1056 lines
39 KiB
C++
1056 lines
39 KiB
C++
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#ifndef YOLO_V2_CLASS_HPP
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#define YOLO_V2_CLASS_HPP
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#ifndef LIB_API
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#ifdef LIB_EXPORTS
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#if defined(_MSC_VER)
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#define LIB_API __declspec(dllexport)
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#else
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#define LIB_API __attribute__((visibility("default")))
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#endif
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#else
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#if defined(_MSC_VER)
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#define LIB_API
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#else
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#define LIB_API
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#endif
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#endif
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#endif
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#define OPENCV
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#define C_SHARP_MAX_OBJECTS 1000
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struct bbox_t {
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unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box
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float prob; // confidence - probability that the object was found correctly
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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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int h; // height
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int w; // width
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int c; // number of chanels (3 - for RGB)
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float *data; // pointer to the image data
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};
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struct bbox_t_container {
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bbox_t candidates[C_SHARP_MAX_OBJECTS];
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};
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#ifdef __cplusplus
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#include <memory>
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#include <vector>
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#include <deque>
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#include <algorithm>
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#include <chrono>
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#include <string>
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#include <sstream>
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#include <iostream>
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#include <cmath>
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#ifdef OPENCV
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#include <opencv2/opencv.hpp> // C++
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#include <opencv2/highgui/highgui_c.h> // C
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#include <opencv2/imgproc/imgproc_c.h> // C
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#endif
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extern "C" LIB_API int init(const char *configurationFilename, const char *weightsFilename, int gpu, int batch_size);
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extern "C" LIB_API int detect_image(const char *filename, bbox_t_container &container);
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extern "C" LIB_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container);
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extern "C" LIB_API int dispose();
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extern "C" LIB_API int get_device_count();
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extern "C" LIB_API int get_device_name(int gpu, char* deviceName);
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extern "C" LIB_API bool built_with_cuda();
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extern "C" LIB_API bool built_with_cudnn();
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extern "C" LIB_API bool built_with_opencv();
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extern "C" LIB_API void send_json_custom(char const* send_buf, int port, int timeout);
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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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std::string _cfg_filename, _weight_filename;
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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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LIB_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0, int batch_size = 1);
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LIB_API ~Detector();
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LIB_API std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
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LIB_API std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
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LIB_API std::vector<std::vector<bbox_t>> detectBatch(image_t img, int batch_size, int width, int height, float thresh, bool make_nms = true);
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static LIB_API image_t load_image(std::string image_filename);
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static LIB_API void free_image(image_t m);
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LIB_API int get_net_width() const;
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LIB_API int get_net_height() const;
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LIB_API int get_net_color_depth() const;
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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 = std::string(), 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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throw std::runtime_error("Image is empty");
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auto detection_boxes = detect(img, thresh, use_mean);
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float wk = (float)init_w / img.w, hk = (float)init_h / img.h;
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for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk;
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return detection_boxes;
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}
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#ifdef OPENCV
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std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false)
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{
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if(mat.data == NULL)
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throw std::runtime_error("Image is empty");
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auto image_ptr = mat_to_image_resize(mat);
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return detect_resized(*image_ptr, mat.cols, mat.rows, thresh, use_mean);
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}
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std::shared_ptr<image_t> mat_to_image_resize(cv::Mat mat) const
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{
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if (mat.data == NULL) return std::shared_ptr<image_t>(NULL);
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cv::Size network_size = cv::Size(get_net_width(), get_net_height());
