| | |
| | | #pragma once |
| | | #include <memory> |
| | | #include <vector> |
| | | #include <deque> |
| | | #include <algorithm> |
| | | |
| | | #ifdef OPENCV |
| | | #include <opencv2/opencv.hpp> // C++ |
| | |
| | | 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) |
| | | }; |
| | | |
| | | struct image_t { |
| | |
| | | class Detector { |
| | | std::shared_ptr<void> detector_gpu_ptr; |
| | | public: |
| | | float nms = .4; |
| | | |
| | | YOLODLL_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); |
| | | YOLODLL_API ~Detector(); |
| | |
| | | static YOLODLL_API image_t load_image(std::string image_filename); |
| | | static YOLODLL_API void free_image(image_t m); |
| | | |
| | | YOLODLL_API std::vector<bbox_t> tracking(std::vector<bbox_t> cur_bbox_vec, int const frames_story = 4); |
| | | |
| | | #ifdef OPENCV |
| | | std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2) |
| | | { |
| | |
| | | } |
| | | |
| | | #endif // OPENCV |
| | | |
| | | std::deque<std::vector<bbox_t>> prev_bbox_vec_deque; |
| | | }; |
| | | |
| | | |