| 1 | /**
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| 2 | * @license
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| 3 | * Copyright 2020 Google LLC. All Rights Reserved.
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| 4 | * Licensed under the Apache License, Version 2.0 (the "License");
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| 5 | * you may not use this file except in compliance with the License.
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| 6 | * You may obtain a copy of the License at
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| 7 | *
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| 8 | * http://www.apache.org/licenses/LICENSE-2.0
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| 9 | *
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| 10 | * Unless required by applicable law or agreed to in writing, software
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| 11 | * distributed under the License is distributed on an "AS IS" BASIS,
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| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 13 | * See the License for the specific language governing permissions and
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| 14 | * limitations under the License.
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| 15 | * =============================================================================
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| 16 | */
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| 17 | import { ENGINE } from '../../engine';
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| 18 | import { CropAndResize } from '../../kernel_names';
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| 19 | import { convertToTensor } from '../../tensor_util_env';
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| 20 | import * as util from '../../util';
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| 21 | import { op } from '../operation';
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| 22 | /**
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| 23 | * Extracts crops from the input image tensor and resizes them using bilinear
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| 24 | * sampling or nearest neighbor sampling (possibly with aspect ratio change)
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| 25 | * to a common output size specified by cropSize.
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| 26 | *
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| 27 | * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,
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| 28 | * where imageHeight and imageWidth must be positive, specifying the
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| 29 | * batch of images from which to take crops
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| 30 | * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is
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| 31 | * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized
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| 32 | * coordinates of the box in the boxInd[i]'th image in the batch
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| 33 | * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range
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| 34 | * `[0, batch)` that specifies the image that the `i`-th box refers to.
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| 35 | * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`
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| 36 | * specifying the size to which all crops are resized to.
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| 37 | * @param method Optional string from `'bilinear' | 'nearest'`,
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| 38 | * defaults to bilinear, which specifies the sampling method for resizing
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| 39 | * @param extrapolationValue A threshold for deciding when to remove boxes based
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| 40 | * on score. Defaults to 0.
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| 41 | * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`
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| 42 | *
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| 43 | * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
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| 44 | */
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| 45 | function cropAndResize_(image, boxes, boxInd, cropSize, method = 'bilinear', extrapolationValue = 0) {
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| 46 | const $image = convertToTensor(image, 'image', 'cropAndResize');
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| 47 | const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');
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| 48 | const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');
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| 49 | const numBoxes = $boxes.shape[0];
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| 50 | util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +
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| 51 | `but got rank ${$image.rank}.`);
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| 52 | util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +
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| 53 | `but had shape ${$boxes.shape}.`);
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| 54 | util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +
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| 55 | `but had shape ${$boxes.shape}.`);
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| 56 | util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +
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| 57 | `length ${cropSize.length}.`);
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| 58 | util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);
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| 59 | util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);
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| 60 | const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };
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| 61 | const attrs = { method, extrapolationValue, cropSize };
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| 62 | const res = ENGINE.runKernel(CropAndResize, inputs, attrs);
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| 63 | return res;
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| 64 | }
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| 65 | export const cropAndResize = op({ cropAndResize_ });
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor1D, Tensor2D, Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\n\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by cropSize.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(\n    image: Tensor4D|TensorLike,\n    boxes: Tensor2D|TensorLike,\n    boxInd: Tensor1D|TensorLike,\n    cropSize: [number, number],\n    method: 'bilinear'|'nearest' = 'bilinear',\n    extrapolationValue = 0,\n    ): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'cropAndResize');\n  const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n  const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n\n  const numBoxes = $boxes.shape[0];\n\n  util.assert(\n      $image.rank === 4,\n      () => 'Error in cropAndResize: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n  util.assert(\n      $boxes.rank === 2 && $boxes.shape[1] === 4,\n      () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      $boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,\n      () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      cropSize.length === 2,\n      () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n          `length ${cropSize.length}.`);\n  util.assert(\n      cropSize[0] >= 1 && cropSize[1] >= 1,\n      () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n  util.assert(\n      method === 'bilinear' || method === 'nearest',\n      () => `method must be bilinear or nearest, but was ${method}`);\n\n  const inputs:\n      CropAndResizeInputs = {image: $image, boxes: $boxes, boxInd: $boxInd};\n  const attrs: CropAndResizeAttrs = {method, extrapolationValue, cropSize};\n  const res = ENGINE.runKernel(\n      CropAndResize, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n  return res as Tensor4D;\n}\n\nexport const cropAndResize = op({cropAndResize_});\n"]} |
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