#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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#else
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#define YOLODLL_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 YOLODLL_API __declspec(dllimport)
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#else
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#define YOLODLL_API
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#endif
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#endif
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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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};
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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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class Detector {
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std::shared_ptr<void> detector_gpu_ptr;
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public:
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float nms = .4;
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YOLODLL_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
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YOLODLL_API ~Detector();
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YOLODLL_API std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
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YOLODLL_API std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
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static YOLODLL_API image_t load_image(std::string image_filename);
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static YOLODLL_API void free_image(image_t m);
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YOLODLL_API int get_net_width();
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YOLODLL_API int get_net_height();
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YOLODLL_API std::vector<bbox_t> tracking(std::vector<bbox_t> cur_bbox_vec, int const frames_story = 6);
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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("file not found");
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cv::Mat det_mat;
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cv::resize(mat, det_mat, cv::Size(get_net_width(), get_net_height()));
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auto image_ptr = mat_to_image(det_mat);
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auto detection_boxes = detect(*image_ptr, thresh, use_mean);
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float wk = (float)mat.cols / det_mat.cols, hk = (float)mat.rows / det_mat.rows;
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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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static std::shared_ptr<image_t> mat_to_image(cv::Mat img)
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{
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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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std::shared_ptr<IplImage> ipl_small = std::make_shared<IplImage>(img);
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*image_ptr = ipl_to_image(ipl_small.get());
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rgbgr_image(*image_ptr);
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return image_ptr;
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}
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private:
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static image_t ipl_to_image(IplImage* src)
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{
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unsigned char *data = (unsigned char *)src->imageData;
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int h = src->height;
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int w = src->width;
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int c = src->nChannels;
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int step = src->widthStep;
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image_t out = make_image_custom(w, h, c);
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int i, j, k, count = 0;;
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for (k = 0; k < c; ++k) {
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for (i = 0; i < h; ++i) {
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for (j = 0; j < w; ++j) {
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out.data[count++] = data[i*step + j*c + k] / 255.;
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}
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}
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}
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return out;
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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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static void rgbgr_image(image_t im)
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{
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int i;
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for (i = 0; i < im.w*im.h; ++i) {
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float swap = im.data[i];
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im.data[i] = im.data[i + im.w*im.h * 2];
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im.data[i + im.w*im.h * 2] = swap;
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}
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}
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#endif // OPENCV
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std::deque<std::vector<bbox_t>> prev_bbox_vec_deque;
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};
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