for the iterative optimization algorithm.
@brief Stereo rectification for fisheye camera model
@param K1 First camera matrix.
@param D1 First camera distortion parameters.
@param K2 Second camera matrix.
@param D2 Second camera distortion parameters.
@param imageSize Size of the image used for stereo calibration.
@param R Rotation matrix between the coordinate systems of the first and the second
cameras.
@param tvec Translation vector between coordinate systems of the cameras.
@param R1 Output 3x3 rectification transform (rotation matrix) for the first camera.
@param R2 Output 3x3 rectification transform (rotation matrix) for the second camera.
@param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
camera.
@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera.
@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ).
@param flags Operation flags that may be zero or CV_CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.
@param newImageSize New image resolution after rectification. The same size should be passed to
initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to larger value can help you
preserve details in the original image, especially when there is a big radial distortion.
@param balance Sets the new focal length in range between the min focal length and the max focal
length. Balance is in range of [0, 1].
@param fov_scale Divisor for new focal length.
@brief Performs camera calibaration
@param objectPoints vector of vectors of calibration pattern points in the calibration pattern
coordinate space.
@param imagePoints vector of vectors of the projections of calibration pattern points.
imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to
objectPoints[i].size() for each i.
@param image_size Size of the image used only to initialize the intrinsic camera matrix.
@param K Output 3x3 floating-point camera matrix
\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If
fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be
initialized before calling the function.
@param D Output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
@param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view.
That is, each k-th rotation vector together with the corresponding k-th translation vector (see
the next output parameter description) brings the calibration pattern from the model coordinate
space (in which object points are specified) to the world coordinate space, that is, a real
position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
@param tvecs Output vector of translation vectors estimated for each pattern view.
@param flags Different flags that may be zero or a combination of the following values:
- **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of
fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
center ( imageSize is used), and focal distances are computed in a least-squares fashion.
- **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration
of intrinsic optimization.
- **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number.
- **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero.
- **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay
zero.
@param criteria Termination criteria for the iterative optimization algorithm.
@brief Estimates new camera matrix for undistortion or rectification.
@param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
@param image_size
@param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
@param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
1-channel or 1x1 3-channel
@param P New camera matrix (3x3) or new projection matrix (3x4)
@param balance Sets the new focal length in range between the min focal length and the max focal
length. Balance is in range of [0, 1].
@param new_size
@param fov_scale Divisor for new focal length.
@brief Transforms an image to compensate for fisheye lens distortion.
@param distorted image with fisheye lens distortion.
@param undistorted Output image with compensated fisheye lens distortion.
@param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
@param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
@param Knew Camera matrix of the distorted image. By default, it is the identity matrix but you
may additionally scale and shift the result by using a different matrix.
@param new_size
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap
(with bilinear interpolation). See the former function for details of the transformation being
performed.
See below the results of undistortImage.
- a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3,
k_4, k_5, k_6) of distortion were optimized under calibration)
- b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2,
k_3, k_4) of fisheye distortion were optimized under calibration)
- c\) original image was captured with fisheye lens
Pictures a) and b) almost the same. But if we consider points of image located far from the center
of image, we can notice that on image a) these points are distorted.
@brief Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero
distortion is used, if R or P is empty identity matrixes are used.
@param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
@param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
@param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
1-channel or 1x1 3-channel
@param P New camera matrix (3x3) or new projection matrix (3x4)
@param size Undistorted image size.
@param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps()
for details.
@param map1 The first output map.
@param map2 The second output map.
@overload
@brief Creates StereoSGBM object
@param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes
rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
@param numDisparities Maximum disparity minus minimum disparity. The value is always greater than
zero. In the current implementation, this parameter must be divisible by 16.
@param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be
somewhere in the 3..11 range.
@param P1 The first parameter controlling the disparity smoothness. See below.
@param P2 The second parameter controlling the disparity smoothness. The larger the values are,
the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and
32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively).
@param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
disparity check. Set it to a non-positive value to disable the check.
@param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first
computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
The result values are passed to the Birchfield-Tomasi pixel cost function.
@param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function
value should "win" the second best value to consider the found match correct. Normally, a value
within the 5-15 range is good enough.
@param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles
and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
50-200 range.
@param speckleRange Maximum disparity variation within each connected component. If you do speckle
filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
Normally, 1 or 2 is good enough.
@param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
huge for HD-size pictures. By default, it is set to false .
The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
to a custom value.
@brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original
one as follows:
- By default, the algorithm is single-pass, which means that you consider only 5 directions
instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the
algorithm but beware that it may consume a lot of memory.
- The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the
blocks to single pixels.
- Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi
sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well.
- Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for
example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness
check, quadratic interpolation and speckle filtering).
@note
- (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found
at opencv_source_code/samples/python2/stereo_match.py
@brief Creates StereoBM object
@param numDisparities the disparity search range. For each pixel algorithm will find the best
disparity from 0 (default minimum disparity) to numDisparities. The search range can then be
shifted by changing the minimum disparity.
@param blockSize the linear size of the blocks compared by the algorithm. The size should be odd
(as the block is centered at the current pixel). Larger block size implies smoother, though less
accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher
chance for algorithm to find a wrong correspondence.
The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for
a specific stereo pair.
@brief Class for computing stereo correspondence using the block matching algorithm, introduced and
contributed to OpenCV by K. Konolige.
@brief Computes disparity map for the specified stereo pair
@param left Left 8-bit single-channel image.
@param right Right image of the same size and the same type as the left one.
@param disparity Output disparity map. It has the same size as the input images. Some algorithms,
like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value
has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map.
@brief The base class for stereo correspondence algorithms.
@brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
@param H The input homography matrix between two images.
@param K The input intrinsic camera calibration matrix.
@param rotations Array of rotation matrices.
@param translations Array of translation matrices.
@param normals Array of plane normal matrices.
This function extracts relative camera motion between two views observing a planar object from the
homography H induced by the plane. The intrinsic camera matrix K must also be provided. The function
may return up to four mathematical solution sets. At least two of the solutions may further be
invalidated if point correspondences are available by applying positive depth constraint (all points
must be in front of the camera). The decomposition method is described in detail in @cite Malis .
@brief Computes an optimal affine transformation between two 3D point sets.
@param src First input 3D point set.
@param dst Second input 3D point set.
@param out Output 3D affine transformation matrix \f$3 \times 4\f$ .
@param inliers Output vector indicating which points are inliers.
@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as
an inlier.
@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
The function estimates an optimal 3D affine transformation between two 3D point sets using the
RANSAC algorithm.
@brief Reprojects a disparity image to 3D space.
@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
floating-point disparity image.
@param _3dImage Output 3-channel floating-point image of the same size as disparity . Each
element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity
map.
@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify.
@param handleMissingValues Indicates, whether the function should handle missing values (i.e.
points where the disparity was not computed). If handleMissingValues=true, then pixels with the
minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
to 3D points with a very large Z value (currently set to 10000).
@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F
depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
The function transforms a single-channel disparity map to a 3-channel image representing a 3D
surface. That is, for each pixel (x,y) andthe corresponding disparity d=disparity(x,y) , it
computes:
\f[\begin{array}{l} [X \; Y \; Z \; W]^T = \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f]
The matrix Q can be an arbitrary \f$4 \times 4\f$ matrix (for example, the one computed by
stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use
perspectiveTransform .
@brief Filters off small noise blobs (speckles) in the disparity map
@param img The input 16-bit signed disparity image
@param newVal The disparity value used to paint-off the speckles
@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not
affected by the algorithm
@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same
blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
account when specifying this parameter value.
@param buf The optional temporary buffer to avoid memory allocation within the function.
@brief Reconstructs points by triangulation.
@param projMatr1 3x4 projection matrix of the first camera.
@param projMatr2 3x4 projection matrix of the second camera.
@param projPoints1 2xN array of feature points in the first image. In case of c++ version it can
be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
@param projPoints2 2xN array of corresponding points in the second image. In case of c++ version
it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
@param points4D 4xN array of reconstructed points in homogeneous coordinates.
The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their
observations with a stereo camera. Projections matrices can be obtained from stereoRectify.
@note
Keep in mind that all input data should be of float type in order for this function to work.
@sa
reprojectImageTo3D
@brief Decompose an essential matrix to possible rotations and translation.
@param E The input essential matrix.
@param R1 One possible rotation matrix.
@param R2 Another possible rotation matrix.
@param t One possible translation.
This function decompose an essential matrix E using svd decomposition @cite HartleyZ00 . Generally 4
possible poses exists for a given E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. By
decomposing E, you can only get the direction of the translation, so the function returns unit t.
@overload
@brief Converts points to/from homogeneous coordinates.
@param src Input array or vector of 2D, 3D, or 4D points.
@param dst Output vector of 2D, 3D, or 4D points.
The function converts 2D or 3D points from/to homogeneous coordinates by calling either
convertPointsToHomogeneous or convertPointsFromHomogeneous.
@note The function is obsolete. Use one of the previous two functions instead.
@brief Converts points from homogeneous to Euclidean space.
@param src Input vector of N-dimensional points.
@param dst Output vector of N-1-dimensional points.
The function converts points homogeneous to Euclidean space using perspective projection. That is,
each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the
output point coordinates will be (0,0,0,...).
@brief Converts points from Euclidean to homogeneous space.
@param src Input vector of N-dimensional points.
@param dst Output vector of N+1-dimensional points.
The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of
point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1).
@brief Returns the new camera matrix based on the free scaling parameter.
@param cameraMatrix Input camera matrix.
@param distCoeffs Input vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements. If
the vector is NULL/empty, the zero distortion coefficients are assumed.
@param imageSize Original image size.
@param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are
valid) and 1 (when all the source image pixels are retained in the undistorted image). See
stereoRectify for details.
@param newImgSize Image size after rectification. By default,it is set to imageSize .
@param validPixROI Optional output rectangle that outlines all-good-pixels region in the
undistorted image. See roi1, roi2 description in stereoRectify .
@param centerPrincipalPoint Optional flag that indicates whether in the new camera matrix the
principal point should be at the image center or not. By default, the principal point is chosen to
best fit a subset of the source image (determined by alpha) to the corrected image.
@return new_camera_matrix Output new camera matrix.
The function computes and returns the optimal new camera matrix based on the free scaling parameter.
By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original
image pixels if there is valuable information in the corners alpha=1 , or get something in between.
When alpha\>0 , the undistortion result is likely to have some black pixels corresponding to
"virtual" pixels outside of the captured distorted image. The original camera matrix, distortion
coefficients, the computed new camera matrix, and newImageSize should be passed to
initUndistortRectifyMap to produce the maps for remap .
@brief Computes a rectification transform for an uncalibrated stereo camera.
@param points1 Array of feature points in the first image.
@param points2 The corresponding points in the second image. The same formats as in
findFundamentalMat are supported.
@param F Input fundamental matrix. It can be computed from the same set of point pairs using
findFundamentalMat .
@param imgSize Size of the image.
@param H1 Output rectification homography matrix for the first image.
@param H2 Output rectification homography matrix for the second image.
@param threshold Optional threshold used to filter out the outliers. If the parameter is greater
than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points
for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are
rejected prior to computing the homographies. Otherwise,all the points are considered inliers.
The function computes the rectification transformations without knowing intrinsic parameters of the
cameras and their relative position in the space, which explains the suffix "uncalibrated". Another
related difference from stereoRectify is that the function outputs not the rectification
transformations in the object (3D) space, but the planar perspective transformations encoded by the
homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 .
@note
While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily
depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion,
it would be better to correct it before computing the fundamental matrix and calling this
function. For example, distortion coefficients can be estimated for each head of stereo camera
separately by using calibrateCamera . Then, the images can be corrected using undistort , or
just the point coordinates can be corrected with undistortPoints .
@brief Calibrates the stereo camera.
@param objectPoints Vector of vectors of the calibration pattern points.
@param imagePoints1 Vector of vectors of the projections of the calibration pattern points,
observed by the first camera.
@param imagePoints2 Vector of vectors of the projections of the calibration pattern points,
observed by the second camera.
@param cameraMatrix1 Input/output first camera matrix:
\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If
any of CV_CALIB_USE_INTRINSIC_GUESS , CV_CALIB_FIX_ASPECT_RATIO ,
CV_CALIB_FIX_INTRINSIC , or CV_CALIB_FIX_FOCAL_LENGTH are specified, some or all of the
matrix components must be initialized. See the flags description for details.
@param distCoeffs1 Input/output vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 ot 12 elements. The
output vector length depends on the flags.
@param cameraMatrix2 Input/output second camera matrix. The parameter is similar to cameraMatrix1
@param distCoeffs2 Input/output lens distortion coefficients for the second camera. The parameter
is similar to distCoeffs1 .
@param imageSize Size of the image used only to initialize intrinsic camera matrix.
@param R Output rotation matrix between the 1st and the 2nd camera coordinate systems.
@param T Output translation vector between the coordinate systems of the cameras.
@param E Output essential matrix.
@param F Output fundamental matrix.
@param flags Different flags that may be zero or a combination of the following values:
- **CV_CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F
matrices are estimated.
- **CV_CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters
according to the specified flags. Initial values are provided by the user.
- **CV_CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization.
- **CV_CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ .
- **CV_CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$
.
- **CV_CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ .
- **CV_CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to
zeros and fix there.
- **CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6** Do not change the corresponding radial
distortion coefficient during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set,
the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- **CV_CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward
compatibility, this extra flag should be explicitly specified to make the calibration
function use the rational model and return 8 coefficients. If the flag is not set, the
function computes and returns only 5 distortion coefficients.
- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
backward compatibility, this extra flag should be explicitly specified to make the
calibration function use the thin prism model and return 12 coefficients. If the flag is not
set, the function computes and returns only 5 distortion coefficients.
- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during
the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
supplied distCoeffs matrix is used. Otherwise, it is set to 0.
@param criteria Termination criteria for the iterative optimization algorithm.
The function estimates transformation between two cameras making a stereo pair. If you have a stereo
camera where the relative position and orientation of two cameras is fixed, and if you computed
poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2),
respectively (this can be done with solvePnP ), then those poses definitely relate to each other.
This means that, given ( \f$R_1\f$,\f$T_1\f$ ), it should be possible to compute ( \f$R_2\f$,\f$T_2\f$ ). You only
need to know the position and orientation of the second camera relative to the first camera. This is
what the described function does. It computes ( \f$R\f$,\f$T\f$ ) so that:
\f[R_2=R*R_1
T_2=R*T_1 + T,\f]
Optionally, it computes the essential matrix E:
\f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} *R\f]
where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . And the function
can also compute the fundamental matrix F:
\f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\f]
Besides the stereo-related information, the function can also perform a full calibration of each of
two cameras. However, due to the high dimensionality of the parameter space and noise in the input
data, the function can diverge from the correct solution. If the intrinsic parameters can be
estimated with high accuracy for each of the cameras individually (for example, using
calibrateCamera ), you are recommended to do so and then pass CV_CALIB_FIX_INTRINSIC flag to the
function along with the computed intrinsic parameters. Otherwise, if all the parameters are
estimated at once, it makes sense to restrict some parameters, for example, pass
CV_CALIB_SAME_FOCAL_LENGTH and CV_CALIB_ZERO_TANGENT_DIST flags, which is usually a
reasonable assumption.
Similarly to calibrateCamera , the function minimizes the total re-projection error for all the
points in all the available views from both cameras. The function returns the final value of the
re-projection error.
@brief Computes useful camera characteristics from the camera matrix.
@param cameraMatrix Input camera matrix that can be estimated by calibrateCamera or
stereoCalibrate .
@param imageSize Input image size in pixels.
@param apertureWidth Physical width in mm of the sensor.
@param apertureHeight Physical height in mm of the sensor.
@param fovx Output field of view in degrees along the horizontal sensor axis.
@param fovy Output field of view in degrees along the vertical sensor axis.
@param focalLength Focal length of the lens in mm.
@param principalPoint Principal point in mm.
@param aspectRatio \f$f_y/f_x\f$
The function computes various useful camera characteristics from the previously estimated camera
matrix.
@note
Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for
the chessboard pitch (it can thus be any value).
@brief Renders the detected chessboard corners.
@param image Destination image. It must be an 8-bit color image.
@param patternSize Number of inner corners per a chessboard row and column
(patternSize = cv::Size(points_per_row,points_per_column)).
@param corners Array of detected corners, the output of findChessboardCorners.
@param patternWasFound Parameter indicating whether the complete board was found or not. The
return value of findChessboardCorners should be passed here.
The function draws individual chessboard corners detected either as red circles if the board was not
found, or as colored corners connected with lines if the board was found.
@brief Finds an initial camera matrix from 3D-2D point correspondences.
@param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern
coordinate space. In the old interface all the per-view vectors are concatenated. See
calibrateCamera for details.
@param imagePoints Vector of vectors of the projections of the calibration pattern points. In the
old interface all the per-view vectors are concatenated.
@param imageSize Image size in pixels used to initialize the principal point.
@param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently.
Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ .
The function estimates and returns an initial camera matrix for the camera calibration process.
Currently, the function only supports planar calibration patterns, which are patterns where each
object point has z-coordinate =0.
@brief Combines two rotation-and-shift transformations.
@param rvec1 First rotation vector.
@param tvec1 First translation vector.
@param rvec2 Second rotation vector.
@param tvec2 Second translation vector.
@param rvec3 Output rotation vector of the superposition.
@param tvec3 Output translation vector of the superposition.
@param dr3dr1
@param dr3dt1
@param dr3dr2
@param dr3dt2
@param dt3dr1
@param dt3dt1
@param dt3dr2
@param dt3dt2 Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and
tvec2, respectively.
The functions compute:
\f[\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\f]
where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and
\f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input
vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in
your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
function that contains a matrix multiplication.
@brief Computes partial derivatives of the matrix product for each multiplied matrix.
@param A First multiplied matrix.
@param B Second multiplied matrix.
@param dABdA First output derivative matrix d(A\*B)/dA of size
\f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ .
@param dABdB Second output derivative matrix d(A\*B)/dB of size
\f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ .
The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to
the elements of each of the two input matrices. The function is used to compute the Jacobian
matrices in stereoCalibrate but can also be used in any other similar optimization function.
@brief Decomposes a projection matrix into a rotation matrix and a camera matrix.
@param projMatrix 3x4 input projection matrix P.
@param cameraMatrix Output 3x3 camera matrix K.
@param rotMatrix Output 3x3 external rotation matrix R.
@param transVect Output 4x1 translation vector T.
@param rotMatrixX Optional 3x3 rotation matrix around x-axis.
@param rotMatrixY Optional 3x3 rotation matrix around y-axis.
@param rotMatrixZ Optional 3x3 rotation matrix around z-axis.
@param eulerAngles Optional three-element vector containing three Euler angles of rotation in
degrees.
The function computes a decomposition of a projection matrix into a calibration and a rotation
matrix and the position of a camera.
It optionally returns three rotation matrices, one for each axis, and three Euler angles that could
be used in OpenGL. Note, there is always more than one sequence of rotations about the three
principle axes that results in the same orientation of an object, eg. see @cite Slabaugh . Returned
tree rotation matrices and corresponding three Euler angules are only one of the possible solutions.
The function is based on RQDecomp3x3 .
@brief Computes an RQ decomposition of 3x3 matrices.
@param src 3x3 input matrix.
@param mtxR Output 3x3 upper-triangular matrix.
@param mtxQ Output 3x3 orthogonal matrix.
@param Qx Optional output 3x3 rotation matrix around x-axis.
@param Qy Optional output 3x3 rotation matrix around y-axis.
@param Qz Optional output 3x3 rotation matrix around z-axis.
The function computes a RQ decomposition using the given rotations. This function is used in
decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera
and a rotation matrix.
It optionally returns three rotation matrices, one for each axis, and the three Euler angles in
degrees (as the return value) that could be used in OpenGL. Note, there is always more than one
sequence of rotations about the three principle axes that results in the same orientation of an
object, eg. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angules
are only one of the possible solutions.
@overload
@brief Converts a rotation matrix to a rotation vector or vice versa.
@param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
@param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.
@param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial
derivatives of the output array components with respect to the input array components.
\f[\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos{\theta} I + (1- \cos{\theta} ) r r^T + \sin{\theta} \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f]
Inverse transformation can be also done easily, since
\f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f]
A rotation vector is a convenient and most compact representation of a rotation matrix (since any
rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry
optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP .
@addtogroup objdetect_c
@{
@overload
if `outputRejectLevels` is `true` returns `rejectLevels` and `levelWeights`
@overload
@param image Matrix of the type CV_8U containing an image where objects are detected.
@param objects Vector of rectangles where each rectangle contains the detected object, the
rectangles may be partially outside the original image.
@param numDetections Vector of detection numbers for the corresponding objects. An object's number
of detections is the number of neighboring positively classified rectangles that were joined
together to form the object.
@param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
to retain it.
@param flags Parameter with the same meaning for an old cascade as in the function
cvHaarDetectObjects. It is not used for a new cascade.
@param minSize Minimum possible object size. Objects smaller than that are ignored.
@param maxSize Maximum possible object size. Objects larger than that are ignored.
@brief Detects objects of different sizes in the input image. The detected objects are returned as a list
of rectangles.
@param image Matrix of the type CV_8U containing an image where objects are detected.
@param objects Vector of rectangles where each rectangle contains the detected object, the
rectangles may be partially outside the original image.
@param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
to retain it.
@param flags Parameter with the same meaning for an old cascade as in the function
cvHaarDetectObjects. It is not used for a new cascade.
@param minSize Minimum possible object size. Objects smaller than that are ignored.
@param maxSize Maximum possible object size. Objects larger than that are ignored.
The function is parallelized with the TBB library.
@note
- (Python) A face detection example using cascade classifiers can be found at
opencv_source_code/samples/python2/facedetect.py
@brief Reads a classifier from a FileStorage node.
@note The file may contain a new cascade classifier (trained traincascade application) only.
@brief Loads a classifier from a file.
@param filename Name of the file from which the classifier is loaded. The file may contain an old
HAAR classifier trained by the haartraining application or a new cascade classifier trained by the
traincascade application.
@brief Checks whether the classifier has been loaded.
@brief Loads a classifier from a file.
@param filename Name of the file from which the classifier is loaded.
@brief Cascade classifier class for object detection.
@overload
@overload
@overload
@overload
@brief Groups the object candidate rectangles.
@param rectList Input/output vector of rectangles. Output vector includes retained and grouped
rectangles. (The Python list is not modified in place.)
@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a
group of rectangles to retain it.
@param eps Relative difference between sides of the rectangles to merge them into a group.
The function is a wrapper for the generic function partition . It clusters all the input rectangles
using the rectangle equivalence criteria that combines rectangles with similar sizes and similar
locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If
\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small
clusters containing less than or equal to groupThreshold rectangles are rejected. In each other
cluster, the average rectangle is computed and put into the output rectangle list.
@brief Returns an image descriptor type.
@brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.
@overload
@param keypointDescriptors Computed descriptors to match with vocabulary.
@param imgDescriptor Computed output image descriptor.
@param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
returned if it is non-zero.
@brief Computes an image descriptor using the set visual vocabulary.
@param image Image, for which the descriptor is computed.
@param keypoints Keypoints detected in the input image.
@param imgDescriptor Computed output image descriptor.
@param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
returned if it is non-zero.
@param descriptors Descriptors of the image keypoints that are returned if they are non-zero.
@brief Returns the set vocabulary.
@brief Sets a visual vocabulary.
@param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the
vocabulary is a visual word (cluster center).
@overload
@brief The constructor.
@param dextractor Descriptor extractor that is used to compute descriptors for an input image and
its keypoints.
@param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary
for each keypoint descriptor of the image.
@brief Class to compute an image descriptor using the *bag of visual words*.
Such a computation consists of the following steps:
1. Compute descriptors for a given image and its keypoints set.
2. Find the nearest visual words from the vocabulary for each keypoint descriptor.
3. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words
encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the
vocabulary in the given image.
@brief The constructor.
@see cv::kmeans
@brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. :
@brief Clusters train descriptors.
@param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor.
Descriptors are not added to the inner train descriptor set.
The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first
variant of the method, train descriptors stored in the object are clustered. In the second variant,
input descriptors are clustered.
@overload
@brief Returns the count of all descriptors stored in the training set.
@brief Returns a training set of descriptors.
@brief Adds descriptors to a training set.
@param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a
descriptor.
The training set is clustered using clustermethod to construct the vocabulary.
@brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors.
For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka,
Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :
@overload
@brief Draws the found matches of keypoints from two images.
@param img1 First source image.
@param keypoints1 Keypoints from the first source image.
@param img2 Second source image.
@param keypoints2 Keypoints from the second source image.
@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i]
has a corresponding point in keypoints2[matches[i]] .
@param outImg Output image. Its content depends on the flags value defining what is drawn in the
output image. See possible flags bit values below.
@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1)
, the color is generated randomly.
@param singlePointColor Color of single keypoints (circles), which means that keypoints do not
have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are
drawn.
@param flags Flags setting drawing features. Possible flags bit values are defined by
DrawMatchesFlags.
This function draws matches of keypoints from two images in the output image. Match is a line
connecting two keypoints (circles). See cv::DrawMatchesFlags.
@brief Draws keypoints.
@param image Source image.
@param keypoints Keypoints from the source image.
@param outImage Output image. Its content depends on the flags value defining what is drawn in the
output image. See possible flags bit values below.
@param color Color of keypoints.
@param flags Flags setting drawing features. Possible flags bit values are defined by
DrawMatchesFlags. See details above in drawMatches .
@note
For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT,
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG,
cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS
@brief Flann-based descriptor matcher.
This matcher trains flann::Index_ on a train descriptor collection and calls its nearest search
methods to find the best matches. So, this matcher may be faster when matching a large train
collection than the brute force matcher. FlannBasedMatcher does not support masking permissible
matches of descriptor sets because flann::Index does not support this. :
@brief Brute-force matcher constructor.
@param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are
preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor
description).
@param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k
nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with
k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the
matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent
pairs. Such technique usually produces best results with minimal number of outliers when there are
enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
@brief Brute-force descriptor matcher.
For each descriptor in the first set, this matcher finds the closest descriptor in the second set
by trying each one. This descriptor matcher supports masking permissible matches of descriptor
sets.
Class to work with descriptors from several images as with one merged matrix.
It is used e.g. in FlannBasedMatcher.
@brief Creates a descriptor matcher of a given type with the default parameters (using default
constructor).
@param descriptorMatcherType Descriptor matcher type. Now the following matcher types are
supported:
- `BruteForce` (it uses L2 )
- `BruteForce-L1`
- `BruteForce-Hamming`
- `BruteForce-Hamming(2)`
- `FlannBased`
@brief Clones the matcher.
@param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object,
that is, copies both parameters and train data. If emptyTrainData is true, the method creates an
object copy with the current parameters but with empty train data.
@overload
@param queryDescriptors Query set of descriptors.
@param matches Found matches.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param masks Set of masks. Each masks[i] specifies permissible matches between the input query
descriptors and stored train descriptors from the i-th image trainDescCollection[i].
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@overload
@param queryDescriptors Query set of descriptors.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param masks Set of masks. Each masks[i] specifies permissible matches between the input query
descriptors and stored train descriptors from the i-th image trainDescCollection[i].
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@overload
@param queryDescriptors Query set of descriptors.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param masks Set of masks. Each masks[i] specifies permissible matches between the input query
descriptors and stored train descriptors from the i-th image trainDescCollection[i].
@brief For each query descriptor, finds the training descriptors not farther than the specified distance.
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Found matches.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.
@brief Finds the k best matches for each descriptor from a query set.
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
These extended variants of DescriptorMatcher::match methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
for the details about query and train descriptors.
@brief Trains a descriptor matcher
Trains a descriptor matcher (for example, the flann index). In all methods to match, the method
train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher)
have an empty implementation of this method. Other matchers really train their inner structures (for
example, FlannBasedMatcher trains flann::Index ).
@brief Returns true if the descriptor matcher supports masking permissible matches.
@brief Returns true if there are no train descriptors in the both collections.
@brief Clears the train descriptor collections.
@brief Returns a constant link to the train descriptor collection trainDescCollection .
@brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor
collection.
If the collection is not empty, the new descriptors are added to existing train descriptors.
@param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
train image.
@brief Abstract base class for matching keypoint descriptors.
It has two groups of match methods: for matching descriptors of an image with another image or with
an image set.
@brief The AKAZE constructor
@param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE,
DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
@param descriptor_size Size of the descriptor in bits. 0 -\> Full size
@param descriptor_channels Number of channels in the descriptor (1, 2, 3)
@param threshold Detector response threshold to accept point
@param nOctaves Maximum octave evolution of the image
@param nOctaveLayers Default number of sublevels per scale level
@param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
DIFF_CHARBONNIER
@brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13 . :
@note AKAZE descriptors can only be used with KAZE or AKAZE keypoints. Try to avoid using *extract*
and *detect* instead of *operator()* due to performance reasons. .. [ANB13] Fast Explicit Diffusion
for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, JesĂşs Nuevo and Adrien
Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
@brief The KAZE constructor
@param extended Set to enable extraction of extended (128-byte) descriptor.
@param upright Set to enable use of upright descriptors (non rotation-invariant).
@param threshold Detector response threshold to accept point
@param nOctaves Maximum octave evolution of the image
@param nOctaveLayers Default number of sublevels per scale level
@param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
DIFF_CHARBONNIER
@brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 .
@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo
F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision
(ECCV), Fiorenze, Italy, October 2012.
@brief Class for extracting blobs from an image. :
The class implements a simple algorithm for extracting blobs from an image:
1. Convert the source image to binary images by applying thresholding with several thresholds from
minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between
neighboring thresholds.
2. Extract connected components from every binary image by findContours and calculate their
centers.
3. Group centers from several binary images by their coordinates. Close centers form one group that
corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter.
4. From the groups, estimate final centers of blobs and their radiuses and return as locations and
sizes of keypoints.
This class performs several filtrations of returned blobs. You should set filterBy\* to true/false
to turn on/off corresponding filtration. Available filtrations:
- **By color**. This filter compares the intensity of a binary image at the center of a blob to
blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs
and blobColor = 255 to extract light blobs.
- **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).
- **By circularity**. Extracted blobs have circularity
(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and
maxCircularity (exclusive).
- **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio
between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive).
- **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between
minConvexity (inclusive) and maxConvexity (exclusive).
Default values of parameters are tuned to extract dark circular blobs.
@brief Wrapping class for feature detection using the goodFeaturesToTrack function. :
@brief Wrapping class for feature detection using the AGAST method. :
@brief Detects corners using the AGAST algorithm
@param image grayscale image where keypoints (corners) are detected.
@param keypoints keypoints detected on the image.
@param threshold threshold on difference between intensity of the central pixel and pixels of a
circle around this pixel.
@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
(keypoints).
@param type one of the four neighborhoods as defined in the paper:
AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,
AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16
Detects corners using the AGAST algorithm by @cite mair2010_agast .
@overload
@brief Wrapping class for feature detection using the FAST method. :
@brief Detects corners using the FAST algorithm
@param image grayscale image where keypoints (corners) are detected.
@param keypoints keypoints detected on the image.
@param threshold threshold on difference between intensity of the central pixel and pixels of a
circle around this pixel.
@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
(keypoints).
@param type one of the three neighborhoods as defined in the paper:
FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12,
FastFeatureDetector::TYPE_5_8
Detects corners using the FAST algorithm by @cite Rosten06 .
@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8,
cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner
detection, use cv2.FAST.detect() method.
@overload
@brief The ORB constructor
@param nfeatures The maximum number of features to retain.
@param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.
@param nlevels The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels).
@param edgeThreshold This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.
@param firstLevel It should be 0 in the current implementation.
@param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
@param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
@param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
pyramid layers the perceived image area covered by a feature will be larger.
@param fastThreshold
@brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects
the strongest features using FAST or Harris response, finds their orientation using first-order
moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or
k-tuples) are rotated according to the measured orientation).
@brief The BRISK constructor for a custom pattern
@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).
@param indexChange index remapping of the bits.
@brief The BRISK constructor
@param thresh AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
keypoint.
@brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 .
Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you
to easily switch between different algorithms solving the same problem. This section is devoted to
computing descriptors represented as vectors in a multidimensional space. All objects that implement
the vector descriptor extractors inherit the DescriptorExtractor interface.
Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch
between different algorithms solving the same problem. All objects that implement keypoint detectors
inherit the FeatureDetector interface.
Detects keypoints and computes the descriptors
@overload
@param images Image set.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.
@brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set
(second variant).
@param image Image.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.
@overload
@param images Image set.
@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
of keypoints detected in images[i] .
@param masks Masks for each input image specifying where to look for keypoints (optional).
masks[i] is a mask for images[i].
@brief Detects keypoints in an image (first variant) or image set (second variant).
@param image Image.
@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
of keypoints detected in images[i] .
@param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
matrix with non-zero values in the region of interest.
@brief Abstract base class for 2D image feature detectors and descriptor extractors
@brief A class filters a vector of keypoints.
Because now it is difficult to provide a convenient interface for all usage scenarios of the
keypoints filter class, it has only several needed by now static methods.
@addtogroup video_c
@{
@brief Creates KNN Background Subtractor
@param history Length of the history.
@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
whether a pixel is close to that sample. This parameter does not affect the background update.
@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
speed a bit, so if you do not need this feature, set the parameter to false.
@brief Sets the shadow threshold
@brief Returns the shadow threshold
A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra,
*Detecting Moving Shadows...*, IEEE PAMI,2003.
@brief Sets the shadow value
@brief Returns the shadow value
Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
in the mask always means background, 255 means foreground.
@brief Enables or disables shadow detection
@brief Returns the shadow detection flag
If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for
details.
@brief Sets the k in the kNN. How many nearest neigbours need to match.
@brief Returns the number of neighbours, the k in the kNN.
K is the number of samples that need to be within dist2Threshold in order to decide that that
pixel is matching the kNN background model.
@brief Sets the threshold on the squared distance
@brief Returns the threshold on the squared distance between the pixel and the sample
The threshold on the squared distance between the pixel and the sample to decide whether a pixel is
close to a data sample.
@brief Sets the number of data samples in the background model.
The model needs to be reinitalized to reserve memory.
@brief Returns the number of data samples in the background model
@brief Sets the number of last frames that affect the background model
@brief Returns the number of last frames that affect the background model
@brief K-nearest neigbours - based Background/Foreground Segmentation Algorithm.
The class implements the K-nearest neigbours background subtraction described in @cite Zivkovic2006 .
Very efficient if number of foreground pixels is low.
@brief Creates MOG2 Background Subtractor
@param history Length of the history.
@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
to decide whether a pixel is well described by the background model. This parameter does not
affect the background update.
@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
speed a bit, so if you do not need this feature, set the parameter to false.
@brief Sets the shadow threshold
@brief Returns the shadow threshold
A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra,
*Detecting Moving Shadows...*, IEEE PAMI,2003.
@brief Sets the shadow value
@brief Returns the shadow value
Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
in the mask always means background, 255 means foreground.
@brief Enables or disables shadow detection
@brief Returns the shadow detection flag
If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for
details.
@brief Sets the complexity reduction threshold
@brief Sets the initial variance of each gaussian component
@brief Returns the initial variance of each gaussian component
@brief Sets the variance threshold for the pixel-model match used for new mixture component generation
@brief Returns the variance threshold for the pixel-model match used for new mixture component generation
Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the
existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it
is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg
value generates more components. A higher Tg value may result in a small number of components but
they can grow too large.
@brief Sets the variance threshold for the pixel-model match
@brief Returns the variance threshold for the pixel-model match
The main threshold on the squared Mahalanobis distance to decide if the sample is well described by
the background model or not. Related to Cthr from the paper.
@brief Sets the "background ratio" parameter of the algorithm
@brief Returns the "background ratio" parameter of the algorithm
If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's
considered background and added to the model as a center of a new component. It corresponds to TB
parameter in the paper.
@brief Sets the number of gaussian components in the background model.
The model needs to be reinitalized to reserve memory.
@brief Returns the number of gaussian components in the background model
@brief Sets the number of last frames that affect the background model
@brief Returns the number of last frames that affect the background model
@brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004
and @cite Zivkovic2006 .
@brief Computes a background image.
@param backgroundImage The output background image.
@note Sometimes the background image can be very blurry, as it contain the average background
statistics.
@brief Computes a foreground mask.
@param image Next video frame.
@param fgmask The output foreground mask as an 8-bit binary image.
@param learningRate The value between 0 and 1 that indicates how fast the background model is
learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
rate. 0 means that the background model is not updated at all, 1 means that the background model
is completely reinitialized from the last frame.
@brief Base class for background/foreground segmentation. :
The class is only used to define the common interface for the whole family of background/foreground
segmentation algorithms.
@brief Creates instance of cv::DenseOpticalFlow
@copybrief getMedianFiltering @see getMedianFiltering
@see setMedianFiltering
@copybrief getScaleStep @see getScaleStep
@see setScaleStep
@copybrief getUseInitialFlow @see getUseInitialFlow
@see setUseInitialFlow
@copybrief getOuterIterations @see getOuterIterations
@see setOuterIterations
@copybrief getInnerIterations @see getInnerIterations
@see setInnerIterations
@copybrief getEpsilon @see getEpsilon
@see setEpsilon
@copybrief getWarpingsNumber @see getWarpingsNumber
@see setWarpingsNumber
@copybrief getScalesNumber @see getScalesNumber
@see setScalesNumber
@copybrief getGamma @see getGamma
@see setGamma
@copybrief getTheta @see getTheta
@see setTheta
@copybrief getLambda @see getLambda
@see setLambda
@copybrief getTau @see getTau
@see setTau
@brief "Dual TV L1" Optical Flow Algorithm.
The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and
@cite Javier2012 .
Here are important members of the class that control the algorithm, which you can set after
constructing the class instance:
- member double tau
Time step of the numerical scheme.
- member double lambda
Weight parameter for the data term, attachment parameter. This is the most relevant
parameter, which determines the smoothness of the output. The smaller this parameter is,
the smoother the solutions we obtain. It depends on the range of motions of the images, so
its value should be adapted to each image sequence.
- member double theta
Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the
attachment and the regularization terms. In theory, it should have a small value in order
to maintain both parts in correspondence. The method is stable for a large range of values
of this parameter.
- member int nscales
Number of scales used to create the pyramid of images.
- member int warps
Number of warpings per scale. Represents the number of times that I1(x+u0) and grad(
I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the
method. It also affects the running time, so it is a compromise between speed and
accuracy.
- member double epsilon
Stopping criterion threshold used in the numerical scheme, which is a trade-off between
precision and running time. A small value will yield more accurate solutions at the
expense of a slower convergence.
- member int iterations
Stopping criterion iterations number used in the numerical scheme.
C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
@brief Releases all inner buffers.
@brief Calculates an optical flow.
@param I0 first 8-bit single-channel input image.
@param I1 second input image of the same size and the same type as prev.
@param flow computed flow image that has the same size as prev and type CV_32FC2.
@brief Updates the predicted state from the measurement.
@param measurement The measured system parameters
@brief Computes a predicted state.
@param control The optional input control
@brief Re-initializes Kalman filter. The previous content is destroyed.
@param dynamParams Dimensionality of the state.
@param measureParams Dimensionality of the measurement.
@param controlParams Dimensionality of the control vector.
@param type Type of the created matrices that should be CV_32F or CV_64F.
@overload
@param dynamParams Dimensionality of the state.
@param measureParams Dimensionality of the measurement.
@param controlParams Dimensionality of the control vector.
@param type Type of the created matrices that should be CV_32F or CV_64F.
@brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 .
@param templateImage single-channel template image; CV_8U or CV_32F array.
@param inputImage single-channel input image which should be warped with the final warpMatrix in
order to provide an image similar to templateImage, same type as temlateImage.
@param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp).
@param motionType parameter, specifying the type of motion:
- **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with
the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being
estimated.
- **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three
parameters are estimated; warpMatrix is \f$2\times 3\f$.
- **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated;
warpMatrix is \f$2\times 3\f$.
- **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are
estimated;\`warpMatrix\` is \f$3\times 3\f$.
@param criteria parameter, specifying the termination criteria of the ECC algorithm;
criteria.epsilon defines the threshold of the increment in the correlation coefficient between two
iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion).
Default values are shown in the declaration above.
@param inputMask An optional mask to indicate valid values of inputImage.
The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion
(@cite EP08), that is
\f[\texttt{warpMatrix} = \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f]
where
\f[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\f]
(the equation holds with homogeneous coordinates for homography). It returns the final enhanced
correlation coefficient, that is the correlation coefficient between the template image and the
final warped input image. When a \f$3\times 3\f$ matrix is given with motionType =0, 1 or 2, the third
row is ignored.
Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an
area-based alignment that builds on intensity similarities. In essence, the function updates the
initial transformation that roughly aligns the images. If this information is missing, the identity
warp (unity matrix) should be given as input. Note that if images undergo strong
displacements/rotations, an initial transformation that roughly aligns the images is necessary
(e.g., a simple euclidean/similarity transform that allows for the images showing the same image
content approximately). Use inverse warping in the second image to take an image close to the first
one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV
sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws
an exception if algorithm does not converges.
