#ifndef YOLO_V2_CLASS_HPP #define YOLO_V2_CLASS_HPP #ifndef LIB_API #ifdef LIB_EXPORTS #if defined(_MSC_VER) #define LIB_API __declspec(dllexport) #else #define LIB_API __attribute__((visibility("default"))) #endif #else #if defined(_MSC_VER) #define LIB_API #else #define LIB_API #endif #endif #endif #define C_SHARP_MAX_OBJECTS 1000 struct bbox_t { unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box float prob; // confidence - probability that the object was found correctly unsigned int obj_id; // class of object - from range [0, classes-1] unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) unsigned int frames_counter; // counter of frames on which the object was detected float x_3d, y_3d, z_3d; // center of object (in Meters) if ZED 3D Camera is used }; struct image_t { int h; // height int w; // width int c; // number of chanels (3 - for RGB) float *data; // pointer to the image data }; struct bbox_t_container { bbox_t candidates[C_SHARP_MAX_OBJECTS]; }; #ifdef __cplusplus #include #include #include #include #include #include #include #include #include #ifdef OPENCV #include // C++ #include // C #include // C #endif extern "C" LIB_API int init(const char *configurationFilename, const char *weightsFilename, int gpu, int batch_size); extern "C" LIB_API int detect_image(const char *filename, bbox_t_container &container); extern "C" LIB_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); extern "C" LIB_API int dispose(); extern "C" LIB_API int get_device_count(); extern "C" LIB_API int get_device_name(int gpu, char* deviceName); extern "C" LIB_API bool built_with_cuda(); extern "C" LIB_API bool built_with_cudnn(); extern "C" LIB_API bool built_with_opencv(); extern "C" LIB_API void send_json_custom(char const* send_buf, int port, int timeout); class Detector { std::shared_ptr detector_gpu_ptr; std::deque> prev_bbox_vec_deque; std::string _cfg_filename, _weight_filename; public: const int cur_gpu_id; float nms = .4; bool wait_stream; LIB_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0, int batch_size = 1); LIB_API ~Detector(); LIB_API std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); LIB_API std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); LIB_API std::vector> detectBatch(image_t img, int batch_size, int width, int height, float thresh, bool make_nms = true); static LIB_API image_t load_image(std::string image_filename); static LIB_API void free_image(image_t m); LIB_API int get_net_width() const; LIB_API int get_net_height() const; LIB_API int get_net_color_depth() const; LIB_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, int const frames_story = 5, int const max_dist = 40); LIB_API void *get_cuda_context(); //LIB_API bool send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, // std::string filename = std::string(), int timeout = 400000, int port = 8070); std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) { if (img.data == NULL) throw std::runtime_error("Image is empty"); auto detection_boxes = detect(img, thresh, use_mean); float wk = (float)init_w / img.w, hk = (float)init_h / img.h; for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk; return detection_boxes; } #ifdef OPENCV std::vector detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false) { if(mat.data == NULL) throw std::runtime_error("Image is empty"); auto image_ptr = mat_to_image_resize(mat); return detect_resized(*image_ptr, mat.cols, mat.rows, thresh, use_mean); } std::shared_ptr mat_to_image_resize(cv::Mat mat) const { if (mat.data == NULL) return std::shared_ptr(NULL); cv::Size network_size = cv::Size(get_net_width(), get_net_height()); cv::Mat det_mat; if (mat.size() != network_size) cv::resize(mat, det_mat, network_size); else det_mat = mat; // only reference is copied return mat_to_image(det_mat); } static std::shared_ptr mat_to_image(cv::Mat img_src) { cv::Mat