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https://github.com/pjreddie/darknet.git
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C wrappers
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@ -1,15 +1,4 @@
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#pragma once
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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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#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 // OPENCV
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#ifdef YOLODLL_EXPORTS
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#if defined(_MSC_VER)
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#define YOLODLL_API __declspec(dllexport)
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@ -39,6 +28,17 @@ struct image_t {
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float *data; // pointer to the image data
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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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#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 // OPENCV
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class Detector {
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std::shared_ptr<void> detector_gpu_ptr;
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@ -61,23 +61,23 @@ public:
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YOLODLL_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 = 10, int const max_dist = 150);
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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.size(), thresh, use_mean);
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}
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std::vector<bbox_t> detect_resized(image_t img, cv::Size init_size, 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_size.width / img.w, hk = (float)init_size.height / 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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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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@ -588,3 +588,54 @@ public:
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}
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};
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#endif // OPENCV
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//extern "C" {
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#endif // __cplusplus
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/*
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// C - wrappers
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YOLODLL_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id);
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YOLODLL_API void delete_detector();
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YOLODLL_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size);
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YOLODLL_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size);
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YOLODLL_API bbox_t* detect(image_t img, int *result_size);
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YOLODLL_API image_t load_img(char *image_filename);
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YOLODLL_API void free_img(image_t m);
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#ifdef __cplusplus
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} // extern "C"
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static std::shared_ptr<void> c_detector_ptr;
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static std::vector<bbox_t> c_result_vec;
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void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id) {
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c_detector_ptr = std::make_shared<YOLODLL_API Detector>(cfg_filename, weight_filename, gpu_id);
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}
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void delete_detector() { c_detector_ptr.reset(); }
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bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size) {
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c_result_vec = static_cast<Detector*>(c_detector_ptr.get())->detect(img, thresh, use_mean);
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*result_size = c_result_vec.size();
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return c_result_vec.data();
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}
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bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size) {
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c_result_vec = static_cast<Detector*>(c_detector_ptr.get())->detect_resized(img, init_w, init_h, thresh, use_mean);
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*result_size = c_result_vec.size();
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return c_result_vec.data();
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}
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bbox_t* detect(image_t img, int *result_size) {
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return detect_custom(img, 0.24, true, result_size);
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}
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image_t load_img(char *image_filename) {
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return static_cast<Detector*>(c_detector_ptr.get())->load_image(image_filename);
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}
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void free_img(image_t m) {
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static_cast<Detector*>(c_detector_ptr.get())->free_image(m);
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}
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#endif // __cplusplus
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*/
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