@sa
estimateRigidTransform, findHomography
@brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
pyramids.
@param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
@param nextImg second input image or pyramid of the same size and the same type as prevImg.
@param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
single-precision floating-point numbers.
@param nextPts output vector of 2D points (with single-precision floating-point coordinates)
containing the calculated new positions of input features in the second image; when
OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
@param status output status vector (of unsigned chars); each element of the vector is set to 1 if
the flow for the corresponding features has been found, otherwise, it is set to 0.
@param err output vector of errors; each element of the vector is set to an error for the
corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
found then the error is not defined (use the status parameter to find such cases).
@param winSize size of the search window at each pyramid level.
@param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
algorithm will use as many levels as pyramids have but no more than maxLevel.
@param criteria parameter, specifying the termination criteria of the iterative search algorithm
(after the specified maximum number of iterations criteria.maxCount or when the search window
moves by less than criteria.epsilon.
@param flags operation flags:
- **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is
not set, then prevPts is copied to nextPts and is considered the initial estimate.
- **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see
minEigThreshold description); if the flag is not set, then L1 distance between patches
around the original and a moved point, divided by number of pixels in a window, is used as a
error measure.
@param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of
optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided
by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
performance boost.
The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
@cite Bouguet00 . The function is parallelized with the TBB library.
@note
- An example using the Lucas-Kanade optical flow algorithm can be found at
opencv_source_code/samples/cpp/lkdemo.cpp
- (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
opencv_source_code/samples/python2/lk_track.py
- (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
opencv_source_code/samples/python2/lk_homography.py
@brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
@param img 8-bit input image.
@param pyramid output pyramid.
@param winSize window size of optical flow algorithm. Must be not less than winSize argument of
calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
@param maxLevel 0-based maximal pyramid level number.
@param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
@param pyrBorder the border mode for pyramid layers.
@param derivBorder the border mode for gradients.
@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false
to force data copying.
@return number of levels in constructed pyramid. Can be less than maxLevel.
@brief Finds an object on a back projection image.
@param probImage Back projection of the object histogram. See calcBackProject for details.
@param window Initial search window.
@param criteria Stop criteria for the iterative search algorithm.
returns
: Number of iterations CAMSHIFT took to converge.
The function implements the iterative object search algorithm. It takes the input back projection of
an object and the initial position. The mass center in window of the back projection image is
computed and the search window center shifts to the mass center. The procedure is repeated until the
specified number of iterations criteria.maxCount is done or until the window center shifts by less
than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search
window size or orientation do not change during the search. You can simply pass the output of
calcBackProject to this function. But better results can be obtained if you pre-filter the back
projection and remove the noise. For example, you can do this by retrieving connected components
with findContours , throwing away contours with small area ( contourArea ), and rendering the
remaining contours with drawContours.
@note
- A mean-shift tracking sample can be found at opencv_source_code/samples/cpp/camshiftdemo.cpp
@brief Finds an object center, size, and orientation.
@param probImage Back projection of the object histogram. See calcBackProject.
@param window Initial search window.
@param criteria Stop criteria for the underlying meanShift.
returns
(in old interfaces) Number of iterations CAMSHIFT took to converge
The function implements the CAMSHIFT object tracking algorithm @cite Bradski98 . First, it finds an
object center using meanShift and then adjusts the window size and finds the optimal rotation. The
function returns the rotated rectangle structure that includes the object position, size, and
orientation. The next position of the search window can be obtained with RotatedRect::boundingRect()
See the OpenCV sample camshiftdemo.c that tracks colored objects.
@note
- (Python) A sample explaining the camshift tracking algorithm can be found at
opencv_source_code/samples/python2/camshift.py
@addtogroup photo_c
@{
@brief Stylization aims to produce digital imagery with a wide variety of effects not focused on
photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low
contrast while preserving, or enhancing, high-contrast features.
@param src Input 8-bit 3-channel image.
@param dst Output image with the same size and type as src.
@param sigma_s Range between 0 to 200.
@param sigma_r Range between 0 to 1.
@brief Pencil-like non-photorealistic line drawing
@param src Input 8-bit 3-channel image.
@param dst1 Output 8-bit 1-channel image.
@param dst2 Output image with the same size and type as src.
@param sigma_s Range between 0 to 200.
@param sigma_r Range between 0 to 1.
@param shade_factor Range between 0 to 0.1.
@brief This filter enhances the details of a particular image.
@param src Input 8-bit 3-channel image.
@param dst Output image with the same size and type as src.
@param sigma_s Range between 0 to 200.
@param sigma_r Range between 0 to 1.
@brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing
filters are used in many different applications @cite EM11 .
@param src Input 8-bit 3-channel image.
@param dst Output 8-bit 3-channel image.
@param flags Edge preserving filters:
- **RECURS_FILTER** = 1
- **NORMCONV_FILTER** = 2
@param sigma_s Range between 0 to 200.
@param sigma_r Range between 0 to 1.
@brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one
washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge
Detector is used.
@param src Input 8-bit 3-channel image.
@param mask Input 8-bit 1 or 3-channel image.
@param dst Output image with the same size and type as src.
@param low_threshold Range from 0 to 100.
@param high_threshold Value \> 100.
@param kernel_size The size of the Sobel kernel to be used.
**NOTE:**
The algorithm assumes that the color of the source image is close to that of the destination. This
assumption means that when the colors don't match, the source image color gets tinted toward the
color of the destination image.
@brief Applying an appropriate non-linear transformation to the gradient field inside the selection and
then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
@param src Input 8-bit 3-channel image.
@param mask Input 8-bit 1 or 3-channel image.
@param dst Output image with the same size and type as src.
@param alpha Value ranges between 0-2.
@param beta Value ranges between 0-2.
This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
@brief Given an original color image, two differently colored versions of this image can be mixed
seamlessly.
@param src Input 8-bit 3-channel image.
@param mask Input 8-bit 1 or 3-channel image.
@param dst Output image with the same size and type as src .
@param red_mul R-channel multiply factor.
@param green_mul G-channel multiply factor.
@param blue_mul B-channel multiply factor.
Multiplication factor is between .5 to 2.5.
@brief Image editing tasks concern either global changes (color/intensity corrections, filters,
deformations) or local changes concerned to a selection. Here we are interested in achieving local
changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless
manner. The extent of the changes ranges from slight distortions to complete replacement by novel
content @cite PM03 .
@param src Input 8-bit 3-channel image.
@param dst Input 8-bit 3-channel image.
@param mask Input 8-bit 1 or 3-channel image.
@param p Point in dst image where object is placed.
@param blend Output image with the same size and type as dst.
@param flags Cloning method that could be one of the following:
- **NORMAL_CLONE** The power of the method is fully expressed when inserting objects with
complex outlines into a new background
- **MIXED_CLONE** The classic method, color-based selection and alpha masking might be time
consuming and often leaves an undesirable halo. Seamless cloning, even averaged with the
original image, is not effective. Mixed seamless cloning based on a loose selection proves
effective.
- **FEATURE_EXCHANGE** Feature exchange allows the user to easily replace certain features of
one object by alternative features.
@brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized
black-and-white photograph rendering, and in many single channel image processing applications
@cite CL12 .
@param src Input 8-bit 3-channel image.
@param grayscale Output 8-bit 1-channel image.
@param color_boost Output 8-bit 3-channel image.
This function is to be applied on color images.
@brief Creates MergeRobertson object
@brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
values and camera response.
For more information see @cite RB99 .
@brief Creates MergeMertens object
@param contrast_weight contrast measure weight. See MergeMertens.
@param saturation_weight saturation measure weight
@param exposure_weight well-exposedness measure weight
@brief Short version of process, that doesn't take extra arguments.
@param src vector of input images
@param dst result image
@brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are
combined using laplacian pyramids.
The resulting image weight is constructed as weighted average of contrast, saturation and
well-exposedness measures.
The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying
by 255, but it's recommended to apply gamma correction and/or linear tonemapping.
For more information see @cite MK07 .
@brief Creates MergeDebevec object
@brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
values and camera response.
For more information see @cite DM97 .
@brief Merges images.
@param src vector of input images
@param dst result image
@param times vector of exposure time values for each image
@param response 256x1 matrix with inverse camera response function for each pixel value, it should
have the same number of channels as images.
@brief The base class algorithms that can merge exposure sequence to a single image.
@brief Creates CalibrateRobertson object
@param max_iter maximal number of Gauss-Seidel solver iterations.
@param threshold target difference between results of two successive steps of the minimization.
@brief Inverse camera response function is extracted for each brightness value by minimizing an objective
function as linear system. This algorithm uses all image pixels.
For more information see @cite RB99 .
@brief Creates CalibrateDebevec object
@param samples number of pixel locations to use
@param lambda smoothness term weight. Greater values produce smoother results, but can alter the
response.
@param random if true sample pixel locations are chosen at random, otherwise the form a
rectangular grid.
@brief Inverse camera response function is extracted for each brightness value by minimizing an objective
function as linear system. Objective function is constructed using pixel values on the same position
in all images, extra term is added to make the result smoother.
For more information see @cite DM97 .
@brief Recovers inverse camera response.
@param src vector of input images
@param dst 256x1 matrix with inverse camera response function
@param times vector of exposure time values for each image
@brief The base class for camera response calibration algorithms.
@brief Creates AlignMTB object
@param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are
usually good enough (31 and 63 pixels shift respectively).
@param exclude_range range for exclusion bitmap that is constructed to suppress noise around the
median value.
@param cut if true cuts images, otherwise fills the new regions with zeros.
@brief Computes median threshold and exclude bitmaps of given image.
@param img input image
@param tb median threshold bitmap
@param eb exclude bitmap
@brief Helper function, that shift Mat filling new regions with zeros.
@param src input image
@param dst result image
@param shift shift value
@brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the
first.
@param img0 first image
@param img1 second image
@brief Short version of process, that doesn't take extra arguments.
@param src vector of input images
@param dst vector of aligned images
@brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median
luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations.
It is invariant to exposure, so exposure values and camera response are not necessary.
In this implementation new image regions are filled with zeros.
For more information see @cite GW03 .
@brief Aligns images
@param src vector of input images
@param dst vector of aligned images
@param times vector of exposure time values for each image
@param response 256x1 matrix with inverse camera response function for each pixel value, it should
have the same number of channels as images.
@brief The base class for algorithms that align images of the same scene with different exposures
@brief Creates TonemapMantiuk object
@param gamma gamma value for gamma correction. See createTonemap
@param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing
dynamic range. Values from 0.6 to 0.9 produce best results.
@param saturation saturation enhancement value. See createTonemapDrago
@brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid,
transforms contrast values to HVS response and scales the response. After this the image is
reconstructed from new contrast values.
For more information see @cite MM06 .
@brief Creates TonemapReinhard object
@param gamma gamma value for gamma correction. See createTonemap
@param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results.
@param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel
value, if 0 it's global, otherwise it's a weighted mean of this two cases.
@param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently,
if 0 adaptation level is the same for each channel.
@brief This is a global tonemapping operator that models human visual system.
Mapping function is controlled by adaptation parameter, that is computed using light adaptation and
color adaptation.
For more information see @cite RD05 .
@brief Creates TonemapDurand object
@param gamma gamma value for gamma correction. See createTonemap
@param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min
are maximum and minimum luminance values of the resulting image.
@param saturation saturation enhancement value. See createTonemapDrago
@param sigma_space bilateral filter sigma in color space
@param sigma_color bilateral filter sigma in coordinate space
@brief This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter
and compresses contrast of the base layer thus preserving all the details.
This implementation uses regular bilateral filter from opencv.
Saturation enhancement is possible as in ocvTonemapDrago.
For more information see @cite DD02 .
@brief Creates TonemapDrago object
@param gamma gamma value for gamma correction. See createTonemap
@param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater
than 1 increase saturation and values less than 1 decrease it.
@param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best
results, default value is 0.85.
@brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in
logarithmic domain.
Since it's a global operator the same function is applied to all the pixels, it is controlled by the
bias parameter.
Optional saturation enhancement is possible as described in @cite FL02 .
For more information see @cite DM03 .
@brief Tonemaps image
@param src source image - 32-bit 3-channel Mat
@param dst destination image - 32-bit 3-channel Mat with values in [0, 1] range
@brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
@brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is,
finding a function to minimize some functional). As the image denoising, in particular, may be seen
as the variational problem, primal-dual algorithm then can be used to perform denoising and this is
exactly what is implemented.
It should be noted, that this implementation was taken from the July 2013 blog entry
@cite MA13 , which also contained (slightly more general) ready-to-use source code on Python.
Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end
of July 2013 and finally it was slightly adapted by later authors.
Although the thorough discussion and justification of the algorithm involved may be found in
@cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin
with, we consider the 1-byte gray-level images as the functions from the rectangular domain of
pixels (it may be seen as set
\f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some
\f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with
this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula
\f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f]
\f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our
image to be smooth (ideally, having zero gradient, thus being constant) and the second states that
we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is
exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
@param observations This array should contain one or more noised versions of the image that is to
be restored.
@param result Here the denoised image will be stored. There is no need to do pre-allocation of
storage space, as it will be automatically allocated, if necessary.
@param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth
(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly
speaking, as it becomes smaller, the result will be more blur but more sever outliers will be
removed.
@param niters Number of iterations that the algorithm will run. Of course, as more iterations as
better, but it is hard to quantitatively refine this statement, so just use the default and
increase it if the results are poor.
@brief Modification of fastNlMeansDenoisingMulti function for colored images sequences
@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and
size.
@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
srcImgs[imgToDenoiseIndex] image.
@param dst Output image with the same size and type as srcImgs images.
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
removes noise but also removes image details, smaller h value preserves details but also preserves
some noise.
@param hColor The same as h but for color components.
The function converts images to CIELAB colorspace and then separately denoise L and AB components
with given h parameters using fastNlMeansDenoisingMulti function.
@brief Modification of fastNlMeansDenoising function for colored images
@param src Input 8-bit 3-channel image.
@param dst Output image with the same size and type as src .
@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
Should be odd. Recommended value 7 pixels
@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
denoising time. Recommended value 21 pixels
@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
removes noise but also removes image details, smaller h value preserves details but also preserves
some noise
@param hColor The same as h but for color components. For most images value equals 10
will be enough to remove colored noise and do not distort colors
The function converts image to CIELAB colorspace and then separately denoise L and AB components
with given h parameters using fastNlMeansDenoising function.
@brief Draws contour outlines or filled interiors on the image
@see cv::drawContours
@brief Returns the polygon points which make up the given ellipse.
The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial
sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than
0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total
number of points stored into 'pts' is returned by this function.
@see cv::ellipse2Poly
@brief Unpacks color value
if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels
(depending on arrtype) of destination scalar are set to the same value = _color_
@brief Calculates bounding box of text stroke (useful for alignment)
@see cv::getTextSize
@brief Renders text stroke with specified font and color at specified location.
CvFont should be initialized with cvInitFont
@see cvInitFont, cvGetTextSize, cvFont, cv::putText
@brief Initializes font structure (OpenCV 1.x API).
The function initializes the font structure that can be passed to text rendering functions.
@param font Pointer to the font structure initialized by the function
@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers.
@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width
depending on the font type. If equal to 0.5f , the characters are of half the original width.
@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending
on the font type. If equal to 0.5f , the characters are of half the original height.
@param shear Approximate tangent of the character slope relative to the vertical line. A zero
value means a non-italic font, 1.0f means about a 45 degree slope, etc.
@param thickness Thickness of the text strokes
@param line_type Type of the strokes, see line description
@sa cvPutText
Font structure
@brief Initializes line iterator.
Initially, line_iterator->ptr will point to pt1 (or pt2, see left_to_right description) location in
the image. Returns the number of pixels on the line between the ending points.
@see cv::LineIterator
@brief Draws one or more polygonal curves
@see cv::polylines
@brief Fills an area bounded by one or more arbitrary polygons
@see cv::fillPoly
@brief Fills convex or monotonous polygon.
@see cv::fillConvexPoly
@brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector
depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure
is rotated by _angle_. All the angles are in degrees
@see cv::ellipse
@brief Draws a circle with specified center and radius.
Thickness works in the same way as with cvRectangle
@see cv::circle
@brief Draws a rectangle specified by a CvRect structure
@see cv::rectangle
@brief Draws 4-connected, 8-connected or antialiased line segment connecting two points
@see cv::line
@brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique)
@see cv::fitLine
@brief Finds circles in the image
@see cv::HoughCircles
@brief Finds lines on binary image using one of several methods.
line_storage is either memory storage or 1 x _max number of lines_ CvMat, its
number of columns is changed by the function.
method is one of CV_HOUGH_*;
rho, theta and threshold are used for each of those methods;
param1 ~ line length, param2 ~ line gap - for probabilistic,
param1 ~ srn, param2 ~ stn - for multi-scale
@see cv::HoughLines
@brief Finds a sparse set of points within the selected region
that seem to be easy to track
@see cv::goodFeaturesToTrack
@brief Adjust corner position using some sort of gradient search
@see cv::cornerSubPix
@brief Harris corner detector:
Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel
@see cv::cornerHarris
@brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at
every image pixel
@see cv::cornerMinEigenVal
@brief Calculates eigen values and vectors of 2x2
gradient covariation matrix at every image pixel
@see cv::cornerEigenValsAndVecs
@brief Calculates constraint image for corner detection
Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy.
Applying threshold to the result gives coordinates of corners
@see cv::preCornerDetect
@brief Runs canny edge detector
@see cv::Canny
@brief Fills the connected component until the color difference gets large enough
@see cv::floodFill
@brief Applies adaptive threshold to grayscale image.
The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and
CV_ADAPTIVE_THRESH_GAUSSIAN_C are:
neighborhood size (3, 5, 7 etc.),
and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...)
@see cv::adaptiveThreshold
@brief Applies fixed-level threshold to grayscale image.
This is a basic operation applied before retrieving contours
@see cv::threshold
@brief Applies distance transform to binary image
@see cv::distanceTransform
@brief equalizes histogram of 8-bit single-channel image
@see cv::equalizeHist
@brief Divides one histogram by another.
The function calculates the object probability density from two histograms as:
\f[\texttt{disthist} (I)= \forkthree{0}{if \(\texttt{hist1}(I)=0\)}{\texttt{scale}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) > \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f]
@param hist1 First histogram (the divisor).
@param hist2 Second histogram.
@param dst_hist Destination histogram.
@param scale Scale factor for the destination histogram.
@brief Locates a template within an image by using a histogram comparison.
The function calculates the back projection by comparing histograms of the source image patches with
the given histogram. The function is similar to matchTemplate, but instead of comparing the raster
patch with all its possible positions within the search window, the function CalcBackProjectPatch
compares histograms. See the algorithm diagram below:
@param image Source images (though, you may pass CvMat\*\* as well).
@param dst Destination image.
@param range
@param hist Histogram.
@param method Comparison method passed to cvCompareHist (see the function description).
@param factor Normalization factor for histograms that affects the normalization scale of the
destination image. Pass 1 if not sure.
@see cvCalcBackProjectPatch
@brief Calculates back project
@see cvCalcBackProject, cv::calcBackProject
@overload
@brief Calculates array histogram
@see cv::calcHist
@brief Calculates bayesian probabilistic histograms
(each or src and dst is an array of _number_ histograms
@brief Copies a histogram.
The function makes a copy of the histogram. If the second histogram pointer \*dst is NULL, a new
histogram of the same size as src is created. Otherwise, both histograms must have equal types and
sizes. Then the function copies the bin values of the source histogram to the destination histogram
and sets the same bin value ranges as in src.
@param src Source histogram.
@param dst Pointer to the destination histogram.
Compares two histogram
@brief Thresholds the histogram.
The function clears histogram bins that are below the specified threshold.
@param hist Pointer to the histogram.
@param threshold Threshold level.
@brief Normalizes the histogram.
The function normalizes the histogram bins by scaling them so that the sum of the bins becomes equal
to factor.
@param hist Pointer to the histogram.
@param factor Normalization factor.
@brief Finds the minimum and maximum histogram bins.
The function finds the minimum and maximum histogram bins and their positions. All of output
arguments are optional. Among several extremas with the same value the ones with the minimum index
(in the lexicographical order) are returned. In case of several maximums or minimums, the earliest
in the lexicographical order (extrema locations) is returned.
@param hist Histogram.
@param min_value Pointer to the minimum value of the histogram.
@param max_value Pointer to the maximum value of the histogram.
@param min_idx Pointer to the array of coordinates for the minimum.
@param max_idx Pointer to the array of coordinates for the maximum.
@brief Clears the histogram.
The function sets all of the histogram bins to 0 in case of a dense histogram and removes all
histogram bins in case of a sparse array.
@param hist Histogram.
@brief Releases the histogram.
The function releases the histogram (header and the data). The pointer to the histogram is cleared
by the function. If \*hist pointer is already NULL, the function does nothing.
@param hist Double pointer to the released histogram.
@brief Makes a histogram out of an array.
The function initializes the histogram, whose header and bins are allocated by the user.
cvReleaseHist does not need to be called afterwards. Only dense histograms can be initialized this
way. The function returns hist.
@param dims Number of the histogram dimensions.
@param sizes Array of the histogram dimension sizes.
@param hist Histogram header initialized by the function.
@param data Array used to store histogram bins.
@param ranges Histogram bin ranges. See cvCreateHist for details.
@param uniform Uniformity flag. See cvCreateHist for details.
@brief Sets the bounds of the histogram bins.
This is a standalone function for setting bin ranges in the histogram. For a more detailed
description of the parameters ranges and uniform, see the :ocvCalcHist function that can initialize
the ranges as well. Ranges for the histogram bins must be set before the histogram is calculated or
the backproject of the histogram is calculated.
@param hist Histogram.
@param ranges Array of bin ranges arrays. See :ocvCreateHist for details.
@param uniform Uniformity flag. See :ocvCreateHist for details.
@brief Checks whether the point is inside polygon, outside, on an edge (at a vertex).
Returns positive, negative or zero value, correspondingly.
Optionally, measures a signed distance between
the point and the nearest polygon edge (measure_dist=1)
@see cv::pointPolygonTest
@brief Initializes sequence header for a matrix (column or row vector) of points
a wrapper for cvMakeSeqHeaderForArray (it does not initialize bounding rectangle!!!)
@brief Finds coordinates of the box vertices
@brief Finds minimum rectangle containing two given rectangles
@brief Fits ellipse into a set of 2d points
@see cv::fitEllipse
@brief Finds convexity defects for the contour
@see cv::convexityDefects
@brief Checks whether the contour is convex or not (returns 1 if convex, 0 if not)
@see cv::isContourConvex
@brief Calculates exact convex hull of 2d point set
@see cv::convexHull
@brief Compares two contours by matching their moments
@see cv::matchShapes
@brief Finds minimum enclosing circle for a set of points
@see cv::minEnclosingCircle
@brief Finds minimum area rotated rectangle bounding a set of points
@see cv::minAreaRect
@brief Calculates area of a contour or contour segment
@see cv::contourArea
@brief Calculates contour bounding rectangle (update=1) or
just retrieves pre-calculated rectangle (update=0)
@see cv::boundingRect
same as cvArcLength for closed contour
@brief Calculates perimeter of a contour or length of a part of contour
@see cv::arcLength
@brief Approximates a single polygonal curve (contour) or
a tree of polygonal curves (contours)
@see cv::approxPolyDP
@brief Retrieves the next chain point
@see cvApproxChains
@brief Initializes Freeman chain reader.
The reader is used to iteratively get coordinates of all the chain points.
If the Freeman codes should be read as is, a simple sequence reader should be used
@see cvApproxChains
@brief Approximates Freeman chain(s) with a polygonal curve.
This is a standalone contour approximation routine, not represented in the new interface. When
cvFindContours retrieves contours as Freeman chains, it calls the function to get approximated
contours, represented as polygons.
@param src_seq Pointer to the approximated Freeman chain that can refer to other chains.
@param storage Storage location for the resulting polylines.
@param method Approximation method (see the description of the function :ocvFindContours ).
@param parameter Method parameter (not used now).
@param minimal_perimeter Approximates only those contours whose perimeters are not less than
minimal_perimeter . Other chains are removed from the resulting structure.
@param recursive Recursion flag. If it is non-zero, the function approximates all chains that can
be obtained from chain by using the h_next or v_next links. Otherwise, the single input chain is
approximated.
@see cvStartReadChainPoints, cvReadChainPoint
@brief Releases contour scanner and returns pointer to the first outer contour
@see cvFindContours
@brief Substitutes the last retrieved contour with the new one
(if the substitutor is null, the last retrieved contour is removed from the tree)
@see cvFindContours
@brief Retrieves next contour
@see cvFindContours
@brief Initializes contour retrieving process.
Calls cvStartFindContours.
Calls cvFindNextContour until null pointer is returned
or some other condition becomes true.
Calls cvEndFindContours at the end.
@see cvFindContours
@brief Retrieves outer and optionally inner boundaries of white (non-zero) connected
components in the black (zero) background
@see cv::findContours, cvStartFindContours, cvFindNextContour, cvSubstituteContour, cvEndFindContours
@brief Computes earth mover distance between
two weighted point sets (called signatures)
@see cv::EMD
@brief Measures similarity between template and overlapped windows in the source image
and fills the resultant image with the measurements
@see cv::matchTemplate
@brief Fetches pixels that belong to the specified line segment and stores them to the buffer.
Returns the number of retrieved points.
@see cv::LineSegmentDetector
@brief Calculates 7 Hu's invariants from precalculated spatial and central moments
@see cv::HuMoments
@brief Retrieve normalized central moments
@brief Retrieve central moments
@brief Retrieve spatial moments
@brief Calculates all spatial and central moments up to the 3rd order
@see cv::moments
@brief Performs complex morphological transformation
@see cv::morphologyEx
@brief dilates input image (applies maximum filter) one or more times.
If element pointer is NULL, 3x3 rectangular element is used
@see cv::dilate
@brief erodes input image (applies minimum filter) one or more times.
If element pointer is NULL, 3x3 rectangular element is used
@see cv::erode
@brief releases structuring element
@see cvCreateStructuringElementEx
@brief Computes the original (undistorted) feature coordinates
from the observed (distorted) coordinates
@see cv::undistortPoints
@brief Computes undistortion+rectification map for a head of stereo camera
@see cv::initUndistortRectifyMap
@brief Computes transformation map from intrinsic camera parameters
that can used by cvRemap
@brief Transforms the input image to compensate lens distortion
@see cv::undistort
Performs forward or inverse linear-polar image transform
@see cv::linearPolar
@brief Performs forward or inverse log-polar image transform
@see cv::logPolar
@brief Performs generic geometric transformation using the specified coordinate maps
@see cv::remap
@brief Computes perspective transform matrix for mapping src[i] to dst[i] (i=0,1,2,3)
@see cv::getPerspectiveTransform
@brief Warps image with perspective (projective) transform
@see cv::warpPerspective
@brief Computes rotation_matrix matrix
@see cv::getRotationMatrix2D
@brief Computes affine transform matrix for mapping src[i] to dst[i] (i=0,1,2)
@see cv::getAffineTransform
@brief Warps image with affine transform
@note ::cvGetQuadrangleSubPix is similar to ::cvWarpAffine, but the outliers are extrapolated using
replication border mode.
@see cv::warpAffine
@brief Resizes image (input array is resized to fit the destination array)
@see cv::resize
@brief Converts input array pixels from one color space to another
@see cv::cvtColor
@brief Calculates the image Laplacian: (d2/dx + d2/dy)I
@see cv::Laplacian
@brief Calculates an image derivative using generalized Sobel
(aperture_size = 1,3,5,7) or Scharr (aperture_size = -1) operator.
Scharr can be used only for the first dx or dy derivative
@see cv::Sobel
@brief Segments image using seed "markers"
@see cv::watershed
@brief Filters image using meanshift algorithm
@see cv::pyrMeanShiftFiltering
@brief Releases pyramid
@brief Builds pyramid for an image
@see buildPyramid
@brief Up-samples image and smoothes the result with gaussian kernel.
dst_width = src_width*2,
dst_height = src_height*2
@see cv::pyrUp
@brief Smoothes the input image with gaussian kernel and then down-samples it.
dst_width = floor(src_width/2)[+1],
dst_height = floor(src_height/2)[+1]
@see cv::pyrDown
@brief Convolves an image with the kernel.
@param src input image.
@param dst output image of the same size and the same number of channels as src.
@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
matrix; if you want to apply different kernels to different channels, split the image into
separate color planes using split and process them individually.
@param anchor anchor of the kernel that indicates the relative position of a filtered point within
the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
is at the kernel center.
@see cv::filter2D
@brief Smooths the image in one of several ways.
@param src The source image
@param dst The destination image
@param smoothtype Type of the smoothing, see SmoothMethod_c
@param size1 The first parameter of the smoothing operation, the aperture width. Must be a
positive odd number (1, 3, 5, ...)
@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by
CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if
size2 is zero, it is set to size1. Otherwise it must be a positive odd number.
@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$
(standard deviation). If it is zero, it is calculated from the kernel size:
\f[\sigma = 0.3 (n/2 - 1) + 0.8 \quad \text{where} \quad n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f]
Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If
sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the
sigma (to provide accurate enough operation).
@param sigma2 additional parameter for bilateral filtering
@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter.
Copies source 2D array inside of the larger destination array and
makes a border of the specified type (IPL_BORDER_*) around the copied area.
@brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha
@see cv::accumulateWeighted
@brief Adds a product of two images to accumulator
@see cv::accumulateProduct
@brief Adds squared image to accumulator
@see cv::accumulateSquare
@addtogroup imgproc_c
@{
@brief Adds image to accumulator
@see cv::accumulate
Convexity defect
@brief Shape matching methods
\f$A\f$ denotes object1,\f$B\f$ denotes object2
\f$\begin{array}{l} m^A_i = \mathrm{sign} (h^A_i) \cdot \log{h^A_i} \\ m^B_i = \mathrm{sign} (h^B_i) \cdot \log{h^B_i} \end{array}\f$
and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively.
Freeman chain reader state
Hu invariants
Spatial and central moments
Shapes of a structuring element for morphological operations
@see cv::MorphShapes, cv::getStructuringElement
Image smooth methods
bilateral filter with a \f$\texttt{size1}\times\texttt{size1}\f$ square aperture, color
sigma= sigma1 and spatial sigma= sigma2. If size1=0, the aperture square side is set to
cvRound(sigma2\*1.5)\*2+1. See cv::bilateralFilter
median filter with a \f$\texttt{size1}\times\texttt{size1}\f$ square aperture
linear convolution with a \f$\texttt{size1}\times\texttt{size2}\f$ Gaussian kernel
linear convolution with \f$\texttt{size1}\times\texttt{size2}\f$ box kernel (all
1's) with subsequent scaling by \f$1/(\texttt{size1}\cdot\texttt{size2})\f$
linear convolution with \f$\texttt{size1}\times\texttt{size2}\f$ box kernel (all 1's). If
you want to smooth different pixels with different-size box kernels, you can use the integral
image that is computed using integral
@addtogroup imgproc_c
@{
Connected component structure
@brief returns coordinates of the current pixel
@brief postfix increment operator (it++). shifts iterator to the next pixel
@brief prefix increment operator (++it). shifts iterator to the next pixel
@brief returns pointer to the current pixel
@brief intializes the iterator
creates iterators for the line connecting pt1 and pt2
the line will be clipped on the image boundaries
the line is 8-connected or 4-connected
If leftToRight=true, then the iteration is always done
from the left-most point to the right most,
not to depend on the ordering of pt1 and pt2 parameters
@brief Draws a text string.
The function putText renders the specified text string in the image. Symbols that cannot be rendered
using the specified font are replaced by question marks. See getTextSize for a text rendering code
example.
@param img Image.
@param text Text string to be drawn.
@param org Bottom-left corner of the text string in the image.
@param fontFace Font type, see cv::HersheyFonts.
@param fontScale Font scale factor that is multiplied by the font-specific base size.
@param color Text color.
@param thickness Thickness of the lines used to draw a text.
@param lineType Line type. See the line for details.
@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise,
it is at the top-left corner.
@brief Approximates an elliptic arc with a polyline.
The function ellipse2Poly computes the vertices of a polyline that approximates the specified
elliptic arc. It is used by cv::ellipse.
@param center Center of the arc.
@param axes Half of the size of the ellipse main axes. See the ellipse for details.
@param angle Rotation angle of the ellipse in degrees. See the ellipse for details.
@param arcStart Starting angle of the elliptic arc in degrees.
@param arcEnd Ending angle of the elliptic arc in degrees.
@param delta Angle between the subsequent polyline vertices. It defines the approximation
accuracy.
@param pts Output vector of polyline vertices.
@overload
@param imgRect Image rectangle.
@param pt1 First line point.
@param pt2 Second line point.
@brief Clips the line against the image rectangle.
The functions clipLine calculate a part of the line segment that is entirely within the specified
rectangle. They return false if the line segment is completely outside the rectangle. Otherwise,
they return true .
@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) .
@param pt1 First line point.
@param pt2 Second line point.
@brief Draws several polygonal curves.
@param img Image.
@param pts Array of polygonal curves.
@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
the function draws a line from the last vertex of each curve to its first vertex.
@param color Polyline color.
@param thickness Thickness of the polyline edges.
@param lineType Type of the line segments. See the line description.
@param shift Number of fractional bits in the vertex coordinates.
The function polylines draws one or more polygonal curves.
@overload
@brief Fills the area bounded by one or more polygons.
The function fillPoly fills an area bounded by several polygonal contours. The function can fill
complex areas, for example, areas with holes, contours with self-intersections (some of their
parts), and so forth.
@param img Image.
@param pts Array of polygons where each polygon is represented as an array of points.
@param color Polygon color.
@param lineType Type of the polygon boundaries. See the line description.
@param shift Number of fractional bits in the vertex coordinates.
@param offset Optional offset of all points of the contours.
@overload
@brief Fills a convex polygon.
The function fillConvexPoly draws a filled convex polygon. This function is much faster than the
function cv::fillPoly . It can fill not only convex polygons but any monotonic polygon without
self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line)
twice at the most (though, its top-most and/or the bottom edge could be horizontal).
@param img Image.
@param points Polygon vertices.
@param color Polygon color.
@param lineType Type of the polygon boundaries. See the line description.
@param shift Number of fractional bits in the vertex coordinates.
@overload
@overload
@param img Image.
@param box Alternative ellipse representation via RotatedRect. This means that the function draws
an ellipse inscribed in the rotated rectangle.
@param color Ellipse color.
@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
a filled ellipse sector is to be drawn.
@param lineType Type of the ellipse boundary. See the line description.
@brief Draws a simple or thick elliptic arc or fills an ellipse sector.
The functions ellipse with less parameters draw an ellipse outline, a filled ellipse, an elliptic
arc, or a filled ellipse sector. A piecewise-linear curve is used to approximate the elliptic arc
boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
ellipse2Poly and then render it with polylines or fill it with fillPoly . If you use the first
variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and
endAngle=360 . The figure below explains the meaning of the parameters.
@param img Image.
@param center Center of the ellipse.
@param axes Half of the size of the ellipse main axes.
@param angle Ellipse rotation angle in degrees.
@param startAngle Starting angle of the elliptic arc in degrees.
@param endAngle Ending angle of the elliptic arc in degrees.
@param color Ellipse color.
@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
a filled ellipse sector is to be drawn.
@param lineType Type of the ellipse boundary. See the line description.
@param shift Number of fractional bits in the coordinates of the center and values of axes.
@brief Draws a circle.
The function circle draws a simple or filled circle with a given center and radius.
@param img Image where the circle is drawn.
@param center Center of the circle.
@param radius Radius of the circle.
@param color Circle color.
@param thickness Thickness of the circle outline, if positive. Negative thickness means that a
filled circle is to be drawn.
@param lineType Type of the circle boundary. See the line description.
@param shift Number of fractional bits in the coordinates of the center and in the radius value.
@overload
use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and
r.br()-Point(1,1)` are opposite corners
@brief Draws a simple, thick, or filled up-right rectangle.
The function rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
are pt1 and pt2.
@param img Image.
@param pt1 Vertex of the rectangle.
@param pt2 Vertex of the rectangle opposite to pt1 .
@param color Rectangle color or brightness (grayscale image).
@param thickness Thickness of lines that make up the rectangle. Negative values, like CV_FILLED ,
mean that the function has to draw a filled rectangle.
@param lineType Type of the line. See the line description.
@param shift Number of fractional bits in the point coordinates.
@brief Draws a arrow segment pointing from the first point to the second one.
The function arrowedLine draws an arrow between pt1 and pt2 points in the image. See also cv::line.
@param img Image.
@param pt1 The point the arrow starts from.
@param pt2 The point the arrow points to.
@param color Line color.
@param thickness Line thickness.
@param line_type Type of the line, see cv::LineTypes
@param shift Number of fractional bits in the point coordinates.
@param tipLength The length of the arrow tip in relation to the arrow length
@brief Draws a line segment connecting two points.
The function line draws the line segment between pt1 and pt2 points in the image. The line is
clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
lines are drawn using Gaussian filtering.
@param img Image.
@param pt1 First point of the line segment.
@param pt2 Second point of the line segment.
@param color Line color.
@param thickness Line thickness.
@param lineType Type of the line, see cv::LineTypes.
@param shift Number of fractional bits in the point coordinates.
@brief Applies a GNU Octave/MATLAB equivalent colormap on a given image.
@param src The source image, grayscale or colored does not matter.
@param dst The result is the colormapped source image. Note: Mat::create is called on dst.
@param colormap The colormap to apply, see cv::ColormapTypes
@brief Performs a point-in-contour test.
The function determines whether the point is inside a contour, outside, or lies on an edge (or
coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge)
value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively.
Otherwise, the return value is a signed distance between the point and the nearest contour edge.
See below a sample output of the function where each image pixel is tested against the contour:
@param contour Input contour.
@param pt Point tested against the contour.
@param measureDist If true, the function estimates the signed distance from the point to the
nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.
@brief Finds the convexity defects of a contour.
The figure below displays convexity defects of a hand contour:
@param contour Input contour.
@param convexhull Convex hull obtained using convexHull that should contain indices of the contour
points that make the hull.
@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java
interface each convexity defect is represented as 4-element integer vector (a.k.a. cv::Vec4i):
(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices
in the original contour of the convexity defect beginning, end and the farthest point, and
fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the
farthest contour point and the hull. That is, to get the floating-point value of the depth will be
fixpt_depth/256.0.
@brief Compares two shapes.
The function compares two shapes. All three implemented methods use the Hu invariants (see cv::HuMoments)
@param contour1 First contour or grayscale image.
@param contour2 Second contour or grayscale image.
@param method Comparison method, see ::ShapeMatchModes
@param parameter Method-specific parameter (not supported now).
@brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
The function finds the four vertices of a rotated rectangle. This function is useful to draw the
rectangle. In C++, instead of using this function, you can directly use box.points() method. Please
visit the [tutorial on bounding
rectangle](http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html#bounding-rects-circles)
for more information.
@param box The input rotated rectangle. It may be the output of
@param points The output array of four vertices of rectangles.
@brief Calculates the up-right bounding rectangle of a point set.
The function calculates and returns the minimal up-right bounding rectangle for the specified point set.
@param points Input 2D point set, stored in std::vector or Mat.
@brief Calculates a contour perimeter or a curve length.
The function computes a curve length or a closed contour perimeter.
@param curve Input vector of 2D points, stored in std::vector or Mat.
@param closed Flag indicating whether the curve is closed or not.
@overload
@brief Finds contours in a binary image.
The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours
are a useful tool for shape analysis and object detection and recognition. See squares.c in the
OpenCV sample directory.
@note Source image is modified by this function. Also, the function does not take into account
1-pixel border of the image (it's filled with 0's and used for neighbor analysis in the algorithm),
therefore the contours touching the image border will be clipped.
@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
pixels remain 0's, so the image is treated as binary . You can use compare , inRange , threshold ,
adaptiveThreshold , Canny , and others to create a binary image out of a grayscale or color one.
The function modifies the image while extracting the contours. If mode equals to RETR_CCOMP
or RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
@param contours Detected contours. Each contour is stored as a vector of points.
@param hierarchy Optional output vector, containing information about the image topology. It has
as many elements as the number of contours. For each i-th contour contours[i] , the elements
hierarchy[i][0] , hiearchy[i][1] , hiearchy[i][2] , and hiearchy[i][3] are set to 0-based indices
in contours of the next and previous contours at the same hierarchical level, the first child
contour and the parent contour, respectively. If for the contour i there are no next, previous,
parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
@param mode Contour retrieval mode, see cv::RetrievalModes
@param method Contour approximation method, see cv::ContourApproximationModes
@param offset Optional offset by which every contour point is shifted. This is useful if the
contours are extracted from the image ROI and then they should be analyzed in the whole image
context.
