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ZED 3D Camera support added to ./uselib (yolo_console_cpp.exe) example
This commit is contained in:
AlexeyAB
@ -25,6 +25,7 @@ struct bbox_t {
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unsigned int obj_id; // class of object - from range [0, classes-1]
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unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object)
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unsigned int frames_counter; // counter of frames on which the object was detected
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float x_3d, y_3d, z_3d; // center of object (in Meters) if ZED 3D Camera is used
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};
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struct image_t {
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@ -60,8 +61,8 @@ extern "C" LIB_API int get_device_name(int gpu, char* deviceName);
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class Detector {
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std::shared_ptr<void> detector_gpu_ptr;
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std::deque<std::vector<bbox_t>> prev_bbox_vec_deque;
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const int cur_gpu_id;
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public:
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const int cur_gpu_id;
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float nms = .4;
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bool wait_stream;
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@ -79,6 +80,11 @@ public:
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LIB_API std::vector<bbox_t> tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history = true,
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int const frames_story = 5, int const max_dist = 40);
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LIB_API void *get_cuda_context();
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LIB_API bool send_json_http(std::vector<bbox_t> cur_bbox_vec, std::vector<std::string> obj_names, int frame_id,
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std::string filename = "", int timeout = 400000, int port = 8070);
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std::vector<bbox_t> detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false)
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{
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if (img.data == NULL)
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@ -115,7 +121,10 @@ public:
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static std::shared_ptr<image_t> mat_to_image(cv::Mat img_src)
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{
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cv::Mat img;
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cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR);
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if (img_src.channels() == 4) cv::cvtColor(img_src, img, cv::COLOR_RGBA2BGR);
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else if (img_src.channels() == 3) cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR);
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else if (img_src.channels() == 1) cv::cvtColor(img_src, img, cv::COLOR_GRAY2BGR);
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else std::cerr << " Warning: img_src.channels() is not 1, 3 or 4. It is = " << img_src.channels() << std::endl;
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std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; });
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std::shared_ptr<IplImage> ipl_small = std::make_shared<IplImage>(img);
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*image_ptr = ipl_to_image(ipl_small.get());
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@ -166,7 +175,7 @@ private:
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#endif // OPENCV
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};
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// --------------------------------------------------------------------------------
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#if defined(TRACK_OPTFLOW) && defined(OPENCV) && defined(GPU)
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@ -183,7 +192,7 @@ public:
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const int flow_error;
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Tracker_optflow(int _gpu_id = 0, int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
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Tracker_optflow(int _gpu_id = 0, int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
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gpu_count(cv::cuda::getCudaEnabledDeviceCount()), gpu_id(std::min(_gpu_id, gpu_count-1)),
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flow_error((_flow_error > 0)? _flow_error:(win_size*4))
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{
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@ -249,18 +258,32 @@ public:
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if (old_gpu_id != gpu_id)
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cv::cuda::setDevice(gpu_id);
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if (src_mat.channels() == 3) {
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if (src_mat.channels() == 1 || src_mat.channels() == 3 || src_mat.channels() == 4) {
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if (src_mat_gpu.cols == 0) {
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src_mat_gpu = cv::cuda::GpuMat(src_mat.size(), src_mat.type());
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src_grey_gpu = cv::cuda::GpuMat(src_mat.size(), CV_8UC1);
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}
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update_cur_bbox_vec(_cur_bbox_vec);
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if (src_mat.channels() == 1) {
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src_mat_gpu.upload(src_mat, stream);
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src_mat_gpu.copyTo(src_grey_gpu);
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}
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else if (src_mat.channels() == 3) {
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src_mat_gpu.upload(src_mat, stream);
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cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream);
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}
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else if (src_mat.channels() == 4) {
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src_mat_gpu.upload(src_mat, stream);
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cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGRA2GRAY, 1, stream);
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}
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else {
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std::cerr << " Warning: src_mat.channels() is not: 1, 3 or 4. It is = " << src_mat.channels() << " \n";
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return;
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}
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//src_grey_gpu.upload(src_mat, stream); // use BGR
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src_mat_gpu.upload(src_mat, stream);
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cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream);
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}
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update_cur_bbox_vec(_cur_bbox_vec);
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if (old_gpu_id != gpu_id)
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cv::cuda::setDevice(old_gpu_id);
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}
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@ -355,7 +378,7 @@ public:
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const int flow_error;
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Tracker_optflow(int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
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Tracker_optflow(int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
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flow_error((_flow_error > 0)? _flow_error:(win_size*4))
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{
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sync_PyrLKOpticalFlow = cv::SparsePyrLKOpticalFlow::create();
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@ -396,12 +419,20 @@ public:
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void update_tracking_flow(cv::Mat new_src_mat, std::vector<bbox_t> _cur_bbox_vec)
