mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 11:56:44 +08:00
b565c15bf7
* Add tinypose model * Add PPTinypose python API * Fix picodet preprocess bug && Add Tinypose examples * Update tinypose example code * Update ppseg preprocess if condition * Update ppseg backend support type * Update permute.h * Update README.md * Update code with comments * Move files dir * Delete premute.cc * Add single model pptinypose * Delete pptinypose old code in ppdet * Code format * Add ppdet + pptinypose pipeline model * Fix bug for posedetpipeline * Change Frontend to ModelFormat * Change Frontend to ModelFormat in __init__.py * Add python posedetpipeline/ * Update pptinypose example dir name * Update README.md * Update README.md * Update README.md * Update README.md * Create keypointdetection_result.md * Create README.md * Create README.md * Create README.md * Update README.md * Update README.md * Create README.md * Fix det_keypoint_unite_infer.py bug * Create README.md * Update PP-Tinypose by comment * Update by comment * Add pipeline directory * Add pptinypose dir * Update pptinypose to align accuracy * Addd warpAffine processor * Update GetCpuMat to GetOpenCVMat * Add comment for pptinypose && pipline * Update docs/main_page.md * Add README.md for pptinypose * Add README for det_keypoint_unite * Remove ENABLE_PIPELINE option * Remove ENABLE_PIPELINE option * Change pptinypose default backend * PP-TinyPose Pipeline support multi PP-Detection models * Update pp-tinypose comment * Update by comments * Add single test example Co-authored-by: Jason <jiangjiajun@baidu.com>
81 lines
3.4 KiB
C++
81 lines
3.4 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. 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.
|
|
|
|
#include "fastdeploy/vision/utils/utils.h"
|
|
|
|
namespace fastdeploy {
|
|
namespace vision {
|
|
namespace utils {
|
|
|
|
void DarkParse(const std::vector<float>& heatmap, const std::vector<int>& dim,
|
|
std::vector<float>* coords, const int px, const int py,
|
|
const int index, const int ch) {
|
|
/*DARK postpocessing, Zhang et al. Distribution-Aware Coordinate
|
|
Representation for Human Pose Estimation (CVPR 2020).
|
|
1) offset = - hassian.inv() * derivative
|
|
2) dx = (heatmap[x+1] - heatmap[x-1])/2.
|
|
3) dxx = (dx[x+1] - dx[x-1])/2.
|
|
4) derivative = Mat([dx, dy])
|
|
5) hassian = Mat([[dxx, dxy], [dxy, dyy]])
|
|
*/
|
|
std::vector<float>::const_iterator first1 = heatmap.begin() + index;
|
|
std::vector<float>::const_iterator last1 =
|
|
heatmap.begin() + index + dim[2] * dim[3];
|
|
std::vector<float> heatmap_ch(first1, last1);
|
|
cv::Mat heatmap_mat = cv::Mat(heatmap_ch).reshape(0, dim[2]);
|
|
heatmap_mat.convertTo(heatmap_mat, CV_32FC1);
|
|
cv::GaussianBlur(heatmap_mat, heatmap_mat, cv::Size(3, 3), 0, 0);
|
|
heatmap_mat = heatmap_mat.reshape(1, 1);
|
|
heatmap_ch = std::vector<float>(heatmap_mat.reshape(1, 1));
|
|
|
|
float epsilon = 1e-10;
|
|
// sample heatmap to get values in around target location
|
|
float xy = log(fmax(heatmap_ch[py * dim[3] + px], epsilon));
|
|
float xr = log(fmax(heatmap_ch[py * dim[3] + px + 1], epsilon));
|
|
float xl = log(fmax(heatmap_ch[py * dim[3] + px - 1], epsilon));
|
|
|
|
float xr2 = log(fmax(heatmap_ch[py * dim[3] + px + 2], epsilon));
|
|
float xl2 = log(fmax(heatmap_ch[py * dim[3] + px - 2], epsilon));
|
|
float yu = log(fmax(heatmap_ch[(py + 1) * dim[3] + px], epsilon));
|
|
float yd = log(fmax(heatmap_ch[(py - 1) * dim[3] + px], epsilon));
|
|
float yu2 = log(fmax(heatmap_ch[(py + 2) * dim[3] + px], epsilon));
|
|
float yd2 = log(fmax(heatmap_ch[(py - 2) * dim[3] + px], epsilon));
|
|
float xryu = log(fmax(heatmap_ch[(py + 1) * dim[3] + px + 1], epsilon));
|
|
float xryd = log(fmax(heatmap_ch[(py - 1) * dim[3] + px + 1], epsilon));
|
|
float xlyu = log(fmax(heatmap_ch[(py + 1) * dim[3] + px - 1], epsilon));
|
|
float xlyd = log(fmax(heatmap_ch[(py - 1) * dim[3] + px - 1], epsilon));
|
|
|
|
// compute dx/dy and dxx/dyy with sampled values
|
|
float dx = 0.5 * (xr - xl);
|
|
float dy = 0.5 * (yu - yd);
|
|
float dxx = 0.25 * (xr2 - 2 * xy + xl2);
|
|
float dxy = 0.25 * (xryu - xryd - xlyu + xlyd);
|
|
float dyy = 0.25 * (yu2 - 2 * xy + yd2);
|
|
|
|
// finally get offset by derivative and hassian, which combined by dx/dy and
|
|
// dxx/dyy
|
|
if (dxx * dyy - dxy * dxy != 0) {
|
|
float M[2][2] = {dxx, dxy, dxy, dyy};
|
|
float D[2] = {dx, dy};
|
|
cv::Mat hassian(2, 2, CV_32F, M);
|
|
cv::Mat derivative(2, 1, CV_32F, D);
|
|
cv::Mat offset = -hassian.inv() * derivative;
|
|
(*coords)[ch * 2] += offset.at<float>(0, 0);
|
|
(*coords)[ch * 2 + 1] += offset.at<float>(1, 0);
|
|
}
|
|
}
|
|
|
|
} // namespace utils
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|