| | |
| | | #pragma once |
| | | #include <memory> |
| | | #include <vector> |
| | | #include <deque> |
| | | #include <algorithm> |
| | | |
| | | #ifdef OPENCV |
| | | #include <opencv2/opencv.hpp> // C++ |
| | | #include "opencv2/highgui/highgui_c.h" // C |
| | | #include "opencv2/imgproc/imgproc_c.h" // C |
| | | #endif // OPENCV |
| | | |
| | | #ifdef YOLODLL_EXPORTS |
| | | #if defined(_MSC_VER) |
| | | #define YOLODLL_API __declspec(dllexport) |
| | |
| | | float *data; // pointer to the image data |
| | | }; |
| | | |
| | | #ifdef __cplusplus |
| | | #include <memory> |
| | | #include <vector> |
| | | #include <deque> |
| | | #include <algorithm> |
| | | |
| | | #ifdef OPENCV |
| | | #include <opencv2/opencv.hpp> // C++ |
| | | #include "opencv2/highgui/highgui_c.h" // C |
| | | #include "opencv2/imgproc/imgproc_c.h" // C |
| | | #endif // OPENCV |
| | | |
| | | class Detector { |
| | | std::shared_ptr<void> detector_gpu_ptr; |
| | |
| | | YOLODLL_API std::vector<bbox_t> tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history = true, |
| | | int const frames_story = 10, int const max_dist = 150); |
| | | |
| | | std::vector<bbox_t> 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<bbox_t> 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.size(), thresh, use_mean); |
| | | } |
| | | |
| | | std::vector<bbox_t> detect_resized(image_t img, cv::Size init_size, 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_size.width / img.w, hk = (float)init_size.height / img.h; |
| | | for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk; |
| | | return detection_boxes; |
| | | return detect_resized(*image_ptr, mat.cols, mat.rows, thresh, use_mean); |
| | | } |
| | | |
| | | std::shared_ptr<image_t> mat_to_image_resize(cv::Mat mat) const |
| | |
| | | |
| | | #elif defined(TRACK_OPTFLOW) && defined(OPENCV) |
| | | |
| | | #include <opencv2/optflow.hpp> |
| | | //#include <opencv2/optflow.hpp> |
| | | #include <opencv2/video/tracking.hpp> |
| | | |
| | | class Tracker_optflow { |
| | | public: |
| | |
| | | } |
| | | |
| | | // just to avoid extra allocations |
| | | cv::Mat src_mat; |
| | | cv::Mat dst_mat, dst_grey; |
| | | cv::Mat dst_grey; |
| | | cv::Mat prev_pts_flow, cur_pts_flow; |
| | | cv::Mat status, err; |
| | | |
| | |
| | | void update_tracking_flow(cv::Mat new_src_mat, std::vector<bbox_t> _cur_bbox_vec) |
| | | { |
| | | if (new_src_mat.channels() == 3) { |
| | | if (src_mat.cols == 0) { |
| | | src_mat = cv::Mat(new_src_mat.size(), new_src_mat.type()); |
| | | src_grey = cv::Mat(new_src_mat.size(), CV_8UC1); |
| | | } |
| | | |
| | | update_cur_bbox_vec(_cur_bbox_vec); |
| | | |
| | | src_mat = new_src_mat; |
| | | cv::cvtColor(src_mat, src_grey, CV_BGR2GRAY, 1); |
| | | cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1); |
| | | } |
| | | } |
| | | |
| | |
| | | return cur_bbox_vec; |
| | | } |
| | | |
| | | if (dst_mat.cols == 0) { |
| | | dst_mat = cv::Mat(new_dst_mat.size(), new_dst_mat.type()); |
| | | dst_grey = cv::Mat(new_dst_mat.size(), CV_8UC1); |
| | | } |
| | | |
| | | dst_mat = new_dst_mat; |
| | | cv::cvtColor(dst_mat, dst_grey, CV_BGR2GRAY, 1); |
| | | 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(); |
| | | 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 |
| | | |
| | |
| | | |
| | | std::vector<bbox_t> result_bbox_vec; |
| | | |
| | | if (err.cols == cur_bbox_vec.size() && status.cols == cur_bbox_vec.size()) |
| | | if (err.rows == cur_bbox_vec.size() && status.rows == cur_bbox_vec.size()) |
| | | { |
| | | for (size_t i = 0; i < cur_bbox_vec.size(); ++i) |
| | | { |
| | |
| | | } |
| | | } |
| | | |
| | | prev_pts_flow = cur_pts_flow; |
| | | prev_pts_flow = cur_pts_flow.clone(); |
| | | |
| | | return result_bbox_vec; |
| | | } |
| | |
| | | |
| | | #ifdef OPENCV |
| | | |
| | | cv::Scalar obj_id_to_color(int obj_id); |
| | | 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 |
| | |
| | | } |
| | | }; |
| | | #endif // OPENCV |
| | | |
| | | //extern "C" { |
| | | #endif // __cplusplus |
| | | |
| | | /* |
| | | // C - wrappers |
| | | YOLODLL_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id); |
| | | YOLODLL_API void delete_detector(); |
| | | YOLODLL_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size); |
| | | YOLODLL_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size); |
| | | YOLODLL_API bbox_t* detect(image_t img, int *result_size); |
| | | YOLODLL_API image_t load_img(char *image_filename); |
| | | YOLODLL_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<YOLODLL_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 |
| | | */ |