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{
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if (new_src_mat.channels() == 3) {
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update_cur_bbox_vec(_cur_bbox_vec);
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if (new_src_mat.channels() == 1) {
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src_grey = new_src_mat.clone();
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}
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else if (new_src_mat.channels() == 3) {
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cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1);
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}
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else if (new_src_mat.channels() == 4) {
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cv::cvtColor(new_src_mat, src_grey, CV_BGRA2GRAY, 1);
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}
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else {
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std::cerr << " Warning: new_src_mat.channels() is not: 1, 3 or 4. It is = " << new_src_mat.channels() << " \n";
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return;
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}
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update_cur_bbox_vec(_cur_bbox_vec);
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}
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@ -416,6 +447,7 @@ public:
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if (src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols) {
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src_grey = dst_grey.clone();
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//std::cerr << " Warning: src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols \n";
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return cur_bbox_vec;
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}
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@ -611,56 +643,361 @@ public:
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}
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}
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};
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class track_kalman_t
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{
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int track_id_counter;
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std::chrono::steady_clock::time_point global_last_time;
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float dT;
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public:
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int max_objects; // max objects for tracking
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int min_frames; // min frames to consider an object as detected
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const float max_dist; // max distance (in px) to track with the same ID
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cv::Size img_size; // max value of x,y,w,h
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struct tst_t {
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int track_id;
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int state_id;
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std::chrono::steady_clock::time_point last_time;
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int detection_count;
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tst_t() : track_id(-1), state_id(-1) {}
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};
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std::vector<tst_t> track_id_state_id_time;
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std::vector<bbox_t> result_vec_pred;
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struct one_kalman_t;
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std::vector<one_kalman_t> kalman_vec;
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struct one_kalman_t
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{
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cv::KalmanFilter kf;
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cv::Mat state;
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cv::Mat meas;
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int measSize, stateSize, contrSize;
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void set_delta_time(float dT) {
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kf.transitionMatrix.at<float>(2) = dT;
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kf.transitionMatrix.at<float>(9) = dT;
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}
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void set(bbox_t box)
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{
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initialize_kalman();
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kf.errorCovPre.at<float>(0) = 1; // px
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kf.errorCovPre.at<float>(7) = 1; // px
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kf.errorCovPre.at<float>(14) = 1;
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kf.errorCovPre.at<float>(21) = 1;
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kf.errorCovPre.at<float>(28) = 1; // px
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kf.errorCovPre.at<float>(35) = 1; // px
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state.at<float>(0) = box.x;
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state.at<float>(1) = box.y;
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state.at<float>(2) = 0;
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state.at<float>(3) = 0;
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state.at<float>(4) = box.w;
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state.at<float>(5) = box.h;
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// <<<< Initialization
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kf.statePost = state;
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}
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// Kalman.correct() calculates: statePost = statePre + gain * (z(k)-measurementMatrix*statePre);
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// corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
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void correct(bbox_t box) {
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meas.at<float>(0) = box.x;
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meas.at<float>(1) = box.y;
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meas.at<float>(2) = box.w;
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meas.at<float>(3) = box.h;
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kf.correct(meas);
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bbox_t new_box = predict();
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if (new_box.w == 0 || new_box.h == 0) {
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set(box);
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//std::cerr << " force set(): track_id = " << box.track_id <<
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// ", x = " << box.x << ", y = " << box.y << ", w = " << box.w << ", h = " << box.h << std::endl;
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}
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}
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// Kalman.predict() calculates: statePre = TransitionMatrix * statePost;
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// predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
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bbox_t predict() {
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bbox_t box;
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state = kf.predict();
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box.x = state.at<float>(0);
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box.y = state.at<float>(1);
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box.w = state.at<float>(4);
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box.h = state.at<float>(5);
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return box;
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}
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void initialize_kalman()
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{
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kf = cv::KalmanFilter(stateSize, measSize, contrSize, CV_32F);
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// Transition State Matrix A
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// Note: set dT at each processing step!