@overload
@param image the image to be labeled
@param labels destination labeled image
@param stats statistics output for each label, including the background label, see below for
available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
cv::ConnectedComponentsTypes
@param centroids floating point centroid (x,y) output for each label, including the background label
@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
@param ltype output image label type. Currently CV_32S and CV_16U are supported.
@brief computes the connected components labeled image of boolean image
image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
represents the background label. ltype specifies the output label image type, an important
consideration based on the total number of labels or alternatively the total number of pixels in
the source image.
@param image the image to be labeled
@param labels destination labeled image
@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
@param ltype output image label type. Currently CV_32S and CV_16U are supported.
@brief Compares a template against overlapped image regions.
The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against
templ using the specified method and stores the comparison results in result . Here are the formulae
for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation
is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$
After the function finishes the comparison, the best matches can be found as global minimums (when
TM_SQDIFF was used) or maximums (when TM_CCORR or TM_CCOEFF was used) using the
minMaxLoc function. In case of a color image, template summation in the numerator and each sum in
the denominator is done over all of the channels and separate mean values are used for each channel.
That is, the function can take a color template and a color image. The result will still be a
single-channel image, which is easier to analyze.
@param image Image where the search is running. It must be 8-bit or 32-bit floating-point.
@param templ Searched template. It must be not greater than the source image and have the same
data type.
@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image
is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ .
@param method Parameter specifying the comparison method, see cv::TemplateMatchModes
@param mask Mask of searched template. It must have the same datatype and size with templ. It is
not set by default.
@overload
@brief Calculates all of the moments up to the third order of a polygon or rasterized shape.
The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
results are returned in the structure cv::Moments.
@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array (
\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ).
@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is
used for images only.
@returns moments.
@sa contourArea, arcLength
@brief Converts an image from one color space to another.
The function converts an input image from one color space to another. In case of a transformation
to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note
that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the
bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue
component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and
sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.
The conventional ranges for R, G, and B channel values are:
- 0 to 255 for CV_8U images
- 0 to 65535 for CV_16U images
- 0 to 1 for CV_32F images
In case of linear transformations, the range does not matter. But in case of a non-linear
transformation, an input RGB image should be normalized to the proper value range to get the correct
results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a
32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will
have the 0..255 value range instead of 0..1 assumed by the function. So, before calling cvtColor ,
you need first to scale the image down:
@code
img *= 1./255;
cvtColor(img, img, COLOR_BGR2Luv);
@endcode
If you use cvtColor with 8-bit images, the conversion will have some information lost. For many
applications, this will not be noticeable but it is recommended to use 32-bit images in applications
that need the full range of colors or that convert an image before an operation and then convert
back.
If conversion adds the alpha channel, its value will set to the maximum of corresponding channel
range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision
floating-point.
@param dst output image of the same size and depth as src.
@param code color space conversion code (see cv::ColorConversionCodes).
@param dstCn number of channels in the destination image; if the parameter is 0, the number of the
channels is derived automatically from src and code.
@see @ref imgproc_color_conversions
@example ffilldemo.cpp
An example using the FloodFill technique
@overload
variant without `mask` parameter
@overload
@param src 8-bit, single-channel (binary) source image.
@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
single-channel image of the same size as src .
@param distanceType Type of distance, see cv::DistanceTypes
@param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks. In case of the
DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives
the same result as \f$5\times 5\f$ or any larger aperture.
@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for
the first variant of the function and distanceType == DIST_L1.
@example distrans.cpp
An example on using the distance transform
@brief Calculates the distance to the closest zero pixel for each pixel of the source image.
The functions distanceTransform calculate the approximate or precise distance from every binary
image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
When maskSize == DIST_MASK_PRECISE and distanceType == DIST_L2 , the function runs the
algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library.
In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function
finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical,
diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall
distance is calculated as a sum of these basic distances. Since the distance function should be
symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all
the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the
same cost (denoted as `c`). For the cv::DIST_C and cv::DIST_L1 types, the distance is calculated
precisely, whereas for cv::DIST_L2 (Euclidean distance) the distance can be calculated only with a
relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV
uses the values suggested in the original paper:
- DIST_L1: `a = 1, b = 2`
- DIST_L2:
- `3 x 3`: `a=0.955, b=1.3693`
- `5 x 5`: `a=1, b=1.4, c=2.1969`
- DIST_C: `a = 1, b = 1`
Typically, for a fast, coarse distance estimation DIST_L2, a \f$3\times 3\f$ mask is used. For a
more accurate distance estimation DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used.
Note that both the precise and the approximate algorithms are linear on the number of pixels.
This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$
but also identifies the nearest connected component consisting of zero pixels
(labelType==DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==DIST_LABEL_PIXEL). Index of the
component/pixel is stored in `labels(x, y)`. When labelType==DIST_LABEL_CCOMP, the function
automatically finds connected components of zero pixels in the input image and marks them with
distinct labels. When labelType==DIST_LABEL_CCOMP, the function scans through the input image and
marks all the zero pixels with distinct labels.
In this mode, the complexity is still linear. That is, the function provides a very fast way to
compute the Voronoi diagram for a binary image. Currently, the second variant can use only the
approximate distance transform algorithm, i.e. maskSize=DIST_MASK_PRECISE is not supported
yet.
@param src 8-bit, single-channel (binary) source image.
@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
single-channel image of the same size as src.
@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type
CV_32SC1 and the same size as src.
@param distanceType Type of distance, see cv::DistanceTypes
@param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks.
DIST_MASK_PRECISE is not supported by this variant. In case of the DIST_L1 or DIST_C distance type,
the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times
5\f$ or any larger aperture.
@param labelType Type of the label array to build, see cv::DistanceTransformLabelTypes.
@example grabcut.cpp
An example using the GrabCut algorithm
@brief Runs the GrabCut algorithm.
The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).
@param img Input 8-bit 3-channel image.
@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when
mode is set to GC_INIT_WITH_RECT. Its elements may have one of the cv::GrabCutClasses.
@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as
"obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
@param bgdModel Temporary array for the background model. Do not modify it while you are
processing the same image.
@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are
processing the same image.
@param iterCount Number of iterations the algorithm should make before returning the result. Note
that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or
mode==GC_EVAL .
@param mode Operation mode that could be one of the cv::GrabCutModes
@example watershed.cpp
An example using the watershed algorithm
@brief Performs a marker-based image segmentation using the watershed algorithm.
The function implements one of the variants of watershed, non-parametric marker-based segmentation
algorithm, described in @cite Meyer92 .
Before passing the image to the function, you have to roughly outline the desired regions in the
image markers with positive (\>0) indices. So, every region is represented as one or more connected
components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary
mask using findContours and drawContours (see the watershed.cpp demo). The markers are "seeds" of
the future image regions. All the other pixels in markers , whose relation to the outlined regions
is not known and should be defined by the algorithm, should be set to 0's. In the function output,
each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the
regions.
@note Any two neighbor connected components are not necessarily separated by a watershed boundary
(-1's pixels); for example, they can touch each other in the initial marker image passed to the
function.
@param image Input 8-bit 3-channel image.
@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same
size as image .
@sa findContours
@ingroup imgproc_misc
@brief Computes the "minimal work" distance between two weighted point configurations.
The function computes the earth mover distance and/or a lower boundary of the distance between the
two weighted point configurations. One of the applications described in @cite RubnerSept98,
@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation
problem that is solved using some modification of a simplex algorithm, thus the complexity is
exponential in the worst case, though, on average it is much faster. In the case of a real metric
the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used
to determine roughly whether the two signatures are far enough so that they cannot relate to the
same object.
@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix.
Each row stores the point weight followed by the point coordinates. The matrix is allowed to have
a single column (weights only) if the user-defined cost matrix is used.
@param signature2 Second signature of the same format as signature1 , though the number of rows
may be different. The total weights may be different. In this case an extra "dummy" point is added
to either signature1 or signature2 .
@param distType Used metric. See cv::DistanceTypes.
@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix
is used, lower boundary lowerBound cannot be calculated because it needs a metric function.
@param lowerBound Optional input/output parameter: lower boundary of a distance between the two
signatures that is a distance between mass centers. The lower boundary may not be calculated if
the user-defined cost matrix is used, the total weights of point configurations are not equal, or
if the signatures consist of weights only (the signature matrices have a single column). You
**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or
equal to \*lowerBound (it means that the signatures are far enough), the function does not
calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on
return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound
should be set to 0.
@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is
a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 .
@overload
@brief Compares two histograms.
The function compare two dense or two sparse histograms using the specified method.
The function returns \f$d(H_1, H_2)\f$ .
While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable
for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling
problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms
or more general sparse configurations of weighted points, consider using the cv::EMD function.
@param H1 First compared histogram.
@param H2 Second compared histogram of the same size as H1 .
@param method Comparison method, see cv::HistCompMethods
@overload
@overload
@brief Calculates the back projection of a histogram.
The functions calcBackProject calculate the back project of the histogram. That is, similarly to
cv::calcHist , at each location (x, y) the function collects the values from the selected channels
in the input images and finds the corresponding histogram bin. But instead of incrementing it, the
function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of
statistics, the function computes probability of each element value in respect with the empirical
probability distribution represented by the histogram. See how, for example, you can find and track
a bright-colored object in a scene:
- Before tracking, show the object to the camera so that it covers almost the whole frame.
Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant
colors in the object.
- When tracking, calculate a back projection of a hue plane of each input video frame using that
pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make
sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels.
- Find connected components in the resulting picture and choose, for example, the largest
component.
This is an approximate algorithm of the CamShift color object tracker.
@param images Source arrays. They all should have the same depth, CV_8U or CV_32F , and the same
size. Each of them can have an arbitrary number of channels.
@param nimages Number of source images.
@param channels The list of channels used to compute the back projection. The number of channels
must match the histogram dimensionality. The first array channels are numerated from 0 to
images[0].channels()-1 , the second array channels are counted from images[0].channels() to
images[0].channels() + images[1].channels()-1, and so on.
@param hist Input histogram that can be dense or sparse.
@param backProject Destination back projection array that is a single-channel array of the same
size and depth as images[0] .
@param ranges Array of arrays of the histogram bin boundaries in each dimension. See calcHist .
@param scale Optional scale factor for the output back projection.
@param uniform Flag indicating whether the histogram is uniform or not (see above).
@sa cv::calcHist, cv::compareHist
@overload
@overload
this variant uses cv::SparseMat for output
@brief Computes the ideal point coordinates from the observed point coordinates.
The function is similar to cv::undistort and cv::initUndistortRectifyMap but it operates on a
sparse set of points instead of a raster image. Also the function performs a reverse transformation
to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
planar object, it does, up to a translation vector, if the proper R is specified.
@code
// (u,v) is the input point, (u', v') is the output point
// camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1]
// P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz]
x" = (u - cx)/fx
y" = (v - cy)/fy
(x',y') = undistort(x",y",dist_coeffs)
[X,Y,W]T = R*[x' y' 1]T
x = X/W, y = Y/W
// only performed if P=[fx' 0 cx' [tx]; 0 fy' cy' [ty]; 0 0 1 [tz]] is specified
u' = x*fx' + cx'
v' = y*fy' + cy',
@endcode
where cv::undistort is an approximate iterative algorithm that estimates the normalized original
point coordinates out of the normalized distorted point coordinates ("normalized" means that the
coordinates do not depend on the camera matrix).
The function can be used for both a stereo camera head or a monocular camera (when R is empty).
@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
@param dst Output ideal point coordinates after undistortion and reverse perspective
transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@param distCoeffs Input vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])\f$ of 4, 5, or 8 elements. If the vector is
NULL/empty, the zero distortion coefficients are assumed.
@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by
cv::stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
@param P New camera matrix (3x3) or new projection matrix (3x4). P1 or P2 computed by
cv::stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.
@brief Computes the undistortion and rectification transformation map.
The function computes the joint undistortion and rectification transformation and represents the
result in the form of maps for remap. The undistorted image looks like original, as if it is
captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a
monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by
cv::getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera,
newCameraMatrix is normally set to P1 or P2 computed by cv::stereoRectify .
Also, this new camera is oriented differently in the coordinate space, according to R. That, for
example, helps to align two heads of a stereo camera so that the epipolar lines on both images
become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
The function actually builds the maps for the inverse mapping algorithm that is used by remap. That
is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function
computes the corresponding coordinates in the source image (that is, in the original image from
camera). The following process is applied:
\f[\begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ x" \leftarrow x' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + 2p_1 x' y' + p_2(r^2 + 2 x'^2) \\ y" \leftarrow y' (1 + k_1 r^2 + k_2 r^4 + k_3 r^6) + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' \\ map_x(u,v) \leftarrow x" f_x + c_x \\ map_y(u,v) \leftarrow y" f_y + c_y \end{array}\f]
where \f$(k_1, k_2, p_1, p_2[, k_3])\f$ are the distortion coefficients.
In case of a stereo camera, this function is called twice: once for each camera head, after
stereoRectify, which in its turn is called after cv::stereoCalibrate. But if the stereo camera
was not calibrated, it is still possible to compute the rectification transformations directly from
the fundamental matrix using cv::stereoRectifyUncalibrated. For each camera, the function computes
homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
space. R can be computed from H as
\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f]
where cameraMatrix can be chosen arbitrarily.
@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@param distCoeffs Input vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])\f$ of 4, 5, or 8 elements. If the vector is
NULL/empty, the zero distortion coefficients are assumed.
@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 ,
computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation
is assumed. In cvInitUndistortMap R assumed to be an identity matrix.
@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$.
@param size Undistorted image size.
@param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2, see cv::convertMaps
@param map1 The first output map.
@param map2 The second output map.
@brief Transforms an image to compensate for lens distortion.
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of cv::initUndistortRectifyMap (with unity R ) and cv::remap
(with bilinear interpolation). See the former function for details of the transformation being
performed.
Those pixels in the destination image, for which there is no correspondent pixels in the source
image, are filled with zeros (black color).
A particular subset of the source image that will be visible in the corrected image can be regulated
by newCameraMatrix. You can use cv::getOptimalNewCameraMatrix to compute the appropriate
newCameraMatrix depending on your requirements.
The camera matrix and the distortion parameters can be determined using cv::calibrateCamera. If
the resolution of images is different from the resolution used at the calibration stage, \f$f_x,
f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain
the same.
@param src Input (distorted) image.
@param dst Output (corrected) image that has the same size and type as src .
@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@param distCoeffs Input vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6]])\f$ of 4, 5, or 8 elements. If the vector is
NULL/empty, the zero distortion coefficients are assumed.
@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as
cameraMatrix but you may additionally scale and shift the result by using a different matrix.
@brief Constructs the Gaussian pyramid for an image.
The function constructs a vector of images and builds the Gaussian pyramid by recursively applying
pyrDown to the previously built pyramid layers, starting from `dst[0]==src`.
@param src Source image. Check pyrDown for the list of supported types.
@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the
same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on.
@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative.
@param borderType Pixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported)
@brief Upsamples an image and then blurs it.
By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any
case, the following conditions should be satisfied:
\f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\f]
The function performs the upsampling step of the Gaussian pyramid construction, though it can
actually be used to construct the Laplacian pyramid. First, it upsamples the source image by
injecting even zero rows and columns and then convolves the result with the same kernel as in
pyrDown multiplied by 4.
@param src input image.
@param dst output image. It has the specified size and the same type as src .
@param dstsize size of the output image.
@param borderType Pixel extrapolation method, see cv::BorderTypes (only BORDER_DEFAULT is supported)
@brief Applies an adaptive threshold to an array.
The function transforms a grayscale image to a binary image according to the formulae:
- **THRESH_BINARY**
\f[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f]
- **THRESH_BINARY_INV**
\f[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f]
where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter).
The function can process the image in-place.
@param src Source 8-bit single-channel image.
@param dst Destination image of the same size and the same type as src.
@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied
@param adaptiveMethod Adaptive thresholding algorithm to use, see cv::AdaptiveThresholdTypes
@param thresholdType Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV,
see cv::ThresholdTypes.
@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the
pixel: 3, 5, 7, and so on.
@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it
is positive but may be zero or negative as well.
@sa threshold, blur, GaussianBlur
@brief Applies a fixed-level threshold to each array element.
The function applies fixed-level thresholding to a single-channel array. The function is typically
used to get a bi-level (binary) image out of a grayscale image ( cv::compare could be also used for
this purpose) or for removing a noise, that is, filtering out pixels with too small or too large
values. There are several types of thresholding supported by the function. They are determined by
type parameter.
Also, the special values cv::THRESH_OTSU or cv::THRESH_TRIANGLE may be combined with one of the
above values. In these cases, the function determines the optimal threshold value using the Otsu's
or Triangle algorithm and uses it instead of the specified thresh . The function returns the
computed threshold value. Currently, the Otsu's and Triangle methods are implemented only for 8-bit
images.
@param src input array (single-channel, 8-bit or 32-bit floating point).
@param dst output array of the same size and type as src.
@param thresh threshold value.
@param maxval maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding
types.
@param type thresholding type (see the cv::ThresholdTypes).
@sa adaptiveThreshold, findContours, compare, min, max
@brief This function computes a Hanning window coefficients in two dimensions.
See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function)
for more information.
An example is shown below:
@code
// create hanning window of size 100x100 and type CV_32F
Mat hann;
createHanningWindow(hann, Size(100, 100), CV_32F);
@endcode
@param dst Destination array to place Hann coefficients in
@param winSize The window size specifications
@param type Created array type
@brief Updates a running average.
The function calculates the weighted sum of the input image src and the accumulator dst so that dst
becomes a running average of a frame sequence:
\f[\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f]
That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images).
The function supports multi-channel images. Each channel is processed independently.
@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
floating-point.
@param alpha Weight of the input image.
@param mask Optional operation mask.
@sa accumulate, accumulateSquare, accumulateProduct
@brief Adds the per-element product of two input images to the accumulator.
The function adds the product of two images or their selected regions to the accumulator dst :
\f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f]
The function supports multi-channel images. Each channel is processed independently.
@param src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
@param src2 Second input image of the same type and the same size as src1 .
@param dst %Accumulator with the same number of channels as input images, 32-bit or 64-bit
floating-point.
@param mask Optional operation mask.
@sa accumulate, accumulateSquare, accumulateWeighted
@brief Adds the square of a source image to the accumulator.
The function adds the input image src or its selected region, raised to a power of 2, to the
accumulator dst :
\f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y)^2 \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f]
The function supports multi-channel images. Each channel is processed independently.
@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
floating-point.
@param mask Optional operation mask.
@sa accumulateSquare, accumulateProduct, accumulateWeighted
@brief Adds an image to the accumulator.
The function adds src or some of its elements to dst :
\f[\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\f]
The function supports multi-channel images. Each channel is processed independently.
The functions accumulate\* can be used, for example, to collect statistics of a scene background
viewed by a still camera and for the further foreground-background segmentation.
@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
floating-point.
@param mask Optional operation mask.
@sa accumulateSquare, accumulateProduct, accumulateWeighted
@overload
@overload
@brief Remaps an image to polar space.
transforms the source image using the following transformation:
\f[dst( \phi , \rho ) = src(x,y)\f]
where
\f[\rho = (src.width/maxRadius) \cdot \sqrt{x^2 + y^2} , \phi =atan(y/x)\f]
The function can not operate in-place.
@param src Source image
@param dst Destination image
@param center The transformation center;
@param maxRadius Inverse magnitude scale parameter
@param flags A combination of interpolation methods, see cv::InterpolationFlags
@example polar_transforms.cpp
An example using the cv::linearPolar and cv::logPolar operations
@brief Remaps an image to log-polar space.
transforms the source image using the following transformation:
\f[dst( \phi , \rho ) = src(x,y)\f]
where
\f[\rho = M \cdot \log{\sqrt{x^2 + y^2}} , \phi =atan(y/x)\f]
The function emulates the human "foveal" vision and can be used for fast scale and
rotation-invariant template matching, for object tracking and so forth. The function can not operate
in-place.
@param src Source image
@param dst Destination image
@param center The transformation center; where the output precision is maximal
@param M Magnitude scale parameter.
@param flags A combination of interpolation methods, see cv::InterpolationFlags
@brief Retrieves a pixel rectangle from an image with sub-pixel accuracy.
The function getRectSubPix extracts pixels from src:
\f[dst(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f]
where the values of the pixels at non-integer coordinates are retrieved using bilinear
interpolation. Every channel of multi-channel images is processed independently. While the center of
the rectangle must be inside the image, parts of the rectangle may be outside. In this case, the
replication border mode (see cv::BorderTypes) is used to extrapolate the pixel values outside of
the image.
@param image Source image.
@param patchSize Size of the extracted patch.
@param center Floating point coordinates of the center of the extracted rectangle within the
source image. The center must be inside the image.
@param patch Extracted patch that has the size patchSize and the same number of channels as src .
@param patchType Depth of the extracted pixels. By default, they have the same depth as src .
@sa warpAffine, warpPerspective
@brief Calculates a perspective transform from four pairs of the corresponding points.
The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that:
\f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map\_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
where
\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f]
@param src Coordinates of quadrangle vertices in the source image.
@param dst Coordinates of the corresponding quadrangle vertices in the destination image.
@sa findHomography, warpPerspective, perspectiveTransform
@brief Calculates an affine transform from three pairs of the corresponding points.
The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that:
\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map\_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
where
\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f]
@param src Coordinates of triangle vertices in the source image.
@param dst Coordinates of the corresponding triangle vertices in the destination image.
@sa warpAffine, transform
@brief Converts image transformation maps from one representation to another.
The function converts a pair of maps for remap from one representation to another. The following
options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are
supported:
- \f$\texttt{(CV\_32FC1, CV\_32FC1)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}\f$. This is the
most frequently used conversion operation, in which the original floating-point maps (see remap )
are converted to a more compact and much faster fixed-point representation. The first output array
contains the rounded coordinates and the second array (created only when nninterpolation=false )
contains indices in the interpolation tables.
- \f$\texttt{(CV\_32FC2)} \rightarrow \texttt{(CV\_16SC2, CV\_16UC1)}\f$. The same as above but
the original maps are stored in one 2-channel matrix.
- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same
as the originals.
@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix),
respectively.
@param dstmap1 The first output map that has the type dstmap1type and the same size as src .
@param dstmap2 The second output map.
@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or
CV_32FC2 .
@param nninterpolation Flag indicating whether the fixed-point maps are used for the
nearest-neighbor or for a more complex interpolation.
@sa remap, undistort, initUndistortRectifyMap
@brief Applies a generic geometrical transformation to an image.
The function remap transforms the source image using the specified map:
\f[\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\f]
where values of pixels with non-integer coordinates are computed using one of available
interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps
in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in
\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to
convert from floating to fixed-point representations of a map is that they can yield much faster
(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x),
cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients.
This function cannot operate in-place.
@param src Source image.
@param dst Destination image. It has the same size as map1 and the same type as src .
@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 ,
CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point
representation to fixed-point for speed.
@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
if map1 is (x,y) points), respectively.
@param interpolation Interpolation method (see cv::InterpolationFlags). The method INTER_AREA is
not supported by this function.
@param borderMode Pixel extrapolation method (see cv::BorderTypes). When
borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image that
corresponds to the "outliers" in the source image are not modified by the function.
@param borderValue Value used in case of a constant border. By default, it is 0.
@brief Applies a perspective transformation to an image.
The function warpPerspective transforms the source image using the specified matrix:
\f[\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
\frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
and then put in the formula above instead of M. The function cannot operate in-place.
@param src input image.
@param dst output image that has the size dsize and the same type as src .
@param M \f$3\times 3\f$ transformation matrix.
@param dsize size of the output image.
@param flags combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the
optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation (
\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
@param borderMode pixel extrapolation method (BORDER_CONSTANT or BORDER_REPLICATE).
@param borderValue value used in case of a constant border; by default, it equals 0.
@sa warpAffine, resize, remap, getRectSubPix, perspectiveTransform
@brief Applies an affine transformation to an image.
The function warpAffine transforms the source image using the specified matrix:
\f[\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\f]
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted
with cv::invertAffineTransform and then put in the formula above instead of M. The function cannot
operate in-place.
@param src input image.
@param dst output image that has the size dsize and the same type as src .
@param M \f$2\times 3\f$ transformation matrix.
@param dsize size of the output image.
@param flags combination of interpolation methods (see cv::InterpolationFlags) and the optional
flag WARP_INVERSE_MAP that means that M is the inverse transformation (
\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
@param borderMode pixel extrapolation method (see cv::BorderTypes); when
borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to
the "outliers" in the source image are not modified by the function.
@param borderValue value used in case of a constant border; by default, it is 0.
@sa warpPerspective, resize, remap, getRectSubPix, transform
@brief Resizes an image.
The function resize resizes the image src down to or up to the specified size. Note that the
initial dst type or size are not taken into account. Instead, the size and type are derived from
the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst,
you may call the function as follows:
@code
// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);
@endcode
If you want to decimate the image by factor of 2 in each direction, you can call the function this
way:
@code
// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);
@endcode
To shrink an image, it will generally look best with CV_INTER_AREA interpolation, whereas to
enlarge an image, it will generally look best with CV_INTER_CUBIC (slow) or CV_INTER_LINEAR
(faster but still looks OK).
@param src input image.
@param dst output image; it has the size dsize (when it is non-zero) or the size computed from
src.size(), fx, and fy; the type of dst is the same as of src.
@param dsize output image size; if it equals zero, it is computed as:
\f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
Either dsize or both fx and fy must be non-zero.
@param fx scale factor along the horizontal axis; when it equals 0, it is computed as
\f[\texttt{(double)dsize.width/src.cols}\f]
@param fy scale factor along the vertical axis; when it equals 0, it is computed as
\f[\texttt{(double)dsize.height/src.rows}\f]
@param interpolation interpolation method, see cv::InterpolationFlags
@sa warpAffine, warpPerspective, remap
@brief Performs advanced morphological transformations.
The function can perform advanced morphological transformations using an erosion and dilation as
basic operations.
Any of the operations can be done in-place. In case of multi-channel images, each channel is
processed independently.
@param src Source image. The number of channels can be arbitrary. The depth should be one of
CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
@param dst Destination image of the same size and type as src\` .
@param kernel Structuring element. It can be created using getStructuringElement.
@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
kernel center.
@param op Type of a morphological operation, see cv::MorphTypes
@param iterations Number of times erosion and dilation are applied.
@param borderType Pixel extrapolation method, see cv::BorderTypes
@param borderValue Border value in case of a constant border. The default value has a special
meaning.
@sa dilate, erode, getStructuringElement
@brief Dilates an image by using a specific structuring element.
The function dilates the source image using the specified structuring element that determines the
shape of a pixel neighborhood over which the maximum is taken:
\f[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In
case of multi-channel images, each channel is processed independently.
@param src input image; the number of channels can be arbitrary, but the depth should be one of
CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
@param dst output image of the same size and type as src\`.
@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular
structuring element is used. Kernel can be created using getStructuringElement
@param anchor position of the anchor within the element; default value (-1, -1) means that the
anchor is at the element center.
@param iterations number of times dilation is applied.
@param borderType pixel extrapolation method, see cv::BorderTypes
@param borderValue border value in case of a constant border
@sa erode, morphologyEx, getStructuringElement
@example morphology2.cpp
An example using the morphological operations
@brief Erodes an image by using a specific structuring element.
The function erodes the source image using the specified structuring element that determines the
shape of a pixel neighborhood over which the minimum is taken:
\f[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In
case of multi-channel images, each channel is processed independently.
@param src input image; the number of channels can be arbitrary, but the depth should be one of
CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
@param dst output image of the same size and type as src.
@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular
structuring element is used. Kernel can be created using getStructuringElement.
@param anchor position of the anchor within the element; default value (-1, -1) means that the
anchor is at the element center.
@param iterations number of times erosion is applied.
@param borderType pixel extrapolation method, see cv::BorderTypes
@param borderValue border value in case of a constant border
@sa dilate, morphologyEx, getStructuringElement
@brief Determines strong corners on an image.
The function finds the most prominent corners in the image or in the specified image region, as
described in @cite Shi94
- Function calculates the corner quality measure at every source image pixel using the
cornerMinEigenVal or cornerHarris .
- Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
retained).
- The corners with the minimal eigenvalue less than
\f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected.
- The remaining corners are sorted by the quality measure in the descending order.
- Function throws away each corner for which there is a stronger corner at a distance less than
maxDistance.
The function can be used to initialize a point-based tracker of an object.
@note If the function is called with different values A and B of the parameter qualityLevel , and
A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
with qualityLevel=B .
@param image Input 8-bit or floating-point 32-bit, single-channel image.
@param corners Output vector of detected corners.
@param maxCorners Maximum number of corners to return. If there are more corners than are found,
the strongest of them is returned.
@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
(see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the
quality measure less than the product are rejected. For example, if the best corner has the
quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
less than 15 are rejected.
@param minDistance Minimum possible Euclidean distance between the returned corners.
@param mask Optional region of interest. If the image is not empty (it needs to have the type
CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
@param blockSize Size of an average block for computing a derivative covariation matrix over each
pixel neighborhood. See cornerEigenValsAndVecs .
@param useHarrisDetector Parameter indicating whether to use a Harris detector (see cornerHarris)
or cornerMinEigenVal.
@param k Free parameter of the Harris detector.
@sa cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
@brief Refines the corner locations.
The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as
shown on the figure below.
Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$
to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$
subject to image and measurement noise. Consider the expression:
\f[\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\f]
where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The
value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up
with \f$\epsilon_i\f$ set to zero:
\f[\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\f]
where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first
gradient term \f$G\f$ and the second gradient term \f$b\f$ gives:
\f[q = G^{-1} \cdot b\f]
The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates
until the center stays within a set threshold.
@param image Input image.
@param corners Initial coordinates of the input corners and refined coordinates provided for
output.
@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) ,
then a \f$5*2+1 \times 5*2+1 = 11 \times 11\f$ search window is used.
@param zeroZone Half of the size of the dead region in the middle of the search zone over which
the summation in the formula below is not done. It is used sometimes to avoid possible
singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such
a size.
@param criteria Criteria for termination of the iterative process of corner refinement. That is,
the process of corner position refinement stops either after criteria.maxCount iterations or when
the corner position moves by less than criteria.epsilon on some iteration.
@brief Calculates a feature map for corner detection.
The function calculates the complex spatial derivative-based function of the source image
\f[\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\f]
where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image
derivatives, and \f$D_{xy}\f$ is the mixed derivative.
The corners can be found as local maximums of the functions, as shown below:
@code
Mat corners, dilated_corners;
preCornerDetect(image, corners, 3);
// dilation with 3x3 rectangular structuring element
dilate(corners, dilated_corners, Mat(), 1);
Mat corner_mask = corners == dilated_corners;
@endcode
@param src Source single-channel 8-bit of floating-point image.
@param dst Output image that has the type CV_32F and the same size as src .
@param ksize %Aperture size of the Sobel .
@param borderType Pixel extrapolation method. See cv::BorderTypes.
@brief Harris corner detector.
The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and
cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance
matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it
computes the following characteristic:
\f[\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f]
Corners in the image can be found as the local maxima of this response map.
@param src Input single-channel 8-bit or floating-point image.
@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same
size as src .
@param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ).
@param ksize Aperture parameter for the Sobel operator.
@param k Harris detector free parameter. See the formula below.
@param borderType Pixel extrapolation method. See cv::BorderTypes.
@brief Calculates the minimal eigenvalue of gradient matrices for corner detection.
The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal
eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms
of the formulae in the cornerEigenValsAndVecs description.
@param src Input single-channel 8-bit or floating-point image.
@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as
src .
@param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ).
@param ksize Aperture parameter for the Sobel operator.
@param borderType Pixel extrapolation method. See cv::BorderTypes.
@example laplace.cpp
An example using Laplace transformations for edge detection
@brief Calculates the Laplacian of an image.
The function calculates the Laplacian of the source image by adding up the second x and y
derivatives calculated using the Sobel operator:
\f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f]
This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image
with the following \f$3 \times 3\f$ aperture:
\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]
@param src Source image.
@param dst Destination image of the same size and the same number of channels as src .
@param ddepth Desired depth of the destination image.
@param ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for
details. The size must be positive and odd.
@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
applied. See getDerivKernels for details.
@param delta Optional delta value that is added to the results prior to storing them in dst .
@param borderType Pixel extrapolation method, see cv::BorderTypes
@sa Sobel, Scharr
@brief Calculates the first x- or y- image derivative using Scharr operator.
The function computes the first x- or y- spatial image derivative using the Scharr operator. The
call
\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f]
is equivalent to
\f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV\_SCHARR, scale, delta, borderType)} .\f]
@param src input image.
@param dst output image of the same size and the same number of channels as src.
@param ddepth output image depth, see @ref filter_depths "combinations"
@param dx order of the derivative x.
@param dy order of the derivative y.
@param scale optional scale factor for the computed derivative values; by default, no scaling is
applied (see getDerivKernels for details).
@param delta optional delta value that is added to the results prior to storing them in dst.
@param borderType pixel extrapolation method, see cv::BorderTypes
@sa cartToPolar
@brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first
or the second x- or y- derivatives.
There is also the special value `ksize = CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr
filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
for the x-derivative, or transposed for the y-derivative.
The function calculates an image derivative by convolving the image with the appropriate kernel:
\f[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less
resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3)
or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first
case corresponds to a kernel of:
\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
The second case corresponds to a kernel of:
\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
@param src input image.
@param dst output image of the same size and the same number of channels as src .
@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
8-bit input images it will result in truncated derivatives.
@param dx order of the derivative x.
@param dy order of the derivative y.
@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
@param scale optional scale factor for the computed derivative values; by default, no scaling is
applied (see cv::getDerivKernels for details).
@param delta optional delta value that is added to the results prior to storing them in dst.
@param borderType pixel extrapolation method, see cv::BorderTypes
@sa Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
@brief Applies a separable linear filter to an image.
The function applies a separable linear filter to the image. That is, first, every row of src is
filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
kernel kernelY. The final result shifted by delta is stored in dst .
@param src Source image.
@param dst Destination image of the same size and the same number of channels as src .
@param ddepth Destination image depth, see @ref filter_depths "combinations"
@param kernelX Coefficients for filtering each row.
@param kernelY Coefficients for filtering each column.
@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
is at the kernel center.
@param delta Value added to the filtered results before storing them.
@param borderType Pixel extrapolation method, see cv::BorderTypes
@sa filter2D, Sobel, GaussianBlur, boxFilter, blur
@brief Calculates the normalized sum of squares of the pixel values overlapping the filter.
For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring
pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$.
The unnormalized square box filter can be useful in computing local image statistics such as the the local
variance and standard deviation around the neighborhood of a pixel.
@param _src input image
@param _dst output image of the same size and type as _src
@param ddepth the output image depth (-1 to use src.depth())
@param ksize kernel size
@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
center.
@param normalize flag, specifying whether the kernel is to be normalized by it's area or not.
@param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
@sa boxFilter
@brief Blurs an image using a Gaussian filter.
The function convolves the source image with the specified Gaussian kernel. In-place filtering is
supported.
@param src input image; the image can have any number of channels, which are processed
independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
@param dst output image of the same size and type as src.
@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
positive and odd. Or, they can be zero's and then they are computed from sigma.
@param sigmaX Gaussian kernel standard deviation in X direction.
@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
respectively (see cv::getGaussianKernel for details); to fully control the result regardless of
possible future modifications of all this semantics, it is recommended to specify all of ksize,
sigmaX, and sigmaY.
@param borderType pixel extrapolation method, see cv::BorderTypes
@sa sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
@brief Blurs an image using the median filter.
The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently.
In-place operation is supported.
@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be
CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
@param dst destination array of the same size and type as src.
@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
@sa bilateralFilter, blur, boxFilter, GaussianBlur
@brief Returns a structuring element of the specified size and shape for morphological operations.
The function constructs and returns the structuring element that can be further passed to cv::erode,
cv::dilate or cv::morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as
the structuring element.
@param shape Element shape that could be one of cv::MorphShapes
@param ksize Size of the structuring element.
@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the
anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor
position. In other cases the anchor just regulates how much the result of the morphological
operation is shifted.
@brief Returns Gabor filter coefficients.
For more details about gabor filter equations and parameters, see: [Gabor
Filter](http://en.wikipedia.org/wiki/Gabor_filter).
@param ksize Size of the filter returned.
@param sigma Standard deviation of the gaussian envelope.
@param theta Orientation of the normal to the parallel stripes of a Gabor function.
@param lambd Wavelength of the sinusoidal factor.
@param gamma Spatial aspect ratio.
@param psi Phase offset.
@param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
@brief Returns filter coefficients for computing spatial image derivatives.
The function computes and returns the filter coefficients for spatial image derivatives. When
`ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see cv::Scharr). Otherwise, Sobel
kernels are generated (see cv::Sobel). The filters are normally passed to sepFilter2D or to
@param kx Output matrix of row filter coefficients. It has the type ktype .
@param ky Output matrix of column filter coefficients. It has the type ktype .
@param dx Derivative order in respect of x.
@param dy Derivative order in respect of y.
@param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7.
@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not.
Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are
going to filter floating-point images, you are likely to use the normalized kernels. But if you
compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve
all the fractional bits, you may want to set normalize=false .
@param ktype Type of filter coefficients. It can be CV_32f or CV_64F .
@brief Returns Gaussian filter coefficients.
The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter
coefficients:
\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f]
where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$.
Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize
smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly.
You may also use the higher-level GaussianBlur.
@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive.
@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as
`sigma = 0.3\*((ksize-1)\*0.5 - 1) + 0.8`.
@param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
@sa sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur
@brief Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
to edit those, as to tailor it for their own application.
@param _refine The way found lines will be refined, see cv::LineSegmentDetectorModes
@param _scale The scale of the image that will be used to find the lines. Range (0..1].
@param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale.
@param _quant Bound to the quantization error on the gradient norm.
@param _ang_th Gradient angle tolerance in degrees.
@param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advancent refinement
is chosen.
@param _density_th Minimal density of aligned region points in the enclosing rectangle.
@param _n_bins Number of bins in pseudo-ordering of gradient modulus.
@brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.
@param size The size of the image, where lines1 and lines2 were found.
@param lines1 The first group of lines that needs to be drawn. It is visualized in blue color.
@param lines2 The second group of lines. They visualized in red color.
@param _image Optional image, where the lines will be drawn. The image should be color(3-channel)
in order for lines1 and lines2 to be drawn in the above mentioned colors.
@brief Draws the line segments on a given image.
@param _image The image, where the liens will be drawn. Should be bigger or equal to the image,
where the lines were found.
@param lines A vector of the lines that needed to be drawn.
@brief Finds lines in the input image.
This is the output of the default parameters of the algorithm on the above shown image.
@param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use:
`lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);`
@param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where
Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly
oriented depending on the gradient.
@param width Vector of widths of the regions, where the lines are found. E.g. Width of line.
@param prec Vector of precisions with which the lines are found.
@param nfa Vector containing number of false alarms in the line region, with precision of 10%. The
bigger the value, logarithmically better the detection.
- -1 corresponds to 10 mean false alarms
- 0 corresponds to 1 mean false alarm
- 1 corresponds to 0.1 mean false alarms
This vector will be calculated only when the objects type is LSD_REFINE_ADV.
@example lsd_lines.cpp
An example using the LineSegmentDetector
@brief Line segment detector class
following the algorithm described at @cite Rafael12 .
types of intersection between rectangles
@ingroup imgproc_shape
the color conversion code
@see @ref imgproc_color_conversions
@ingroup imgproc_misc
Histogram comparison methods
@ingroup imgproc_hist
Kullback-Leibler divergence
\f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f]
Alternative Chi-Square
\f[d(H_1,H_2) = 2 * \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f]
This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997
Bhattacharyya distance
(In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.)
\f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f]
Intersection
\f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f]
Chi-Square
\f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f]
Correlation
\f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f]
where
\f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f]
and \f$N\f$ is a total number of histogram bins.
multi-scale variant of the classical Hough transform. The lines are encoded the same way as
HOUGH_STANDARD.
probabilistic Hough transform (more efficient in case if the picture contains a few long
linear segments). It returns line segments rather than the whole line. Each segment is
represented by starting and ending points, and the matrix must be (the created sequence will
be) of the CV_32SC4 type.
classical or standard Hough transform. Every line is represented by two floating-point
numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line,
and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must
be (the created sequence will be) of CV_32FC2 type
applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89
applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89
compresses horizontal, vertical, and diagonal segments and leaves only their end points.
For example, an up-right rectangular contour is encoded with 4 points.
stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and
(x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is,
max(abs(x1-x2),abs(y2-y1))==1.
retrieves all of the contours and reconstructs a full hierarchy of nested contours.
retrieves all of the contours and organizes them into a two-level hierarchy. At the top
level, there are external boundaries of the components. At the second level, there are
boundaries of the holes. If there is another contour inside a hole of a connected component, it
is still put at the top level.
retrieves all of the contours without establishing any hierarchical relationships.
retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for
all the contours.
