人脸关键点定位CLM框架的使用

CLM框架的使用也就是CLM函数的使用方法，所以下面我的代码是用来进行照片中人脸关键点的定位，看完之后对这个流程应该会变得清晰起来。

#include "CLM_core.h"#include <fstream>#include <sstream>#include <opencv2/videoio/videoio.hpp>#include <opencv2/videoio/videoio_c.h> using namespace std;using namespace cv;vector<string> get_arguments(int argc, char **argv){vector<string> arguments;for (int i = 0; i < argc; ++i){arguments.push_back(string(argv[i]));}return arguments;}void image_show(Mat& captured_image, Mat_<float>& depth_image, const CLMTracker::CLM& clm_model, const CLMTracker::CLMParameters& clm_parameters, double fx, double fy, double cx, double cy){double detection_certainty = clm_model.detection_certainty;bool detection_success = clm_model.detection_success;double visualisation_boundary = 0.2;if (detection_certainty < visualisation_boundary){CLMTracker::Draw(captured_image, clm_model);double vis_certainty = detection_certainty;if (vis_certainty > 1)vis_certainty = 1;if (vis_certainty < -1)vis_certainty = -1;vis_certainty = (vis_certainty + 1) / (visualisation_boundary + 1);// A rough heuristic for box around the face widthint thickness = (int)std::ceil(2.0* ((double)captured_image.cols) / 640.0);Vec6d pose_estimate_to_draw = CLMTracker::GetCorrectedPoseWorld(clm_model, fx, fy, cx, cy);// Draw it in reddish if uncertain, blueish if certainCLMTracker::DrawBox(captured_image, pose_estimate_to_draw, Scalar((1 - vis_certainty)*255.0, 0, vis_certainty * 255), thickness, fx, fy, cx, cy);}if (!clm_parameters.quiet_mode){namedWindow("tracking_result", 1);imshow("tracking_result", captured_image);if (!depth_image.empty()){// Division needed for visualisation purposesimshow("depth", depth_image / 2000.0);}}}int main(int argc, char **argv){vector<string> arguments = get_arguments(argc, argv);vector<string> files, depth_directories, pose_output_files, tracked_videos_output, landmark_output_files, landmark_3D_output_files;int device = 0;CLMTracker::CLMParameters clm_parameters(arguments);bool use_world_coordinates;CLMTracker::get_video_input_output_params(files, depth_directories, pose_output_files, tracked_videos_output, landmark_output_files, landmark_3D_output_files, use_world_coordinates, arguments);CLMTracker::CLM clm_model(clm_parameters.model_location);float fx = 0, fy = 0, cx = 0, cy = 0;CLMTracker::get_camera_params(device, fx, fy, cx, cy, arguments);bool cx_undefined = false;bool fx_undefined = false;if (cx == 0 || cy == 0){cx_undefined = true;}if (fx == 0 || fy == 0){fx_undefined = true;}bool done = false;Mat src = imread(files[0]);imshow("image", src);// 未定义光学中心，利用图像中心if (cx_undefined){cx = src.cols / 2.0f;cy = src.rows / 2.0f;}// 假设焦距if (fx_undefined){fx = 500 * (src.cols / 640.0);fy = 500 * (src.rows / 480.0);fx = (fx + fy) / 2.0;fy = fx;}if (!src.empty()){Mat_<float> depth_image;Mat_<uchar> grayscale_image;if (src.channels() == 3){cvtColor(src, grayscale_image, CV_BGR2GRAY);}else{grayscale_image = src.clone();}// 面部捕捉追踪的函数bool detection_success = CLMTracker::DetectLandmarksInVideo(grayscale_image, depth_image, clm_model, clm_parameters);// Work out the pose of the head from the tracked modelVec6d pose_estimate_CLM;if (use_world_coordinates){pose_estimate_CLM = CLMTracker::GetCorrectedPoseWorld(clm_model, fx, fy, cx, cy);}else{pose_estimate_CLM = CLMTracker::GetCorrectedPoseCamera(clm_model, fx, fy, cx, cy);}// 可视化结果// 面部标记点和边界框的显示;image_show(src, depth_image, clm_model, clm_parameters, fx, fy, cx, cy);}waitKey(0);return 0;}