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// [ 1 0 dT 0 0 0 ]
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// [ 0 1 0 dT 0 0 ]
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// [ 0 0 1 0 0 0 ]
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// [ 0 0 0 1 0 0 ]
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// [ 0 0 0 0 1 0 ]
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// [ 0 0 0 0 0 1 ]
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cv::setIdentity(kf.transitionMatrix);
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// Measure Matrix H
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// [ 1 0 0 0 0 0 ]
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// [ 0 1 0 0 0 0 ]
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// [ 0 0 0 0 1 0 ]
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// [ 0 0 0 0 0 1 ]
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kf.measurementMatrix = cv::Mat::zeros(measSize, stateSize, CV_32F);
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kf.measurementMatrix.at<float>(0) = 1.0f;
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kf.measurementMatrix.at<float>(7) = 1.0f;
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kf.measurementMatrix.at<float>(16) = 1.0f;
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kf.measurementMatrix.at<float>(23) = 1.0f;
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// Process Noise Covariance Matrix Q - result smoother with lower values (1e-2)
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// [ Ex 0 0 0 0 0 ]
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// [ 0 Ey 0 0 0 0 ]
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// [ 0 0 Ev_x 0 0 0 ]
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// [ 0 0 0 Ev_y 0 0 ]
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// [ 0 0 0 0 Ew 0 ]
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// [ 0 0 0 0 0 Eh ]
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//cv::setIdentity(kf.processNoiseCov, cv::Scalar(1e-3));
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kf.processNoiseCov.at<float>(0) = 1e-2;
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kf.processNoiseCov.at<float>(7) = 1e-2;
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kf.processNoiseCov.at<float>(14) = 1e-2;// 5.0f;
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kf.processNoiseCov.at<float>(21) = 1e-2;// 5.0f;
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kf.processNoiseCov.at<float>(28) = 1e-2;
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kf.processNoiseCov.at<float>(35) = 1e-2;
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// Measures Noise Covariance Matrix R - result smoother with higher values (1e-1)
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cv::setIdentity(kf.measurementNoiseCov, cv::Scalar(1e-1));
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//cv::setIdentity(kf.errorCovPost, cv::Scalar::all(1e-2));
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// <<<< Kalman Filter
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set_delta_time(0);
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}
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one_kalman_t(int _stateSize = 6, int _measSize = 4, int _contrSize = 0) :
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kf(_stateSize, _measSize, _contrSize, CV_32F), measSize(_measSize), stateSize(_stateSize), contrSize(_contrSize)
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{
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state = cv::Mat(stateSize, 1, CV_32F); // [x,y,v_x,v_y,w,h]
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meas = cv::Mat(measSize, 1, CV_32F); // [z_x,z_y,z_w,z_h]
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//cv::Mat procNoise(stateSize, 1, type)
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// [E_x,E_y,E_v_x,E_v_y,E_w,E_h]
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initialize_kalman();
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}
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};
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// ------------------------------------------
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track_kalman_t(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :
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max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size),
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track_id_counter(0)
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{
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kalman_vec.resize(max_objects);
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track_id_state_id_time.resize(max_objects);
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result_vec_pred.resize(max_objects);
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}
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float calc_dt() {
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dT = std::chrono::duration<double>(std::chrono::steady_clock::now() - global_last_time).count();
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return dT;
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}
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static float get_distance(float src_x, float src_y, float dst_x, float dst_y) {