If set, the function does not change the image ( newVal is ignored), and only fills the
mask with the value specified in bits 8-16 of flags as described above. This option only make
sense in function variants that have the mask parameter.
If set, the difference between the current pixel and seed pixel is considered. Otherwise,
the difference between neighbor pixels is considered (that is, the range is floating).
each zero pixel (and all the non-zero pixels closest to it) gets its own label.
each connected component of zeros in src (as well as all the non-zero pixels closest to the
connected component) will be assigned the same label
The value means that the algorithm should just resume.
The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT
and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are
automatically initialized with GC_BGD .
The function initializes the state and the mask using the provided rectangle. After that it
runs iterCount iterations of the algorithm.
the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian
window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$
minus C . The default sigma (standard deviation) is used for the specified blockSize . See
cv::getGaussianKernel
the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times
\texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C
flag, inverse transformation
For example, polar transforms:
- flag is __not__ set: \f$dst( \phi , \rho ) = src(x,y)\f$
- flag is set: \f$dst(x,y) = src( \phi , \rho )\f$
flag, fills all of the destination image pixels. If some of them correspond to outliers in the
source image, they are set to zero
mask for interpolation codes
Lanczos interpolation over 8x8 neighborhood
resampling using pixel area relation. It may be a preferred method for image decimation, as
it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST
method.
bicubic interpolation
bilinear interpolation
nearest neighbor interpolation
@addtogroup imgproc
@{
@brief This function returns the reference to the ready-to-use ConjGradSolver object.
All the parameters are optional, so this procedure can be called even without parameters at
all. In this case, the default values will be used. As default value for terminal criteria are
the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained
object, if the function was not given to create(). Otherwise, the two ways (submit it to
create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent
(and will drop the same errors in the same way, should invalid input be detected).
@param f Pointer to the function that will be minimized, similarly to the one you submit via
MinProblemSolver::setFunction.
@param termcrit Terminal criteria to the algorithm, similarly to the one you submit via
MinProblemSolver::setTermCriteria.
@brief This function returns the reference to the ready-to-use DownhillSolver object.
All the parameters are optional, so this procedure can be called even without parameters at
all. In this case, the default values will be used. As default value for terminal criteria are
the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep()
should be called upon the obtained object, if the respective parameters were not given to
create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out
and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely
equivalent (and will drop the same errors in the same way, should invalid input be detected).
@param f Pointer to the function that will be minimized, similarly to the one you submit via
MinProblemSolver::setFunction.
@param initStep Initial step, that will be used to construct the initial simplex, similarly to the one
you submit via MinProblemSolver::setInitStep.
@param termcrit Terminal criteria to the algorithm, similarly to the one you submit via
MinProblemSolver::setTermCriteria.
@brief Returns the initial step that will be used in downhill simplex algorithm.
@param step Initial step that will be used in algorithm. Note, that although corresponding setter
accepts column-vectors as well as row-vectors, this method will return a row-vector.
@see DownhillSolver::setInitStep
@brief actually runs the algorithm and performs the minimization.
The sole input parameter determines the centroid of the starting simplex (roughly, it tells
where to start), all the others (terminal criteria, initial step, function to be minimized) are
supposed to be set via the setters before the call to this method or the default values (not
always sensible) will be used.
@param x The initial point, that will become a centroid of an initial simplex. After the algorithm
will terminate, it will be setted to the point where the algorithm stops, the point of possible
minimum.
@return The value of a function at the point found.
@brief Set terminal criteria for solver.
This method *is not necessary* to be called before the first call to minimize(), as the default
value is sensible.
Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when
the function values at the vertices of simplex are within termcrit.epsilon range or simplex
becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes
first.
@param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure.
@brief Getter for the previously set terminal criteria for this algorithm.
@return Deep copy of the terminal criteria used at the moment.
@brief Setter for the optimized function.
*It should be called at least once before the call to* minimize(), as default value is not usable.
@param f The new function to optimize.
@brief Getter for the optimized function.
The optimized function is represented by Function interface, which requires derivatives to
implement the sole method calc(double*) to evaluate the function.
@return Smart-pointer to an object that implements Function interface - it represents the
function that is being optimized. It can be empty, if no function was given so far.
@brief Represents function being optimized
@addtogroup core_optim
The algorithms in this section minimize or maximize function value within specified constraints or
without any constraints.
@{
@brief Basic interface for all solvers
Output to MessageBox(WIN32)
Output to console(fprintf(stderr,...))
Output nothing
Assigns a new error-handling function
Maps IPP error codes to the counterparts from OpenCV
Retrieves detailed information about the last error occured
Retrieves textual description of the error given its code
Sets error status and performs some additonal actions (displaying message box,
writing message to stderr, terminating application etc.)
depending on the current error mode
Sets error processing mode, returns previously used mode
Retrives current error processing mode
Sets error status silently
Get current OpenCV error status
get index of the thread being executed
retrieve/set the number of threads used in OpenMP implementations
helper functions for RNG initialization and accurate time measurement:
uses internal clock counter on x86
@brief Loads an object from a file.
The function loads an object from a file. It basically reads the specified file, find the first
top-level node and calls cvRead for that node. If the file node does not have type information or
the type information can not be found by the type name, the function returns NULL. After the object
is loaded, the file storage is closed and all the temporary buffers are deleted. Thus, to load a
dynamic structure, such as a sequence, contour, or graph, one should pass a valid memory storage
destination to the function.
@param filename File name
@param memstorage Memory storage for dynamic structures, such as CvSeq or CvGraph . It is not used
for matrices or images.
@param name Optional object name. If it is NULL, the first top-level object in the storage will be
loaded.
@param real_name Optional output parameter that will contain the name of the loaded object
(useful if name=NULL )
@brief Saves an object to a file.
The function saves an object to a file. It provides a simple interface to cvWrite .
@param filename File name
@param struct_ptr Object to save
@param name Optional object name. If it is NULL, the name will be formed from filename .
@param comment Optional comment to put in the beginning of the file
@param attributes Optional attributes passed to cvWrite
@brief Makes a clone of an object.
The function finds the type of a given object and calls clone with the passed object. Of course, if
you know the object type, for example, struct_ptr is CvMat\*, it is faster to call the specific
function, like cvCloneMat.
@param struct_ptr The object to clone
@brief Releases an object.
The function finds the type of a given object and calls release with the double pointer.
@param struct_ptr Double pointer to the object
@brief Returns the type of an object.
The function finds the type of a given object. It iterates through the list of registered types and
calls the is_instance function/method for every type info structure with that object until one of
them returns non-zero or until the whole list has been traversed. In the latter case, the function
returns NULL.
@param struct_ptr The object pointer
@brief Finds a type by its name.
The function finds a registered type by its name. It returns NULL if there is no type with the
specified name.
@param type_name Type name
@brief Returns the beginning of a type list.
The function returns the first type in the list of registered types. Navigation through the list can
be done via the prev and next fields of the CvTypeInfo structure.
@brief Unregisters the type.
The function unregisters a type with a specified name. If the name is unknown, it is possible to
locate the type info by an instance of the type using cvTypeOf or by iterating the type list,
starting from cvFirstType, and then calling cvUnregisterType(info-\>typeName).
@param type_name Name of an unregistered type
@brief Registers a new type.
The function registers a new type, which is described by info . The function creates a copy of the
structure, so the user should delete it after calling the function.
@param info Type info structure
@brief Returns the name of a file node.
The function returns the name of a file node or NULL, if the file node does not have a name or if
node is NULL.
@param node File node
@brief Writes a file node to another file storage.
The function writes a copy of a file node to file storage. Possible applications of the function are
merging several file storages into one and conversion between XML and YAML formats.
@param fs Destination file storage
@param new_node_name New name of the file node in the destination file storage. To keep the
existing name, use cvcvGetFileNodeName
@param node The written node
@param embed If the written node is a collection and this parameter is not zero, no extra level of
hierarchy is created. Instead, all the elements of node are written into the currently written
structure. Of course, map elements can only be embedded into another map, and sequence elements
can only be embedded into another sequence.
@brief Reads multiple numbers.
The function reads elements from a file node that represents a sequence of scalars.
@param fs File storage
@param src The file node (a sequence) to read numbers from
@param dst Pointer to the destination array
@param dt Specification of each array element. It has the same format as in cvWriteRawData .
@brief Initializes file node sequence reader.
The function reads one or more elements from the file node, representing a sequence, to a
user-specified array. The total number of read sequence elements is a product of total and the
number of components in each array element. For example, if dt=2if, the function will read total\*3
sequence elements. As with any sequence, some parts of the file node sequence can be skipped or read
repeatedly by repositioning the reader using cvSetSeqReaderPos.
@param fs File storage
@param reader The sequence reader. Initialize it with cvStartReadRawData .
@param count The number of elements to read
@param dst Pointer to the destination array
@param dt Specification of each array element. It has the same format as in cvWriteRawData .
@brief Initializes the file node sequence reader.
The function initializes the sequence reader to read data from a file node. The initialized reader
can be then passed to cvReadRawDataSlice.
@param fs File storage
@param src The file node (a sequence) to read numbers from
@param reader Pointer to the sequence reader
@brief Finds an object by name and decodes it.
The function is a simple superposition of cvGetFileNodeByName and cvRead.
@param fs File storage
@param map The parent map. If it is NULL, the function searches a top-level node.
@param name The node name
@param attributes Unused parameter
@brief Decodes an object and returns a pointer to it.
The function decodes a user object (creates an object in a native representation from the file
storage subtree) and returns it. The object to be decoded must be an instance of a registered type
that supports the read method (see CvTypeInfo). The type of the object is determined by the type
name that is encoded in the file. If the object is a dynamic structure, it is created either in
memory storage and passed to cvOpenFileStorage or, if a NULL pointer was passed, in temporary
memory storage, which is released when cvReleaseFileStorage is called. Otherwise, if the object is
not a dynamic structure, it is created in a heap and should be released with a specialized function
or by using the generic cvRelease.
@param fs File storage
@param node The root object node
@param attributes Unused parameter
@brief Finds a file node by its name and returns its value.
The function is a simple superposition of cvGetFileNodeByName and cvReadString .
@param fs File storage
@param map The parent map. If it is NULL, the function searches a top-level node.
@param name The node name
@param default_value The value that is returned if the file node is not found
@brief Retrieves a text string from a file node.
The function returns a text string that is represented by the file node. If the file node is NULL,
the default_value is returned (thus, it is convenient to call the function right after
cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_STR , then
node-:math:\>data.str.ptr is returned. Otherwise the result is not determined.
@param node File node
@param default_value The value that is returned if node is NULL
@brief Finds a file node and returns its value.
The function is a simple superposition of cvGetFileNodeByName and cvReadReal .
@param fs File storage
@param map The parent map. If it is NULL, the function searches a top-level node.
@param name The node name
@param default_value The value that is returned if the file node is not found
@brief Retrieves a floating-point value from a file node.
The function returns a floating-point value that is represented by the file node. If the file node
is NULL, the default_value is returned (thus, it is convenient to call the function right after
cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_REAL ,
then node-\>data.f is returned. If the file node has type CV_NODE_INT , then node-:math:\>data.f
is converted to floating-point and returned. Otherwise the result is not determined.
@param node File node
@param default_value The value that is returned if node is NULL
@brief Finds a file node and returns its value.
The function is a simple superposition of cvGetFileNodeByName and cvReadInt.
@param fs File storage
@param map The parent map. If it is NULL, the function searches a top-level node.
@param name The node name
@param default_value The value that is returned if the file node is not found
@brief Retrieves an integer value from a file node.
The function returns an integer that is represented by the file node. If the file node is NULL, the
default_value is returned (thus, it is convenient to call the function right after cvGetFileNode
without checking for a NULL pointer). If the file node has type CV_NODE_INT, then node-\>data.i is
returned. If the file node has type CV_NODE_REAL, then node-\>data.f is converted to an integer
and returned. Otherwise the error is reported.
@param node File node
@param default_value The value that is returned if node is NULL
@brief Finds a node in a map or file storage.
The function finds a file node by name. The node is searched either in map or, if the pointer is
NULL, among the top-level file storage nodes. Using this function for maps and cvGetSeqElem (or
sequence reader) for sequences, it is possible to navigate through the file storage. To speed up
multiple queries for a certain key (e.g., in the case of an array of structures) one may use a
combination of cvGetHashedKey and cvGetFileNode.
@param fs File storage
@param map The parent map. If it is NULL, the function searches in all the top-level nodes
(streams), starting with the first one.
@param name The file node name
@brief Finds a node in a map or file storage.
The function finds a file node. It is a faster version of cvGetFileNodeByName (see
cvGetHashedKey discussion). Also, the function can insert a new node, if it is not in the map yet.
@param fs File storage
@param map The parent map. If it is NULL, the function searches a top-level node. If both map and
key are NULLs, the function returns the root file node - a map that contains top-level nodes.
@param key Unique pointer to the node name, retrieved with cvGetHashedKey
@param create_missing Flag that specifies whether an absent node should be added to the map
@brief Retrieves one of the top-level nodes of the file storage.
The function returns one of the top-level file nodes. The top-level nodes do not have a name, they
correspond to the streams that are stored one after another in the file storage. If the index is out
of range, the function returns a NULL pointer, so all the top-level nodes can be iterated by
subsequent calls to the function with stream_index=0,1,..., until the NULL pointer is returned.
This function can be used as a base for recursive traversal of the file storage.
@param fs File storage
@param stream_index Zero-based index of the stream. See cvStartNextStream . In most cases,
there is only one stream in the file; however, there can be several.
@brief Writes multiple numbers.
The function writes an array, whose elements consist of single or multiple numbers. The function
call can be replaced with a loop containing a few cvWriteInt and cvWriteReal calls, but a single
call is more efficient. Note that because none of the elements have a name, they should be written
to a sequence rather than a map.
@param fs File storage
@param src Pointer to the written array
@param len Number of the array elements to write
@param dt Specification of each array element, see @ref format_spec "format specification"
@brief Starts the next stream.
The function finishes the currently written stream and starts the next stream. In the case of XML
the file with multiple streams looks like this:
@code{.xml}
...
@endcode
The YAML file will look like this:
@code{.yaml}
%YAML:1.0
# stream #1 data
...
---
# stream #2 data
@endcode
This is useful for concatenating files or for resuming the writing process.
@param fs File storage
@brief Writes a comment.
The function writes a comment into file storage. The comments are skipped when the storage is read.
@param fs File storage
@param comment The written comment, single-line or multi-line
@param eol_comment If non-zero, the function tries to put the comment at the end of current line.
If the flag is zero, if the comment is multi-line, or if it does not fit at the end of the current
line, the comment starts a new line.
@brief Writes a text string.
The function writes a text string to file storage.
@param fs File storage
@param name Name of the written string . Should be NULL if and only if the parent structure is a
sequence.
@param str The written text string
@param quote If non-zero, the written string is put in quotes, regardless of whether they are
required. Otherwise, if the flag is zero, quotes are used only when they are required (e.g. when
the string starts with a digit or contains spaces).
@brief Writes an integer value.
The function writes a single integer value (with or without a name) to the file storage.
@param fs File storage
@param name Name of the written value. Should be NULL if and only if the parent structure is a
sequence.
@param value The written value
@brief Finishes writing to a file node collection.
@param fs File storage
@sa cvStartWriteStruct.
@brief Starts writing a new structure.
The function starts writing a compound structure (collection) that can be a sequence or a map. After
all the structure fields, which can be scalars or structures, are written, cvEndWriteStruct should
be called. The function can be used to group some objects or to implement the write function for a
some user object (see CvTypeInfo).
@param fs File storage
@param name Name of the written structure. The structure can be accessed by this name when the
storage is read.
@param struct_flags A combination one of the following values:
- **CV_NODE_SEQ** the written structure is a sequence (see discussion of CvFileStorage ),
that is, its elements do not have a name.
- **CV_NODE_MAP** the written structure is a map (see discussion of CvFileStorage ), that
is, all its elements have names.
One and only one of the two above flags must be specified
- **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that
the structure is written as a flow (not as a block), which is more compact. It is
recommended to use this flag for structures or arrays whose elements are all scalars.
@param type_name Optional parameter - the object type name. In
case of XML it is written as a type_id attribute of the structure opening tag. In the case of
YAML it is written after a colon following the structure name (see the example in
CvFileStorage description). Mainly it is used with user objects. When the storage is read, the
encoded type name is used to determine the object type (see CvTypeInfo and cvFindType ).
@param attributes This parameter is not used in the current implementation
returns attribute value or 0 (NULL) if there is no such attribute
@brief Releases file storage.
The function closes the file associated with the storage and releases all the temporary structures.
It must be called after all I/O operations with the storage are finished.
@param fs Double pointer to the released file storage
@brief Opens file storage for reading or writing data.
The function opens file storage for reading or writing data. In the latter case, a new file is
created or an existing file is rewritten. The type of the read or written file is determined by the
filename extension: .xml for XML and .yml or .yaml for YAML. The function returns a pointer to the
CvFileStorage structure. If the file cannot be opened then the function returns NULL.
@param filename Name of the file associated with the storage
@param memstorage Memory storage used for temporary data and for
: storing dynamic structures, such as CvSeq or CvGraph . If it is NULL, a temporary memory
storage is created and used.
@param flags Can be one of the following:
> - **CV_STORAGE_READ** the storage is open for reading
> - **CV_STORAGE_WRITE** the storage is open for writing
@param encoding
@brief Makes OpenCV use IPL functions for allocating IplImage and IplROI structures.
Normally, the function is not called directly. Instead, a simple macro
CV_TURN_ON_IPL_COMPATIBILITY() is used that calls cvSetIPLAllocators and passes there pointers
to IPL allocation functions. :
@code
...
CV_TURN_ON_IPL_COMPATIBILITY()
...
@endcode
@param create_header pointer to a function, creating IPL image header.
@param allocate_data pointer to a function, allocating IPL image data.
@param deallocate pointer to a function, deallocating IPL image.
@param create_roi pointer to a function, creating IPL image ROI (i.e. Region of Interest).
@param clone_image pointer to a function, cloning an IPL image.
Loads optimized functions from IPP, MKL etc. or switches back to pure C code
The function implements the K-means algorithm for clustering an array of sample
vectors in a specified number of classes
Gathers pointers to all the sequences,
accessible from the `first`, to the single sequence
Removes contour from tree (together with the contour children).
Inserts sequence into tree with specified "parent" sequence.
If parent is equal to frame (e.g. the most external contour),
then added contour will have null pointer to parent.
Does look-up transformation. Elements of the source array
(that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table
Creates a copy of graph
Get next graph element
Releases graph scanner.
Creates new graph scanner.
Retrieves graph vertex by given index
Retrieves index of a graph vertex given its pointer
Retrieves index of a graph edge given its pointer
flags for graph vertices and edges
Count number of edges incident to the vertex
Remove all vertices and edges from the graph
Find edge connecting two vertices
Remove edge connecting two vertices
Link two vertices specifed by indices or pointers if they
are not connected or return pointer to already existing edge
connecting the vertices.
Functions return 1 if a new edge was created, 0 otherwise
Removes vertex from the graph together with all incident edges
Adds new vertex to the graph
Creates new graph
Removes all the elements from the set
Returns a set element by index. If the element doesn't belong to the set,
NULL is returned
Removes element from the set by its index
Removes set element given its pointer
Fast variant of cvSetAdd
Adds new element to the set and returns pointer to it
Creates a new set
Splits sequence into one or more equivalence classes using the specified criteria
Reverses order of sequence elements in-place
Finds element in a [sorted] sequence
Sorts sequence in-place given element comparison function
Inserts a sequence or array into another sequence
Removes sequence slice
Extracts sequence slice (with or without copying sequence elements)
Creates sequence header for array.
After that all the operations on sequences that do not alter the content
can be applied to the resultant sequence
Copies sequence content to a continuous piece of memory
Changes sequence reader position. It may seek to an absolute or
to relative to the current position
Returns current sequence reader position (currently observed sequence element)
Initializes sequence reader.
The sequence can be read in forward or backward direction
Updates sequence header. May be useful to get access to some of previously
written elements via cvGetSeqElem or sequence reader
Closes sequence writer, updates sequence header and returns pointer
to the resultant sequence
(which may be useful if the sequence was created using cvStartWriteSeq))
Combination of cvCreateSeq and cvStartAppendToSeq
Initializes sequence writer. The new elements will be added to the end of sequence
Calculates index of the specified sequence element.
Returns -1 if element does not belong to the sequence
Retrieves pointer to specified sequence element.
Negative indices are supported and mean counting from the end
(e.g -1 means the last sequence element)
Removes all the elements from the sequence. The freed memory
can be reused later only by the same sequence unless cvClearMemStorage
or cvRestoreMemStoragePos is called
Removes specified sequence element
Inserts a new element in the middle of sequence.
cvSeqInsert(seq,0,elem) == cvSeqPushFront(seq,elem)
Removes several elements from the end of sequence and optionally saves them
Adds several new elements to the end of sequence
Removes the first element from sequence and optioanally saves it
Removes the last element from sequence and optionally saves it
Adds new element to the beginning of sequence. Returns pointer to it
Adds new element to the end of sequence. Returns pointer to the element
Changes default size (granularity) of sequence blocks.
The default size is ~1Kbyte
Creates new empty sequence that will reside in the specified storage
Allocates string in memory storage
Allocates continuous buffer of the specified size in the storage
Restore a storage "free memory" position
Remember a storage "free memory" position
Clears memory storage. This is the only way(!!!) (besides cvRestoreMemStoragePos)
to reuse memory allocated for the storage - cvClearSeq,cvClearSet ...
do not free any memory.
A child storage returns all the blocks to the parent when it is cleared
Releases memory storage. All the children of a parent must be released before
the parent. A child storage returns all the blocks to parent when it is released
Creates a memory storage that will borrow memory blocks from parent storage
Creates new memory storage.
block_size == 0 means that default,
somewhat optimal size, is used (currently, it is 64K)
Calculates length of sequence slice (with support of negative indices).
Discrete Cosine Transform
@see core_c_DftFlags "flags"
Finds optimal DFT vector size >= size0
Multiply results of DFTs: DFT(X)*DFT(Y) or DFT(X)*conj(DFT(Y))
@see core_c_DftFlags "flags"
@anchor core_c_DftFlags
@name Flags for cvDFT, cvDCT and cvMulSpectrums
@{
@}
Discrete Fourier Transform:
complex->complex,
real->ccs (forward),
ccs->real (inverse)
@see core_c_DftFlags "flags"
@anchor core_c_ReduceFlags
@name Flags for cvReduce
@{
@}
@see @ref core_c_ReduceFlags "flags"
@see ref core_c_NormFlags "flags"
@anchor core_c_NormFlags
@name Flags for cvNorm and cvNormalize
@{
@}
Finds norm, difference norm or relative difference norm for an array (or two arrays)
@see ref core_c_NormFlags "flags"
Finds global minimum, maximum and their positions
Calculates mean and standard deviation of pixel values
Calculates mean value of array elements
Calculates number of non-zero pixels
Finds sum of array elements
Calculates Mahalanobis(weighted) distance
@anchor core_c_CovarFlags
@name Flags for cvCalcCovarMatrix
@see cvCalcCovarMatrix
@{
flag for cvCalcCovarMatrix, transpose([v1-avg, v2-avg,...]) * [v1-avg,v2-avg,...]
flag for cvCalcCovarMatrix, [v1-avg, v2-avg,...] * transpose([v1-avg,v2-avg,...])
flag for cvCalcCovarMatrix, do not calc average (i.e. mean vector) - use the input vector instead
(useful for calculating covariance matrix by parts)
flag for cvCalcCovarMatrix, scale the covariance matrix coefficients by number of the vectors
flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its rows
flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its columns
@}
Calculates covariation matrix for a set of vectors
@see @ref core_c_CovarFlags "flags"
Fills matrix with given range of numbers
* Finds selected eigen values and vectors of a symmetric matrix */
Makes an identity matrix (mat_ij = i == j)
Finds eigen values and vectors of a symmetric matrix
Calculates trace of the matrix (sum of elements on the main diagonal)
Calculates determinant of input matrix
Solves linear system (src1)*(dst) = (src2)
(returns 0 if src1 is a singular and CV_LU method is used)
Inverts matrix
Performs Singular Value Back Substitution (solves A*X = B):
flags must be the same as in cvSVD
Performs Singular Value Decomposition of a matrix
Mirror array data around horizontal (flip=0),
vertical (flip=1) or both(flip=-1) axises:
cvFlip(src) flips images vertically and sequences horizontally (inplace)
Completes the symmetric matrix from the lower (LtoR=0) or from the upper (LtoR!=0) part
Tranposes matrix. Square matrices can be transposed in-place
Calculates (A-delta)*(A-delta)^T (order=0) or (A-delta)^T*(A-delta) (order=1)
Does perspective transform on every element of input array
Transforms each element of source array and stores
resultant vectors in destination array
Matrix transform: dst = A*B + C, C is optional
Extended matrix transform:
dst = alpha*op(A)*op(B) + beta*op(C), where op(X) is X or X^T
@brief Calculates the cross product of two 3D vectors.
The function calculates the cross product of two 3D vectors:
\f[\texttt{dst} = \texttt{src1} \times \texttt{src2}\f]
or:
\f[\begin{array}{l} \texttt{dst} _1 = \texttt{src1} _2 \texttt{src2} _3 - \texttt{src1} _3 \texttt{src2} _2 \\ \texttt{dst} _2 = \texttt{src1} _3 \texttt{src2} _1 - \texttt{src1} _1 \texttt{src2} _3 \\ \texttt{dst} _3 = \texttt{src1} _1 \texttt{src2} _2 - \texttt{src1} _2 \texttt{src2} _1 \end{array}\f]
@param src1 The first source vector
@param src2 The second source vector
@param dst The destination vector
Finds all real and complex roots of a polynomial equation
Finds real roots of a cubic equation
@brief Fills an array with random numbers and updates the RNG state.
The function fills the destination array with uniformly or normally distributed random numbers.
@param rng CvRNG state initialized by cvRNG
@param arr The destination array
@param dist_type Distribution type
> - **CV_RAND_UNI** uniform distribution
> - **CV_RAND_NORMAL** normal or Gaussian distribution
@param param1 The first parameter of the distribution. In the case of a uniform distribution it is
the inclusive lower boundary of the random numbers range. In the case of a normal distribution it
is the mean value of the random numbers.
@param param2 The second parameter of the distribution. In the case of a uniform distribution it
is the exclusive upper boundary of the random numbers range. In the case of a normal distribution
it is the standard deviation of the random numbers.
@sa randu, randn, RNG::fill.
Checks array values for NaNs, Infs or simply for too large numbers
(if CV_CHECK_RANGE is set). If CV_CHECK_QUIET is set,
no runtime errors is raised (function returns zero value in case of "bad" values).
Otherwise cvError is called
Fast cubic root calculation
Fast arctangent calculation
Calculates natural logarithms: dst(idx) = log(abs(src(idx))).
Logarithm of 0 gives large negative number(~-700)
Maximal relative error is ~3e-7 for single-precision output
Does exponention: dst(idx) = exp(src(idx)).
Overflow is not handled yet. Underflow is handled.
Maximal relative error is ~7e-6 for single-precision input
Does powering: dst(idx) = src(idx)^power
Does polar->cartesian coordinates conversion.
Either of output components (magnitude or angle) is optional.
If magnitude is missing it is assumed to be all 1's
Does cartesian->polar coordinates conversion.
Either of output components (magnitude or angle) is optional
dst(x,y,c) = abs(src(x,y,c) - value(c))
dst(x,y,c) = abs(src1(x,y,c) - src2(x,y,c))
dst(idx) = max(src(idx),value)
dst(idx) = min(src(idx),value)
dst(idx) = max(src1(idx),src2(idx))
dst(idx) = min(src1(idx),src2(idx))
dst(idx) = src1(idx) _cmp_op_ value
The comparison operation support single-channel arrays only.
Destination image should be 8uC1 or 8sC1
dst(idx) = src1(idx) _cmp_op_ src2(idx)
dst(idx) = ~src(idx)
dst(idx) = src(idx) ^ value
dst(idx) = src1(idx) ^ src2(idx)
dst(idx) = src(idx) | value
dst(idx) = src1(idx) | src2(idx)
@brief Calculates the dot product of two arrays in Euclidean metrics.
The function calculates and returns the Euclidean dot product of two arrays.
\f[src1 \bullet src2 = \sum _I ( \texttt{src1} (I) \texttt{src2} (I))\f]
In the case of multiple channel arrays, the results for all channels are accumulated. In particular,
cvDotProduct(a,a) where a is a complex vector, will return \f$||\texttt{a}||^2\f$. The function can
process multi-dimensional arrays, row by row, layer by layer, and so on.
@param src1 The first source array
@param src2 The second source array
dst = src1 * alpha + src2 * beta + gamma
dst = src1 * scale + src2
element-wise division/inversion with scaling:
dst(idx) = src1(idx) * scale / src2(idx)
or dst(idx) = scale / src2(idx) if src1 == 0
dst(idx) = src1(idx) * src2(idx) * scale
(scaled element-wise multiplication of 2 arrays)
dst(mask) = value - src(mask)
dst(mask) = src(mask) - value = src(mask) + (-value)
dst(mask) = src1(mask) - src2(mask)
dst(mask) = src(mask) + value
dst(mask) = src1(mask) + src2(mask)
checks termination criteria validity and
sets eps to default_eps (if it is not set),
max_iter to default_max_iters (if it is not set)
Performs linear transformation on every source array element,
stores absolute value of the result:
dst(x,y,c) = abs(scale*src(x,y,c)+shift).
destination array must have 8u type.
In other cases one may use cvConvertScale + cvAbsDiffS
@brief Converts one array to another with optional linear transformation.
The function has several different purposes, and thus has several different names. It copies one
array to another with optional scaling, which is performed first, and/or optional type conversion,
performed after:
\f[\texttt{dst} (I) = \texttt{scale} \texttt{src} (I) + ( \texttt{shift} _0, \texttt{shift} _1,...)\f]
All the channels of multi-channel arrays are processed independently.
The type of conversion is done with rounding and saturation, that is if the result of scaling +
conversion can not be represented exactly by a value of the destination array element type, it is
set to the nearest representable value on the real axis.
@param src Source array
@param dst Destination array
@param scale Scale factor
@param shift Value added to the scaled source array elements
Copies several channels from input arrays to
certain channels of output arrays
Merges a set of single-channel arrays into the single multi-channel array
or inserts one particular [color] plane to the array
Splits a multi-channel array into the set of single-channel arrays or
extracts particular [color] plane
@brief Clears the array.
The function clears the array. In the case of dense arrays (CvMat, CvMatND or IplImage),
cvZero(array) is equivalent to cvSet(array,cvScalarAll(0),0). In the case of sparse arrays all the
elements are removed.
@param arr Array to be cleared
@brief Sets every element of an array to a given value.
The function copies the scalar value to every selected element of the destination array:
\f[\texttt{arr} (I)= \texttt{value} \quad \text{if} \quad \texttt{mask} (I) \ne 0\f]
If array arr is of IplImage type, then is ROI used, but COI must not be set.
@param arr The destination array
@param value Fill value
@param mask Operation mask, 8-bit single channel array; specifies elements of the destination
array to be changed
@brief Copies one array to another.
The function copies selected elements from an input array to an output array:
\f[\texttt{dst} (I)= \texttt{src} (I) \quad \text{if} \quad \texttt{mask} (I) \ne 0.\f]
If any of the passed arrays is of IplImage type, then its ROI and COI fields are used. Both arrays
must have the same type, the same number of dimensions, and the same size. The function can also
copy sparse arrays (mask is not supported in this case).
@param src The source array
@param dst The destination array
@param mask Operation mask, 8-bit single channel array; specifies elements of the destination array
to be changed
@brief Returns size of matrix or image ROI.
The function returns number of rows (CvSize::height) and number of columns (CvSize::width) of the
input matrix or image. In the case of image the size of ROI is returned.
@param arr array header
@brief Assigns user data to the array header.
The function assigns user data to the array header. Header should be initialized before using
cvCreateMatHeader, cvCreateImageHeader, cvCreateMatNDHeader, cvInitMatHeader,
cvInitImageHeader or cvInitMatNDHeader.
@param arr Array header
@param data User data
@param step Full row length in bytes
@brief Releases array data.
The function releases the array data. In the case of CvMat or CvMatND it simply calls
cvDecRefData(), that is the function can not deallocate external data. See also the note to
cvCreateData .
@param arr Array header
@brief Allocates array data
The function allocates image, matrix or multi-dimensional dense array data. Note that in the case of
matrix types OpenCV allocation functions are used. In the case of IplImage they are used unless
CV_TURN_ON_IPL_COMPATIBILITY() has been called before. In the latter case IPL functions are used
to allocate the data.
@param arr Array header
Repeats source 2d array several times in both horizontal and
vertical direction to fill destination array
@brief Returns image header for arbitrary array.
The function returns the image header for the input array that can be a matrix (CvMat) or image
(IplImage). In the case of an image the function simply returns the input pointer. In the case of
CvMat it initializes an image_header structure with the parameters of the input matrix. Note that
if we transform IplImage to CvMat using cvGetMat and then transform CvMat back to IplImage using
this function, we will get different headers if the ROI is set in the original image.
@param arr Input array
@param image_header Pointer to IplImage structure used as a temporary buffer
clears element of ND dense array,
in case of sparse arrays it deletes the specified node
@overload
@param arr Input array
@param idx Array of the element indices
@param value The assigned value
@overload
@overload
@brief Change a specific array element.
The functions assign a new value to a specific element of a single-channel array. If the array has
multiple channels, a runtime error is raised. Note that the Set\*D function can be used safely for
both single-channel and multiple-channel arrays, though they are a bit slower.
In the case of a sparse array the functions create the node if it does not yet exist.
@param arr Input array
@param idx0 The first zero-based component of the element index
@param value The assigned value
@overload
@param arr Input array
@param idx Array of the element indices
@param value The assigned value
@overload
@overload
@brief Change the particular array element.
The functions assign the new value to a particular array element. In the case of a sparse array the
functions create the node if it does not exist yet.
@param arr Input array
@param idx0 The first zero-based component of the element index
@param value The assigned value
@overload
@param arr Input array. Must have a single channel.
@param idx Array of the element indices
@overload
@overload
@brief Return a specific element of single-channel 1D, 2D, 3D or nD array.
Returns a specific element of a single-channel array. If the array has multiple channels, a runtime
error is raised. Note that Get?D functions can be used safely for both single-channel and
multiple-channel arrays though they are a bit slower.
In the case of a sparse array the functions return 0 if the requested node does not exist (no new
node is created by the functions).
@param arr Input array. Must have a single channel.
@param idx0 The first zero-based component of the element index
@overload
@param arr Input array
@param idx Array of the element indices
@overload
@overload
@brief Return a specific array element.
The functions return a specific array element. In the case of a sparse array the functions return 0
if the requested node does not exist (no new node is created by the functions).
@param arr Input array
@param idx0 The first zero-based component of the element index
@overload
@param arr Input array
@param idx Array of the element indices
@param type Optional output parameter: type of matrix elements
@param create_node Optional input parameter for sparse matrices. Non-zero value of the parameter
means that the requested element is created if it does not exist already.
@param precalc_hashval Optional input parameter for sparse matrices. If the pointer is not NULL,
the function does not recalculate the node hash value, but takes it from the specified location.
It is useful for speeding up pair-wise operations (TODO: provide an example)
@overload
@overload
@brief Return pointer to a particular array element.
The functions return a pointer to a specific array element. Number of array dimension should match
to the number of indices passed to the function except for cvPtr1D function that can be used for
sequential access to 1D, 2D or nD dense arrays.
The functions can be used for sparse arrays as well - if the requested node does not exist they
create it and set it to zero.
All these as well as other functions accessing array elements ( cvGetND , cvGetRealND , cvSet
, cvSetND , cvSetRealND ) raise an error in case if the element index is out of range.
@param arr Input array
@param idx0 The first zero-based component of the element index
@param type Optional output parameter: type of matrix elements
@brief Returns array size along the specified dimension.
@param arr Input array
@param index Zero-based dimension index (for matrices 0 means number of rows, 1 means number of
columns; for images 0 means height, 1 means width)
@brief Returns type of array elements.
The function returns type of the array elements. In the case of IplImage the type is converted to
CvMat-like representation. For example, if the image has been created as:
@code
IplImage* img = cvCreateImage(cvSize(640, 480), IPL_DEPTH_8U, 3);
@endcode
The code cvGetElemType(img) will return CV_8UC3.
@param arr Input array
returns zero value if iteration is finished, non-zero (slice length) otherwise
initializes iterator that traverses through several arrays simulteneously
(the function together with cvNextArraySlice is used for
N-ari element-wise operations)
matrix iterator: used for n-ary operations on dense arrays
@brief Initializes sparse array elements iterator.
The function initializes iterator of sparse array elements and returns pointer to the first element,
or NULL if the array is empty.
@param mat Input array
@param mat_iterator Initialized iterator
Creates a copy of CvSparseMat (except, may be, zero items)
@brief Deallocates sparse array.
The function releases the sparse array and clears the array pointer upon exit.
@param mat Double pointer to the array
@brief Creates sparse array.
The function allocates a multi-dimensional sparse array. Initially the array contain no elements,
that is PtrND and other related functions will return 0 for every index.
@param dims Number of array dimensions. In contrast to the dense matrix, the number of dimensions is
practically unlimited (up to \f$2^{16}\f$ ).
@param sizes Array of dimension sizes
@param type Type of array elements. The same as for CvMat
Creates a copy of CvMatND (except, may be, steps)
@brief Deallocates a multi-dimensional array.
The function decrements the array data reference counter and releases the array header. If the
reference counter reaches 0, it also deallocates the data. :
@code
if(*mat )
cvDecRefData(*mat);
cvFree((void**)mat);
@endcode
@param mat Double pointer to the array
@brief Initializes a pre-allocated multi-dimensional array header.
@param mat A pointer to the array header to be initialized
@param dims The number of array dimensions
@param sizes An array of dimension sizes
@param type Type of array elements, see cvCreateMat
@param data Optional data pointer assigned to the matrix header
@brief Creates the header and allocates the data for a multi-dimensional dense array.
This function call is equivalent to the following code:
@code
CvMatND* mat = cvCreateMatNDHeader(dims, sizes, type);
cvCreateData(mat);
@endcode
@param dims Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be
changed at build time).
@param sizes Array of dimension sizes.
@param type Type of array elements, see cvCreateMat .
@brief Creates a new matrix header but does not allocate the matrix data.
The function allocates a header for a multi-dimensional dense array. The array data can further be
allocated using cvCreateData or set explicitly to user-allocated data via cvSetData.
@param dims Number of array dimensions
@param sizes Array of dimension sizes
@param type Type of array elements, see cvCreateMat
@brief Returns one of array diagonals.
The function returns the header, corresponding to a specified diagonal of the input array.
@param arr Input array
@param submat Pointer to the resulting sub-array header
@param diag Index of the array diagonal. Zero value corresponds to the main diagonal, -1
corresponds to the diagonal above the main, 1 corresponds to the diagonal below the main, and so
forth.
@overload
@param arr Input array
@param submat Pointer to the resulting sub-array header
@param col Zero-based index of the selected column
@brief Returns one of more array columns.
The functions return the header, corresponding to a specified column span of the input array. That
is, no data is copied. Therefore, any modifications of the submatrix will affect the original array.
If you need to copy the columns, use cvCloneMat. cvGetCol(arr, submat, col) is a shortcut for
cvGetCols(arr, submat, col, col+1).
@param arr Input array
@param submat Pointer to the resulting sub-array header
@param start_col Zero-based index of the starting column (inclusive) of the span
@param end_col Zero-based index of the ending column (exclusive) of the span
@overload
@param arr Input array
@param submat Pointer to the resulting sub-array header
@param row Zero-based index of the selected row
@brief Returns array row or row span.
The functions return the header, corresponding to a specified row/row span of the input array.
cvGetRow(arr, submat, row) is a shortcut for cvGetRows(arr, submat, row, row+1).
@param arr Input array
@param submat Pointer to the resulting sub-array header
@param start_row Zero-based index of the starting row (inclusive) of the span
@param end_row Zero-based index of the ending row (exclusive) of the span
@param delta_row Index step in the row span. That is, the function extracts every delta_row -th
row from start_row and up to (but not including) end_row .
@brief Returns matrix header corresponding to the rectangular sub-array of input image or matrix.
The function returns header, corresponding to a specified rectangle of the input array. In other
words, it allows the user to treat a rectangular part of input array as a stand-alone array. ROI is
taken into account by the function so the sub-array of ROI is actually extracted.
@param arr Input array
@param submat Pointer to the resultant sub-array header
@param rect Zero-based coordinates of the rectangle of interest
Creates an exact copy of the input matrix (except, may be, step value)
@brief Increments array data reference counter.
