/**
 * @license
 * Copyright 2019 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import { Pose, TensorBuffer3D } from '../types';
/**
 * Detects multiple poses and finds their parts from part scores and
 * displacement vectors. It returns up to `maxDetections` object instance
 * detections in decreasing root score order. It works as follows: We first
 * create a priority queue with local part score maxima above
 * `scoreThreshold`, considering all parts at the same time. Then we
 * iteratively pull the top  element of the queue (in decreasing score order)
 * and treat it as a root candidate for a new object instance. To avoid
 * duplicate detections, we reject the root candidate if it is within a disk
 * of `nmsRadius` pixels from the corresponding part of a previously detected
 * instance, which is a form of part-based non-maximum suppression (NMS). If
 * the root candidate passes the NMS check, we start a new object instance
 * detection, treating the corresponding part as root and finding the
 * positions of the remaining parts by following the displacement vectors
 * along the tree-structured part graph. We assign to the newly detected
 * instance a score equal to the sum of scores of its parts which have not
 * been claimed by a previous instance (i.e., those at least `nmsRadius`
 * pixels away from the corresponding part of all previously detected
 * instances), divided by the total number of parts `numParts`.
 *
 * @param heatmapScores 3-D tensor with shape `[height, width, numParts]`.
 * The value of heatmapScores[y, x, k]` is the score of placing the `k`-th
 * object part at position `(y, x)`.
 *
 * @param offsets 3-D tensor with shape `[height, width, numParts * 2]`.
 * The value of [offsets[y, x, k], offsets[y, x, k + numParts]]` is the
 * short range offset vector of the `k`-th  object part at heatmap
 * position `(y, x)`.
 *
 * @param displacementsFwd 3-D tensor of shape
 * `[height, width, 2 * num_edges]`, where `num_edges = num_parts - 1` is the
 * number of edges (parent-child pairs) in the tree. It contains the forward
 * displacements between consecutive part from the root towards the leaves.
 *
 * @param displacementsBwd 3-D tensor of shape
 * `[height, width, 2 * num_edges]`, where `num_edges = num_parts - 1` is the
 * number of edges (parent-child pairs) in the tree. It contains the backward
 * displacements between consecutive part from the root towards the leaves.
 *
 * @param outputStride The output stride that was used when feed-forwarding
 * through the PoseNet model.  Must be 32, 16, or 8.
 *
 * @param maxPoseDetections Maximum number of returned instance detections per
 * image.
 *
 * @param scoreThreshold Only return instance detections that have root part
 * score greater or equal to this value. Defaults to 0.5.
 *
 * @param nmsRadius Non-maximum suppression part distance. It needs to be
 * strictly positive. Two parts suppress each other if they are less than
 * `nmsRadius` pixels away. Defaults to 20.
 *
 * @return An array of poses and their scores, each containing keypoints and
 * the corresponding keypoint scores.
 */
export declare function decodeMultiplePoses(scoresBuffer: TensorBuffer3D, offsetsBuffer: TensorBuffer3D, displacementsFwdBuffer: TensorBuffer3D, displacementsBwdBuffer: TensorBuffer3D, outputStride: number, maxPoseDetections: number, scoreThreshold?: number, nmsRadius?: number): Pose[];