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return sqrtf((src_x - dst_x)*(src_x - dst_x) + (src_y - dst_y)*(src_y - dst_y));
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}
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void clear_old_states() {
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// clear old bboxes
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for (size_t state_id = 0; state_id < track_id_state_id_time.size(); ++state_id)
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{
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float time_sec = std::chrono::duration<double>(std::chrono::steady_clock::now() - track_id_state_id_time[state_id].last_time).count();
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float time_wait = 0.5; // 0.5 second
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if (track_id_state_id_time[state_id].track_id > -1)
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{
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if ((result_vec_pred[state_id].x > img_size.width) ||
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(result_vec_pred[state_id].y > img_size.height))
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{
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track_id_state_id_time[state_id].track_id = -1;
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}
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if (time_sec >= time_wait || track_id_state_id_time[state_id].detection_count < 0) {
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//std::cerr << " remove track_id = " << track_id_state_id_time[state_id].track_id << ", state_id = " << state_id << std::endl;
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track_id_state_id_time[state_id].track_id = -1; // remove bbox
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}
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}
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}
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}
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tst_t get_state_id(bbox_t find_box, std::vector<bool> &busy_vec)
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{
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tst_t tst;
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tst.state_id = -1;
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float min_dist = std::numeric_limits<float>::max();
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for (size_t i = 0; i < max_objects; ++i)
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{
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if (track_id_state_id_time[i].track_id > -1 && result_vec_pred[i].obj_id == find_box.obj_id && busy_vec[i] == false)
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{
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bbox_t pred_box = result_vec_pred[i];
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float dist = get_distance(pred_box.x, pred_box.y, find_box.x, find_box.y);
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float movement_dist = std::max(max_dist, static_cast<float>(std::max(pred_box.w, pred_box.h)) );
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if ((dist < movement_dist) && (dist < min_dist)) {
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min_dist = dist;
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tst.state_id = i;
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}
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}
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}
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if (tst.state_id > -1) {
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track_id_state_id_time[tst.state_id].last_time = std::chrono::steady_clock::now();
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track_id_state_id_time[tst.state_id].detection_count = std::max(track_id_state_id_time[tst.state_id].detection_count + 2, 10);
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tst = track_id_state_id_time[tst.state_id];
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busy_vec[tst.state_id] = true;
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}
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else {
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//std::cerr << " Didn't find: obj_id = " << find_box.obj_id << ", x = " << find_box.x << ", y = " << find_box.y <<
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// ", track_id_counter = " << track_id_counter << std::endl;
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}
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return tst;
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}
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tst_t new_state_id(std::vector<bool> &busy_vec)
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{
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tst_t tst;
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// find empty cell to add new track_id
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auto it = std::find_if(track_id_state_id_time.begin(), track_id_state_id_time.end(), [&](tst_t &v) { return v.track_id == -1; });
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if (it != track_id_state_id_time.end()) {
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it->state_id = it - track_id_state_id_time.begin();
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//it->track_id = track_id_counter++;