The function increments CvMat or CvMatND data reference counter and returns the new counter value if
the reference counter pointer is not NULL, otherwise it returns zero.
@param arr Array header
@brief Decrements an array data reference counter.
The function decrements the data reference counter in a CvMat or CvMatND if the reference counter
pointer is not NULL. If the counter reaches zero, the data is deallocated. In the current
implementation the reference counter is not NULL only if the data was allocated using the
cvCreateData function. The counter will be NULL in other cases such as: external data was assigned
to the header using cvSetData, header is part of a larger matrix or image, or the header was
converted from an image or n-dimensional matrix header.
@param arr Pointer to an array header
@brief Deallocates a matrix.
The function decrements the matrix data reference counter and deallocates matrix header. If the data
reference counter is 0, it also deallocates the data. :
@code
if(*mat )
cvDecRefData(*mat);
cvFree((void**)mat);
@endcode
@param mat Double pointer to the matrix
@brief Creates a matrix header but does not allocate the matrix data.
The function allocates a new matrix header and returns a pointer to it. The matrix data can then be
allocated using cvCreateData or set explicitly to user-allocated data via cvSetData.
@param rows Number of rows in the matrix
@param cols Number of columns in the matrix
@param type Type of the matrix elements, see cvCreateMat
@brief Returns the image ROI.
If there is no ROI set, cvRect(0,0,image-\>width,image-\>height) is returned.
@param image A pointer to the image header
@brief Resets the image ROI to include the entire image and releases the ROI structure.
This produces a similar result to the following, but in addition it releases the ROI structure. :
@code
cvSetImageROI(image, cvRect(0, 0, image->width, image->height ));
cvSetImageCOI(image, 0);
@endcode
@param image A pointer to the image header
@brief Sets an image Region Of Interest (ROI) for a given rectangle.
If the original image ROI was NULL and the rect is not the whole image, the ROI structure is
allocated.
Most OpenCV functions support the use of ROI and treat the image rectangle as a separate image. For
example, all of the pixel coordinates are counted from the top-left (or bottom-left) corner of the
ROI, not the original image.
@param image A pointer to the image header
@param rect The ROI rectangle
@brief Returns the index of the channel of interest.
Returns the channel of interest of in an IplImage. Returned values correspond to the coi in
cvSetImageCOI.
@param image A pointer to the image header
@brief Sets the channel of interest in an IplImage.
If the ROI is set to NULL and the coi is *not* 0, the ROI is allocated. Most OpenCV functions do
*not* support the COI setting, so to process an individual image/matrix channel one may copy (via
cvCopy or cvSplit) the channel to a separate image/matrix, process it and then copy the result
back (via cvCopy or cvMerge) if needed.
@param image A pointer to the image header
@param coi The channel of interest. 0 - all channels are selected, 1 - first channel is selected,
etc. Note that the channel indices become 1-based.
Creates a copy of IPL image (widthStep may differ)
@brief Deallocates the image header and the image data.
This call is a shortened form of :
@code
if(*image )
{
cvReleaseData(*image);
cvReleaseImageHeader(image);
}
@endcode
@param image Double pointer to the image header
@brief Deallocates an image header.
This call is an analogue of :
@code
if(image )
{
iplDeallocate(*image, IPL_IMAGE_HEADER | IPL_IMAGE_ROI);
*image = 0;
}
@endcode
but it does not use IPL functions by default (see the CV_TURN_ON_IPL_COMPATIBILITY macro).
@param image Double pointer to the image header
@brief Creates an image header and allocates the image data.
This function call is equivalent to the following code:
@code
header = cvCreateImageHeader(size, depth, channels);
cvCreateData(header);
@endcode
@param size Image width and height
@param depth Bit depth of image elements. See IplImage for valid depths.
@param channels Number of channels per pixel. See IplImage for details. This function only creates
images with interleaved channels.
@brief Initializes an image header that was previously allocated.
The returned IplImage\* points to the initialized header.
@param image Image header to initialize
@param size Image width and height
@param depth Image depth (see cvCreateImage )
@param channels Number of channels (see cvCreateImage )
@param origin Top-left IPL_ORIGIN_TL or bottom-left IPL_ORIGIN_BL
@param align Alignment for image rows, typically 4 or 8 bytes
@brief Creates an image header but does not allocate the image data.
@param size Image width and height
@param depth Image depth (see cvCreateImage )
@param channels Number of channels (see cvCreateImage )
`free` wrapper.
Here and further all the memory releasing functions
(that all call cvFree) take double pointer in order to
to clear pointer to the data after releasing it.
Passing pointer to NULL pointer is Ok: nothing happens in this case
@}
@addtogroup core_c
@{
`malloc` wrapper.
If there is no enough memory, the function
(as well as other OpenCV functions that call cvAlloc)
raises an error.
@brief Type information
The structure contains information about one of the standard or user-defined types. Instances of the
type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there
is a way to find the type info structure for a given object using the cvTypeOf function.
Alternatively, type info can be found by type name using cvFindType, which is used when an object
is read from file storage. The user can register a new type with cvRegisterType that adds the type
information structure into the beginning of the type list. Thus, it is possible to create
specialized types from generic standard types and override the basic methods.
Basic element of the file storage - scalar or collection:
All the keys (names) of elements in the readed file storage
are stored in the hash to speed up the lookup operations:
file node flags
initializes CvAttrList structure
Storage flags:
@brief List of attributes. :
In the current implementation, attributes are used to pass extra parameters when writing user
objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type
specification (type_id attribute).
@see cvAttrList, cvAttrValue
Add element to sequence:
Move reader position forward:
Move reader position backward:
Read element and move read position forward:
Read element and move read position backward:
Return next graph edge for given vertex:
"black box" file storage
types of sequences
types of sparse sequences (sets)
flags for curves
flags for graphs
point sets
chain-coded curves
binary tree for the contour
sequence of the connected components
sequence of the integer numbers
flag checking
type checking macros
@}
Graph is "derived" from the set (this is set a of vertices)
and includes another set (edges)
Checks whether the element pointed by ptr belongs to a set or not
@name Graph
We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly,
pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in
another set. There is a singly-linked list of incoming/outcoming edges for each vertex.
Each edge consists of:
- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively).
A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not
distinguished during search operations.
- Two pointers to next edges for the starting and ending vertices, where next[0] points to the
next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1]
adjacency list.
@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph
@{
@brief Set
Order is not preserved. There can be gaps between sequence elements.
After the element has been inserted it stays in the same place all the time.
The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists.
Read/Write sequence.
Elements can be dynamically inserted to or deleted from the sequence.
@sa Scalar_
Pointer to the current point:
Line iterator state:
@sa RotatedRect
constructs CvSize2D32f structure.
constructs CvSize structure.
constructs CvPoint3D64f structure.
constructs CvPoint2D64f structure.
constructs CvPoint3D32f structure.
converts CvPoint2D32f to CvPoint.
converts CvPoint to CvPoint2D32f.
constructs CvPoint2D32f structure.
constructs CvPoint structure.
@sa TermCriteria
constructs CvRect structure.
@sa Rect_
indicates whether bin ranges are set already or not
should be used as a parameter only,
it turns to CV_HIST_UNIFORM_FLAG of hist->type
@deprecated consider using cv::Mat instead
@brief Sets a specific element of a single-channel floating-point matrix.
The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point
matrices. It is faster because it is inline, it does fewer checks for array type and array element
type, and it checks for the row and column ranges only in debug mode.
@param mat The matrix
@param row The zero-based index of row
@param col The zero-based index of column
@param value The new value of the matrix element
@brief Returns the particular element of single-channel floating-point matrix.
The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point
matrices. It is faster because it is inline, it does fewer checks for array type and array element
type, and it checks for the row and column ranges only in debug mode.
@param mat Input matrix
@param row The zero-based index of row
@param col The zero-based index of column
Inline constructor. No data is allocated internally!!!
* (Use together with cvCreateData, or use cvCreateMat instead to
* get a matrix with allocated data):
extra border mode
for storing double-precision
floating point data in IplImage's
get reference to pixel at (col,row),
for multi-channel images (col) should be multiplied by number of channels
Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column
index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro:
uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j)
CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f;
To access multiple-channel matrices, you can use
CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx).
@deprecated CvMat is now obsolete; consider using Mat instead.
The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV
only supports a subset of possible IplImage formats, as outlined in the parameter list above.
In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require
that the image size or ROI size of all source and destination images match exactly. On the other
hand, the Intel Image Processing Library processes the area of intersection between the source and
destination images (or ROIs), allowing them to vary independently.
@brief Returns a floating-point random number and updates RNG.
The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not
included).
@param rng RNG state initialized by cvRNG
@brief Returns a 32-bit unsigned integer and updates RNG.
The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG
state. It is similar to the rand() function from the C runtime library, except that OpenCV functions
always generates a 32-bit random number, regardless of the platform.
@param rng CvRNG state initialized by cvRNG.
@brief Initializes a random number generator state.
The function initializes a random number generator and returns the state. The pointer to the state
can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current
implementation a multiply-with-carry generator is used.
@param seed 64-bit value used to initiate a random sequence
@sa the C++ class RNG replaced CvRNG.
@addtogroup core_c
@{
@brief This is the "metatype" used *only* as a function parameter.
It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even
CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4
bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray.
@brief Print list of errors occured
@sa check
@brief Print help message
This method will print standard help message containing the about message and arguments description.
@sa about
@brief Set the about message
The about message will be shown when @ref printMessage is called, right before arguments table.
@brief Check for parsing errors
Returns true if error occured while accessing the parameters (bad conversion, missing arguments,
etc.). Call @ref printErrors to print error messages list.
@brief Check if field was provided in the command line
@param name argument name to check
@brief Returns application path
This method returns the path to the executable from the command line (`argv[0]`).
For example, if the application has been started with such command:
@code{.sh}
$ ./bin/my-executable
@endcode
this method will return `./bin`.
@brief Destructor
@brief Assignment operator
@brief Copy constructor
@brief Constructor
Initializes command line parser object
@param argc number of command line arguments (from main())
@param argv array of command line arguments (from main())
@param keys string describing acceptable command line parameters (see class description for syntax)
@brief Parallel data processor
@brief Base class for parallel data processors
@brief Returns the status of optimized code usage.
The function returns true if the optimized code is enabled. Otherwise, it returns false.
@brief Enables or disables the optimized code.
The function can be used to dynamically turn on and off optimized code (code that uses SSE2, AVX,
and other instructions on the platforms that support it). It sets a global flag that is further
checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only
safe to call the function on the very top level in your application where you can be sure that no
other OpenCV function is currently executed.
By default, the optimized code is enabled unless you disable it in CMake. The current status can be
retrieved using useOptimized.
@param onoff The boolean flag specifying whether the optimized code should be used (onoff=true)
or not (onoff=false).
@brief Returns the number of logical CPUs available for the process.
@brief Returns true if the specified feature is supported by the host hardware.
The function returns true if the host hardware supports the specified feature. When user calls
setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until
setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code
in OpenCV.
@param feature The feature of interest, one of cv::CpuFeatures
@brief Available CPU features.
remember to keep this list identical to the one in cvdef.h
@brief Returns the number of CPU ticks.
The function returns the current number of CPU ticks on some architectures (such as x86, x64,
PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for
very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU
systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU
with its own counter. So, theoretically (and practically) the subsequent calls to the function do
not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU
frequency depending on the load, the number of CPU clocks spent in some code cannot be directly
converted to time units. Therefore, getTickCount is generally a preferable solution for measuring
execution time.
@brief Returns the number of ticks per second.
The function returns the number of ticks per second. That is, the following code computes the
execution time in seconds:
@code
double t = (double)getTickCount();
// do something ...
t = ((double)getTickCount() - t)/getTickFrequency();
@endcode
@brief Returns the number of ticks.
The function returns the number of ticks after the certain event (for example, when the machine was
turned on). It can be used to initialize RNG or to measure a function execution time by reading the
tick count before and after the function call. See also the tick frequency.
@brief Returns full configuration time cmake output.
Returned value is raw cmake output including version control system revision, compiler version,
compiler flags, enabled modules and third party libraries, etc. Output format depends on target
architecture.
@brief Returns the index of the currently executed thread within the current parallel region. Always
returns 0 if called outside of parallel region.
The exact meaning of return value depends on the threading framework used by OpenCV library:
- `TBB` – Unsupported with current 4.1 TBB release. May be will be supported in future.
- `OpenMP` – The thread number, within the current team, of the calling thread.
- `Concurrency` – An ID for the virtual processor that the current context is executing on (0
for master thread and unique number for others, but not necessary 1,2,3,...).
- `GCD` – System calling thread's ID. Never returns 0 inside parallel region.
- `C=` – The index of the current parallel task.
@sa setNumThreads, getNumThreads
@brief Returns the number of threads used by OpenCV for parallel regions.
Always returns 1 if OpenCV is built without threading support.
The exact meaning of return value depends on the threading framework used by OpenCV library:
- `TBB` – The number of threads, that OpenCV will try to use for parallel regions. If there is
any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns
default number of threads used by TBB library.
- `OpenMP` – An upper bound on the number of threads that could be used to form a new team.
- `Concurrency` – The number of threads, that OpenCV will try to use for parallel regions.
- `GCD` – Unsupported; returns the GCD thread pool limit (512) for compatibility.
- `C=` – The number of threads, that OpenCV will try to use for parallel regions, if before
called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs,
available for the process.
@sa setNumThreads, getThreadNum
@brief Returns a text string formatted using the printf-like expression.
The function acts like sprintf but forms and returns an STL string. It can be used to form an error
message in the Exception constructor.
@param fmt printf-compatible formatting specifiers.
@brief Sets the new error handler and the optional user data.
The function sets the new error handler, called from cv::error().
\param errCallback the new error handler. If NULL, the default error handler is used.
\param userdata the optional user data pointer, passed to the callback.
\param prevUserdata the optional output parameter where the previous user data pointer is stored
\return the previous error handler
@brief Sets/resets the break-on-error mode.
When the break-on-error mode is set, the default error handler issues a hardware exception, which
can make debugging more convenient.
\return the previous state
Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string.
@brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
@brief Reads algorithm parameters from a file storage
@brief Stores algorithm parameters in a file storage
@brief Clears the algorithm state
@todo document
@todo document
@brief returns uniformly distributed double-precision floating-point random number from [a,b) range
@brief returns uniformly distributed floating-point random number from [a,b) range
@brief returns uniformly distributed integer random number from [a,b) range
@brief Mersenne Twister random number generator
Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c
@todo document
@brief Returns the next random number sampled from the Gaussian distribution
@param sigma standard deviation of the distribution.
The method transforms the state using the MWC algorithm and returns the
next random number from the Gaussian distribution N(0,sigma) . That is,
the mean value of the returned random numbers is zero and the standard
deviation is the specified sigma .
@brief Fills arrays with random numbers.
@param mat 2D or N-dimensional matrix; currently matrices with more than
4 channels are not supported by the methods, use Mat::reshape as a
possible workaround.
@param distType distribution type, RNG::UNIFORM or RNG::NORMAL.
@param a first distribution parameter; in case of the uniform
distribution, this is an inclusive lower boundary, in case of the normal
distribution, this is a mean value.
@param b second distribution parameter; in case of the uniform
distribution, this is a non-inclusive upper boundary, in case of the
normal distribution, this is a standard deviation (diagonal of the
standard deviation matrix or the full standard deviation matrix).
@param saturateRange pre-saturation flag; for uniform distribution only;
if true, the method will first convert a and b to the acceptable value
range (according to the mat datatype) and then will generate uniformly
distributed random numbers within the range [saturate(a), saturate(b)),
if saturateRange=false, the method will generate uniformly distributed
random numbers in the original range [a, b) and then will saturate them,
it means, for example, that
theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX) will likely
produce array mostly filled with 0's and 255's, since the range (0, 255)
is significantly smaller than [-DBL_MAX, DBL_MAX).
Each of the methods fills the matrix with the random values from the
specified distribution. As the new numbers are generated, the RNG state
is updated accordingly. In case of multiple-channel images, every
channel is filled independently, which means that RNG cannot generate
samples from the multi-dimensional Gaussian distribution with
non-diagonal covariance matrix directly. To do that, the method
generates samples from multi-dimensional standard Gaussian distribution
with zero mean and identity covariation matrix, and then transforms them
using transform to get samples from the specified Gaussian distribution.
@overload
@overload
@brief returns uniformly distributed integer random number from [a,b) range
The methods transform the state using the MWC algorithm and return the
next uniformly-distributed random number of the specified type, deduced
from the input parameter type, from the range [a, b) . There is a nuance
illustrated by the following sample:
@code{.cpp}
RNG rng;
// always produces 0
double a = rng.uniform(0, 1);
// produces double from [0, 1)
double a1 = rng.uniform((double)0, (double)1);
// produces float from [0, 1)
double b = rng.uniform(0.f, 1.f);
// produces double from [0, 1)
double c = rng.uniform(0., 1.);
// may cause compiler error because of ambiguity:
// RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)?
double d = rng.uniform(0, 0.999999);
@endcode
The compiler does not take into account the type of the variable to
which you assign the result of RNG::uniform . The only thing that
matters to the compiler is the type of a and b parameters. So, if you
want a floating-point random number, but the range boundaries are
integer numbers, either put dots in the end, if they are constants, or
use explicit type cast operators, as in the a1 initialization above.
@param a lower inclusive boundary of the returned random numbers.
@param b upper non-inclusive boundary of the returned random numbers.
@overload
@param N upper non-inclusive boundary of the returned random number.
@brief returns a random integer sampled uniformly from [0, N).
The methods transform the state using the MWC algorithm and return the
next random number. The first form is equivalent to RNG::next . The
second form returns the random number modulo N , which means that the
result is in the range [0, N) .
@overload
@overload
@overload
@overload
@overload
@overload
@overload
Each of the methods updates the state using the MWC algorithm and
returns the next random number of the specified type. In case of integer
types, the returned number is from the available value range for the
specified type. In case of floating-point types, the returned value is
from [0,1) range.
The method updates the state using the MWC algorithm and returns the
next 32-bit random number.
@overload
@param state 64-bit value used to initialize the RNG.
@brief constructor
These are the RNG constructors. The first form sets the state to some
pre-defined value, equal to 2\*\*32-1 in the current implementation. The
second form sets the state to the specified value. If you passed state=0
, the constructor uses the above default value instead to avoid the
singular random number sequence, consisting of all zeros.
@brief performs a singular value back substitution.
The method calculates a back substitution for the specified right-hand
side:
\f[\texttt{x} = \texttt{vt} ^T \cdot diag( \texttt{w} )^{-1} \cdot \texttt{u} ^T \cdot \texttt{rhs} \sim \texttt{A} ^{-1} \cdot \texttt{rhs}\f]
Using this technique you can either get a very accurate solution of the
convenient linear system, or the best (in the least-squares terms)
pseudo-solution of an overdetermined linear system.
@param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to
be solved, where A has been previously decomposed.
@param dst found solution of the system.
@note Explicit SVD with the further back substitution only makes sense
if you need to solve many linear systems with the same left-hand side
(for example, src ). If all you need is to solve a single system
(possibly with multiple rhs immediately available), simply call solve
add pass DECOMP_SVD there. It does absolutely the same thing.
@brief solves an under-determined singular linear system
The method finds a unit-length solution x of a singular linear system
A\*x = 0. Depending on the rank of A, there can be no solutions, a
single solution or an infinite number of solutions. In general, the
algorithm solves the following problem:
\f[dst = \arg \min _{x: \| x \| =1} \| src \cdot x \|\f]
@param src left-hand-side matrix.
@param dst found solution.
@brief performs back substitution
@overload
computes singular values of a matrix
@param src decomposed matrix
@param w calculated singular values
@param flags operation flags - see SVD::Flags.
@brief decomposes matrix and stores the results to user-provided matrices
The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor
and SVD::operator(), they store the results to the user-provided
matrices:
@code{.cpp}
Mat A, w, u, vt;
SVD::compute(A, w, u, vt);
@endcode
@param src decomposed matrix
@param w calculated singular values
@param u calculated left singular vectors
@param vt transposed matrix of right singular values
@param flags operation flags - see SVD::SVD.
@brief the operator that performs SVD. The previously allocated u, w and vt are released.
The operator performs the singular value decomposition of the supplied
matrix. The u,`vt` , and the vector of singular values w are stored in
the structure. The same SVD structure can be reused many times with
different matrices. Each time, if needed, the previous u,`vt` , and w
are reclaimed and the new matrices are created, which is all handled by
Mat::create.
@param src decomposed matrix.
@param flags operation flags (SVD::Flags)
@overload
initializes an empty SVD structure and then calls SVD::operator()
@param src decomposed matrix.
@param flags operation flags (SVD::Flags)
@brief the default constructor
initializes an empty SVD structure
when the matrix is not square, by default the algorithm produces u and vt matrices of
sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is
specified, u and vt will be full-size square orthogonal matrices.
indicates that only a vector of singular values `w` is to be processed, while u and vt
will be set to empty matrices
allow the algorithm to modify the decomposed matrix; it can save space and speed up
processing. currently ignored.
@brief Singular Value Decomposition
Class for computing Singular Value Decomposition of a floating-point
matrix. The Singular Value Decomposition is used to solve least-square
problems, under-determined linear systems, invert matrices, compute
condition numbers, and so on.
If you want to compute a condition number of a matrix or an absolute value of
its determinant, you do not need `u` and `vt`. You can pass
flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u
and vt must be computed, which is not necessary most of the time.
@sa invert, solve, eigen, determinant
Returns the eigenvalues of this LDA.
Returns the eigenvectors of this LDA.
Reconstructs projections from the LDA subspace.
Projects samples into the LDA subspace.
Compute the discriminants for data in src and labels.
destructor
Deserializes this object from a given cv::FileStorage.
Serializes this object to a given cv::FileStorage.
Deserializes this object from a given filename.
Serializes this object to a given filename.
Initializes and performs a Discriminant Analysis with Fisher's
Optimization Criterion on given data in src and corresponding labels
in labels. If 0 (or less) number of components are given, they are
automatically determined for given data in computation.
@brief constructor
Initializes a LDA with num_components (default 0) and specifies how
samples are aligned (default dataAsRow=true).
@example pca.cpp
An example using %PCA for dimensionality reduction while maintaining an amount of variance
@brief Linear Discriminant Analysis
@todo document this class
@brief write and load PCA matrix
@overload
@param vec coordinates of the vectors in the principal component
subspace, the layout and size are the same as of PCA::project output
vectors.
@param result reconstructed vectors; the layout and size are the same as
of PCA::project input vectors.
@brief Reconstructs vectors from their PC projections.
The methods are inverse operations to PCA::project. They take PC
coordinates of projected vectors and reconstruct the original vectors.
Unless all the principal components have been retained, the
reconstructed vectors are different from the originals. But typically,
the difference is small if the number of components is large enough (but
still much smaller than the original vector dimensionality). As a
result, PCA is used.
@param vec coordinates of the vectors in the principal component
subspace, the layout and size are the same as of PCA::project output
vectors.
@overload
@param vec input vector(s); must have the same dimensionality and the
same layout as the input data used at PCA phase, that is, if
DATA_AS_ROW are specified, then `vec.cols==data.cols`
(vector dimensionality) and `vec.rows` is the number of vectors to
project, and the same is true for the PCA::DATA_AS_COL case.
@param result output vectors; in case of PCA::DATA_AS_COL, the
output matrix has as many columns as the number of input vectors, this
means that `result.cols==vec.cols` and the number of rows match the
number of principal components (for example, `maxComponents` parameter
passed to the constructor).
@brief Projects vector(s) to the principal component subspace.
The methods project one or more vectors to the principal component
subspace, where each vector projection is represented by coefficients in
the principal component basis. The first form of the method returns the
matrix that the second form writes to the result. So the first form can
be used as a part of expression while the second form can be more
efficient in a processing loop.
@param vec input vector(s); must have the same dimensionality and the
same layout as the input data used at %PCA phase, that is, if
DATA_AS_ROW are specified, then `vec.cols==data.cols`
(vector dimensionality) and `vec.rows` is the number of vectors to
project, and the same is true for the PCA::DATA_AS_COL case.
@overload
@param data input samples stored as the matrix rows or as the matrix
columns.
@param mean optional mean value; if the matrix is empty (noArray()),
the mean is computed from the data.
@param flags operation flags; currently the parameter is only used to
specify the data layout. (PCA::Flags)
@param retainedVariance Percentage of variance that %PCA should retain.
Using this parameter will let the %PCA decided how many components to
retain but it will always keep at least 2.
@brief performs %PCA
The operator performs %PCA of the supplied dataset. It is safe to reuse
the same PCA structure for multiple datasets. That is, if the structure
has been previously used with another dataset, the existing internal
data is reclaimed and the new eigenvalues, @ref eigenvectors , and @ref
mean are allocated and computed.
The computed eigenvalues are sorted from the largest to the smallest and
the corresponding eigenvectors are stored as eigenvectors rows.
@param data input samples stored as the matrix rows or as the matrix
columns.
@param mean optional mean value; if the matrix is empty (noArray()),
the mean is computed from the data.
@param flags operation flags; currently the parameter is only used to
specify the data layout. (Flags)
@param maxComponents maximum number of components that PCA should
retain; by default, all the components are retained.
@overload
@param data input samples stored as matrix rows or matrix columns.
@param mean optional mean value; if the matrix is empty (noArray()),
the mean is computed from the data.
@param flags operation flags; currently the parameter is only used to
specify the data layout (PCA::Flags)
@param retainedVariance Percentage of variance that PCA should retain.
Using this parameter will let the PCA decided how many components to
retain but it will always keep at least 2.
@overload
@param data input samples stored as matrix rows or matrix columns.
@param mean optional mean value; if the matrix is empty (@c noArray()),
the mean is computed from the data.
@param flags operation flags; currently the parameter is only used to
specify the data layout (PCA::Flags)
@param maxComponents maximum number of components that %PCA should
retain; by default, all the components are retained.
@brief default constructor
The default constructor initializes an empty %PCA structure. The other
constructors initialize the structure and call PCA::operator()().
@brief Shuffles the array elements randomly.
The function randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and
swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor .
@param dst input/output numerical 1D array.
@param iterFactor scale factor that determines the number of random swap operations (see the details
below).
@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used
instead.
@sa RNG, sort
@brief Fills the array with normally distributed random numbers.
The function randn fills the matrix dst with normally distributed random numbers with the specified
mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the
value range of the output array data type.
@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels.
@param mean mean value (expectation) of the generated random numbers.
@param stddev standard deviation of the generated random numbers; it can be either a vector (in
which case a diagonal standard deviation matrix is assumed) or a square matrix.
@sa RNG, randu
@brief Returns the default random number generator.
The function theRNG returns the default random number generator. For each thread, there is a
separate random number generator, so you can use the function safely in multi-thread environments.
If you just need to get a single random number using this generator or initialize an array, you can
use randu or randn instead. But if you are going to generate many random numbers inside a loop, it
is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() .
@sa RNG, randu, randn
@brief Returns the optimal DFT size for a given vector size.
DFT performance is not a monotonic function of a vector size. Therefore, when you calculate
convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to
pad the input data with zeros to get a bit larger array that can be transformed much faster than the
original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process.
Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2)
are also processed quite efficiently.
The function getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize
so that the DFT of a vector of size N can be processed efficiently. In the current implementation N
= 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r.
The function returns a negative number if vecsize is too large (very close to INT_MAX ).
While the function cannot be used directly to estimate the optimal vector size for DCT transform
(since the current DCT implementation supports only even-size vectors), it can be easily processed
as getOptimalDFTSize((vecsize+1)/2)\*2.
@param vecsize vector size.
@sa dft , dct , idft , idct , mulSpectrums
@brief Performs the per-element multiplication of two Fourier spectrums.
The function mulSpectrums performs the per-element multiplication of the two CCS-packed or complex
matrices that are results of a real or complex Fourier transform.
The function, together with dft and idft , may be used to calculate convolution (pass conjB=false )
or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are
simply multiplied (per element) with an optional conjugation of the second-array elements. When the
arrays are real, they are assumed to be CCS-packed (see dft for details).
@param a first input array.
@param b second input array of the same size and type as src1 .
@param c output array of the same size and type as src1 .
@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value.
@param conjB optional flag that conjugates the second input array before the multiplication (true)
or not (false).
@brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array.
idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE).
@param src input floating-point single-channel array.
@param dst output array of the same size and type as src.
@param flags operation flags.
@sa dct, dft, idft, getOptimalDFTSize
@brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array.
idft(src, dst, flags) is equivalent to dft(src, dst, flags | DFT_INVERSE) .
@note None of dft and idft scales the result by default. So, you should pass DFT_SCALE to one of
dft or idft explicitly to make these transforms mutually inverse.
@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize
@param src input floating-point real or complex array.
@param dst output array whose size and type depend on the flags.
@param flags operation flags (see dft and cv::DftFlags).
@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see
the convolution sample in dft description.
@brief Calculates the Mahalanobis distance between two vectors.
The function Mahalanobis calculates and returns the weighted distance between two vectors:
\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f]
The covariance matrix may be calculated using the cv::calcCovarMatrix function and then inverted using
the invert function (preferably using the cv::DECOMP_SVD method, as the most accurate).
@param v1 first 1D input vector.
@param v2 second 1D input vector.
@param icovar inverse covariance matrix.
wrap SVD::backSubst
wrap SVD::compute
wrap PCA::backProject
wrap PCA::project
wrap PCA::operator()
wrap PCA::operator()
@overload
@note use cv::COVAR_ROWS or cv::COVAR_COLS flag
@param samples samples stored as rows/columns of a single matrix.
@param covar output covariance matrix of the type ctype and square size.
@param mean input or output (depending on the flags) array as the average value of the input vectors.
@param flags operation flags as a combination of cv::CovarFlags
@param ctype type of the matrixl; it equals 'CV_64F' by default.
@brief Calculates the covariance matrix of a set of vectors.
The functions calcCovarMatrix calculate the covariance matrix and, optionally, the mean vector of
the set of input vectors.
@param samples samples stored as separate matrices
@param nsamples number of samples
@param covar output covariance matrix of the type ctype and square size.
@param mean input or output (depending on the flags) array as the average value of the input vectors.
@param flags operation flags as a combination of cv::CovarFlags
@param ctype type of the matrixl; it equals 'CV_64F' by default.
@sa PCA, mulTransposed, Mahalanobis
@todo InputArrayOfArrays
@brief Finds the real or complex roots of a polynomial equation.
The function solvePoly finds real and complex roots of a polynomial equation:
\f[\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\f]
@param coeffs array of polynomial coefficients.
@param roots output (complex) array of roots.
@param maxIters maximum number of iterations the algorithm does.
@brief Finds the real roots of a cubic equation.
The function solveCubic finds the real roots of a cubic equation:
- if coeffs is a 4-element vector:
\f[\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\f]
- if coeffs is a 3-element vector:
\f[x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\f]
The roots are stored in the roots array.
@param coeffs equation coefficients, an array of 3 or 4 elements.
@param roots output array of real roots that has 1 or 3 elements.
@brief Sorts each row or each column of a matrix.
The function sortIdx sorts each matrix row or each matrix column in the
ascending or descending order. So you should pass two operation flags to
get desired behaviour. Instead of reordering the elements themselves, it
stores the indices of sorted elements in the output array. For example:
@code
Mat A = Mat::eye(3,3,CV_32F), B;
sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING);
// B will probably contain
// (because of equal elements in A some permutations are possible):
// [[1, 2, 0], [0, 2, 1], [0, 1, 2]]
@endcode
@param src input single-channel array.
@param dst output integer array of the same size as src.
@param flags operation flags that could be a combination of cv::SortFlags
@sa sort, randShuffle
@brief Sorts each row or each column of a matrix.
The function sort sorts each matrix row or each matrix column in
ascending or descending order. So you should pass two operation flags to
get desired behaviour. If you want to sort matrix rows or columns
lexicographically, you can use STL std::sort generic function with the
proper comparison predicate.
@param src input single-channel array.
@param dst output array of the same size and type as src.
@param flags operation flags, a combination of cv::SortFlags
@sa sortIdx, randShuffle
@brief Solves one or more linear systems or least-squares problems.
The function solve solves a linear system or least-squares problem (the
latter is possible with SVD or QR methods, or by specifying the flag
DECOMP_NORMAL ):
\f[\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\f]
If DECOMP_LU or DECOMP_CHOLESKY method is used, the function returns 1
if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise,
it returns 0. In the latter case, dst is not valid. Other methods find a
pseudo-solution in case of a singular left-hand side part.
@note If you want to find a unity-norm solution of an under-defined
singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve
will not do the work. Use SVD::solveZ instead.
@param src1 input matrix on the left-hand side of the system.
@param src2 input matrix on the right-hand side of the system.
@param dst output solution.
@param flags solution (matrix inversion) method (cv::DecompTypes)
@sa invert, SVD, eigen
@brief Finds the inverse or pseudo-inverse of a matrix.
The function invert inverts the matrix src and stores the result in dst
. When the matrix src is singular or non-square, the function calculates
the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is
minimal, where I is an identity matrix.
In case of the DECOMP_LU method, the function returns non-zero value if
the inverse has been successfully calculated and 0 if src is singular.
In case of the DECOMP_SVD method, the function returns the inverse
condition number of src (the ratio of the smallest singular value to the
largest singular value) and 0 if src is singular. The SVD method
calculates a pseudo-inverse matrix if src is singular.
Similarly to DECOMP_LU, the method DECOMP_CHOLESKY works only with
non-singular square matrices that should also be symmetrical and
positively defined. In this case, the function stores the inverted
matrix in dst and returns non-zero. Otherwise, it returns 0.
@param src input floating-point M x N matrix.
@param dst output matrix of N x M size and the same type as src.
@param flags inversion method (cv::DecompTypes)
@sa solve, SVD
@brief Returns the trace of a matrix.
The function trace returns the sum of the diagonal elements of the
matrix mtx .
\f[\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)\f]
@param mtx input matrix.
@brief Initializes a scaled identity matrix.
The function setIdentity initializes a scaled identity matrix:
\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f]
The function can also be emulated using the matrix initializers and the
matrix expressions:
@code
Mat A = Mat::eye(4, 3, CV_32F)*5;
// A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]]
@endcode
@param mtx matrix to initialize (not necessarily square).
@param s value to assign to diagonal elements.
@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator=
@brief Performs the matrix transformation of every array element.
The function transform performs the matrix transformation of every
element of the array src and stores the results in dst :
\f[\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\f]
(when m.cols=src.channels() ), or
\f[\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\f]
(when m.cols=src.channels()+1 )
Every element of the N -channel array src is interpreted as N -element
vector that is transformed using the M x N or M x (N+1) matrix m to
M-element vector - the corresponding element of the output array dst .
The function may be used for geometrical transformation of
N -dimensional points, arbitrary linear color space transformation (such
as various kinds of RGB to YUV transforms), shuffling the image
channels, and so forth.
@param src input array that must have as many channels (1 to 4) as
m.cols or m.cols-1.
@param dst output array of the same size and depth as src; it has as
many channels as m.rows.
@param m transformation 2x2 or 2x3 floating-point matrix.
@sa perspectiveTransform, getAffineTransform, estimateRigidTransform, warpAffine, warpPerspective
@brief Transposes a matrix.
The function transpose transposes the matrix src :
\f[\texttt{dst} (i,j) = \texttt{src} (j,i)\f]
@note No complex conjugation is done in case of a complex matrix. It it
should be done separately if needed.
@param src input array.
@param dst output array of the same type as src.
@brief Calculates the product of a matrix and its transposition.
The function mulTransposed calculates the product of src and its
transposition:
\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f]
if aTa=true , and
\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f]
otherwise. The function is used to calculate the covariance matrix. With
zero delta, it can be used as a faster substitute for general matrix
product A\*B when B=A'
@param src input single-channel matrix. Note that unlike gemm, the
function can multiply not only floating-point matrices.
@param dst output square matrix.
@param aTa Flag specifying the multiplication ordering. See the
description below.
@param delta Optional delta matrix subtracted from src before the
multiplication. When the matrix is empty ( delta=noArray() ), it is
assumed to be zero, that is, nothing is subtracted. If it has the same
size as src , it is simply subtracted. Otherwise, it is "repeated" (see
repeat ) to cover the full src and then subtracted. Type of the delta
matrix, when it is not empty, must be the same as the type of created
output matrix. See the dtype parameter description below.
@param scale Optional scale factor for the matrix product.
@param dtype Optional type of the output matrix. When it is negative,
the output matrix will have the same type as src . Otherwise, it will be
type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F .
@sa calcCovarMatrix, gemm, repeat, reduce
@brief Performs generalized matrix multiplication.
The function performs generalized matrix multiplication similar to the
gemm functions in BLAS level 3. For example,
`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)`
corresponds to
\f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f]
In case of complex (two-channel) data, performed a complex matrix
multiplication.
The function can be replaced with a matrix expression. For example, the
above call can be replaced with:
@code{.cpp}
dst = alpha*src1.t()*src2 + beta*src3.t();
@endcode
@param src1 first multiplied input matrix that could be real(CV_32FC1,
CV_64FC1) or complex(CV_32FC2, CV_64FC2).
@param src2 second multiplied input matrix of the same type as src1.
@param alpha weight of the matrix product.
@param src3 third optional delta matrix added to the matrix product; it
should have the same type as src1 and src2.
@param beta weight of src3.
@param dst output matrix; it has the proper size and the same type as
input matrices.
@param flags operation flags (cv::GemmFlags)
@sa mulTransposed , transform
@brief converts NaN's to the given number
@brief Calculates the magnitude of 2D vectors.
The function magnitude calculates the magnitude of 2D vectors formed
from the corresponding elements of x and y arrays:
\f[\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f]
@param x floating-point array of x-coordinates of the vectors.
@param y floating-point array of y-coordinates of the vectors; it must
have the same size as x.
@param magnitude output array of the same size and type as x.
@sa cartToPolar, polarToCart, phase, sqrt
@brief Calculates the rotation angle of 2D vectors.
The function phase calculates the rotation angle of each 2D vector that
is formed from the corresponding elements of x and y :
\f[\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f]
The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 ,
the corresponding angle(I) is set to 0.
@param x input floating-point array of x-coordinates of 2D vectors.
@param y input array of y-coordinates of 2D vectors; it must have the
same size and the same type as x.
@param angle output array of vector angles; it has the same size and
same type as x .
@param angleInDegrees when true, the function calculates the angle in
degrees, otherwise, they are measured in radians.
@brief Calculates the magnitude and angle of 2D vectors.
The function cartToPolar calculates either the magnitude, angle, or both
for every 2D vector (x(I),y(I)):
\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f]
The angles are calculated with accuracy about 0.3 degrees. For the point
(0,0), the angle is set to 0.
@param x array of x-coordinates; this must be a single-precision or
double-precision floating-point array.
@param y array of y-coordinates, that must have the same size and same type as x.
@param magnitude output array of magnitudes of the same size and type as x.
@param angle output array of angles that has the same size and type as
x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees).
@param angleInDegrees a flag, indicating whether the angles are measured
in radians (which is by default), or in degrees.
@sa Sobel, Scharr
@brief Calculates x and y coordinates of 2D vectors from their magnitude and angle.
The function polarToCart calculates the Cartesian coordinates of each 2D
vector represented by the corresponding elements of magnitude and angle:
\f[\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f]
The relative accuracy of the estimated coordinates is about 1e-6.
@param magnitude input floating-point array of magnitudes of 2D vectors;
it can be an empty matrix (=Mat()), in this case, the function assumes
that all the magnitudes are =1; if it is not empty, it must have the
same size and type as angle.
@param angle input floating-point array of angles of 2D vectors.
@param x output array of x-coordinates of 2D vectors; it has the same
size and type as angle.
@param y output array of y-coordinates of 2D vectors; it has the same
size and type as angle.
@param angleInDegrees when true, the input angles are measured in
degrees, otherwise, they are measured in radians.
@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt
@brief Calculates the natural logarithm of every array element.
The function log calculates the natural logarithm of the absolute value
of every element of the input array:
\f[\texttt{dst} (I) = \fork{\log |\texttt{src}(I)|}{if \(\texttt{src}(I) \ne 0\) }{\texttt{C}}{otherwise}\f]
where C is a large negative number (about -700 in the current
implementation). The maximum relative error is about 7e-6 for
single-precision input and less than 1e-10 for double-precision input.
Special values (NaN, Inf) are not handled.
@param src input array.
@param dst output array of the same size and type as src .
@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude
@brief Calculates the exponent of every array element.
The function exp calculates the exponent of every element of the input
array:
\f[\texttt{dst} [I] = e^{ src(I) }\f]
The maximum relative error is about 7e-6 for single-precision input and
less than 1e-10 for double-precision input. Currently, the function
converts denormalized values to zeros on output. Special values (NaN,
Inf) are not handled.
@param src input array.
@param dst output array of the same size and type as src.