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cv::Mat det_mat;
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if (mat.size() != network_size)
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cv::resize(mat, det_mat, network_size);
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else
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det_mat = mat; // only reference is copied
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return mat_to_image(det_mat);
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}
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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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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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*image_ptr = mat_to_image_custom(img);
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return image_ptr;
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}
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private:
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static image_t mat_to_image_custom(cv::Mat mat)
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{
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int w = mat.cols;
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int h = mat.rows;
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int c = mat.channels();
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image_t im = make_image_custom(w, h, c);
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unsigned char *data = (unsigned char *)mat.data;
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int step = mat.step;
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for (int y = 0; y < h; ++y) {
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for (int k = 0; k < c; ++k) {
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for (int x = 0; x < w; ++x) {
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im.data[k*w*h + y*w + x] = data[y*step + x*c + k] / 255.0f;
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}
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}
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}
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return im;
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}
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static image_t make_empty_image(int w, int h, int c)
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{
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image_t out;
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out.data = 0;
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out.h = h;
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out.w = w;
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out.c = c;
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return out;
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}
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static image_t make_image_custom(int w, int h, int c)
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{
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image_t out = make_empty_image(w, h, c);
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out.data = (float *)calloc(h*w*c, sizeof(float));
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return out;
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}
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#endif // OPENCV
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public:
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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 = std::string(), int timeout = 400000, int port = 8070)
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{
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std::string send_str;
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char *tmp_buf = (char *)calloc(1024, sizeof(char));
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if (!filename.empty()) {
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sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename.c_str());
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}
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else {
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sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id);
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}
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send_str = tmp_buf;
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free(tmp_buf);
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for (auto & i : cur_bbox_vec) {
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char *buf = (char *)calloc(2048, sizeof(char));
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sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"absolute_coordinates\":{\"center_x\":%d, \"center_y\":%d, \"width\":%d, \"height\":%d}, \"confidence\":%f",
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i.obj_id, obj_names[i.obj_id].c_str(), i.x, i.y, i.w, i.h, i.prob);
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//sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f",
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// i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob);
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send_str += buf;
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if (!std::isnan(i.z_3d)) {
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sprintf(buf, "\n , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}",
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i.x_3d, i.y_3d, i.z_3d);
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send_str += buf;
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}
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send_str += "}\n";
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free(buf);
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}
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//send_str += "\n ] \n}, \n";
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send_str += "\n ] \n}";
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send_json_custom(send_str.c_str(), port, timeout);
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return true;
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}
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};
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// --------------------------------------------------------------------------------
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#if defined(TRACK_OPTFLOW) && defined(OPENCV) && defined(GPU)
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#include <opencv2/cudaoptflow.hpp>
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#include <opencv2/cudaimgproc.hpp>
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#include <opencv2/cudaarithm.hpp>
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#include <opencv2/core/cuda.hpp>
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class Tracker_optflow {
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public:
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const int gpu_count;
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const int gpu_id;
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const int flow_error;
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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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int const old_gpu_id = cv::cuda::getDevice();
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cv::cuda::setDevice(gpu_id);
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stream = cv::cuda::Stream();
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sync_PyrLKOpticalFlow_gpu = cv::cuda::SparsePyrLKOpticalFlow::create();