img; if (img_src.channels() == 4) cv::cvtColor(img_src, img, cv::COLOR_RGBA2BGR); else if (img_src.channels() == 3) cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR); else if (img_src.channels() == 1) cv::cvtColor(img_src, img, cv::COLOR_GRAY2BGR); else std::cerr << " Warning: img_src.channels() is not 1, 3 or 4. It is = " << img_src.channels() << std::endl; std::shared_ptr image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; }); *image_ptr = mat_to_image_custom(img); return image_ptr; } private: static image_t mat_to_image_custom(cv::Mat mat) { int w = mat.cols; int h = mat.rows; int c = mat.channels(); image_t im = make_image_custom(w, h, c); unsigned char *data = (unsigned char *)mat.data; int step = mat.step; for (int y = 0; y < h; ++y) { for (int k = 0; k < c; ++k) { for (int x = 0; x < w; ++x) { im.data[k*w*h + y*w + x] = data[y*step + x*c + k] / 255.0f; } } } return im; } static image_t make_empty_image(int w, int h, int c) { image_t out; out.data = 0; out.h = h; out.w = w; out.c = c; return out; } static image_t make_image_custom(int w, int h, int c) { image_t out = make_empty_image(w, h, c); out.data = (float *)calloc(h*w*c, sizeof(float)); return out; } #endif // OPENCV public: bool send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, std::string filename = std::string(), int timeout = 400000, int port = 8070) { std::string send_str; char *tmp_buf = (char *)calloc(1024, sizeof(char)); if (!filename.empty()) { sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename.c_str()); } else { sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id); } send_str = tmp_buf; free(tmp_buf); for (auto & i : cur_bbox_vec) { char *buf = (char *)calloc(2048, sizeof(char)); sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"absolute_coordinates\":{\"center_x\":%d, \"center_y\":%d, \"width\":%d, \"height\":%d}, \"confidence\":%f", i.obj_id, obj_names[i.obj_id].c_str(), i.x, i.y, i.w, i.h, i.prob); //sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f", // i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob); send_str += buf; if (!std::isnan(i.z_3d)) { sprintf(buf, "\n , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}", i.x_3d, i.y_3d, i.z_3d); send_str += buf; } send_str += "}\n"; free(buf); } //send_str += "\n ] \n}, \n"; send_str += "\n ] \n}"; send_json_custom(send_str.c_str(), port, timeout); return true; } }; // -------------------------------------------------------------------------------- #if defined(TRACK_OPTFLOW) && defined(OPENCV) && defined(GPU) #include #include #include #include class Tracker_optflow { public: const int gpu_count; const int gpu_id; const int flow_error; Tracker_optflow(int _gpu_id = 0, int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) : gpu_count(cv::cuda::getCudaEnabledDeviceCount()), gpu_id(std::min(_gpu_id, gpu_count-1)), flow_error((_flow_error > 0)? _flow_error:(win_size*4)) { int const old_gpu_id = cv::cuda::getDevice(); cv::cuda::setDevice(gpu_id); stream = cv::cuda::Stream(); sync_PyrLKOpticalFlow_gpu = cv::cuda::SparsePyrLKOpticalFlow::create(); sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31 sync_PyrLKOpticalFlow_gpu->setMaxLevel(max_level); // +- 3 pt sync_PyrLKOpticalFlow_gpu->setNumIters(iterations); // 2000, def: 30 cv::cuda::setDevice(old_gpu_id); } // just to avoid extra allocations cv::cuda::GpuMat src_mat_gpu; cv::cuda::GpuMat dst_mat_gpu, dst_grey_gpu; cv::cuda::GpuMat prev_pts_flow_gpu, cur_pts_flow_gpu; cv::cuda::GpuMat status_gpu, err_gpu; cv::cuda::GpuMat src_grey_gpu; // used in both functions cv::Ptr sync_PyrLKOpticalFlow_gpu; cv::cuda::Stream stream; std::vector cur_bbox_vec; std::vector good_bbox_vec_flags; cv::Mat prev_pts_flow_cpu; void update_cur_bbox_vec(std::vector _cur_bbox_vec) { cur_bbox_vec = _cur_bbox_vec; good_bbox_vec_flags = std::vector(cur_bbox_vec.size(), true); cv::Mat