@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude
@brief Calculates a square root of array elements.
The functions sqrt calculate a square root of each input array element.
In case of multi-channel arrays, each channel is processed
independently. The accuracy is approximately the same as of the built-in
std::sqrt .
@param src input floating-point array.
@param dst output array of the same size and type as src.
@brief Calculates per-element maximum of two arrays or an array and a scalar.
The functions max calculate the per-element maximum of two arrays:
\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f]
or array and a scalar:
\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f]
@param src1 first input array.
@param src2 second input array of the same size and type as src1 .
@param dst output array of the same size and type as src1.
@sa min, compare, inRange, minMaxLoc, @ref MatrixExpressions
@brief Calculates per-element minimum of two arrays or an array and a scalar.
The functions min calculate the per-element minimum of two arrays:
\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f]
or array and a scalar:
\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f]
@param src1 first input array.
@param src2 second input array of the same size and type as src1.
@param dst output array of the same size and type as src1.
@sa max, compare, inRange, minMaxLoc
@brief Checks if array elements lie between the elements of two other arrays.
The function checks the range as follows:
- For every element of a single-channel input array:
\f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\f]
- For two-channel arrays:
\f[\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\f]
- and so forth.
That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the
specified 1D, 2D, 3D, ... box and 0 otherwise.
When the lower and/or upper boundary parameters are scalars, the indexes
(I) at lowerb and upperb in the above formulas should be omitted.
@param src first input array.
@param lowerb inclusive lower boundary array or a scalar.
@param upperb inclusive upper boundary array or a scalar.
@param dst output array of the same size as src and CV_8U type.
@brief Inverts every bit of an array.
The function calculates per-element bit-wise inversion of the input
array:
\f[\texttt{dst} (I) = \neg \texttt{src} (I)\f]
In case of a floating-point input array, its machine-specific bit
representation (usually IEEE754-compliant) is used for the operation. In
case of multi-channel arrays, each channel is processed independently.
@param src input array.
@param dst output array that has the same size and type as the input
array.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.
@brief Calculates the per-element bit-wise "exclusive or" operation on two
arrays or an array and a scalar.
The function calculates the per-element bit-wise logical "exclusive-or"
operation for:
* Two arrays when src1 and src2 have the same size:
\f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
* An array and a scalar when src2 is constructed from Scalar or has
the same number of elements as `src1.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
* A scalar and an array when src1 is constructed from Scalar or has
the same number of elements as `src2.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
In case of floating-point arrays, their machine-specific bit
representations (usually IEEE754-compliant) are used for the operation.
In case of multi-channel arrays, each channel is processed
independently. In the 2nd and 3rd cases above, the scalar is first
converted to the array type.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as the input
arrays.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.
@brief Calculates the per-element bit-wise disjunction of two arrays or an
array and a scalar.
The function calculates the per-element bit-wise logical disjunction for:
* Two arrays when src1 and src2 have the same size:
\f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
* An array and a scalar when src2 is constructed from Scalar or has
the same number of elements as `src1.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
* A scalar and an array when src1 is constructed from Scalar or has
the same number of elements as `src2.channels()`:
\f[\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
In case of floating-point arrays, their machine-specific bit
representations (usually IEEE754-compliant) are used for the operation.
In case of multi-channel arrays, each channel is processed
independently. In the second and third cases above, the scalar is first
converted to the array type.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as the input
arrays.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.
@brief Applies vertical concatenation to given matrices.
The function vertically concatenates two or more cv::Mat matrices (with the same number of cols).
@code{.cpp}
cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)),
cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)),
cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};
cv::Mat out;
cv::vconcat( matArray, 3, out );
//out:
//[1, 1, 1, 1;
// 2, 2, 2, 2;
// 3, 3, 3, 3]
@endcode
@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth.
@param nsrc number of matrices in src.
@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src.
@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray)
@brief Applies horizontal concatenation to given matrices.
The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows).
@code{.cpp}
cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)),
cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)),
cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};
cv::Mat out;
cv::hconcat( matArray, 3, out );
//out:
//[1, 2, 3;
// 1, 2, 3;
// 1, 2, 3;
// 1, 2, 3]
@endcode
@param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
@param nsrc number of matrices in src.
@param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
@sa cv::vconcat(const Mat*, size_t, OutputArray), @sa cv::vconcat(InputArrayOfArrays, OutputArray) and @sa cv::vconcat(InputArray, InputArray, OutputArray)
@overload
@param src input array to replicate.
@param ny Flag to specify how many times the src is repeated along the
vertical axis.
@param nx Flag to specify how many times the src is repeated along the
horizontal axis.
@brief Fills the output array with repeated copies of the input array.
The functions repeat duplicate the input array one or more times along each of the two axes:
\f[\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }\f]
The second variant of the function is more convenient to use with @ref MatrixExpressions.
@param src input array to replicate.
@param dst output array of the same type as src.
@param ny Flag to specify how many times the src is repeated along the
vertical axis.
@param nx Flag to specify how many times the src is repeated along the
horizontal axis.
@sa reduce
@brief inserts a single channel to dst (coi is 0-based index)
@todo document
@brief extracts a single channel from src (coi is 0-based index)
@todo document
@overload
@param src input array or vector of matricesl; all of the matrices must have the same size and the
same depth.
@param dst output array or vector of matrices; all the matrices *must be allocated*; their size and
depth must be the same as in src[0].
@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
filled with zero .
@overload
@param src input array or vector of matricesl; all of the matrices must have the same size and the
same depth.
@param dst output array or vector of matrices; all the matrices *must be allocated*; their size and
depth must be the same as in src[0].
@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
filled with zero .
@param npairs number of index pairs in fromTo.
@overload
@param m input multi-channel array.
@param mv output vector of arrays; the arrays themselves are reallocated, if needed.
@brief Divides a multi-channel array into several single-channel arrays.
The functions split split a multi-channel array into separate single-channel arrays:
\f[\texttt{mv} [c](I) = \texttt{src} (I)_c\f]
If you need to extract a single channel or do some other sophisticated channel permutation, use
mixChannels .
@param src input multi-channel array.
@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are
reallocated, if needed.
@sa merge, mixChannels, cvtColor
@overload
@param mv input vector of matrices to be merged; all the matrices in mv must have the same
size and the same depth.
@param dst output array of the same size and the same depth as mv[0]; The number of channels will
be the total number of channels in the matrix array.
@brief Creates one multichannel array out of several single-channel ones.
The functions merge merge several arrays to make a single multi-channel array. That is, each
element of the output array will be a concatenation of the elements of the input arrays, where
elements of i-th input array are treated as mv[i].channels()-element vectors.
The function split does the reverse operation. If you need to shuffle channels in some other
advanced way, use mixChannels .
@param mv input array of matrices to be merged; all the matrices in mv must have the same
size and the same depth.
@param count number of input matrices when mv is a plain C array; it must be greater than zero.
@param dst output array of the same size and the same depth as mv[0]; The number of channels will
be the total number of channels in the matrix array.
@sa mixChannels, split, Mat::reshape
@brief Reduces a matrix to a vector.
The function reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of
1D vectors and performing the specified operation on the vectors until a single row/column is
obtained. For example, the function can be used to compute horizontal and vertical projections of a
raster image. In case of REDUCE_SUM and REDUCE_AVG , the output may have a larger element
bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction
modes.
@param src input 2D matrix.
@param dst output vector. Its size and type is defined by dim and dtype parameters.
@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to
a single row. 1 means that the matrix is reduced to a single column.
@param rtype reduction operation that could be one of cv::ReduceTypes
@param dtype when negative, the output vector will have the same type as the input matrix,
otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()).
@sa repeat
@overload
@param a input single-channel array.
@param minVal pointer to the returned minimum value; NULL is used if not required.
@param maxVal pointer to the returned maximum value; NULL is used if not required.
@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required;
Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element
in each dimension are stored there sequentially.
@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required.
@brief Finds the global minimum and maximum in an array
The function minMaxIdx finds the minimum and maximum element values and their positions. The
extremums are searched across the whole array or, if mask is not an empty array, in the specified
array region. The function does not work with multi-channel arrays. If you need to find minimum or
maximum elements across all the channels, use Mat::reshape first to reinterpret the array as
single-channel. Or you may extract the particular channel using either extractImageCOI , or
mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements
only.
@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is
a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2
dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be
(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be
(0,j1)/(0,j2)).
@param src input single-channel array.
@param minVal pointer to the returned minimum value; NULL is used if not required.
@param maxVal pointer to the returned maximum value; NULL is used if not required.
@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required;
Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element
in each dimension are stored there sequentially.
@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required.
@param mask specified array region
@brief Finds the global minimum and maximum in an array.
The functions minMaxLoc find the minimum and maximum element values and their positions. The
extremums are searched across the whole array or, if mask is not an empty array, in the specified
array region.
The functions do not work with multi-channel arrays. If you need to find minimum or maximum
elements across all the channels, use Mat::reshape first to reinterpret the array as
single-channel. Or you may extract the particular channel using either extractImageCOI , or
mixChannels , or split .
@param src input single-channel array.
@param minVal pointer to the returned minimum value; NULL is used if not required.
@param maxVal pointer to the returned maximum value; NULL is used if not required.
@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required.
@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required.
@param mask optional mask used to select a sub-array.
@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape
@overload
@param src input array.
@param dst output array of the same size as src .
@param alpha norm value to normalize to or the lower range boundary in case of the range
normalization.
@param normType normalization type (see cv::NormTypes).
@brief Normalizes the norm or value range of an array.
The functions normalize scale and shift the input array elements so that
\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f]
(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that
\f[\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\f]
when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be
normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this
sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or
min-max but modify the whole array, you can use norm and Mat::convertTo.
In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this,
the range transformation for sparse matrices is not allowed since it can shift the zero level.
@param src input array.
@param dst output array of the same size as src .
@param alpha norm value to normalize to or the lower range boundary in case of the range
normalization.
@param beta upper range boundary in case of the range normalization; it is not used for the norm
normalization.
@param norm_type normalization type (see cv::NormTypes).
@param dtype when negative, the output array has the same type as src; otherwise, it has the same
number of channels as src and the depth =CV_MAT_DEPTH(dtype).
@param mask optional operation mask.
@sa norm, Mat::convertTo, SparseMat::convertTo
@brief naive nearest neighbor finder
see http://en.wikipedia.org/wiki/Nearest_neighbor_search
@todo document
@brief computes PSNR image/video quality metric
see http://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio for details
@todo document
@overload
@param src first input array.
@param normType type of the norm (see cv::NormTypes).
@overload
@param src1 first input array.
@param src2 second input array of the same size and the same type as src1.
@param normType type of the norm (cv::NormTypes).
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
@brief Calculates an absolute array norm, an absolute difference norm, or a
relative difference norm.
The functions norm calculate an absolute norm of src1 (when there is no
src2 ):
\f[norm = \forkthree{\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM\_INF}\) }
{ \| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM\_L1}\) }
{ \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM\_L2}\) }\f]
or an absolute or relative difference norm if src2 is there:
\f[norm = \forkthree{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM\_INF}\) }
{ \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM\_L1}\) }
{ \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM\_L2}\) }\f]
or
\f[norm = \forkthree{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM\_RELATIVE\_INF}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM\_RELATIVE\_L1}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM\_RELATIVE\_L2}\) }\f]
The functions norm return the calculated norm.
When the mask parameter is specified and it is not empty, the norm is
calculated only over the region specified by the mask.
A multi-channel input arrays are treated as a single-channel, that is,
the results for all channels are combined.
@param src1 first input array.
@param normType type of the norm (see cv::NormTypes).
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Calculates a mean and standard deviation of array elements.
The function meanStdDev calculates the mean and the standard deviation M
of array elements independently for each channel and returns it via the
output parameters:
\f[\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\f]
When all the mask elements are 0's, the functions return
mean=stddev=Scalar::all(0).
@note The calculated standard deviation is only the diagonal of the
complete normalized covariance matrix. If the full matrix is needed, you
can reshape the multi-channel array M x N to the single-channel array
M\*N x mtx.channels() (only possible when the matrix is continuous) and
then pass the matrix to calcCovarMatrix .
@param src input array that should have from 1 to 4 channels so that the results can be stored in
Scalar_ 's.
@param mean output parameter: calculated mean value.
@param stddev output parameter: calculateded standard deviation.
@param mask optional operation mask.
@sa countNonZero, mean, norm, minMaxLoc, calcCovarMatrix
@brief Calculates an average (mean) of array elements.
The function mean calculates the mean value M of array elements,
independently for each channel, and return it:
\f[\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f]
When all the mask elements are 0's, the functions return Scalar::all(0)
@param src input array that should have from 1 to 4 channels so that the result can be stored in
Scalar_ .
@param mask optional operation mask.
@sa countNonZero, meanStdDev, norm, minMaxLoc
@brief Counts non-zero array elements.
The function returns the number of non-zero elements in src :
\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f]
@param src single-channel array.
@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix
@brief Calculates the sum of array elements.
The functions sum calculate and return the sum of array elements,
independently for each channel.
@param src input array that must have from 1 to 4 channels.
@sa countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce
@brief Performs a look-up table transform of an array.
The function LUT fills the output array with values from the look-up table. Indices of the entries
are taken from the input array. That is, the function processes each element of src as follows:
\f[\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\f]
where
\f[d = \fork{0}{if \texttt{src} has depth \texttt{CV\_8U}}{128}{if \texttt{src} has depth \texttt{CV\_8S}}\f]
@param src input array of 8-bit elements.
@param lut look-up table of 256 elements; in case of multi-channel input array, the table should
either have a single channel (in this case the same table is used for all channels) or the same
number of channels as in the input array.
@param dst output array of the same size and number of channels as src, and the same depth as lut.
@sa convertScaleAbs, Mat::convertTo
@brief Calculates the weighted sum of two arrays.
The function addWeighted calculates the weighted sum of two arrays as follows:
\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f]
where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.
The function can be replaced with a matrix expression:
@code{.cpp}
dst = src1*alpha + src2*beta + gamma;
@endcode
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array.
@param alpha weight of the first array elements.
@param src2 second input array of the same size and channel number as src1.
@param beta weight of the second array elements.
@param gamma scalar added to each sum.
@param dst output array that has the same size and number of channels as the input arrays.
@param dtype optional depth of the output array; when both input arrays have the same depth, dtype
can be set to -1, which will be equivalent to src1.depth().
@sa add, subtract, scaleAdd, Mat::convertTo
@brief Calculates the sum of a scaled array and another array.
The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY
or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates
the sum of a scaled array and another array:
\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\f]
The function can also be emulated with a matrix expression, for example:
@code{.cpp}
Mat A(3, 3, CV_64F);
...
A.row(0) = A.row(1)*2 + A.row(2);
@endcode
@param src1 first input array.
@param alpha scale factor for the first array.
@param src2 second input array of the same size and type as src1.
@param dst output array of the same size and type as src1.
@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo
@overload
@brief Performs per-element division of two arrays or a scalar by an array.
The functions divide divide one array by another:
\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f]
or a scalar by an array when there is no src1 :
\f[\texttt{dst(I) = saturate(scale/src2(I))}\f]
When src2(I) is zero, dst(I) will also be zero. Different channels of
multi-channel arrays are processed independently.
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array.
@param src2 second input array of the same size and type as src1.
@param scale scalar factor.
@param dst output array of the same size and type as src2.
@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in
case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth().
@sa multiply, add, subtract
@brief Calculates the per-element scaled product of two arrays.
The function multiply calculates the per-element product of two arrays:
\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\f]
There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul .
For a not-per-element matrix product, see gemm .
@note Saturation is not applied when the output array has the depth
CV_32S. You may even get result of an incorrect sign in the case of
overflow.
@param src1 first input array.
@param src2 second input array of the same size and the same type as src1.
@param dst output array of the same size and type as src1.
@param scale optional scale factor.
@param dtype optional depth of the output array
@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare,
Mat::convertTo
@brief Calculates the per-element difference between two arrays or array and a scalar.
The function subtract calculates:
- Difference between two arrays, when both input arrays have the same size and the same number of
channels:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same
number of elements as `src1.channels()`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same
number of elements as `src2.channels()`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
- The reverse difference between a scalar and an array in the case of `SubRS`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f]
where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.
The first function in the list above can be replaced with matrix expressions:
@code{.cpp}
dst = src1 - src2;
dst -= src1; // equivalent to subtract(dst, src1, dst);
@endcode
The input arrays and the output array can all have the same or different depths. For example, you
can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of
the output array is determined by dtype parameter. In the second and third cases above, as well as
in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this
case the output array will have the same depth as the input array, be it src1, src2 or both.
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array of the same size and the same number of channels as the input array.
@param mask optional operation mask; this is an 8-bit single channel array that specifies elements
of the output array to be changed.
@param dtype optional depth of the output array
@sa add, addWeighted, scaleAdd, Mat::convertTo
@brief Calculates the per-element sum of two arrays or an array and a scalar.
The function add calculates:
- Sum of two arrays when both input arrays have the same size and the same number of channels:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of
elements as `src1.channels()`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of
elements as `src2.channels()`:
\f[\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.
The first function in the list above can be replaced with matrix expressions:
@code{.cpp}
dst = src1 + src2;
dst += src1; // equivalent to add(dst, src1, dst);
@endcode
The input arrays and the output array can all have the same or different depths. For example, you
can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit
floating-point array. Depth of the output array is determined by the dtype parameter. In the second
and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can
be set to the default -1. In this case, the output array will have the same depth as the input
array, be it src1, src2 or both.
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and number of channels as the input array(s); the
depth is defined by dtype or src1/src2.
@param mask optional operation mask - 8-bit single channel array, that specifies elements of the
output array to be changed.
@param dtype optional depth of the output array (see the discussion below).
@sa subtract, addWeighted, scaleAdd, Mat::convertTo
@brief Forms a border around an image.
The function copies the source image into the middle of the destination image. The areas to the
left, to the right, above and below the copied source image will be filled with extrapolated
pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but
what other more complex functions, including your own, may do to simplify image boundary handling.
The function supports the mode when src is already in the middle of dst . In this case, the
function does not copy src itself but simply constructs the border, for example:
@code{.cpp}
// let border be the same in all directions
int border=2;
// constructs a larger image to fit both the image and the border
Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth());
// select the middle part of it w/o copying data
Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows));
// convert image from RGB to grayscale
cvtColor(rgb, gray, COLOR_RGB2GRAY);
// form a border in-place
copyMakeBorder(gray, gray_buf, border, border,
border, border, BORDER_REPLICATE);
// now do some custom filtering ...
...
@endcode
@note When the source image is a part (ROI) of a bigger image, the function will try to use the
pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as
if src was not a ROI, use borderType | BORDER_ISOLATED.
@param src Source image.
@param dst Destination image of the same type as src and the size Size(src.cols+left+right,
src.rows+top+bottom) .
@param top
@param bottom
@param left
@param right Parameter specifying how many pixels in each direction from the source image rectangle
to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs
to be built.
@param borderType Border type. See borderInterpolate for details.
@param value Border value if borderType==BORDER_CONSTANT .
@sa borderInterpolate
@overload
@brief Swaps two matrices
During the first (and possibly the only) attempt, use the
user-supplied labels instead of computing them from the initial centers. For the second and
further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag
to specify the exact method.
Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].
Select random initial centers in each attempt.
If the flag is
specified, all the input vectors are stored as columns of the samples matrix. mean should be a
single-column vector in this case.
If the flag is
specified, all the input vectors are stored as rows of the samples matrix. mean should be a
single-row vector in this case.
If the flag is specified, the covariance matrix is scaled. In the
"normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the
total number of elements in each input vector. By default (if the flag is not specified), the
covariance matrix is not scaled ( scale=1 ).
If the flag is specified, the function does not calculate mean from
the input vectors but, instead, uses the passed mean vector. This is useful if mean has been
pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In
this case, mean is not a mean vector of the input sub-set of vectors but rather the mean
vector of the whole set.
The output covariance matrix is calculated as:
\f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...] \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T,\f]
covar will be a square matrix of the same size as the total number of elements in each input
vector. One and only one of COVAR_SCRAMBLED and COVAR_NORMAL must be specified.
The output covariance matrix is calculated as:
\f[\texttt{scale} \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...]^T \cdot [ \texttt{vects} [0]- \texttt{mean} , \texttt{vects} [1]- \texttt{mean} ,...],\f]
The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used
for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for
face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true
covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of
the "scrambled" covariance matrix.
@brief Writes data to a file storage.
@brief Writes data to a file storage.
@brief Writes string to a file storage.
@relates cv::FileStorage
@brief Reads node elements to the buffer with the specified format.
Usually it is more convenient to use operator `>>` instead of this method.
@param fmt Specification of each array element. See @ref format_spec "format specification"
@param vec Pointer to the destination array.
@param maxCount Number of elements to read. If it is greater than number of remaining elements then
all of them will be read.
@overload
@param it Iterator to be used as initialization for the created iterator.
@overload
@param fs File storage for the iterator.
@param node File node for the iterator.
@param ofs Index of the element in the node. The created iterator will point to this element.
@brief The constructors.
These constructors are used to create a default iterator, set it to specific element in a file node
or construct it from another iterator.
@brief used to iterate through sequences and mappings.
A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a
sequence, stored in node. See the data reading sample in the beginning of the section.
@brief Reads node elements to the buffer with the specified format.
Usually it is more convenient to use operator `>>` instead of this method.
@param fmt Specification of each array element. See @ref format_spec "format specification"
@param vec Pointer to the destination array.
@param len Number of elements to read. If it is greater than number of remaining elements then all
of them will be read.
@brief Returns type of the node.
@returns Type of the node. See FileNode::Type
@overload
@param i Index of an element in the sequence node.
@overload
@param nodename Name of an element in the mapping node.
@brief Returns element of a mapping node or a sequence node.
@param nodename Name of an element in the mapping node.
@returns Returns the element with the given identifier.
@overload
@param node File node to be used as initialization for the created file node.
@overload
@param fs Pointer to the obsolete file storage structure.
@param node File node to be used as initialization for the created file node.
@brief The constructors.
These constructors are used to create a default file node, construct it from obsolete structures or
from the another file node.
@brief File Storage Node class.
The node is used to store each and every element of the file storage opened for reading. When
XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of
nodes. Each node can be a “leaf” that is contain a single number or a string, or be a collection of
other nodes. There can be named collections (mappings) where each element has a name and it is
accessed by a name, and ordered collections (sequences) where elements do not have names but rather
accessed by index. Type of the file node can be determined using FileNode::type method.
Note that file nodes are only used for navigating file storages opened for reading. When a file
storage is opened for writing, no data is stored in memory after it is written.
@brief Returns the normalized object name for the specified name of a file.
@param filename Name of a file
@returns The normalized object name.
@brief Writes the registered C structure (CvMat, CvMatND, CvSeq).
@param name Name of the written object.
@param obj Pointer to the object.
@see ocvWrite for details.
@brief Returns the obsolete C FileStorage structure.
@returns Pointer to the underlying C FileStorage structure
@overload
@overload
@brief Returns the specified element of the top-level mapping.
@param nodename Name of the file node.
@returns Node with the given name.
@brief Returns the top-level mapping
@param streamidx Zero-based index of the stream. In most cases there is only one stream in the file.
However, YAML supports multiple streams and so there can be several.
@returns The top-level mapping.
@brief Returns the first element of the top-level mapping.
@returns The first element of the top-level mapping.
@brief Closes the file and releases all the memory buffers.
Call this method after all I/O operations with the storage are finished. If the storage was
opened for writing data and FileStorage::WRITE was specified
@brief Closes the file and releases all the memory buffers.
Call this method after all I/O operations with the storage are finished.
@brief Checks whether the file is opened.
@returns true if the object is associated with the current file and false otherwise. It is a
good practice to call this method after you tried to open a file.
@brief Opens a file.
See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release
before opening the file.
@param filename Name of the file to open or the text string to read the data from.
Extension of the file (.xml or .yml/.yaml) determines its format (XML or YAML respectively).
Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both
FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify
the output file format (e.g. mydata.xml, .yml etc.).
@param flags Mode of operation. One of FileStorage::Mode
@param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and
you should use 8-bit encoding instead of it.
@overload
@overload
@param source Name of the file to open or the text string to read the data from. Extension of the
file (.xml or .yml/.yaml) determines its format (XML or YAML respectively). Also you can append .gz
to work with compressed files, for example myHugeMatrix.xml.gz. If both FileStorage::WRITE and
FileStorage::MEMORY flags are specified, source is used just to specify the output file format (e.g.
mydata.xml, .yml etc.).
@param flags Mode of operation. See FileStorage::Mode
@param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and
you should use 8-bit encoding instead of it.
@brief The constructors.
The full constructor opens the file. Alternatively you can use the default constructor and then
call FileStorage::open.
@brief XML/YAML file storage class that encapsulates all the information necessary for writing or reading
data to/from a file.
@overload
@param e matrix expression.
@brief Read-write Sparse Matrix Iterator
The class is similar to cv::SparseMatConstIterator,
but can be used for in-place modification of the matrix elements.
@overload
@param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted
to sparse representation.
@overload
@param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted
to sparse representation.
@overload
@param dims Array dimensionality.
@param _sizes Sparce matrix size on all dementions.
@param _type Sparse matrix data type.
@brief Various SparseMat constructors.
@todo document
@overload
@overload
@overload
@overload
@overload
@overload
@brief Returns a pointer to the specified matrix row.
The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in
Mat::isContinuous to know how to use these methods.
@param i0 A 0-based row index.
@overload
@brief Returns the total number of array elements.
The method returns the number of array elements (a number of pixels if the array represents an
image).
@brief Returns true if the array has no elements.
The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and
resize() methods `M.total() == 0` does not imply that `M.data == NULL`.
@brief Returns a normalized step.
The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an
arbitrary matrix element.
@brief Returns the number of matrix channels.
The method returns the number of matrix channels.
@brief Returns the depth of a matrix element.
The method returns the identifier of the matrix element depth (the type of each individual channel).
For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of
matrix types contains the following values:
- CV_8U - 8-bit unsigned integers ( 0..255 )
- CV_8S - 8-bit signed integers ( -128..127 )
- CV_16U - 16-bit unsigned integers ( 0..65535 )
- CV_16S - 16-bit signed integers ( -32768..32767 )
- CV_32S - 32-bit signed integers ( -2147483648..2147483647 )
- CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN )
- CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN )
@brief Returns the type of a matrix element.
The method returns a matrix element type. This is an identifier compatible with the CvMat type
system, like CV_16SC3 or 16-bit signed 3-channel array, and so on.
@brief Returns the size of each matrix element channel in bytes.
The method returns the matrix element channel size in bytes, that is, it ignores the number of
channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2.
@brief Returns the matrix element size in bytes.
The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 ,
the method returns 3\*sizeof(short) or 6.
@overload
@param ranges Array of selected ranges along each array dimension.
@overload
@param roi Extracted submatrix specified as a rectangle.
@brief Extracts a rectangular submatrix.
The operators make a new header for the specified sub-array of \*this . They are the most
generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example,
`A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above,
the operators are O(1) operations, that is, no matrix data is copied.
@param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To
select all the rows, use Range::all().
@param colRange Start and end column of the extracted submatrix. The upper boundary is not included.
To select all the columns, use Range::all().
@brief Adjusts a submatrix size and position within the parent matrix.
The method is complimentary to Mat::locateROI . The typical use of these functions is to determine
the submatrix position within the parent matrix and then shift the position somehow. Typically, it
can be required for filtering operations when pixels outside of the ROI should be taken into
account. When all the method parameters are positive, the ROI needs to grow in all directions by the
specified amount, for example:
@code
A.adjustROI(2, 2, 2, 2);
@endcode
In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted
by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the
filtering with the 5x5 kernel.
adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the
adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is
located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not
be increased in the upward direction.
The function is used internally by the OpenCV filtering functions, like filter2D , morphological
operations, and so on.
@param dtop Shift of the top submatrix boundary upwards.
@param dbottom Shift of the bottom submatrix boundary downwards.
@param dleft Shift of the left submatrix boundary to the left.
@param dright Shift of the right submatrix boundary to the right.
@sa copyMakeBorder
@brief Locates the matrix header within a parent matrix.
After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange,
Mat::colRange, and others, the resultant submatrix points just to the part of the original big
matrix. However, each submatrix contains information (represented by datastart and dataend
fields) that helps reconstruct the original matrix size and the position of the extracted
submatrix within the original matrix. The method locateROI does exactly that.
@param wholeSize Output parameter that contains the size of the whole matrix containing *this*
as a part.
@param ofs Output parameter that contains an offset of *this* inside the whole matrix.
@brief Removes elements from the bottom of the matrix.
The method removes one or more rows from the bottom of the matrix.
@param nelems Number of removed rows. If it is greater than the total number of rows, an exception
is thrown.
@overload
@param m Added line(s).
@overload
@param sz New number of rows.
@param s Value assigned to the newly added elements.
@brief Changes the number of matrix rows.
The methods change the number of matrix rows. If the matrix is reallocated, the first
min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL
vector class.
@param sz New number of rows.
@brief Reserves space for the certain number of rows.
The method reserves space for sz rows. If the matrix already has enough space to store sz rows,
nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method
emulates the corresponding method of the STL vector class.
@param sz Number of rows.
@brief Decrements the reference counter and deallocates the matrix if needed.
The method decrements the reference counter associated with the matrix data. When the reference
counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers
are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the
reference counter is NULL, and the method has no effect in this case.
This method can be called manually to force the matrix data deallocation. But since this method is
automatically called in the destructor, or by any other method that changes the data pointer, it is
usually not needed. The reference counter decrement and check for 0 is an atomic operation on the
platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in
different threads.
@brief Increments the reference counter.
The method increments the reference counter associated with the matrix data. If the matrix header
points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no
effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It
is called implicitly by the matrix assignment operator. The reference counter increment is an atomic
operation on the platforms that support it. Thus, it is safe to operate on the same matrices
asynchronously in different threads.
@overload
@param ndims New array dimensionality.
@param sizes Array of integers specifying a new array shape.
@param type New matrix type.
@overload
@param size Alternative new matrix size specification: Size(cols, rows)
@param type New matrix type.
@brief Allocates new array data if needed.
This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays
call this method for each output array. The method uses the following algorithm:
-# If the current array shape and the type match the new ones, return immediately. Otherwise,
de-reference the previous data by calling Mat::release.
-# Initialize the new header.
-# Allocate the new data of total()\*elemSize() bytes.
-# Allocate the new, associated with the data, reference counter and set it to 1.
Such a scheme makes the memory management robust and efficient at the same time and helps avoid
extra typing for you. This means that usually there is no need to explicitly allocate output arrays.
That is, instead of writing:
@code
Mat color;
...
Mat gray(color.rows, color.cols, color.depth());
cvtColor(color, gray, COLOR_BGR2GRAY);
@endcode
you can simply write:
@code
Mat color;
...
Mat gray;
cvtColor(color, gray, COLOR_BGR2GRAY);
@endcode
because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array
internally.
@param rows New number of rows.
@param cols New number of columns.
@param type New matrix type.
@overload
@param size Alternative matrix size specification as Size(cols, rows) .
@param type Created matrix type.
@brief Returns an identity matrix of the specified size and type.
The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to
Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently:
@code
// make a 4x4 diagonal matrix with 0.1's on the diagonal.
Mat A = Mat::eye(4, 4, CV_32F)*0.1;
@endcode
@param rows Number of rows.
@param cols Number of columns.
@param type Created matrix type.
@overload
@param ndims Array dimensionality.
@param sz Array of integers specifying the array shape.
@param type Created matrix type.
@overload
@param size Alternative to the matrix size specification Size(cols, rows) .
@param type Created matrix type.
@brief Returns an array of all 1's of the specified size and type.
The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using
this method you can initialize an array with an arbitrary value, using the following Matlab idiom:
@code
Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3.
@endcode
The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it
just remembers the scale factor (3 in this case) and use it when actually invoking the matrix
initializer.
@param rows Number of rows.
@param cols Number of columns.
@param type Created matrix type.
@overload
@param ndims Array dimensionality.
@param sz Array of integers specifying the array shape.
@param type Created matrix type.
@overload
@param size Alternative to the matrix size specification Size(cols, rows) .
@param type Created matrix type.
@brief Returns a zero array of the specified size and type.
The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant
array as a function parameter, part of a matrix expression, or as a matrix initializer. :
@code
Mat A;
A = Mat::zeros(3, 3, CV_32F);
@endcode
In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix.
Otherwise, the existing matrix A is filled with zeros.
@param rows Number of rows.
@param cols Number of columns.
@param type Created matrix type.
@brief Computes a dot-product of two vectors.
The method computes a dot-product of two matrices. If the matrices are not single-column or
single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D
vectors. The vectors must have the same size and type. If the matrices have more than one channel,
the dot products from all the channels are summed together.
@param m another dot-product operand.
@brief Computes a cross-product of two 3-element vectors.
The method computes a cross-product of two 3-element vectors. The vectors must be 3-element
floating-point vectors of the same shape and size. The result is another 3-element vector of the
same shape and type as operands.
@param m Another cross-product operand.
@brief Performs an element-wise multiplication or division of the two matrices.
The method returns a temporary object encoding per-element array multiplication, with optional
scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator.
Example:
@code
Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5)
@endcode
@param m Another array of the same type and the same size as \*this, or a matrix expression.
@param scale Optional scale factor.
@brief Inverses a matrix.
The method performs a matrix inversion by means of matrix expressions. This means that a temporary
matrix inversion object is returned by the method and can be used further as a part of more complex
matrix expressions or can be assigned to a matrix.
@param method Matrix inversion method. One of cv::DecompTypes
@brief Transposes a matrix.
The method performs matrix transposition by means of matrix expressions. It does not perform the
actual transposition but returns a temporary matrix transposition object that can be further used as
a part of more complex matrix expressions or can be assigned to a matrix:
@code
Mat A1 = A + Mat::eye(A.size(), A.type())*lambda;
Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I)
@endcode
@overload
@brief Sets all or some of the array elements to the specified value.
@param s Assigned scalar converted to the actual array type.
@brief Provides a functional form of convertTo.
This is an internally used method called by the @ref MatrixExpressions engine.
@param m Destination array.
@param type Desired destination array depth (or -1 if it should be the same as the source type).
@overload
@param m Destination matrix. If it does not have a proper size or type before the operation, it is
reallocated.
@param mask Operation mask. Its non-zero elements indicate which matrix elements need to be copied.
@brief Copies the matrix to another one.
The method copies the matrix data to another matrix. Before copying the data, the method invokes :
@code
m.create(this->size(), this->type());
@endcode
so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the
function does not handle the case of a partial overlap between the source and the destination
matrices.
When the operation mask is specified, if the Mat::create call shown above reallocates the matrix,
the newly allocated matrix is initialized with all zeros before copying the data.
@param m Destination matrix. If it does not have a proper size or type before the operation, it is
reallocated.
@brief Creates a full copy of the array and the underlying data.
The method creates a full copy of the array. The original step[] is not taken into account. So, the
array copy is a continuous array occupying total()*elemSize() bytes.
@brief creates a diagonal matrix
The method makes a new header for the specified matrix diagonal. The new matrix is represented as a
single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation.
@param d Single-column matrix that forms a diagonal matrix
@overload
@param r Range structure containing both the start and the end indices.
@brief Creates a matrix header for the specified column span.
The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and
Mat::col , this is an O(1) operation.
@param startcol An inclusive 0-based start index of the column span.
@param endcol An exclusive 0-based ending index of the column span.
@overload
@param r Range structure containing both the start and the end indices.
@brief Creates a matrix header for the specified row span.
The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and
Mat::col , this is an O(1) operation.
@param startrow An inclusive 0-based start index of the row span.
@param endrow An exclusive 0-based ending index of the row span.
@brief Creates a matrix header for the specified matrix column.
The method makes a new header for the specified matrix column and returns it. This is an O(1)
operation, regardless of the matrix size. The underlying data of the new matrix is shared with the
original matrix. See also the Mat::row description.
@param x A 0-based column index.
@overload
@param expr Assigned matrix expression object. As opposite to the first form of the assignment
operation, the second form can reuse already allocated matrix if it has the right size and type to
fit the matrix expression result. It is automatically handled by the real function that the matrix
expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of
automatic C reallocation.
@brief assignment operators
These are available assignment operators. Since they all are very different, make sure to read the
operator parameters description.
@param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that
no data is copied but the data is shared and the reference counter, if any, is incremented. Before
assigning new data, the old data is de-referenced via Mat::release .
@overload
@param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
formed using such a constructor, you also modify the corresponding elements of m . If you want to
have an independent copy of the sub-array, use Mat::clone() .
@param ranges Array of selected ranges of m along each dimensionality.
@overload
@param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
formed using such a constructor, you also modify the corresponding elements of m . If you want to
have an independent copy of the sub-array, use Mat::clone() .
@param roi Region of interest.
@overload
@param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
formed using such a constructor, you also modify the corresponding elements of m . If you want to
have an independent copy of the sub-array, use Mat::clone() .
@param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range
end is exclusive. Use Range::all() to take all the rows.
@param colRange Range of the m columns to take. Use Range::all() to take all the columns.
@overload
@param ndims Array dimensionality.
@param sizes Array of integers specifying an n-dimensional array shape.
@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
@param data Pointer to the user data. Matrix constructors that take data and step parameters do not
allocate matrix data. Instead, they just initialize the matrix header that points to the specified
data, which means that no data is copied. This operation is very efficient and can be used to
process external data using OpenCV functions. The external data is not automatically deallocated, so
you should take care of it.
@param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always
set to the element size). If not specified, the matrix is assumed to be continuous.
@overload
@param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
number of columns go in the reverse order.
@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
@param data Pointer to the user data. Matrix constructors that take data and step parameters do not
allocate matrix data. Instead, they just initialize the matrix header that points to the specified
data, which means that no data is copied. This operation is very efficient and can be used to
process external data using OpenCV functions. The external data is not automatically deallocated, so
you should take care of it.
@param step Number of bytes each matrix row occupies. The value should include the padding bytes at
the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed
and the actual step is calculated as cols*elemSize(). See Mat::elemSize.
@overload
@param rows Number of rows in a 2D array.
@param cols Number of columns in a 2D array.
@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
@param data Pointer to the user data. Matrix constructors that take data and step parameters do not
allocate matrix data. Instead, they just initialize the matrix header that points to the specified
data, which means that no data is copied. This operation is very efficient and can be used to
process external data using OpenCV functions. The external data is not automatically deallocated, so
you should take care of it.
@param step Number of bytes each matrix row occupies. The value should include the padding bytes at
the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed
and the actual step is calculated as cols*elemSize(). See Mat::elemSize.
@overload
@param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
formed using such a constructor, you also modify the corresponding elements of m . If you want to
have an independent copy of the sub-array, use Mat::clone() .
@overload
@param ndims Array dimensionality.
@param sizes Array of integers specifying an n-dimensional array shape.
@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
@overload
@param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
number of columns go in the reverse order.
@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
@overload
@param rows Number of rows in a 2D array.
@param cols Number of columns in a 2D array.
@param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
These are various constructors that form a matrix. As noted in the AutomaticAllocation, often
the default constructor is enough, and the proper matrix will be allocated by an OpenCV function.
The constructed matrix can further be assigned to another matrix or matrix expression or can be
allocated with Mat::create . In the former case, the old content is de-referenced.
@brief Custom array allocator
@param type The type of termination criteria, one of TermCriteria::Type
@param maxCount The maximum number of iterations or elements to compute.
@param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops.
Criteria type, can be one of: COUNT, EPS or COUNT + EPS
@brief The class defining termination criteria for iterative algorithms.
You can initialize it by default constructor and then override any parameters, or the structure may
be fully initialized using the advanced variant of the constructor.
@brief Class for matching keypoint descriptors
query descriptor index, train descriptor index, train image index, and distance between
descriptors.
This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint
regions' intersection and area of keypoint regions' union (considering keypoint region as circle).
If they don't overlap, we get zero. If they coincide at same location with same size, we get 1.
@param kp1 First keypoint
@param kp2 Second keypoint
@overload
@param points2f Array of (x,y) coordinates of each keypoint
@param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
@param size keypoint diameter
@param response keypoint detector response on the keypoint (that is, strength of the keypoint)
@param octave pyramid octave in which the keypoint has been detected
@param class_id object id
This method converts vector of keypoints to vector of points or the reverse, where each keypoint is
assigned the same size and the same orientation.
@param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
@param points2f Array of (x,y) coordinates of each keypoint
@param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to
convert only specified keypoints)
@param x x-coordinate of the keypoint
@param y y-coordinate of the keypoint
@param _size keypoint diameter
@param _angle keypoint orientation
@param _response keypoint detector response on the keypoint (that is, strength of the keypoint)
@param _octave pyramid octave in which the keypoint has been detected
@param _class_id object id
@brief Data structure for salient point detectors.
The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint
detectors, such as Harris corner detector, cv::FAST, cv::StarDetector, cv::SURF, cv::SIFT,
cv::LDetector etc.