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it->track_id = 0;
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it->last_time = std::chrono::steady_clock::now();
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it->detection_count = 1;
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tst = *it;
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busy_vec[it->state_id] = true;
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}
|
||||
|
||||
return tst;
|
||||
}
|
||||
|
||||
std::vector<tst_t> find_state_ids(std::vector<bbox_t> result_vec)
|
||||
{
|
||||
std::vector<tst_t> tst_vec(result_vec.size());
|
||||
|
||||
std::vector<bool> busy_vec(max_objects, false);
|
||||
|
||||
for (size_t i = 0; i < result_vec.size(); ++i)
|
||||
{
|
||||
tst_t tst = get_state_id(result_vec[i], busy_vec);
|
||||
int state_id = tst.state_id;
|
||||
int track_id = tst.track_id;
|
||||
|
||||
// if new state_id
|
||||
if (state_id < 0) {
|
||||
tst = new_state_id(busy_vec);
|
||||
state_id = tst.state_id;
|
||||
track_id = tst.track_id;
|
||||
if (state_id > -1) {
|
||||
kalman_vec[state_id].set(result_vec[i]);
|
||||
//std::cerr << " post: ";
|
||||
}
|
||||
}
|
||||
|
||||
//std::cerr << " track_id = " << track_id << ", state_id = " << state_id <<
|
||||
// ", x = " << result_vec[i].x << ", det_count = " << tst.detection_count << std::endl;
|
||||
|
||||
if (state_id > -1) {
|
||||
tst_vec[i] = tst;
|
||||
result_vec_pred[state_id] = result_vec[i];
|
||||
result_vec_pred[state_id].track_id = track_id;
|
||||
}
|
||||
}
|
||||
|
||||
return tst_vec;
|
||||
}
|
||||
|
||||
std::vector<bbox_t> predict()
|
||||
{
|
||||
clear_old_states();
|
||||
std::vector<bbox_t> result_vec;
|
||||
|
||||
for (size_t i = 0; i < max_objects; ++i)
|
||||
{
|
||||
tst_t tst = track_id_state_id_time[i];
|
||||
if (tst.track_id > -1) {
|
||||
bbox_t box = kalman_vec[i].predict();
|
||||
|
||||
result_vec_pred[i].x = box.x;
|
||||
result_vec_pred[i].y = box.y;
|
||||
result_vec_pred[i].w = box.w;
|
||||
result_vec_pred[i].h = box.h;
|
||||
|
||||
if (tst.detection_count >= min_frames)
|
||||
{
|
||||
if (track_id_state_id_time[i].track_id == 0) {
|
||||
track_id_state_id_time[i].track_id = ++track_id_counter;
|
||||
result_vec_pred[i].track_id = track_id_counter;
|
||||
}
|
||||
|
||||
result_vec.push_back(result_vec_pred[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
//std::cerr << " result_vec.size() = " << result_vec.size() << std::endl;
|
||||
|
||||
//global_last_time = std::chrono::steady_clock::now();
|
||||
|
||||
return result_vec;
|
||||
}
|
||||
|
||||
|
||||
std::vector<bbox_t> correct(std::vector<bbox_t> result_vec)
|
||||
{
|
||||
calc_dt();
|
||||
clear_old_states();
|
||||
|
||||
for (size_t i = 0; i < max_objects; ++i)
|
||||
track_id_state_id_time[i].detection_count--;
|
||||
|
||||
std::vector<tst_t> tst_vec = find_state_ids(result_vec);
|
||||
|
||||
for (size_t i = 0; i < tst_vec.size(); ++i) {
|
||||
tst_t tst = tst_vec[i];
|
||||
int state_id = tst.state_id;
|
||||
if (state_id > -1)
|
||||
{
|
||||
kalman_vec[state_id].set_delta_time(dT);
|
||||
kalman_vec[state_id].correct(result_vec_pred[state_id]);
|
||||
}
|
||||
}
|
||||
|
||||
result_vec = predict();
|
||||
|
||||
global_last_time = std::chrono::steady_clock::now();
|
||||
|
||||
return result_vec;
|
||||
}
|
||||
|
||||
};
|
||||
// ----------------------------------------------
|
||||
#endif // OPENCV
|
||||
|
||||
//extern "C" {
|
||||
#endif // __cplusplus
|
||||
|
||||
/*
|
||||
// C - wrappers
|
||||
LIB_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id);
|
||||
LIB_API void delete_detector();
|
||||
LIB_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size);
|
||||
LIB_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size);
|
||||
LIB_API bbox_t* detect(image_t img, int *result_size);
|
||||
LIB_API image_t load_img(char *image_filename);
|
||||
LIB_API void free_img(image_t m);
|
||||
|
||||
#ifdef __cplusplus
|
||||
} // extern "C"
|
||||
|
||||
static std::shared_ptr<void> c_detector_ptr;
|
||||
static std::vector<bbox_t> c_result_vec;
|
||||
|
||||
void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id) {
|
||||
c_detector_ptr = std::make_shared<LIB_API Detector>(cfg_filename, weight_filename, gpu_id);
|
||||
}
|
||||
|
||||
void delete_detector() { c_detector_ptr.reset(); }
|
||||
|
||||
bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size) {
|
||||
c_result_vec = static_cast<Detector*>(c_detector_ptr.get())->detect(img, thresh, use_mean);
|
||||
*result_size = c_result_vec.size();
|
||||
return c_result_vec.data();
|
||||
}
|
||||
|
||||
bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size) {
|
||||
c_result_vec = static_cast<Detector*>(c_detector_ptr.get())->detect_resized(img, init_w, init_h, thresh, use_mean);
|
||||
*result_size = c_result_vec.size();
|
||||
return c_result_vec.data();
|
||||
}
|
||||
|
||||
bbox_t* detect(image_t img, int *result_size) {
|
||||
return detect_custom(img, 0.24, true, result_size);
|
||||
}
|
||||
|
||||
image_t load_img(char *image_filename) {
|
||||
return static_cast<Detector*>(c_detector_ptr.get())->load_image(image_filename);
|
||||
}
|
||||
void free_img(image_t m) {
|
||||
static_cast<Detector*>(c_detector_ptr.get())->free_image(m);
|
||||
}
|
||||
|
||||
#endif // __cplusplus
|
||||
*/
|
||||
#endif
|
||||
#endif // YOLO_V2_CLASS_HPP
|
||||
|
||||
Reference in New Issue
Block a user