prev_pts, cur_pts_flow_cpu; for (auto &i : cur_bbox_vec) { float x_center = (i.x + i.w / 2.0F); float y_center = (i.y + i.h / 2.0F); prev_pts.push_back(cv::Point2f(x_center, y_center)); } if (prev_pts.rows == 0) prev_pts_flow_cpu = cv::Mat(); else cv::transpose(prev_pts, prev_pts_flow_cpu); if (prev_pts_flow_gpu.cols < prev_pts_flow_cpu.cols) { prev_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type()); cur_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type()); status_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_8UC1); err_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_32FC1); } prev_pts_flow_gpu.upload(cv::Mat(prev_pts_flow_cpu), stream); } void update_tracking_flow(cv::Mat src_mat, std::vector _cur_bbox_vec) { int const old_gpu_id = cv::cuda::getDevice(); if (old_gpu_id != gpu_id) cv::cuda::setDevice(gpu_id); if (src_mat.channels() == 1 || src_mat.channels() == 3 || src_mat.channels() == 4) { if (src_mat_gpu.cols == 0) { src_mat_gpu = cv::cuda::GpuMat(src_mat.size(), src_mat.type()); src_grey_gpu = cv::cuda::GpuMat(src_mat.size(), CV_8UC1); } if (src_mat.channels() == 1) { src_mat_gpu.upload(src_mat, stream); src_mat_gpu.copyTo(src_grey_gpu); } else if (src_mat.channels() == 3) { src_mat_gpu.upload(src_mat, stream); cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream); } else if (src_mat.channels() == 4) { src_mat_gpu.upload(src_mat, stream); cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGRA2GRAY, 1, stream); } else { std::cerr << " Warning: src_mat.channels() is not: 1, 3 or 4. It is = " << src_mat.channels() << " \n"; return; } } update_cur_bbox_vec(_cur_bbox_vec); if (old_gpu_id != gpu_id) cv::cuda::setDevice(old_gpu_id); } std::vector tracking_flow(cv::Mat dst_mat, bool check_error = true) { if (sync_PyrLKOpticalFlow_gpu.empty()) { std::cout << "sync_PyrLKOpticalFlow_gpu isn't initialized \n"; return cur_bbox_vec; } int const old_gpu_id = cv::cuda::getDevice(); if(old_gpu_id != gpu_id) cv::cuda::setDevice(gpu_id); if (dst_mat_gpu.cols == 0) { dst_mat_gpu = cv::cuda::GpuMat(dst_mat.size(), dst_mat.type()); dst_grey_gpu = cv::cuda::GpuMat(dst_mat.size(), CV_8UC1); } //dst_grey_gpu.upload(dst_mat, stream); // use BGR dst_mat_gpu.upload(dst_mat, stream); cv::cuda::cvtColor(dst_mat_gpu, dst_grey_gpu, CV_BGR2GRAY, 1, stream); if (src_grey_gpu.rows != dst_grey_gpu.rows || src_grey_gpu.cols != dst_grey_gpu.cols) { stream.waitForCompletion(); src_grey_gpu = dst_grey_gpu.clone(); cv::cuda::setDevice(old_gpu_id); return cur_bbox_vec; } ////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x sync_PyrLKOpticalFlow_gpu->calc(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, err_gpu, stream); // OpenCV 3.x cv::Mat cur_pts_flow_cpu; cur_pts_flow_gpu.download(cur_pts_flow_cpu, stream); dst_grey_gpu.copyTo(src_grey_gpu, stream); cv::Mat err_cpu, status_cpu; err_gpu.download(err_cpu, stream); status_gpu.download(status_cpu, stream); stream.waitForCompletion(); std::vector result_bbox_vec; if (err_cpu.cols == cur_bbox_vec.size() && status_cpu.cols == cur_bbox_vec.size()) { for (size_t i = 0; i < cur_bbox_vec.size(); ++i) { cv::Point2f cur_key_pt = cur_pts_flow_cpu.at(0, i); cv::Point2f prev_key_pt = prev_pts_flow_cpu.at(0, i); float moved_x = cur_key_pt.x - prev_key_pt.x; float moved_y = cur_key_pt.y - prev_key_pt.y; if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i]) if (err_cpu.at(0, i) < flow_error && status_cpu.at(0, i) != 0 && ((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0) { cur_bbox_vec[i].x += moved_x + 0.5; cur_bbox_vec[i].y += moved_y + 0.5; result_bbox_vec.push_back(cur_bbox_vec[i]); } else good_bbox_vec_flags[i] = false; else good_bbox_vec_flags[i] = false; //if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]); } } cur_pts_flow_gpu.swap(prev_pts_flow_gpu); cur_pts_flow_cpu.copyTo(prev_pts_flow_cpu); if (old_gpu_id != gpu_id) cv::cuda::setDevice(old_gpu_id); return