The keypoint is characterized by the 2D position, scale (proportional to the diameter of the
neighborhood that needs to be taken into account), orientation and some other parameters. The
keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually
represented as a feature vector). The keypoints representing the same object in different images
can then be matched using cv::KDTree or another method.
returns 4 vertices of the rectangle
@param pts The points array for storing rectangle vertices.
Any 3 end points of the RotatedRect. They must be given in order (either clockwise or
anticlockwise).
@param center The rectangle mass center.
@param size Width and height of the rectangle.
@param angle The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc.,
the rectangle becomes an up-right rectangle.
proxy for hal::Cholesky
proxy for hal::Cholesky
proxy for hal::LU
proxy for hal::LU
@brief Calculates the angle of a 2D vector in degrees.
The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured
in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees.
@param x x-coordinate of the vector.
@param y y-coordinate of the vector.
@brief Computes the cube root of an argument.
The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly.
NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for
single-precision data.
@param val A function argument.
this will count the bits in a ^ b
@brief Call the error handler.
Currently, the error handler prints the error code and the error message to the standard
error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that
the execution stack and all the parameters can be analyzed by the debugger. In the Release
configuration, the exception is thrown.
@param code one of Error::Code
@param msg error message
@brief Call the error handler.
This macro can be used to construct an error message on-fly to include some dynamic information,
for example:
@code
// note the extra parentheses around the formatted text message
CV_Error_( CV_StsOutOfRange,
("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue));
@endcode
@param code one of Error::Code
@param args printf-like formatted error message in parentheses
@brief Checks a condition at runtime and throws exception if it fails
The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros
raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release
configurations while CV_DbgAssert is only retained in the Debug configuration.
same as CV_Error(code,msg), but does not return
same as CV_Error_(code,args), but does not return
replaced with CV_Assert(expr) in Debug configuration
same as cv::error, but does not return
performs a forward or inverse transform of every individual row of the input
matrix. This flag enables you to transform multiple vectors simultaneously and can be used to
decrease the overhead (which is sometimes several times larger than the processing itself) to
perform 3D and higher-dimensional transforms and so forth.
performs an inverse 1D or 2D transform instead of the default forward transform.
performs an inverse transformation of a 1D or 2D complex array; the
result is normally a complex array of the same size, however, if the input array has
conjugate-complex symmetry (for example, it is a result of forward transformation with
DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not
check whether the input is symmetrical or not, you can pass the flag and then the function
will assume the symmetry and produce the real output array (note that when the input is packed
into a real array and inverse transformation is executed, the function treats the input as a
packed complex-conjugate symmetrical array, and the output will also be a real array).
performs a forward transformation of 1D or 2D real array; the result,
though being a complex array, has complex-conjugate symmetry (*CCS*, see the function
description below for details), and such an array can be packed into a real array of the same
size as input, which is the fastest option and which is what the function does by default;
however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) -
pass the flag to enable the function to produce a full-size complex output array.
performs a forward or inverse transform of every individual row of the input
matrix; this flag enables you to transform multiple vectors simultaneously and can be used to
decrease the overhead (which is sometimes several times larger than the processing itself) to
perform 3D and higher-dimensional transformations and so forth.
scales the result: divide it by the number of array elements. Normally, it is
combined with DFT_INVERSE.
performs an inverse 1D or 2D transform instead of the default forward
transform.
norm types
- For one array:
\f[norm = \forkthree{\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM\_INF}\) }
{ \| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM\_L1}\) }
{ \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM\_L2}\) }\f]
- Absolute norm for two arrays
\f[norm = \forkthree{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM\_INF}\) }
{ \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM\_L1}\) }
{ \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM\_L2}\) }\f]
- Relative norm for two arrays
\f[norm = \forkthree{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM\_RELATIVE\_INF}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM\_RELATIVE\_L1}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM\_RELATIVE\_L2}\) }\f]
while all the previous flags are mutually exclusive, this flag can be used together with
any of the previous; it means that the normal equations
\f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are
solved instead of the original system
\f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$
QR factorization; the system can be over-defined and/or the matrix src1 can be singular
Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively
defined
eigenvalue decomposition; the matrix src1 must be symmetrical
singular value decomposition (SVD) method; the system can be over-defined and/or the matrix
src1 can be singular
Gaussian elimination with the optimal pivot element chosen.
@brief Deallocates a memory buffer.
The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the
function does nothing. C version of the function clears the pointer *pptr* to avoid problems with
double memory deallocation.
@param ptr Pointer to the allocated buffer.
@brief Allocates an aligned memory buffer.
The function allocates the buffer of the specified size and returns it. When the buffer size is 16
bytes or more, the returned buffer is aligned to 16 bytes.
@param bufSize Allocated buffer size.
@overload
@overload
@overload
@overload
@overload
@overload
@overload
@overload
@brief Determines if the argument is Infinity.
@param value The input floating-point value
The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard)
and 0 otherwise.
@brief Determines if the argument is Not A Number.
@param value The input floating-point value
The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0
otherwise.
@brief Rounds floating-point number to the nearest integer
@param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
result is not defined.
Resolves the given relative path in CMake build folder to full platform specific path.
@param relativePath relative path in unix form, for example "ZicerOcrModelBuilder/model_all.z"
@return resolved path
Returns the path to CMake build folder.
@return the path to CMake build folder.
Resolves the given relative path in core-photopay folder to full platform specific path.
@param relativePath relative path in unix form, for example "Matlab/mlacs/IMG_0715.jpg"
@return resolved path, for example "/home/dodo/Work/PhotoPay/core-photopay/Matlab/mlacs/IMG_0715.jpg" on unix or "c:\\users\\dodo\\Desktop\\PhotoPay\\core-photopay\\Matlab\\mlacs\\IMG_0715.jpg"
Returns the path to core-photopay folder that can be used in some tests.
@return the path to core-photopay folder that can be used in some tests.
Similar to resolveTestImagesPath, but expects input in form "../TestImages/subpath"
Resolves the given relative path in TestImages folder to full platform specific path.
@param relativePath relative path in unix form, for example "Cro/video/HUB3/Nexus 5/LGE Nexus 5 - 2014-02-20 16-57-51.ppv"
@return resolved path, for example "/home/dodo/PhotoPayImages/Cro/video/HUB3/Nexus 5/LGE Nexus 5 - 2014-02-20 16-57-51.ppv" on unix or "c:\\users\\dodo\\PhotoPayImages\\Cro\\video\\HUB3\\Nexus 5\\LGE Nexus 5 - 2014-02-20 16-57-51.ppv"
Created on: 31/05/2014
Author: dodo
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
Returns the path to TestImages folder that can be used in all tests.
@return the path to TestImages folder that can be used in all tests.
warnings and errors are always printed
Sets a logging callback function for proxying logging to a nother part of the system
Type specifying a logging function callback
Log that doesn't add prefix nor newline at the end of string.
Behaves exactly as printf.
Write information to log.
@param fmt message format with parameters
Log message
@param lvl message lov level
@param funcName name of function that generated message
@param filename of the file where message was generated
@param line line where message was generated
@param fmt message format with parameters
@brief restores default behavious as it were before calling setLogFilename
@brief set the name of the file to which log output will be printed
@param filename name of file to which log will be printed
@param fileIsSecondaryLog if set to true, log outputs will be printed
both to default output and given filename; if
set to false, log outputs will be printed only
to given filename.
nothing is logged
only warnings and errors are logged
warnings, errors and information are logged
warnings, errors, information and debug outputs are logged
everything is logged
PP_WARN_UNUSED_RESULT tells the compiler to emit a warning if a function's
return value is not used by the caller.
Place this attribute at the very beginning of a function definition. For
example, write
PP_WARN_UNUSED_RESULT int foo();
or
PP_WARN_UNUSED_RESULT int foo() { return 42; }
Indicates the recognizer is enabled
Does the recognizer require to be used in landscape orientation
Does the recognizer require camera with autofocus feature
Does the recognizer require on OCR engine
Does the recognizer require quality estimation
of video frames captured by camera.
By setting this to true, you will enabled scanning of lower resolution barcodes
at cost of additional processing time.
This option can significantly increase recognition time. Default is false.
Allow enabling the autodetection of image scale when scanning barcodes.
If set to true, prior reading barcode, image scale will be
corrected. This enables correct reading of barcodes on high
resolution images but slows down the recognition process.
By setting this to true, you will enable scanning of barcodes with inverse
intensity values (i.e. white barcodes on dark background).
This option can significantly increase recognition time. Default is false.
Activates or deactivates the scanning of Code39 1D barcodes.
Default (initial) value is false.
Activates or deactivates the scanning of Code128 1D barcodes.
Default (initial) value is false.
Bardecoder recognizer settings
@brief terminateContext terminates singleton instance of rendering context
@brief getContext return the singleton instance of adequate rendering context
@param status status of the operation
@return singleton instance of adequate rendering context
@brief getContextType returns the type of rendering context (Direct3D or OpenGL)
@return the type of rendering context (Direct3D or OpenGL)
@brief getRenderingSurface returns rendering surface on which scene can be rendered
@return rendering surface on which scene can be rendered
@brief isContextReady returns true if context is ready to be used for rendering
@return true if context is ready to be used for rendering
Specifies family of OpenGL/OpenGL ES contexts
Specifies family of Direct3D contexts
@brief The RenderingContextType enum Defines possible rendering context types.
Returns number of threads available in pool.
@return number of threads available in pool.
List od products this right applies to. When generating licence from web admin dashboard, this right will be available for selection for these products
List of demo products this right applies to. When generating demo licence for these products, this right will always be enabled.
short string defining the name of the right. This name is also used inside keygen
* when parsing command line parameters.
long string describing what is this right for
bit in licence key that defines this right
Returns true if Right is valid (existing).
Tries to unlock the application with given key for given owner.
@param key License key that is used for unlocking the application.
@param licensee Name of the licensee. The key is paired with its licensee.
@param product Product for which license must be OK.
@param status [out] Return parameter for checking whether any error occurred.
@return License token that contains information about license.
Tries to unlock the application with given key for package name obtained via
given package resolver.
@param key License key that is used for unlocking the application.
@param pkgResolver Interface to package resolver that will be used for resolving the package
name of the application. For example, on android the resolver will
return the package name of application context, on ios it will
return the name of the bundle and on windows phone it will return the application id.
@param product Product for which license must be OK.
@param status [out] Return parameter for checking whether any error occured.
@return License token that contains information about license.
@brief getActiveTokens returns list of currently active license tokens.
If there are no active license tokens, function will return empty vector.
@return list of currently active license tokens.
Generates random string of given length. The random generator is initialized with given seed.
@param length Length of the string that will be generated.
@param seed Seed to initialize random generator.
@return The random string.
@note This method is public because it is also used in PaymentDataModifier and other classes that need random strings.
Registers the license token in the system so that it becomes available to use.
@param lt license token to be registered
@param activeTokensConstraint if set to true, up to MAX_ACTIVE_TOKENS license tokens can be active simultaneously.
In order to activate new token when all token slots are used, you have to invalidate
at least one valid token. If set to false, token will be always registered and will
replace currently registered token.
@param status Status of the operation.
Provides package name for this application. On Android, this is app package name,
on iOS, this is bundle name, etc.
@return
Cached grayscale image
Cached BGRA image (original)
Cached BGR image
Is image photo frame or camera frame
visible ROI
Image size in pixels
This method should be implemented in estimators. Method should calculate the quality of given camera frame.
@param frame Camera frame that should be analyzed
@param drawBuffer Pointer to image on which drawing is allowed (for debugging purposes).
@return The quality of the image.
@brief Method transforms given RGB factors in order to achieve white balance correction.
@param analysisResult result of method analyzeWhiteBalance
@param origFactors factors to be transformed
@param transformFactors transformation result
@brief Method updates given pixel converter's factors according to given white balance analysis result.
@param analysisResult result of method analyzeWhiteBalance
@param pixelConverter pixel converter that will be updated
@brief Method analyzes white balance point in image bgrImage with search step searchStep (searchStep == 1 means every pixel will be analyzed).
@param bgrImage BGR or BGRA image that will be analyzed
@param searchStep step by which pixels will be analyzed. searchStep == 1 means every pixel is analyzed, searchStep == 2 means every second pixel is analyzed
@param status status of the operation
@returns Result of white balance analysis. Required for methods updatePixelConverter and transformFactors
Indicates whether results are empty.
Indicates whether the recognized results are valid.
Key for accessing customer number result
Key for accessing bill number result
Key for accessing generic barcode data
Key for accessing the pdf417 2D barcode result in USDL
Key for accessing the code128 1D barcode result in USDL
Key for accessing the code39 1D barcode result in USDL
Key for accessing the library info for barcode readers
Key for accessing the display data information in Austrian payslips
Key for accessing the contract account information in Austrian payslips
Key for accessing the document type information in Austrian payslips
Key for accessing the check digit of Austrian SEPA payslips
Key for accessing the raw result of OCR/barcode decode.
Key for accessing Tax number element.
Key for accessing Tax number element.
Key for accessing PayBull URL element.
Key for accessing Purpose Code element.
Key for accessing Payment Description code element.
Key for accessing Due date element.
Key for accessing Payer name element.
Key for accessing Payer ID element.
Key for accessing Payer reference model element.
Key for accessing Payer reference element.
Key for accessing Payer bank code element.
Key for accessing Payer Account number element.
Key for accessing Payer IBAN element.
Key for accessing PaymentDescription element.
Key for accessing SlipID element.
Key for accessing Form ID element.
Key for accessing Bank name element.
Key for accessing Recipient detailed address element.
Key for accessing Recipient address element.
Key for accessing Recipient name element.
Key for accessing Bank code element.
Key for accessing Reference model element.
Key for accessing Reference element.
Key for accessing Account number element.
Key for accessing BIC element.
Key for accessing IBAN element.
Key for accessing Currency element.
Key for accessing Amount element.
Key for accessing Recognition data type element.
PaymentDataType Constant for QR Code
PaymentDataType Constant for Pdf417 Barcode
Slip ID constant for HUB3
Slip ID constant for HUB1
Adds payment data results from a given barcode string
@param paymentDataType
@param barcodeResult
@param dataIsUncertain
Splits the barcode result into separate lines according to standards
@param barcodeResult
@return
Destructor
Default constructor
Optional data is places in the 14th row
Payment description is placed in the 13th row
Purpose code is placed in the 12th row
Reference number is placed in the 11th row
Reference model is placed in the 10th row
Account number or IBAN is placed in the ninth
Receiver address, 2st part is in the eight row
Receiver address, 1st part is in the seventh row
Receiver name is in the sixth row
Payer name is in the third row
Amount is placed in the second row
Due date is placed in the 17th row of the HUB1 pdf417 result
Payment description is placed in the 16th row of the HUB1 pdf417 result
Payment description code is placed in the 15th row of the HUB1 pdf417 result
Reference number is placed in the 13th row of the HUB1 pdf417 result
Reference model is placed in the 12th row of the HUB1 pdf417 result
Account number is placed in the 11th row of the HUB1 pdf417 result
Receiver address is in the tenth row of the HUB1 pdf427 result
Receiver name is in the ninth row of the HUB1 pdf427 result
Payer name is in the fourth row of the HUB1 pdf417 result
================ HUB1 ===============*
Amount is placed in the third row of the HUB1 pdf417 result
Coding standard is placed in the first row. Standard can be HUB1 or HUB2.
Ecapsulates the decoding of croatian payment data from pdf417 barcode
String constant for payment data type "Croatian slip"
Reference status declaring that reference has model, has checksums and
at least one checksum is not valid.
Reference status declaring that reference has model, but model is not
protected by the checksums.
Reference status declaring that reference has no model.
Reference status declaring that reference has model, has checksums and
all checksums are valid.
Reference status declaring that recognizer cannot conclude anything about
the reference.
Name of the slip id for HUB3 left
Name of the slip id for HUB1 left
Name of the slip id for HUB3 right
Name of the slip id for HUB1 right
Destructor
Resets the data to default values
Assign
Copy constructor
Default constructor
Holds the result of recognition for croatian payments
Threshold for slip id confidence level
Threshold for payment description code confidence level
Threshold for payment description confidence level
Threshold for payment description confidence level
Threshold for reference confidence level
Threshold for account number confidence level
Threshold for amount confidence level
Threshold for reference confidence level
Threshold for account number confidence level
Threshold for amount confidence level
Method sets int element value based on it's extraction history,
filter method and confidence thresholds
@param elementHistory
@param filter
@param elementValidThreshold
@param element
Method sets string element value based on it's extraction history,
filter method and confidence tresholds
@param elementHistory
@param filter
@param elementValidThreshold
@param element
Method performs traversing through result history and estimating the
correct extraction data, based on some criterium
@param resultHistory history of past extractions
@return estimated extraction results
Virtual destructor
Constructor
@param emptyValidator object that will decide if element is empty based
on history of recognitions
Object which goes through the history of extraction results and gives
the best estimate about the observed result.
Method performs traversing through result history and decides if the element is empty.
Class of objects responsible for keeping track if some element is nonexistent on the payment form
Getter method for retrieveing history list
@return history list
Method deletes all results from history
Method takes a new Extraction result and appends it to history
@param result
Method takes other extraction history and appends it to this
@param other
@return
Virtual destructor
Assignment operator
@param other
@return
Copy constructor
@param other
Default constructor
A list of past extraction results
Class responsible for keeping track of the whole history of extractions,
and providing different strategies for using this redundancy for
determining the most probable extraction results.
Screen orientation is in left landscape mode (home button is left of screen)
Screen orientation is in reverse portrait mode (home button is above screen)
Screen orientation is in right landscape mode (home button is right of screen)
Screen orientation is in portrait mode (home button is below screen)
Other settings
which ZXing readers are enabled
PhotoMath recognizer
Use segment scanner recognizer
Stop recognizers on first valid payment data
Use data santization
Fields
Use scanning of payment description field
use OcrQuality estimator recognizer
Scan MRTD
use PhotoMath
use ZXing barcode readers
Scan US Driver's License
Use for decoding barcode with inverse intensities
(white barcode on black surface)
Use to improve detection when image scale is not known.
It may unneccessarily slow down the detection and decoding
on images of known scale.
Use to improve detection when barcode doesn't have quiet zone
(text concatenated with barcode)
Use uncertain scanning
use PDF417 barcode reader
Percentage of image height where OCR line detection starts.
Scan Swiss paymentSlip
Scan Kosovo payment slip
Scan Kosovo code128 barcode
Scan UK Barcodes
Scan UK Giro Credit payment slip
Scan Dutch OCR line
use Belgian slip
use OCROnly recognizer
use PhotoBull
Use German QR code
use German slip
use Austrian QR code
scan Austrian slip
read payment description from Croatian slip
read payer name from Croatian slip
scan Croatian slip
scan Croatian QR Code
scan Croatian Pdf417
scan Slovenian slip
Countries
scan Hungarian slip
Serializes the settings to string for easy logging
Name of application for which rights apply to
ping interval in days. If set to 0, ping interval is disabled.
timestamp until license is valid. If set to 0, timestamp will not be checked.
Returns the number of days since 1.1.2015.
@return the number of days since 1.1.2015.
Returns the maximum allowed interval in days between two pings.
Should be used only if USE_PING right is set.
@return
Returns the reference to the rights manager which can be used for querying
application rights.
@return reference to rights manager
Returns true if licence has time restriction bit set.
@return true if licence has time restriction bit set.
Returns true if licence has ping bit set.
@return true if licence has ping bit set.
Checks if the ping interval has passed since last time successful ping was sent.
Returns true if ping interval has not passed (i.e. it is OK to use the library).
Returns false if ping interval has passed (i.e. library cannot be used until next successful ping).
@param lastPingTimestamp unix timestamp of moment when last ping was done
@return true if ping interval has not passed, false otherwise
Locks the token and invalidates the license.
Call this method when you want to lock your application. Any subsequent calls to
isValid() will return false and any subsequent call to getRightsManager will
return reference to rights manager that has no enabled rights.
@brief returns the string that contains maximum version number for which this license is valid
@return the string that contains maximum version number for which this license is valid
@brief returns the string that contains timestamp until license is valid
@return the string that contains timestamp until license is valid
Constructs a JSON string with information about license.
@return License information summary in JSON.
Returns the error string.
Constructs a string that contains information about license.
@return License information summary.
Calculates the validness for 'isValid' method. Takes into account
time of expiry.
Checks whether application is unlocked by proper license key.
@return True if application is unlocked, otherwise false.
required for clearing rights
contains static member of rights manager which has to be initialized
required for granting rights
Keygen is a friend of this class so that it can access seeds.
required for enabling serialization and deserialization
Clears all rights from this rights manager and purges the owning package name.
move assignment operator (also allowed only to friend classes)
@param other
@return
assignment operator
assignment is allowed only to friend classes
@param other the rights manager to assign
@return reference to self for chaining the operators
Same as with copy constructor, move constructor is also available only to friends.
@param other
Copy constructor is also private. Application parts can only use const reference
to this object obtained via AppProtection::getRightsManager
@param other
Constructor is private because only RightsManagerSerializator, Keygen and AppProtection
classes can construct this object.
Set of rights enabled for this application
Constructs a string that contains information about rights and owning package.
@return License information summary.
Returns the set of rights for this application.
@return The set of rights
Checks if particular right is enabled in application
@param right The particular right to be checked
@return true if right is enabled
destructor
Class responsible for managing application rights.
Object notifying GPU of new frame.
Object for recognition of payment data
Instance of object responsible for ocr
Map: recognizer id, recognizer position in current chain
For testing purposes.
Returns all error messages concatenated into one string.
Returns the initialization error messages.
Returns the internal OCR manager.
@return the internal OCR manager.
Method updates the recognizer chain with given device Info, ocr Manager, and PhotoPaySettings object.
This method enables reconfiguration of recognizer chain.
New recognizers can be created in this method, unneded recognizers can be deleted, and used ones can be updated using
their update method.
Resets the results of the recognizer objects
Performs recognition on the given image
@param cameraFrame camera frame to be recognized
@param cDelegate delegate to communicate progress with UI
@return recognition results
Designated destructor
@param deviceInfo information about device
@param ocrManager OCR manager object
@param photoPayMode recognition, recognition_test or detection_test
@param settings settings for initializing recognizers
@param status status of the initialization operation
@return true if draw buffer is available, otherwise false
@return true if calling showImage will do something
Use this callback to create new draw buffer based on given
image. This is for cases when draw buffer is not standard
frame input.
@param original
This is the callback which shows the image that was returned
via method getDrawBuffer.
This is the callback which shows arbitrary image during recognition process
Method should report back to UI if the whole chain failed
to detect anything.
this is the callback with dewarped image
and dewarped element locations
In release mode, this method should not do anything, and in debug
mode it should for example save images and dewarped elements.
@param dewarpedImage Result of dewarping
@param dewarpedElementLocations Locations of dewarped elements.
This is the callback with dewarped element image and ocr result on this
image. This method is called by recognizers that perform multiple dewarpings.
In release mode, this method should not do anything, and in debug mode
it should save or show given image.
@param dewarpedElement Image of the dewarped element.
@param ocrResult OCR result on given image.
@param elementName Name of the dewarped element.
in debug mode, gets the draw buffer for scanner to draw
@return pointer to cv::Mat which contains draw buffer
Publishes progress of ocr engine
@param progress progress in range [0, 1]
Returns true if the caller wants recognition to stop as soon as possible
Recognizer finished recognition with result
Called when the recognizer starts recognition
Called when recognizer detects the payment form.
Also returns the coordinates of the payment form and the
size of the image on which the form is detected.
Coordinates of the payment form are expected to be in order:
- upper left point
- upper right point
- lower left point
- lower right point
Also, the detection status is provided
Called when the recognizer starts detection
Destructor
Constructor
@param javaEnvironment
@param jDelegateInstance
Dewarped image used by the recognizer.
Successfully scaned image by the recognizer.
Original image used by the recognizer.
Tells the delegate to return paused on next should stop callback.
This method is called from library when all enabled recognizers reported
failed detection on image. This notification can be used for updating the UI.
NOTE: This method will be called from the thread in which the recognition
process is performed.
This method is called from within OCR engine to inform user
about the current progress of the OCR process. The progress
is a integer percentage of the process (integer from interval
[0, 100]).
NOTE: This progress is reported only from OCR engine, and presents
the progress only of the OCR process, not the whole recognition
process which consists of form detection, image enhancing, OCR
and interpretation of OCR result.
NOTE 2: In future this behavior might change to present a percentage of
a whole recognition process.
NOTE 3: This method will be called from the thread in which the recognition
process is performed.
Integer progress of OCR process.
This method is called from within OCR engine and should return
a boolean indicating whether or not OCR process should be canceled.
This is useful in cases OCR process lasts for a longer time, but
user wants to cancel the process.
This method is called ONLY if OCR process is performed.
NOTE: If you cancel the OCR process, you will get an empty result
in onRecognitionFinished callback and as a result of method
recognize in class ManagedPhotoPay.
NOTE 2: This method will be called from the thread in which the recognition
process is performed.
true if OCR process should be canceled, otherwise false
This method is called from library after form recognition ends.
Recognition result is given via parameter result and can be null,
invalid or empty. The same result is given as a result of method
recognize in class ManagedPhotoPay.
No matter if form detection has been successful or not, this
method will ALWAYS be called.
NOTE: This method will be called from the thread in which the recognition
process is performed.
This method is called from library before OCR and form recognition begins, but after the form
detection process has finished. No matter if form detection has been successful or not, this
method will ALWAYS be called.
NOTE: This method will be called from the thread in which the recognition
process is performed.
This method is called from library when payment form detection
process finishes. Library sends list of corner points of detected
form, or empty list - that depends on what has been detected and
that information can be obtained from detectionStatus parameter.
DetectionStatus parameter gives information about detection - whether
detection has failed or has been successful. Furthermore, this parameter
can be used to get information if image is too blurry for OCR or if
it is too far away on image to perform a successful OCR.
Method should return a boolean that should inform library whether
or not it should continue to the OCR process. If this method returns false,
OCR will not be performed (this is useful for just performing payment form
detection test in different conditions without wasting time on OCR).
If this method returns true, library will continue with the OCR and recognition
process.
NOTE: If form was not successfully detected, OCR will not be performed even if
this method returns true. It just makes no sense to perform OCR on images
on which payment slip is too far away on the scene or if image is blurry.
NOTE 2: Even if OCR is not performed (either because this method returned false, or
because form detection has not been successful, callback methods onRecognitionStarted
and onRecognitionFinished WILL be called. The difference is just that onRecognitionFinished
will contain empty result.
NOTE 3: This method will be called from the thread in which the recognition
process is performed.
List of corner points of detected form.
Information about detection
True if you want to continue OCR and recognition process, otherwise false.
This method is called from library when payment form detection
process starts. Method is called for each detection separately
i.e. if multiple recognizers are enabled, method will be called
for each recognizer until one recognizer successfully reads the form.
NOTE: This method will be called from the thread in which the recognition
process is performed.
Returns the row step of the contained image data.
Returns the height of the contained image data.
Returns the width of the contained image data.
Returns the number of color channels contained in the image data.
Returns the contained image data as an array. Note that this method is
expensive since the data needs to be copied from an internal buffer.
Represents a container for recognizer images.
This enum contains possible detection statuses that can be result
payment form detection.
Form detected, but camera is at too big angle
Form is detected, but parts of the form are not in image
PDF417 barcode detected
Fallback detection was successful
QR code detected
Form detected, but the camera is too far above the payment form
Indicates form detection has failed
Indicates payment form is successfully detected
String -> Object map of result elements.
Result data can
be retrieved from this map instead of using the specific
properties in IRecognitionResult implementing classes.
In the instances when specific property is not implemented
the only way of retrieving the data is via this map.
Indicates if the result is empty
Indicates if the result is valid
true if returned data is uncertain.
Only applicable if used with PDF417RecognizerSettings.UncertainScanMode set to true.
Raw barcode data.
String representation of data inside barcode.
PDF417 recognition result
Resets the best results in the whole chain
and clears history
* Method fills the recognition result object with
* payment data recognized from given image
* Returns true if scanning was successful, false otherwise
\file
Runnable.hpp
Created on: May 2, 2013
Author: dodo
Represents anything that can be run. For use with multithreaded and
multi-process execution.
\file
EdgeIntensityBlurEstimator.hpp
Created on: Feb 20, 2013
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
\file
Runnable.hpp
Created on: May 2, 2013
Author: dodo
Represents anything that can be run. For use with multithreaded and
multi-process execution.
Method generates points on a straight
line between start point and end point using Bresenham's
algorithm. Number of points is downscaled by a given factor
@param startPoint
@param endPoint
@param scaleFactor
@param interpolatedPoints
Method generates a given number of points on a straight
line between start point and end point using Bresenham's
algorithm.
@param startPoint
@param endPoint
@param numPoints
@param interpolatedPoints
Method generates points on a straight line between start point
and end point using Bresenham's
algorithm.
This method deals with horizontal or vertical lines.
@param startPoint
@param endPoint
@param interpolatedPoints
Method generates points on a straight line between start point
and end point using Bresenham's
algorithm.
@param startPoint
@param endPoint
@param interpolatedPoints
Method generates points on a straight line starting from startPoint in a direction
perpendicular to direction defined with vector from firstOrientationPoint to
secondOrientationPoint using Bresenham's interpolation. Algorithm generates
given number of points or until it reaches image edge.
@param startPoint
@param firstOrientationPoint
@param secondOrientationPoint
@param imageSize
@param maxNumOfPoints
Method generates a given number of points on a straight
line between start point and end point using Bresenham's
algorithm.
This method deals with horizontal or vertical lines.
The method visits all points as regular bresenham and uses
no divisions and multiplications
@param startPoint
@param endPoint
@param numPoints
@param interpolatedPoints
Method generates a given number of points on a straight
line between start point and end point using Bresenham's
algorithm.
The method visits all points as regular bresenham and uses
no divisions and multiplications
@param startPoint
@param endPoint
@param numPoints
@param interpolatedPoints
Method generates a given number of points on a straight
line between start point and end point using Bresenham's
algorithm.
This method deals with horizontal or vertical lines.
@param startPoint
@param endPoint
@param numPoints
@param interpolatedPoints
Method generates a given number of points on a straight
line between start point and end point using Bresenham's
algorithm.
@param startPoint
@param endPoint
@param numPoints
@param interpolatedPoints
Destructor
Private constructor to prevent instantiation
Interpolation methods
Elements of the reversed pdf417 end pattern => 121113117
Elements of the pdf417 end pattern => 711311121
Sum of elements in the pdf417 end pattern = 18
Number of elements in the pdf417 end pattern = 9
Elements of the reversed pdf417 start pattern => 31111118
Elements of the pdf417 start pattern => 81111113
Sum of elements in the pdf417 start pattern = 17
Number of elements in the pdf417 start pattern = 8
Indicates the recognizer is enabled
Does the recognizer require to be used in landscape orientation
Does the recognizer require camera with autofocus feature
Does the recognizer require on OCR engine
Does the recognizer require quality estimation
of video frames captured by camera.
Whether or not UPCE barcode should be scanned.
Whether or not UPCA barcode should be scanned.
Whether or not QR code should be scanned.
Whether or not ITF barcode should be scanned.
Whether or not EAN8 barcode should be scanned.
Whether or not EAN13 barcode should be scanned.
Whether or not DataMatrix 2D barcode should be scanned.
Whether or not Code39 barcode should be scanned.
Whether or not Code128 barcode should be scanned.
Whether or not Aztec 2D barcode should be scanned.
By setting this to true, you will enable scanning of barcodes with inverse
intensity values (i.e. white barcodes on dark background).
This option can significantly increase recognition time. Default is false.
ZXing recognizer settings
Screen orientation of device when image frame was taken
Was device in motion when image frame was taken
Was camera focused when image frame was taken
Cached images converted with various pixel converters
Cached white balance adjusted BGR or BGRA image
Cached inverted grayscale image
Frame quality estimator used for determining this frame's quality
Result of white balance analysis
White balance analyzer that is used for determining frame's white balance.
BarcodeElement *
Indicates the recognizer is enabled
Does the recognizer require to be used in landscape orientation
Does the recognizer require camera with autofocus feature
Does the recognizer require on OCR engine
Does the recognizer require quality estimation
of video frames captured by camera.
Enable scanning of non-standard elements, but there is no guarantee that all data will be read.
For Pdf417 barcode to be used when multiple rows are missing(e.g.not whole barcode is printed).
Allow scanning barcodes which don't have quiet zone surrounding it(e.g.text concatenated with barcode).
This option can significantly increase recognition time.
US driver's license recognizer settings
This constructor will obtain draft information about library.
Draft information will only contain information about which recognizers
are built in library, but will not contain information such as maximum
texture size, GPU availability, etc.
this constructor will obtain full information about library
it is expected that the library is fully initialized before
creating object with this constructor
@param ocrManager
Provides information about library: which OCR engine is used,
what recognizers are enabled, etc.
Created on: 18/03/2014
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
\file
RCLibraryInfo.hpp
Created on: Jan, 2014
Author: Ljudevit
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
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\file
PPLibInfo.hpp
Created on: Dec 11, 2012
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: 18/03/2014
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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vector of all available engines
@return returns vector with all available engines. Entry that is NULL is unabailable.
Virtual destructor
Constructor, creates a list of available OCR engines.
In order for OCR engine to be available, the define statement for this
OCR engine must be provided, and all required resources for given engine
must be available.
@param status [out parameter] contains result of initialization
Processes multiple image regions and creates mutiple ocr results
Processes the given image region and creats ocr layout
Processes the given image and creates ocr layout
Destructor
Constructor
Abstraction of ocr engines
Creates a new OCR result by appending two diferent results.
Method just appends blocks of the second result to blocks of the first result.
@param first first result
@param second second result
@return result with appended two results
Method performs verification
@return true if verifiable object has no errors, false otherwise
Sets the transform property
@param transform
Returns transform parameteres for tranforming OCR result to device UI
@return transform
Returns true if the result is rotated by 180 degrees
Returns true if the result is empty
Estimates quality of ocr block
@return
Estimates quality of ocr block
@return
Method draws the ocrResult on a given image
Returns the bounding box arount the ocr result
Logs the result to std stream
Returns the object which can iterate through the result
Returns the result as string
Erases block at given iterator and returns the iterator that points
to the block that follows the removed one. If the removed block was
last in result, the returned iterator points to past last block in
the result (value that can be obtained with method @{link getResultEnd}).
Returns the iterator that points to past last block in the result
Returns the iterator that points to past last block in the result
Returns the iterator that points to the first block in the result
Returns the const iterator that points to the first block in the result
Returns number of chars in the result
@return
Returns the number of blocks in result
Destructor
Move-Assignment operator
Copy-Assignment operator
Move constructor
Copy constructor
Constructor which takes string
Constructor which takes prebuild blocks
Transformation matrix for transforming OCR result coordinates into coordiantes on the
device UI.
Bounding box around ocr result
Blocks in result
Encapsulates the OCR results
Finds the index of the current char in the result
compares two iterators for equality and returns false if they are equal
compares two iterators for equality and returns true if they are equal
Resets the iterator to the beginning of the ocr result
Deletes the current character in the layout and moves
iterator to the next character (same as consumeNextChar)
@param status
Consumes the next character in the layout
Iterates through the ocr result from the current point to next n characters
(iterates to end if n==0) and returns the obtained string
Returns true if the interator passed the last char in the whole layout
Returns true if the interator is on the last char in the whole layout
Returns true if the interator is on the last char in the current block
Returns true if the iterator is on the last char in the current line
Returns the length of the line the iterator is currently on.
Returns the pointer to the current ocr character
Destructor
Move-Assignment operator
Copy-Assignment operator
Move constructor
Copy constructor
Constructor
current OCR character
current OCR line
current OCR block
Result over which we wish to iterate
Class responsible for iterating over OcrResult
Method performs verification
@return true if verifiable object has no errors, false otherwise
Estimates quality of ocr block
@return
Logs the result to std stream
Removes the line at specific index and returns the iterator that
points to the line that follows the removed one. If the removed
line was the last in block, the returned iterator points to past
last line in block (the same value that can be obtained with
method @{link getBlockEnd}).
Returns the iterator that points to past last line in block
Returns the iterator that points to past last line in block
Returns the iterator that points to first line in block
Returns the iterator that points to first line in block
Returns number of chars in the block
@return
Returns the character count in the line
Destructor
Move-Assignment operator
Copy-Assignment operator
Move constructor
Copy constructor
Constructor
Bounding box of the block
lines in block
Represents the block of text in ocr result
Method performs verification
@return true if verifiable object has no errors, false otherwise
Estimates quality of ocr line
@return
Returns true if line contains given value
@return
Logs the result to std stream
Method iterates through all chars and returns average char quality
@return
Appends the newline character at the end of the line
Prepends the newline character at the front of the line
Creates the Ocr Line which has trimmed all whitespace from front and end of the line
@return
Removes the char at specific iterator and returns an iterator
to the char after the removed one. If the removed character was
the last in line, the returned iterator points to past the end
of line (value that can be obtained with @{link getLineEnd}).
Returns the iterator that points to past end of line
Returns the iterator that points to past end of line
Returns the iterator that points to first character in line
Returns the iterator that points to first character in line
Returns the character count in the line
Destructor
Move-Assignment operator
Copy-Assignment operator
Move constructor
Copy constructor
Constructor
Bounding box of the line
Chars in line
Represents the line in ocr result
Method performs verification
@return true if verifiable object has no errors, false otherwise
Estimates quality of the char
@return
Logs the result to std stream
Returns the baseline of the character
Sets the uncertain valie
@param uncertain
Returns true if character is uncertain
Returns true if character is italic
Returns true if character is bold
Sets the quality of ocr char
@param quality
Returns the quality factor of the char
Sets the quality of ocr char
@param quality
Returns the quality of the char in interval [0, 100];
Sets the new Box for the char
@param newBox
Returns the bounding box around the character
Sets the new font of the character
Returns the font of the character
Sets the new height of the character
Returns the height of the character
Sets the new value for this char
Returns the value of the character in UNICODE format
Destructor
returns the list of recognition variants for current OcrChar
Move-Assignment operator
Copy-Assignment operator
Move constructor
Copy constructor
Constructor
Char baseline location
Recognition variants for current OCR char
Ocr font
Factor which multiplies quality estimate
Is character uncertain?
Is character italic?
Is character bold?
Quality of the char
Bounding box of the char
Height of the char
Return the value of char
Character representation
Method performs verification
@return true if verifiable object has no errors, false otherwise
Virtual destructor
Constructor
Private to prevent instantiation.
Interface for all classes which integrity can be verified at runtime
\file
PPLibInfo.hpp
Created on: Dec 11, 2012
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
\file
OcrEngine.hpp
Created on: Jun 12, 2012
Author: cerovec
\file
OcrResult.h
Created on: May 6, 2012
Author: cerovec
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
OcrBlock.h
Created on: May 6, 2012
Author: cerovec
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
OcrLine.h
Created on: May 6, 2012
Author: cerovec
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
OcrChar.h
Created on: May 6, 2012
Author: cerovec
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
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\file
wchar.h
Created on: Nov 28, 2012
Author: dodo
Copyright (c)2012 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
OcrEngine.hpp
Created on: Jun 12, 2012
Author: cerovec
\file
OcrResult.h
Created on: May 6, 2012
Author: cerovec
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
OcrBlock.h
Created on: May 6, 2012
Author: cerovec
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
OcrLine.h
Created on: May 6, 2012
Author: cerovec
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ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
OcrChar.h
Created on: May 6, 2012
Author: cerovec
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
wchar.h
Created on: Nov 28, 2012
Author: dodo
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ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Collection of objects notified on resource changes.
Table in which resources are stored.
Notify all registered observer that entry with resID key changed.
Destroys the resources table
Constructor. Creates empty resources table
Method empties the resource map, but does not perform any deallocations.
Retrieves all entries. Used in DetectorStudio to free allocated memory
for all resources. On Android side, memory is freed on Java side.
@return Reference to map with all resources.
Remove an observer from this resource manager.
Register an observer on this resource manager.
Retrieves the entry with given key string. Available resources can be obtained
with static key strings given in this class.
@param resID key string under which the data is stored in the table
* Adds new entry in the table
* @param resID - string key under which the entry is stored
* @param resData - data of the entry
* @param resDataLength - length of the entry in bytes
@brief getResourceManager returns the resource manager singleton instance
@return the resource manager singleton instance
List of keys under which the data entries are stored in the table
class responsible for managing in-memory resources
Creates entry from pointer to data and its length
@param resData - pointer to data
@param resLength - length of the data in bytes
Creates empty entry - data points nowhere (NULL), and the length is 0
Lenght of the data in bytes
Pointer to the data
Entry in the resources table.
Consists of the pointer to the data and length of the data
\file
ResourceManager.hpp
Created on: Dec 23, 2012
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
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Save text to IsolatedStorage of application.
Save cvMat to IsolatedStorage of application. Image is automatically prefixed with timestamp.
Create vector of result data.