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sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31
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sync_PyrLKOpticalFlow_gpu->setMaxLevel(max_level); // +- 3 pt
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sync_PyrLKOpticalFlow_gpu->setNumIters(iterations); // 2000, def: 30
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cv::cuda::setDevice(old_gpu_id);
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}
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// just to avoid extra allocations
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cv::cuda::GpuMat src_mat_gpu;
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cv::cuda::GpuMat dst_mat_gpu, dst_grey_gpu;
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cv::cuda::GpuMat prev_pts_flow_gpu, cur_pts_flow_gpu;
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cv::cuda::GpuMat status_gpu, err_gpu;
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cv::cuda::GpuMat src_grey_gpu; // used in both functions
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cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow_gpu;
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cv::cuda::Stream stream;
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std::vector<bbox_t> cur_bbox_vec;
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std::vector<bool> good_bbox_vec_flags;
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cv::Mat prev_pts_flow_cpu;
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void update_cur_bbox_vec(std::vector<bbox_t> _cur_bbox_vec)
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{
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cur_bbox_vec = _cur_bbox_vec;
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good_bbox_vec_flags = std::vector<bool>(cur_bbox_vec.size(), true);
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cv::Mat prev_pts, cur_pts_flow_cpu;
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for (auto &i : cur_bbox_vec) {
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float x_center = (i.x + i.w / 2.0F);
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float y_center = (i.y + i.h / 2.0F);
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prev_pts.push_back(cv::Point2f(x_center, y_center));
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}
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if (prev_pts.rows == 0)
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prev_pts_flow_cpu = cv::Mat();
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else
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cv::transpose(prev_pts, prev_pts_flow_cpu);
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if (prev_pts_flow_gpu.cols < prev_pts_flow_cpu.cols) {
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prev_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());
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cur_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());
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status_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_8UC1);
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err_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_32FC1);
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}
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prev_pts_flow_gpu.upload(cv::Mat(prev_pts_flow_cpu), stream);
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}
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void update_tracking_flow(cv::Mat src_mat, std::vector<bbox_t> _cur_bbox_vec)
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{
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int const old_gpu_id = cv::cuda::getDevice();
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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() == 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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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);
|
||
|
|
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<bbox_t> 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<bbox_t> 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<cv::Point2f>(0, i);
|
||
|
|
cv::Point2f prev_key_pt = prev_pts_flow_cpu.at<cv::Point2f>(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<float>(0, i) < flow_error && status_cpu.at<unsigned char>(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 <opencv2/optflow.hpp>
|
||
|
|
#include <opencv2/video/tracking.hpp>
|
||
|
|
|
||
|
|
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<cv::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow;
|
||
|
|
|
||
|
|
std::vector<bbox_t> cur_bbox_vec;
|
||
|
|
std::vector<bool> good_bbox_vec_flags;
|
||
|
|
|
||
|
|
void update_cur_bbox_vec(std::vector<bbox_t> _cur_bbox_vec)
|
||
|
|
{
|
||
|
|
cur_bbox_vec = _cur_bbox_vec;
|
||
|
|
good_bbox_vec_flags = std::vector<bool>(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<bbox_t> _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<bbox_t> 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<bbox_t> 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<cv::Point2f>(0, i);
|
||
|
|
cv::Point2f prev_key_pt = prev_pts_flow.at<cv::Point2f>(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<float>(0, i) < flow_error && status.at<unsigned char>(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_t> 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<bbox_t> 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<tst_t> track_id_state_id_time;
|
||
|
|
std::vector<bbox_t> result_vec_pred;
|
||
|
|
|
||
|
|
struct one_kalman_t;
|
||
|
|
std::vector<one_kalman_t> 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<float>(2) = dT;
|
||
|
|
kf.transitionMatrix.at<float>(9) = dT;
|
||
|
|
}
|
||
|
|
|
||
|
|
void set(bbox_t box)
|
||
|
|
{
|
||
|
|
initialize_kalman();
|
||
|
|
|
||
|
|
kf.errorCovPre.at<float>(0) = 1; // px
|
||
|
|
kf.errorCovPre.at<float>(7) = 1; // px
|
||
|
|
kf.errorCovPre.at<float>(14) = 1;
|
||
|
|
kf.errorCovPre.at<float>(21) = 1;
|
||
|
|
kf.errorCovPre.at<float>(28) = 1; // px
|
||
|
|
kf.errorCovPre.at<float>(35) = 1; // px
|
||
|
|
|
||
|
|
state.at<float>(0) = box.x;
|
||
|
|
state.at<float>(1) = box.y;
|
||
|
|
state.at<float>(2) = 0;
|
||
|
|
state.at<float>(3) = 0;
|
||
|
|
state.at<float>(4) = box.w;
|
||
|
|
state.at<float>(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<float>(0) = box.x;
|
||
|
|
meas.at<float>(1) = box.y;
|
||
|
|
meas.at<float>(2) = box.w;
|
||
|
|
meas.at<float>(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<float>(0);
|
||
|
|
box.y = state.at<float>(1);
|
||
|
|
box.w = state.at<float>(4);
|
||
|
|
box.h = state.at<float>(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<float>(0) = 1.0f;
|
||
|
|
kf.measurementMatrix.at<float>(7) = 1.0f;
|
||
|
|
kf.measurementMatrix.at<float>(16) = 1.0f;
|
||
|
|
kf.measurementMatrix.at<float>(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<float>(0) = 1e-2;
|
||
|
|
kf.processNoiseCov.at<float>(7) = 1e-2;
|
||
|
|
kf.processNoiseCov.at<float>(14) = 1e-2;// 5.0f;
|
||
|
|
kf.processNoiseCov.at<float>(21) = 1e-2;// 5.0f;
|
||
|
|
kf.processNoiseCov.at<float>(28) = 5e-3;
|
||
|
|
kf.processNoiseCov.at<float>(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<double>(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<double>(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<bool> &busy_vec)
|
||
|
|
{
|
||
|
|
tst_t tst;
|
||
|
|
tst.state_id = -1;
|
||
|
|
|
||
|
|
float min_dist = std::numeric_limits<float>::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<float>(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<bool> &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<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
|
||
|
|
|
||
|
|
#endif // __cplusplus
|
||
|
|
|
||
|
|
#endif // YOLO_V2_CLASS_HPP
|