result_bbox_vec; } }; #elif defined(TRACK_OPTFLOW) && defined(OPENCV) //#include #include class Tracker_optflow { public: const int flow_error; Tracker_optflow(int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) : flow_error((_flow_error > 0)? _flow_error:(win_size*4)) { sync_PyrLKOpticalFlow = cv::SparsePyrLKOpticalFlow::create(); sync_PyrLKOpticalFlow->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31 sync_PyrLKOpticalFlow->setMaxLevel(max_level); // +- 3 pt } // just to avoid extra allocations cv::Mat dst_grey; cv::Mat prev_pts_flow, cur_pts_flow; cv::Mat status, err; cv::Mat src_grey; // used in both functions cv::Ptr sync_PyrLKOpticalFlow; std::vector cur_bbox_vec; std::vector good_bbox_vec_flags; void update_cur_bbox_vec(std::vector _cur_bbox_vec) { cur_bbox_vec = _cur_bbox_vec; good_bbox_vec_flags = std::vector(cur_bbox_vec.size(), true); cv::Mat prev_pts, cur_pts_flow; for (auto &i : cur_bbox_vec) { float x_center = (i.x + i.w / 2.0F); float y_center = (i.y + i.h / 2.0F); prev_pts.push_back(cv::Point2f(x_center, y_center)); } if (prev_pts.rows == 0) prev_pts_flow = cv::Mat(); else cv::transpose(prev_pts, prev_pts_flow); } void update_tracking_flow(cv::Mat new_src_mat, std::vector _cur_bbox_vec) { if (new_src_mat.channels() == 1) { src_grey = new_src_mat.clone(); } else if (new_src_mat.channels() == 3) { cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1); } else if (new_src_mat.channels() == 4) { cv::cvtColor(new_src_mat, src_grey, CV_BGRA2GRAY, 1); } else { std::cerr << " Warning: new_src_mat.channels() is not: 1, 3 or 4. It is = " << new_src_mat.channels() << " \n"; return; } update_cur_bbox_vec(_cur_bbox_vec); } std::vector tracking_flow(cv::Mat new_dst_mat, bool check_error = true) { if (sync_PyrLKOpticalFlow.empty()) { std::cout << "sync_PyrLKOpticalFlow isn't initialized \n"; return cur_bbox_vec; } cv::cvtColor(new_dst_mat, dst_grey, CV_BGR2GRAY, 1); if (src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols) { src_grey = dst_grey.clone(); //std::cerr << " Warning: src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols \n"; return cur_bbox_vec; } if (prev_pts_flow.cols < 1) { return cur_bbox_vec; } ////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x sync_PyrLKOpticalFlow->calc(src_grey, dst_grey, prev_pts_flow, cur_pts_flow, status, err); // OpenCV 3.x dst_grey.copyTo(src_grey); std::vector result_bbox_vec; if (err.rows == cur_bbox_vec.size() && status.rows == cur_bbox_vec.size()) { for (size_t i = 0; i < cur_bbox_vec.size(); ++i) { cv::Point2f cur_key_pt = cur_pts_flow.at(0, i); cv::Point2f prev_key_pt = prev_pts_flow.at(0, i); float moved_x = cur_key_pt.x - prev_key_pt.x; float moved_y = cur_key_pt.y - prev_key_pt.y; if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i]) if (err.at(0, i) < flow_error && status.at(0, i) != 0 && ((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0) { cur_bbox_vec[i].x += moved_x + 0.5; cur_bbox_vec[i].y += moved_y + 0.5; result_bbox_vec.push_back(cur_bbox_vec[i]); } else good_bbox_vec_flags[i] = false; else good_bbox_vec_flags[i] = false; //if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]); } } prev_pts_flow = cur_pts_flow.clone(); return result_bbox_vec; } }; #else class Tracker_optflow {}; #endif // defined(TRACK_OPTFLOW) && defined(OPENCV) #ifdef OPENCV static cv::Scalar obj_id_to_color(int obj_id) { int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } }; int const offset = obj_id * 123457 % 6; int const color_scale = 150 + (obj_id * 123457) % 100; cv::Scalar color(colors[offset][0], colors[offset][1], colors[offset][2]); color *= color_scale; return color; } class preview_boxes_t { enum { frames_history = 30 }; // how long to keep the history saved struct preview_box_track_t { unsigned int track_id, obj_id, last_showed_frames_ago; bool current_detection; bbox_t bbox; cv::Mat