**
**
Create appropriate result
Indicates the recognizer is enabled
Does the recognizer require to be used in landscape orientation
Does the recognizer require camera with autofocus feature
Does the recognizer require on OCR engine
Does the recognizer require quality estimation
of video frames captured by camera.
By setting this to true, you will enable scanning of barcodes with inverse
intensity values (i.e. white barcodes on dark background).
This option can significantly increase recognition time. Default is false.
Allow scanning barcodes which don't have quiet zone surrounding it(e.g.text concatenated with barcode).
This option can significantly increase recognition time.
Enable scanning of non-standard elements, but there is no guarantee that all data will be read.
For Pdf417 barcode to be used when multiple rows are missing(e.g.not whole barcode is printed).
PDF417 recognizer settings
Indicates the recognizer is enabled
Does the recognizer require to be used in landscape orientation
Does the recognizer require camera with autofocus feature
Does the recognizer require on OCR engine
Does the recognizer require quality estimation
of video frames captured by camera.
Base interface for all recognizer settings classes
will perform only detection and will profile the performance of slip
detection
will perform indefinite scan and will profile the performance of scans
indicates normal scanning operation
String -> Object map of result elements.
Result data can
be retrieved from this map instead of using the specific
properties in IRecognitionResult implementing classes.
In the instances when specific property is not implemented
the only way of retrieving the data is via this map.
Indicates if the result is empty
Indicates if the result is valid
Raw extended barcode data.
Available only on barcodes that support extended data encoding (e.g. code39).
Raw barcode data.
String representation of extended data.
Available only on barcodes that support extended data encoding (e.g. code39).
String representation of data inside barcode.
Type of barcode scanned with BarDecoder recognizer as string.
Currently this can only be CODE128 or CODE39.
Type of barcode scanned with BarDecoder recognizer.
Currently this can only be CODE128 or CODE39.
Bardecoder recognition result
Enables detecting the scale of the image before performing a barcode read.
@param autoScale
Enables decoding of barcodes without quiet zone round the barcode
(e.g. text concatenated with barcode blocks)
@param tryHarder
Enables decoding of barcodes where multiple rows are missing from the end of the barcode
(e.g. when more lines at the end of barcode are not printed, and there is not enough
error correction codewords left to compensate for missing rows)
@param useUncertainDecoding
Resets the best results in the whole chain
and clears history
* Method fills the recognition result object with
* payment data recognized from given image
* Returns true if scanning was successful, false otherwise
Virtual destructor
Constructor
Returns true is we already have given paymentDataType earlier in chain
@param results
@param paymentDataType
Object responsible for reading Code128 barcodes
Object responsible for reading Code39 barcodes
Object which stored barcode result
Recognizer which scans image for Code39 barcodes
Adds barcode results
@param barcodeData - BarDecoder object with barcode information embedded
Virtual destructor
Constructor
Sets paymentDataType to UNKNOWN value. Users are responsible for setting that value.
BarDecoder data for scanning results
Called when the recognizer starts detection
Virtual destructor
Constructor
Private instance of BarcodeRecognizerDelegate
Delegate object which serves as bridge between PaymentRecognizerDelegate
and BarcodeRecognizerDelegate.
Elements of the reversed Code128 end pattern => 2111332
Elements of the Code128 end pattern => 2331112
Sum of elements in the Code128 end pattern = 13
Number of elements in the Code128 end pattern = 7
Elements of the reversed Code128 start pattern for Subtype C => 31111118
Elements of the Code128 start pattern for Subtype C => 211232
Elements of the reversed Code128 start pattern for Subtype B => 412112
Elements of the Code39 start pattern for Subtype B => 211214
Elements of the reversed Code128 start pattern for Subtype A => 214112
Elements of the Code128 start pattern for Subtype A => 211412
Sum of elements in the Code128 start pattern = 11
Number of elements in the Code128 start pattern = 6
Sum of elements in all Code128 patterns = 11
Number of elements in all Code128 patterns = 6
Maximum allowed average difference between patterns to consider them equal
Maximum allowed difference between individual elements of two patterns
Elements of the reversed Code39 pattern => 112121121
Elements of the Code39 pattern => 121121211
Sum of elements in the Code39 pattern = 12
Number of elements in the Code39 pattern = 9
Used to determine if given scanLine passes true barcode. First start and stop pattern are found if they exist,
and then border follow is used to found exact position of each barcode block.
Finds start and stop sequences from given vector with edges of the barcode line
Function determines if the current edge sequence correspondes to a given patterns.
Edge sequence must start with black bar.
Maximum allowed average difference between patterns to consider them equal
Maximum allowed difference between individual elements of two patterns
Creates default BGR to grayscale pixel converter.
@return Reference to grayscale pixel converter.
This is the callback which shows the image that was returned
via method getDrawBuffer.
in debug mode, gets the draw buffer for scanner to draw
@return pointer to cv::Mat which contains draw buffer
Recognizer finished recognition with result
Called when the recognizer starts recognition
Called after detecting barcode for testing purposes
Called when recognizer detects the payment form.
Also returns the coordinates of the payment form and the
size of the image on which the form is detected.
Coordinates of the payment form are expected to be in order:
- upper left point
- upper right point
- lower right point
- lower left point
Also, the detection status is provided
Called when the recognizer starts detection
String -> Object map of result elements.
Result data can
be retrieved from this map instead of using the specific
properties in IRecognitionResult implementing classes.
In the instances when specific property is not implemented
the only way of retrieving the data is via this map.
Indicates if the result is empty
Indicates if the result is valid
Raw extended barcode data.
Available only on barcodes that support extended data encoding (e.g. code39).
Raw barcode data.
String representation of extended data.
Available only on barcodes that support extended data encoding (e.g. code39).
String representation of data inside barcode.
Type of barcode scanned with ZXing recognizer as string.
Type of barcode scanned with ZXing recognizer.
ZXing recognition result
\file
PlatformStringUtils.hpp
Created on: Jan, 2014
Author: Ljudevit
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
\file
RCRecognitionData.hpp
Created on: Jan, 2014
Author: Ljudevit
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
PlatformStringUtils.hpp
Created on: Jan, 2014
Author: Ljudevit
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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* Method fills the recognition result object with
* payment data recognized from given image
* Returns true if scanning was successful, false otherwise
Designated constructor
Sets recognizer to enabled or not enabled
Returns is recognizer enabled or not
Returns true if recognizer using this setting requires GPU acceleration for image processing.
Returns true if recognizer using this setting requires OCR engine to be available.
Returns true if recognizer using this setting requires frame quality analysis to be enabled.
Virtual destructor
Generic recognizer type.
PayBull recognizer
Ocr line recognizer
Library information recognizer
OCR quality estimation recognizer
OCR only recognizer
Generic OCR recognizer
Machine readable travel document recognizer
PhotoMath recognizer
Dutch slip recognizer
Kosovo slip recognizer
Hungarian slip recognizer
German slip recognizer
UK Driver License recognizer
UK slip recognizer
Swiss slip recognizer
Slovenian slip recognizer
Croatian slip recognizer
Belgian slip recognizer
Austrian slip recognizer
Custom 1D barcode recognizer
ZXing recognizer
US Drivers's Licence recognizer
UK QR code recognizer
PDF417 recognizer
Kosovo code128 barcode recognizer
German QR code recognizer
Croatian barcode data code recognizer
Croatian barcode data PDF417 recognizer
Austrian QR code recognizer
Serializes the settings to string for easy logging
Method sorts the given points in clockwise order starting from upperleft
Publishes progress of ocr engine
@param progress progress in range [0, 100]
Method which reports the image which will be OCR-ed
@param image which will be sent to segmentation and classification in the OCR process
Returns true if the caller wants recognition to stop as soon as possible
Method should report back to UI if the whole chain failed
to detect anything.
Method resets the failed detection flag to true, i.e. it appears
as if detection was failed.
Destructor
Default constructor
This method is used to show OCR result in UI.
This is the callback with dewarped element image and ocr result on this
image. This method is called by recognizers that perform multiple dewarpings.
In release mode, this method should not do anything, and in debug mode
it should save or show given image.
@param dewarpedElement Image of the dewarped element.
@param ocrResult OCR result on given image.
@param elementName Name of the dewarped element.
Use this callback to create new draw buffer based on given
image. This is for cases when draw buffer is not standard
frame input. This is also used in MasterRecognizer to set default
draw buffer.
This is the callback which shows the image that was returned
via method getDrawBuffer.
This is the callback that shows camera frame
@param imCache
This is the callback which shows arbitrary image during recognition process.
Image type is passed so that callee can decide whether to save image or not.
Transforms the dewarped location of the object to the location of the object on screen.
@param imageSize size of the video frame
@param imageLocation location of the object on image
@param dewarpedLocation location of the object in dewarped coordinates.
@return cv::Mat with perspective transform from dewarped location to screen location
@return true if calling showImage will do something
in debug mode, gets the draw buffer for scanner to draw
@return pointer to cv::Mat which contains draw buffer
@return true if draw buffer is available, otherwise false
Recognizer finished recognition with result
Called when the recognizer starts recognition
Called when recognizer detects the payment form.
Also returns the coordinates of the payment form and the
size of the image on which the form is detected.
Coordinates of the payment form are expected to be in order:
- upper left point
- upper right point
- lower left point
- lower right point
Also, the detection status is provided
Called when the recognizer starts detection
Abstract delegate object which is responsible for recognition notifications
Appropriate callback functions are called on events:
- recognizer started recognition
- recognizer detected a payment form
- recognizer finished with result
Enumeration of image types which are in delegate
Next recognizer in chain
@brief chainReturn
A method for proper returning from Payment recognizers
@return true if chain resulted with a successful recognition, false otherwise
Resets the best results in the whole chain
@param settings
* Method fills the recognition result object with
* payment data recognized from given image
* Returns true if scanning was successful, false otherwise
Destructor
Constructor
Result with hightest confidence shared between recognitions
Abstract Payment Recognizer
If true, first recognizer in chain that performs successful recognition will stop the recognition chain
Current recognizer delegate for reporting callbacks
Current results in recognition chain
Current frame in recognition. RecognitionContext does not own camera frame.
@brief setStopOnFirstSuccess Sets whether first recognizer that successfully recognizes the frame should break the recognition chain
@param shouldStopOnFirstSuccess whether first recognizer that successfully recognizes the frame should break the recognition chain
@brief shouldStopOnFirstSuccess Returns true if first recognizer that successfully recognizes the frame should break the recognition chain.
@return Returns true if first recognizer that successfully recognizes the frame should break the recognition chain.
@brief setDelegate Sets the pointer to recognizer delegate. Ownership is not acquired.
@param delegate recognizer delegate
@brief getDelegate Returns the pointer to recognizer delegate (if exists)
@return the pointer to recognizer delegate (if exists)
@brief getResults Returns the reference to current vector of recognition results
@return the reference to current vector of recognition results
@brief setCameraFrame Sets the frame to be recognized. Ownership is not acquired.
@param cameraFrame camera frame to be recognized
@brief getCameraFrame Returns current frame in recognition.
@return current frame in recognition.
Sets the maximum number of alternatives that can be produced for
each OcrChar. Default is 0. Allowing more alternatives gives you
ability for more detailed OCR result processing, at a cost of performance.
@param maxCharAlternatives maximum number of alternatives that can be produced for each OcrChar.
Returns the maximum number of alternatives that can be produced for
each OcrChar. Default is 0.
@return maximum number of alternatives that can be produces for each OcrChar.
Return the ROI
@return region of interest
Set the roi
@param roi region of interest in which the MRTD is scanned
If any detector state exists (counting of failed detections) or similar,
this method resets it.
@return All pixel converters used in this detector.
Convenience method for format conversions
Method calculates the dewarped coordinates of decoding elements
@param dewarpedLocation
@param resultCode
@param dewarpedElementLocations
Method calculates the dewarped coordinates of all dewarped areas
@param decodingLocations
@param resultCode
@param dewarpedLocations
Method calculates the dewarped coordinates of the dewarped area
@param detectionLocation
@param dewarpedLocation
Method calculates all locations of the decoding areas on the image
@param detectionLocation
@param resultCode
@param decodingLocations
Method calculates the location of the decoding area on the image
@param detectionLocation
@param resultCode
@param decodingLocation
Method calculates the location of the form which will be displayed
@param detectionLocation
@param displayLocation
Method calculates the detection status for a given detection location
@param detectionLocation
@param resultCode
@return
Method finds the payment form on the given image and returns the result code
@param frame camera's frame
@param detectionLocation Detection result will be saved in this variable.
@param drawBuffer If not NULL, this buffer will be used for debugging of
scanners.
@return detection result code as defined in each specific detector
Destructor
Constructor
Indicates detector failed to find austrian form
Class responsible for detection of payment forms.
Each recognizer has a companion detector for finding location of the payment form.
This object is responsible for determining:
- Location of payment form on the image
- Detections status
- Location of payment form which will be presented on the screen
- Location of the area which will be decoded
- Location of payment form in dewarped perspective
- Locations of payment form elements which need decoding in dewarped perspective
Detection status enum
Method calculates and returns the unique hash value of the converter
that can be used for comparison of different pixel converters.
@return
Used by white balance analyzer to check if current pixelConverter
should also perform contrast stretch.
@return
Method return the type of the converter.
@return
Method returns original offset (float version) of the converter
(not modified by the white balance analyzer).
@param offset
Method returns original factors (float version) of the converter (those factors
are not modified by the white balance analyzer).
@param redFactor
@param greenFactor
@param blueFactor
Method returns original offset of the converter
(not modified by the white balance analyzer).
@param offset
Method returns original factors of the converter (those factors
are not modified by the white balance analyzer).
@param redFactor
@param greenFactor
@param blueFactor
Method returns current offset (float version) of the converter
(might be changed by the white balance analyzer)
@param offset
Method returns current factors (float version) of the converter (those factors
might be modified by the white balance analyzer).
@param redFactor
@param greenFactor
@param blueFactor
Method returns current offset of the converter (might be changed
by the white balance analyzer).
Method returns current offset of the converter (might be changed
by the white balance analyzer).
@param offset
Method returns current factors of the converter (those factors
might be modified by the white balance analyzer).
@param redFactor
@param greenFactor
@param blueFactor
Method converts color pixel to gray
@param pixel
@return
Method converts color pixel to gray
@param pixel
@return
Method sets new offset (float version) of the converter. This method is used
by white balance analyzers to update factors according to
white balance of the frame.
@param offset
Method sets new factors (float version) of the converter. This method is used
by white balance analyzers to update factors according to
white balance of the frame.
@param rFact
@param gFact
@param bFact
Method sets new offset of the converter. This method is used
by white balance analyzers to update factors according to
white balance of the frame.
@param offset
Method sets new factors of the converter. This method is used
by white balance analyzers to update factors according to
white balance of the frame.
@param rFact
@param gFact
@param bFact
Method make an almost exact copy of current object.
'Almost exact' means that frame dependend data (such as
AWB modified factors) are not copied.
@return
Method converts color pixel to gray
@param r
@param g
@param b
@return
method compresses gamma value of given pixel intensity according to
sRGB gamma compression definition {@link http://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale}
This is required for compressing RGB colorspace
@param val pixel intensity
@return compressed pixel intensity
method expands gamma value of given pixel intensity according to
sRGB gamma expansion definition {@link http://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale}
This is required for linearizing RGB colorspace
@param val pixel intensity
@return expanded pixel intesity
Returns the orientation of the device when frame was captured.
Returns true if device was in motion when this frame was captured. It is possible even for photo frames (isPhotoFrame returns true)
to be captured in motion.
Returns true if camera was focused when frame was captured. Note that this can be different from result of
isFocused which is based on multiple parameters.
Returns true if frame thinks it is focused, i.e. sharp enough for recognition. Note that this is frame's own opinion -
recognizers can obtain frame quality with method getFrameQuality to implement their own heuristics for determining
if frame is really good enough for processing.
Also note that this method might trigger a frame quality estimation analysis.
Returns the frame quality estimator that is used for quality estimation of this frame. Returns NULL if no frame quality
estimator is associated with this frame.
Returns the result of white balance analysis, calculating it if necessary. If no white balance analyser exists for this frame,
calculation will fail and this method will return NULL.
Returns white balance analyzer used for analyzing this frame. Returns NULL if no white balance analyzer is associated
with this frame.
Returns true if the originally set frame was in YUV format.
Returns true if the frame is actually a photograph and not a video frame (regardless of its resolution).
Recognition of photographs will not utilize time redundancy.
Retrieves the reference to arbitrary image created with given pixel converter, creating it if necessary.
If pixConv is NULL, returns the same output as getAwbBgrImage.
Retrieves the reference to white balance adjusted BGR or BGRA image, creating it if necessary.
Retrieves the reference to inverted grayscale image, creating it if necessary.
Retrieves the reference to grayscale image, creating it if necessary.
Retrieves the reference to BGR or BGRA image (whichever is available).
If neither is available, creates and returns BGRA image.
Retrieves the reference to BGRA image, creating it if necessary.
Retrieves the reference to BGR image, creating it if necessary.
Returns the original size of the frame
Returns the size of the frame
Number of available screen orientations. This must be last enum entry
Screen orientation is in left landscape mode (home button is left of screen)
Screen orientation is in reverse portrait mode (home button is above screen)
Screen orientation is in right landscape mode (home button is right of screen)
Screen orientation is in portrait mode (home button is below screen)
Returns string representation of the Quadrangle
@return string representation of the Quadrangle
Method returns an OpenCV rectangle from this quandrangle
Method loses some accuracy since quadrangle isn't neccesarily aligned to the axes.
Method returns the vector of points the points to vector
@param points
Method pushes the points to int array in form (x_ul, y_ul, x_ur, y_ur, x_lr, y_lr, x_ll, y_ll).
@param points
Method pushes the points to float array in form (x_ul, y_ul, x_ur, y_ur, x_lr, y_lr, x_ll, y_ll).
@param points
Method pushes the points to vector
@param points
Method warps the perspective of the current quadrangle and creates a new warped quadrangle
@param transformationMatrix
@return
Calculates the affine transform which transform this to other quadrangle
Calculates the perspective transform which transform this to other quadrangle
Method draws the quadrangle on the given image
@param image
@param color
Method created a new rectangle which encloses this quadrangle.
Meothd creates a new quadrangle which is a scaled version of this quadrangle.
Method creates a new quadrangle which is an interpolation between this and given other quadrangle
Interpolation depends on the given interpolation factor
If 1, resulting quadrangle will be equal to other quadrangle
If 0 or smaller, resulting quandrangle will be equal to this quadrangle
If other value, linear interpolation will be performed to obtain result
Calculates the rectangle that has the same left and right bounding lines,
but upper and lower border positiones are given with relative positions to current rectangle
@param upperPos
@param lowerPos
@return
Calculates the rectangle that has the same upper and lower bounding lines,
but left and right border positiones are given with relative positions to current rectangle
@param leftPos
@param rightPos
@return
Spreads the corner points of the rectangle by a certain percentage
@param spreadRatio
@return
Returns true if point is inside this quadrangle
@param point point for which we perform the test
@return true if point is inside this quadrangle
Returns the skew measure of the quadrangle on scale [0, 1].
If the quadrangle is skewed or warped, this measure will be close to 1.
If the quadrangle has 90 degrees angles, the measure will be 0
Returns the width to height aspect ratio
@return aspect ratio
Returns the average width of the quadrangle
@return width
Returns the average height of the quadrangle
@return height
Returns the horizontal line - line connecting centers of left and right quadrangle sides
@return horizontal line
Returns the area of the quadrangle
@return area
Returns the center of the quadrangle
@return
Upper right getter
@return
Upper left getter
@return
Lower right getter
@return
Lower left getter
@return
Operator +=
Destructor
Assignment operator
@param other
@return
Copy constructor
@param other
Constructor for OpenCV rectangle
Constructor for vector
Constructor
Quadrangle class
Division function. Required for calculating mean in K-means algorithm.
@param number
@return
Addition function. Required for calculating mean in K-means algorithm.
@param other
make a clone of itself
@return
Calculates squared distance to point in integer arithmetics
Calculates squared distance to point
Distance function to other segment
Distance function
Logs the line segment to stdout
Method draws the line on the image and sets the errorstatus flag
@param image
Inverts the direction of the line;
Returns the length of the line segment
Returns the point at specified location on the line.
Param represents:
0 - startPoint of the line
1 - endPoint of the line
Returns the end point of the segment
@return
Assignment operator
@param other
@return
Copy constructor
@param other
Virtual destructor
Constructor which takes two points
Line end point
Draws the model with initialization values and inliers
For debugging purposes
@param image
@param initValues
@param inliers
@param color
Evaluation of model based on observed values
@param model
@param values
@return error cost of the model calculated on given values
RansacModel methods
Method returns the distance from a value to the model
@param value
@return
Logs the line to stdout
Inverts the direction of the line
Method calculates projection of given point to line and returns it.
Method calculates and returns the perpendicular line through given point
Method calculates and returnes the vector perpendicular to line direction
@return
Finds the intersection with image rectangle
@param image
@param status
@return
Method returns the line segment intersection of line and rectangle
@param rect
@param segment
@param status
@return
Method draws the line on the image and sets the errorstatus flag
@param image
Checks if the point is collinear to line
@param testPoint
@param distanceThreshold
@return
Checks if the point is collinear to line
@param testPoint
@param distanceThreshold
@return
Method calculates the cosine of the angle between this and other line
@return cosine of the angle
Calculates squared distance to point
Calculates squared distance to point
Calculates the distance to point
Direction getter
@return
End Point getter
Destructor
Assignment operator
Copy constructor
Constructor that fits the line to the points
@param points
@params strategy
- strategy used for line creating. Default is 0. Values mean:
: 0 - least squares method
: 1 - RANSAC method
Constructor which takes point and direction
Constructor which takes two points
Direction vector
Start point of the line
Line object
Class for efficient line direction representation
Generates rotation matrix which remapping coordinates from original coordinate system
(original image) to rotated system (image where text is in horizontal position).
Rotation is performed around the image top left corner.
@param angle - dominant direction of image elements on original image
@param rotationMatrix - computed rotation matrix
Returns the distance between two points in double precision
DEPRECATED METHODS STILL USED FOR BARCODE
Function adds the offset to detected points
TODO:
Method has a flaw because it uses flawed findCornerCoordinates
Method finds start point for line with direction
@deprecated
Use class Line and it's methods
Finds end points of the line on the image
@deprecated
Use class Line and it's methods
@param startPoint
@param pdirection
@param point
@return
@deprecated
Use class Line and it's methods
@param startPoint
@param pdirection
@param point
@return
@deprecated
Use class Line and it's methods
Returns the zoomed image
Calculates the centroid of all points in the vector
Method calculates rectangle that is a bounding box of given points
in image of size imageSize. Corner points of that bounding box is
returned via boundingBoxPoints parameter. Bounding box can be expanded
up to expand pixels in each direction.
@param points Source quadrangle (detected form points)
@param imageSize Size of image
@param boundingBoxPoints Returned bounding box quadrangle
@param expand How much should bounding box be expanded.
Simple crop of the part of the image surrounding the given points.
Resulting image is a bounding box arount the points
Saturates point and clamps its coordinates between minPoint and maxPoint
Returns true if the point a is inside rectangle of size [0, 0], [size.x, size.y]
@deprecated
Use class cv::Rect
Rounds the point to integer values
@param pointf
@return
Private destructor
Private constructor to prevent instantiation
Static class with geometry related methods
method calculates 1/sqrt(x)
taken from: http://www.beyond3d.com/content/articles/8/
other data about device
Maximum CPU frequency in MHz of processor 0
How many processors does the device have?
Is device of high quality?
Device manufacturer
Device model
Device name
OS version
OS name
Available operating systems
Available devices
Returns the speed level of the device
0 - extra slow device
6 - very fast device with at least 1100MHz processor or multicore.
10 - extra fast device
Other values are determined by number of processors and processor speed.
Sets the maximum frequency in MHz of processor 0.
@param maxFreq maximum frequency of processor 0.
Returns the maximum CPU frequency in MHz of processor 0.
@return
Sets the number of available processors on device.
@param procNum number of processors available at the moment
Returns the number of available processors on device.
Not all processors have to be active at the moment.
@return
Method returns true for mobile devices. For now these are only Android and iOS
Method sets whether device is of high quality.
@param hq True if device is HQ, otherwise false.
Method returns true if device is of high quality.
@return True if device is HQ, false otherwise.
Method set the device manufacturer.
@param manufacturer Device manufacturer.
Method returns the device manufacturer
@return Device manufacturer
Method set the device model.
@param model Device model.
Method returns the device model
@return Device model
Method sets the name of the device.
@param deviceName Device name
Method returns the name of the device
@return Device name
Method sets the OS version of the device.
@param osVer OS version
Method returns the OS version of the device
@return OS version
Method sets the OS name of the device.
@param os OS name
Method returns the OS name of the device
@return OS name
Virtual destructor
Constructor
@param operatingSystem Device's operating system (eg. Android)
@param osVersion Device's operating system version (eg. 4.1)
@param manufacturer Manufacturer of the device (eg. Samsung)
@param model Device model
@param deviceName Device name
@param isHQ whether or not device has high quality camera (at least HD)
@param numOfProcessors Number of processors on the device
@param maxCPUFrequency Maximum CPU frequency in MHz of processor 0
Initialize shared instance with values of given device info.
@param deviceInfo Device information that needs to become shared.
Returns the shared instance of device info
@return Pointer to device info structure
Device information holder
Returns the native STL hash map that is contained as member. Useful for efficient iteration over keys.
@return the native STL hash map that is contained as member. Useful for efficient iteration over keys.
Reads the hashmap from JSON.
@param jsonObj
@param status
Writes the hashmap to JSON.
@param writer
@param status
Logs the hashmap to log.
@param loglevel
Return whether or not given key exists in map.
@param key Name of the key.
@return true if key exists, otherwise false
Gets the value for given key. If no value is set for given key, returns empty string.
@param key Name of the key
@return Value for the key or "" if nothing is set for key.
Sets the given value to given key.
@param key Name of the key
@param value Value for the key
\file
DeviceInfo.hpp
Created on: Oct 31, 2012
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
\file
StringMap.hpp
Created on: Sep 29, 2013
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
\file
StringMap.hpp
Created on: Sep 29, 2013
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
Returns true if the file with a given filename is supported by the marker
returns the platform specific path from given unix path
@param unixPath path string in unix form, e.g. c:/windows/system32
@return platform specific path, e.g. on windows: c:\\windows\\system32
\file
Ransac.hpp
Created on: Jul 11, 2012
Author: cerovec
\file
ImageIO.h
Created on: Feb 11, 2012
Author: cerovec
\file
FileUtils.hpp
Created on: Oct 29, 2013
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
Logging method, for convenience
Division function. Required for calculating mean in K-means algorithm.
@param number
@return
Addition function. Required for calculating mean in K-means algorithm.
@param other
make a clone of itself
@return
Distance function
Destructor
Constructor
Class representing a sample for clustering
Publishes progress of ocr engine
@param progress progress in range [0, 100]
Method which reports the image which will be OCR-ed
@param image which will be sent to segmentation and classification in the OCR process
Method which determines if ocr process should stop
@return true if ocr engine should stop, false otherwise
Destructor
Constructor
Delegate object through which the information about OCR process is returned to caller
Performs sanitization
Destructor
Adds extraction results to history
@param extractionResults
@return clone of self
Assignment
Copy constructor
Constructor that does not take currency
Constructor
checks if the payment data is valid. If true, payment is ready for execution
Generic element getter
@param elementName name of the element
@return element value
Ocr Line data element getter.
@param elementName name of the element
@return element value or nullptr
Math data element getter.
@param elementName name of the element
@return element value or nullptr
OCR data element getter
@param elementName name of the element
@return element value or nullptr
barcode data element getter
@param elementName name of the element
@return element value or nullptr
integer element getter
@param elementName name of the element
@param defaultValue default value that will be returned if element does not exist or is not integer
@return element value or default value
Bool element getter
@param element name of the element
@param defaultValue default value that will be returned if element does not exist or is not a bool
@return element value or default value
string element getter
@param elementName name of the element
@param defaultValue default value that will be returned if element does not exist or is not string
@return element value or default value
Returns true if element with given name exists
Returns the PaymentData type
Constructor used by serializer only - should not be used by derived classes
Returns the appropriate sanitizer object for this payment data.
flag indicating empty payment data
flag indicating valid payment data
valid elements - all elements that are in this set are considered
valid and payment recognizer may decide not to perform
additional OCR on these elements
string elements, such as IBAN, BIC, receiver name, ...
Represents information on payment slips. Facade to results of various recognition methods.
number of entries
Information about library and licence
ZXing data
US Drivers's Licence data
UK QR code data
UK Driver License
UK slip data
Swiss slip data
Slovenian slip data
PhotoMath data
PayBull data
PDF417 data
Ocr Quality data
Ocr Line data
Generic OCR data
Dutch slip data
Machine readable travel document data
Kosovo code128 barcode data
Kosovo slip data
Hungarian slip data
German QR code data
German slip data
Croatian barcode data QR code
Croatian barcode data PDF417
Croatian slip data
Belgian slip data
Custom 1D barcode data
Austrian QR code data
Austrian slip data
Generic recognition data type.
Pimpl object
Getter for OcrLineRecognitinResult value. If type is not OcrLineRecognitinResult, this method will return undefined value.
@return a pointer to internal OcrLineRecognitinResult object.
Getter for quadrangle value. If type is not Quadrangle, this method will return undefined value.
@return a pointer to internal quadrangle object.
Getter for PhotoMath data. If type is not PhotoMath, this method will return undefined value.
@return a pointer to internal PhotoMath result structure
Getter for OCR data. If type is not OCR, this method will return undefined value.
@return a pointer to internal OCR structure
Getter for barcode detailed data. If type is not barcode, this method will return undefined value.
@return a pointer to internal barcode structure
Getter for string value.
Returns a pointer to the internal string.
If type is not string, this method will return undefined value.
@return a pointer to the internal string.
Getter for string value.
Returns a copy of the internal string (convenience method)
If type is not string, this method will return undefined value.
Getter for integer value. If type is not integer, this method will return undefined value.
@return integer representation of the value
Getter for boolean value. If type is not boolean, this method will return undefined value.
@return boolean representation of the value;
Returns the string representation of value. For logging
Getter for type
Virtual destructor
Assignment operator with OcrLineRecognitinResult data
Assignment operator with quadrangle data
Assignment operator with boolean data
Assignment operator with PhotoMath data
Assignment operator with OCR data
Assignment operator with barcode data
Assignment operator with c-string data
Assignment operator with string data
Assignment operator with integer data
Assignment operator
Copy constructor
Vreates value with OcrLineRecognitionResult
Creates value with Quadrangle data
Creates value with boolean data
Creates value with PhotoMath data
Creates value with OCR data
Creates value with barcode data
Creates value with c-string data
Creates value with string data
Creates value with integer data
Default constructor used in STL containers
Value stored in Recognition data map
Method performs sanitization
@param value value which needs to be sanitized
@param key key for the given value (e.g. account number, payment description
Returns the string with removed all the chars which are not in allowed chars string
Returns the string with given length, but makes sure that truncation
is performed on UTF8 string, i.e. no multibyte chars are split
Class used for sanitizing of data obtained by scanning
Sets the isEmpty property
Returns true of the result is empty, i.e. nothing was found for this element in extraction process
Confidence level getter
Name getter
Value setter
Value getter
Comparison operator
@return
Assignment operator
Copy constructor
NOTE: isEmpty must be default set to false because when performing multiple
reads if one element is successfuly read the first time, next time
it won't be read and then empty result will be added to history.
To prevent history confusion, default extraction result must be
defined as non-empty!
Frees the memory used to store recognition result
Creates an empty recognition result
position of the result
string name for result
Flag indicating the extraction found nothing
Confidence level
string value for result
Encapsulates the recognition result
Log function
Height of the box
Width of the box
Y coordinate of upper left point
X coordinate of upper left point
Destructor
Returns if the box is empty, i.e. NULL box;
@return
Method is a generalization of getAlignmentMeasure and works for arbitrary number of boxes
@param first
@param second
@return
Method returns a double from 0 to 1 which correspond to how well are the boxes aligned.
@param other
@return
Returns the box translated by given relative factors.
@param xFactor factor of width used for horizontal translation
@param yFactor factor of height used for vertical translation
@return
Returns the box spread by a constant factor in all directions
@param spreadFactor
@return
Gets the horizontal slice of box.
+-----------------+
| |
| |
| |
|-----------------|
| |
+-----------------+
@param sliceIndex
@param numSlices
@return
Gets the vertical slice of box.
+----------------+
| | |
| | |
+----------------+
@param sliceIndex
@param numSlices
@return
Returns vector with corner points
@return
Returns true if this box is a subset of the other box
@param other
@return
Method returns a new Box which represents overlapping area of this box and other
@param other
@return overlapping box
Returns the center of the box
@return
Returns the area of the box
@return area
Comparison operator
Plus operator
Plus equal (increment) operator
Move-Assignment operator
Copy-Assignent operator
Move constructor
Copy constructor
Converts box to a opencv float rectangle format
@return
Converts box to a opencv rect format
@return
Creates a box from opencv rect format
@param rect
Constructor
@param x
@param y
@param width
@param height
Height of empty Box
Width of empty Box
Y coordinate of empty Box
X coordinate of empty Box
Box class.
Has upper left point and size.
Utility method for retrieving member elements from the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param elementHandler Function that will be called for every element in JSON object.
Function should return true if element handling was OK or false
if there was an error while handling the element. In case of error,
element iteration may stop.
Function receives an element, its name in object and ErrorStatus
which it can use to report errors.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving object elements from the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param objectHandler Function that will be called for handling object if it exists.
Function receives JSON object so it can further process it and
ErrorStatus which it can use to report errors.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving array of elements from the JSON object.
@param jsonObj JSON object that will be tested to have required element array
identified by name.
@param name Name of the element array that should be retrieved.
@param elementHandler Function that will be called for every element in JSON array.
Function should return true if element handling was OK or false
if there was an error while handling the element. In case of error,
element iteration may stop.
Function receives array element, its index in array and ErrorStatus
which it can use to report errors.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving string element of the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param val [out] Variable to which result of retrieveal will be saved. If error occurs,
existing value will not be changed.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving 64-bit unsigned int element of the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param val [out] Variable to which result of retrieveal will be saved. If error occurs,
existing value will not be changed.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving 64-bit int element of the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param val [out] Variable to which result of retrieveal will be saved. If error occurs,
existing value will not be changed.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving unsigned int element of the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param val [out] Variable to which result of retrieveal will be saved. If error occurs,
existing value will not be changed.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving int element of the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param val [out] Variable to which result of retrieveal will be saved. If error occurs,
existing value will not be changed.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving bool element of the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param val [out] Variable to which result of retrieveal will be saved. If error occurs,
existing value will not be changed.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving double element even if he doesn't have decimal places of the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param val [out] Variable to which result of retrieveal will be saved. If error occurs,
existing value will not be changed.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving float element even if he doesn't have decimal places of the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param val [out] Variable to which result of retrieveal will be saved. If error occurs,
existing value will not be changed.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Utility method for retrieving float element of the JSON object.
@param jsonObj JSON object that will be tested to have required element
identified by name.
@param name Name of the element that should be retrieved.
@param val [out] Variable to which result of retrieveal will be saved. If error occurs,
existing value will not be changed.
@param status [out] If success, status will be set to ERROR_STATUS_SUCCESS, otherwise it
will be set to ERROR_STATUS_DESERIALIZATION_FAILED and val will not be changed.
@param mandatory If set to true and error occurs, it will be logged and status will be set to ERROR_STATUS_DESERIALIZATION_FAILED. Default is true.
Inits std::string with cString and encoding
Counts the number of occurrences of string string in string instring.
@return number of occurrences
Trim string from right
Trim string from left
Trim from both ends
Converts integer to string.
Converts string to integer.
Ignores chars that are not digits.
@brief Private destructor
@brief Private constructor to prevent instantiation
@brief Utility class for manipulating strings
Converts the input string
string should be null terminated
if s-jis is not null terminated or it has
multiple byte chars with null in them this
will not work, or to provide in other way
input buffer length....
Destructor
Constructor
\file
ZXingRecognizer.hpp
Created on: Apr 9, 2013
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
\file
PaymentRecognizer.h
Created on: Feb 13, 2012
Author: cerovec
\file
PaymentData.h
Created on: Feb 21, 2012
Author: cerovec
\file
StringUtils.h
Created on: Mar 23, 2012
Author: cerovec
Charset conversion utility
\file
PaymentRecognizer.h
Created on: Feb 13, 2012
Author: cerovec
\file
PaymentData.h
Created on: Feb 21, 2012
Author: cerovec
\file
StringUtils.h
Created on: Mar 23, 2012
Author: cerovec
Charset conversion utility
\file
CommonUtil.h
Created on: Jan, 2014
Author: dodo, Ljudevit
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
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Name of folder in which to save debug images
Allow saving of files
Whether image saving is allowed
if saving image is allowed, save all images
if image saving is allowed, save all images
* generated by scanner (no matter if detection was
* successful or not)
if image saving is allowed, save results of
* image dewarping
if image saving is allowed, save results of
* image dewarping with marked OCR char positions
if defined, XML serialized OCR results
* will be saved
Save log output to a file in isolated storage
\file
PaymentData.h
Created on: Feb 21, 2012
Author: cerovec
\file
StringUtils.h
Created on: Mar 23, 2012
Author: cerovec
Charset conversion utility
\file
PaymentData.h
Created on: Feb 21, 2012
Author: cerovec
\file
StringUtils.h
Created on: Mar 23, 2012
Author: cerovec
Charset conversion utility
\file
StringUtils.h
Created on: Mar 23, 2012
Author: cerovec
Charset conversion utility
\file
wchar.h
Created on: Nov 28, 2012
Author: dodo
Copyright (c)2012 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
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REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
\file
StringUtils.h
Created on: Mar 23, 2012
Author: cerovec
Charset conversion utility
Returns all barcode detailed data merged in one byte array
BarcodeDetailedData *
Collection of barcode elements
Collection of text or byte elements that represent results of barcode scanning
Element data represented by string.
This property is null if barcode element type is BYTE_DATA.
Element data in byte array form.
Type of barcode element
Can be text or byte data type
Barcode element, member of barcode detailed data
Type of barcode element in barcode detailed data
Can be text or byte data type
\file
BarcodeDetailedData.hpp
Created on: Aug 21, 2013
Author: dodo
Copyright (c)2015 MicroBlink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
Created on: July 2015
Author: zivac
Copyright (c)2015 Microblink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: July 2015
Author: zivac
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OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: May 2015
Author: zivac
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ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: May 2015
Author: zivac
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Created on: May 2015
Author: zivac
Copyright (c)2015 Microblink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
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Created on: May 2015
Author: zivac
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OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: May 2015
Author: zivac
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OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: May 2015
Author: zivac
Copyright (c)2015 Microblink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: May 2015
Author: zivac
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OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
PaymentRecognizerDelegateBridge.hpp
Created on: Jan, 2014
Author: Ljudevit
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
WITH OR WITHOUT MODIFICATION, WITH THE PURPOSE OF ACQUIRING
UNLAWFUL MATERIAL OR ANY OTHER BENEFIT IS PROHIBITED!
THIS PROGRAM IS PROTECTED BY COPYRIGHT LAWS AND YOU MAY NOT
REVERSE ENGINEER, DECOMPILE, OR DISASSEMBLE IT.
\file
PaymentRecognizer.h
Created on: Feb 13, 2012
Author: cerovec
Created on: May 2015
Author: zivac
Copyright (c)2015 Microblink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: May 2015
Author: zivac
Copyright (c)2015 Microblink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: May 2015
Author: zivac
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OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: May 2015
Author: zivac
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Created on: July 2015
Author: zivac
Copyright (c)2015 Microblink Ltd. All rights reserved.
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OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: July 2015
Author: zivac
Copyright (c)2015 Microblink Ltd. All rights reserved.
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OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Created on: May 2015
Author: zivac
Copyright (c)2015 Microblink Ltd. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
RCBarcodeRecognitionData.hpp
Created on: Jan, 2014
Author: Ljudevit
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
RCPaymentRecognizerDelegate.hpp
Created on: Jan, 2014
Author: Ljudevit
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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\file
PhotoPayComponent.hpp
Created on: Jan, 2014
Author: Ljudevit
Copyright (c)2014 Racuni.hr d.o.o. All rights reserved.
ANY UNAUTHORIZED USE OR SALE, DUPLICATION, OR DISTRIBUTION
OF THIS PROGRAM OR ANY OF ITS PARTS, IN SOURCE OR BINARY FORMS,
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Type of barcode scanned
String -> Object map of result elements.
Result data can
be retrieved from this map instead of using the specific
properties in IRecognitionResult implementing classes.
In the instances when specific property is not implemented
the only way of retrieving the data is via this map.
Indicates if the result is empty
Indicates if the result is valid
Base interface for all recognition result classes