mat_obj, mat_resized_obj; preview_box_track_t() : track_id(0), obj_id(0), last_showed_frames_ago(frames_history), current_detection(false) {} }; std::vector preview_box_track_id; size_t const preview_box_size, bottom_offset; bool const one_off_detections; public: preview_boxes_t(size_t _preview_box_size = 100, size_t _bottom_offset = 100, bool _one_off_detections = false) : preview_box_size(_preview_box_size), bottom_offset(_bottom_offset), one_off_detections(_one_off_detections) {} void set(cv::Mat src_mat, std::vector result_vec) { size_t const count_preview_boxes = src_mat.cols / preview_box_size; if (preview_box_track_id.size() != count_preview_boxes) preview_box_track_id.resize(count_preview_boxes); // increment frames history for (auto &i : preview_box_track_id) i.last_showed_frames_ago = std::min((unsigned)frames_history, i.last_showed_frames_ago + 1); // occupy empty boxes for (auto &k : result_vec) { bool found = false; // find the same (track_id) for (auto &i : preview_box_track_id) { if (i.track_id == k.track_id) { if (!one_off_detections) i.last_showed_frames_ago = 0; // for tracked objects found = true; break; } } if (!found) { // find empty box for (auto &i : preview_box_track_id) { if (i.last_showed_frames_ago == frames_history) { if (!one_off_detections && k.frames_counter == 0) break; // don't show if obj isn't tracked yet i.track_id = k.track_id; i.obj_id = k.obj_id; i.bbox = k; i.last_showed_frames_ago = 0; break; } } } } // draw preview box (from old or current frame) for (size_t i = 0; i < preview_box_track_id.size(); ++i) { // get object image cv::Mat dst = preview_box_track_id[i].mat_resized_obj; preview_box_track_id[i].current_detection = false; for (auto &k : result_vec) { if (preview_box_track_id[i].track_id == k.track_id) { if (one_off_detections && preview_box_track_id[i].last_showed_frames_ago > 0) { preview_box_track_id[i].last_showed_frames_ago = frames_history; break; } bbox_t b = k; cv::Rect r(b.x, b.y, b.w, b.h); cv::Rect img_rect(cv::Point2i(0, 0), src_mat.size()); cv::Rect rect_roi = r & img_rect; if (rect_roi.width > 1 || rect_roi.height > 1) { cv::Mat roi = src_mat(rect_roi); cv::resize(roi, dst, cv::Size(preview_box_size, preview_box_size), cv::INTER_NEAREST); preview_box_track_id[i].mat_obj = roi.clone(); preview_box_track_id[i].mat_resized_obj = dst.clone(); preview_box_track_id[i].current_detection = true; preview_box_track_id[i].bbox = k; } break; } } } } void draw(cv::Mat draw_mat, bool show_small_boxes = false) { // draw preview box (from old or current frame) for (size_t i = 0; i < preview_box_track_id.size(); ++i) { auto &prev_box = preview_box_track_id[i]; // draw object image cv::Mat dst = prev_box.mat_resized_obj; if (prev_box.last_showed_frames_ago < frames_history && dst.size() == cv::Size(preview_box_size, preview_box_size)) { cv::Rect dst_rect_roi(cv::Point2i(i * preview_box_size, draw_mat.rows - bottom_offset), dst.size()); cv::Mat dst_roi = draw_mat(dst_rect_roi); dst.copyTo(dst_roi); cv::Scalar color = obj_id_to_color(prev_box.obj_id); int thickness = (prev_box.current_detection) ? 5 : 1; cv::rectangle(draw_mat, dst_rect_roi, color, thickness); unsigned int const track_id = prev_box.track_id; std::string track_id_str = (track_id > 0) ? std::to_string(track_id) : ""; putText(draw_mat, track_id_str, dst_rect_roi.tl() - cv::Point2i(-4, 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.9, cv::Scalar(0, 0, 0), 2); std::string size_str = std::to_string(prev_box.bbox.w) + "x" + std::to_string(prev_box.bbox.h); putText(draw_mat, size_str, dst_rect_roi.tl() + cv::Point2i(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1); if (!one_off_detections && prev_box.current_detection) { cv::line(draw_mat, dst_rect_roi.tl() + cv::Point2i(preview_box_size, 0), cv::Point2i(prev_box.bbox.x, prev_box.bbox.y + prev_box.bbox.h), color); } if (one_off_detections && show_small_boxes) { cv::Rect src_rect_roi(cv::Point2i(prev_box.bbox.x, prev_box.bbox.y), cv::Size(prev_box.bbox.w, prev_box.bbox.h)); unsigned int const color_history = (255 * prev_box.last_showed_frames_ago) / frames_history; color = cv::Scalar(255 - 3 * color_history, 255 - 2 * color_history, 255 - 1 * color_history); if (prev_box.mat_obj.size() == src_rect_roi.size()) { prev_box.mat_obj.copyTo(draw_mat(src_rect_roi)); } cv::rectangle(draw_mat, src_rect_roi, color, thickness); putText(draw_mat, track_id_str, src_rect_roi.tl() - cv::Point2i(0, 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1); } } } } }; class track_kalman_t { int track_id_counter; std::chrono::steady_clock::time_point global_last_time; float dT; public: int max_objects; // max objects for tracking int min_frames; // min frames to consider an object as detected const float max_dist; // max distance (in px) to track with the same ID cv::Size img_size; // max value of x,y,w,h struct tst_t { int track_id; int state_id; std::chrono::steady_clock::time_point last_time; int detection_count; tst_t() : track_id(-1), state_id(-1) {} }; std::vector track_id_state_id_time; std::vector result_vec_pred; struct one_kalman_t; std::vector kalman_vec; struct one_kalman_t { cv::KalmanFilter kf; cv::Mat state; cv::Mat meas; int measSize, stateSize, contrSize; void set_delta_time(float dT) { kf.transitionMatrix.at(2) = dT; kf.transitionMatrix.at(9) = dT; } void set(bbox_t box) { initialize_kalman(); kf.errorCovPre.at(0) = 1; // px kf.errorCovPre.at(7) = 1; // px kf.errorCovPre.at(14) = 1; kf.errorCovPre.at(21) = 1; kf.errorCovPre.at(28) = 1; // px kf.errorCovPre.at(35) = 1; // px state.at(0) = box.x; state.at(1) = box.y; state.at(2) = 0; state.at(3) = 0; state.at(4) = box.w; state.at(5) = box.h; // <<<< Initialization kf.statePost = state; } // Kalman.correct() calculates: statePost = statePre + gain * (z(k)-measurementMatrix*statePre); // corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) void correct(bbox_t box) { meas.at(0) = box.x; meas.at(1) = box.y; meas.at(2) = box.w; meas.at(3) = box.h; kf.correct(meas); bbox_t new_box = predict(); if (new_box.w == 0 || new_box.h == 0) { set(box); //std::cerr << " force set(): track_id = " << box.track_id << // ", x = " << box.x << ", y = " << box.y << ", w = " << box.w << ", h = " << box.h << std::endl; } } // Kalman.predict() calculates: statePre = TransitionMatrix * statePost; // predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) bbox_t predict() { bbox_t box; state = kf.predict(); box.x = state.at(0); box.y = state.at(1); box.w = state.at(4); box.h = state.at(5); return box; } void initialize_kalman() { kf = cv::KalmanFilter(stateSize, measSize, contrSize, CV_32F); // Transition State Matrix A // Note: set dT at each processing step! // [ 1 0 dT 0 0 0 ] // [ 0 1 0 dT 0 0 ] // [ 0 0 1 0 0 0 ] // [ 0 0 0 1 0 0 ] // [ 0 0 0 0 1 0 ] // [ 0 0 0 0 0 1 ] cv::setIdentity(kf.transitionMatrix); // Measure Matrix H // [ 1 0 0 0 0 0 ] // [ 0 1 0 0 0 0 ] // [ 0 0 0 0 1 0 ] // [ 0 0 0 0 0 1 ] kf.measurementMatrix = cv::Mat::zeros(measSize, stateSize, CV_32F); kf.measurementMatrix.at(0) = 1.0f; kf.measurementMatrix.at(7) = 1.0f; kf.measurementMatrix.at(16) = 1.0f; kf.measurementMatrix.at(23) = 1.0f; // Process Noise Covariance Matrix Q - result smoother with lower values (1e-2) // [ Ex 0 0 0 0 0 ] // [ 0 Ey 0 0 0 0 ] // [ 0 0 Ev_x 0 0 0 ] // [ 0 0 0 Ev_y 0 0 ] // [ 0 0 0 0 Ew 0 ] // [ 0 0 0 0 0 Eh ] //cv::setIdentity(kf.processNoiseCov, cv::Scalar(1e-3)); kf.processNoiseCov.at(0) = 1e-2; kf.processNoiseCov.at(7) = 1e-2; kf.processNoiseCov.at(14) = 1e-2;// 5.0f; kf.processNoiseCov.at(21) = 1e-2;// 5.0f; kf.processNoiseCov.at(28) = 5e-3; kf.processNoiseCov.at(35) = 5e-3; // Measures Noise Covariance Matrix R - result smoother with higher values (1e-1) cv::setIdentity(kf.measurementNoiseCov, cv::Scalar(1e-1)); //cv::setIdentity(kf.errorCovPost, cv::Scalar::all(1e-2)); // <<<< Kalman Filter set_delta_time(0); } one_kalman_t(int _stateSize = 6, int _measSize = 4, int _contrSize = 0) : kf(_stateSize, _measSize, _contrSize, CV_32F), measSize(_measSize), stateSize(_stateSize), contrSize(_contrSize) { state = cv::Mat(stateSize, 1, CV_32F); // [x,y,v_x,v_y,w,h] meas = cv::Mat(measSize, 1, CV_32F); // [z_x,z_y,z_w,z_h] //cv::Mat procNoise(stateSize, 1, type) // [E_x,E_y,E_v_x,E_v_y,E_w,E_h] initialize_kalman(); } }; // ------------------------------------------ track_kalman_t(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) : track_id_counter(0), max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size) { kalman_vec.resize(max_objects); track_id_state_id_time.resize(max_objects); result_vec_pred.resize(max_objects); } float calc_dt() { dT = std::chrono::duration(std::chrono::steady_clock::now() - global_last_time).count(); return dT; } static float get_distance(float src_x, float src_y, float dst_x, float dst_y) { return sqrtf((src_x - dst_x)*(src_x - dst_x) + (src_y - dst_y)*(src_y - dst_y)); } void clear_old_states() { // clear old bboxes for (size_t state_id = 0; state_id < track_id_state_id_time.size(); ++state_id) { float time_sec = std::chrono::duration(std::chrono::steady_clock::now() - track_id_state_id_time[state_id].last_time).count(); float time_wait = 0.5; // 0.5 second if (track_id_state_id_time[state_id].track_id > -1) { if ((result_vec_pred[state_id].x > img_size.width) || (result_vec_pred[state_id].y > img_size.height)) { track_id_state_id_time[state_id].track_id = -1; } if (time_sec >= time_wait || track_id_state_id_time[state_id].detection_count < 0) { //std::cerr << " remove track_id = " << track_id_state_id_time[state_id].track_id << ", state_id = " << state_id << std::endl; track_id_state_id_time[state_id].track_id = -1; // remove bbox } } } } tst_t get_state_id(bbox_t find_box, std::vector &busy_vec) { tst_t tst; tst.state_id = -1; float min_dist = std::numeric_limits::max(); for (size_t i = 0; i < max_objects; ++i) { if (track_id_state_id_time[i].track_id > -1 && result_vec_pred[i].obj_id == find_box.obj_id && busy_vec[i] == false) { bbox_t pred_box = result_vec_pred[i]; float dist = get_distance(pred_box.x, pred_box.y, find_box.x, find_box.y); float movement_dist = std::max(max_dist, static_cast(std::max(pred_box.w, pred_box.h)) ); if ((dist < movement_dist) && (dist < min_dist)) { min_dist = dist; tst.state_id = i; } } } if (tst.state_id > -1) { track_id_state_id_time[tst.state_id].last_time = std::chrono::steady_clock::now(); track_id_state_id_time[tst.state_id].detection_count = std::max(track_id_state_id_time[tst.state_id].detection_count + 2, 10); tst = track_id_state_id_time[tst.state_id]; busy_vec[tst.state_id] = true; } else { //std::cerr << " Didn't find: obj_id = " << find_box.obj_id << ", x = " << find_box.x << ", y = " << find_box.y << // ", track_id_counter = " << track_id_counter << std::endl; } return tst; } tst_t new_state_id(std::vector &busy_vec) { tst_t tst; // find empty cell to add new track_id 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; }); if (it != track_id_state_id_time.end()) { it->state_id = it - track_id_state_id_time.begin(); //it->track_id = track_id_counter++; it->track_id = 0; it->last_time = std::chrono::steady_clock::now(); it->detection_count = 1; tst = *it; busy_vec[it->state_id] = true; } return tst; } std::vector find_state_ids(std::vector result_vec) { std::vector tst_vec(result_vec.size()); std::vector 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 predict() { clear_old_states(); std::vector 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 correct(std::vector 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_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 #endif // __cplusplus #endif // YOLO_V2_CLASS_HPP