| build/darknet/yolo_console_dll.sln | ●●●●● patch | view | raw | blame | history | |
| build/darknet/yolo_console_dll.vcxproj | ●●●●● patch | view | raw | blame | history | |
| build/darknet/yolo_cpp_dll.sln | ●●●●● patch | view | raw | blame | history | |
| build/darknet/yolo_cpp_dll.vcxproj | ●●●●● patch | view | raw | blame | history | |
| src/network.h | ●●●●● patch | view | raw | blame | history | |
| src/yolo.c | ●●●●● patch | view | raw | blame | history | |
| src/yolo_console_dll.cpp | ●●●●● patch | view | raw | blame | history | |
| src/yolo_v2_class.cpp | ●●●●● patch | view | raw | blame | history | |
| src/yolo_v2_class.hpp | ●●●●● patch | view | raw | blame | history |
build/darknet/yolo_console_dll.sln
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@@ -2,8 +2,13 @@ #ifndef NETWORK_H #define NETWORK_H #include "image.h" #include "layer.h" #ifdef __cplusplus extern "C" { #endif #include "image.h" #include "data.h" #include "tree.h" @@ -125,5 +130,9 @@ int get_network_nuisance(network net); int get_network_background(network net); #ifdef __cplusplus } #endif #endif
@@ -6,6 +6,7 @@ #include "box.h" #include "demo.h" #ifdef OPENCV #pragma comment(lib, "opencv_core249.lib") #pragma comment(lib, "opencv_imgproc249.lib") #pragma comment(lib, "opencv_highgui249.lib") @@ -13,7 +14,6 @@ //#pragma comment(lib, "opencv_photo249.lib") //#pragma comment(lib, "opencv_video249.lib") #ifdef OPENCV #include "opencv2/highgui/highgui_c.h" #endif
New file @@ -0,0 +1,57 @@ #include <iostream> #include <string> #include <vector> //#define OPENCV #include "yolo_v2_class.hpp" // imported functions from DLL #ifdef OPENCV #include <opencv2/opencv.hpp> // C++ #pragma comment(lib, "opencv_core249.lib") #pragma comment(lib, "opencv_imgproc249.lib") #pragma comment(lib, "opencv_highgui249.lib") void draw_boxes(cv::Mat mat_img, std::vector<bbox_t> result_vec) { for (auto &i : result_vec) { cv::rectangle(mat_img, cv::Rect(i.x, i.y, i.w, i.h), cv::Scalar(50, 200, 50), 3); } cv::imshow("window name", mat_img); cv::waitKey(0); } #endif // OPENCV void show_result(std::vector<bbox_t> result_vec) { for (auto &i : result_vec) { std::cout << "obj_id = " << i.obj_id << " - x = " << i.x << ", y = " << i.y << ", w = " << i.w << ", h = " << i.h << ", prob = " << i.prob << std::endl; } } int main() { Detector detector("yolo-voc.cfg", "yolo-voc.weights"); while (true) { std::string filename; std::cout << "input image filename: "; std::cin >> filename; if (filename.size() == 0) break; #ifdef OPENCV cv::Mat mat_img = cv::imread(filename); std::vector<bbox_t> result_vec = detector.detect(mat_img); draw_boxes(mat_img, result_vec); #else std::vector<bbox_t> result_vec = detector.detect(filename); #endif show_result(result_vec); } return 0; } src/yolo_v2_class.cpp
New file @@ -0,0 +1,165 @@ #include "yolo_v2_class.hpp" #include "network.h" extern "C" { #include "detection_layer.h" #include "region_layer.h" #include "cost_layer.h" #include "utils.h" #include "parser.h" #include "box.h" #include "image.h" #include "demo.h" #include "option_list.h" } //#include <sys/time.h> #include <vector> #include <iostream> #define FRAMES 3 #define ROI_PER_DETECTOR 100 struct detector_gpu_t{ float **probs; box *boxes; network net; //image det; //image det_s; image images[FRAMES]; float *avg; float *predictions[FRAMES]; }; YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) { int old_gpu_index; cudaGetDevice(&old_gpu_index); detector_gpu_ptr = std::make_shared<detector_gpu_t>(); detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get()); cudaSetDevice(gpu_id); network &net = detector_gpu.net; net.gpu_index = gpu_id; //gpu_index = i; char *cfgfile = const_cast<char *>(cfg_filename.data()); char *weightfile = const_cast<char *>(weight_filename.data()); net = parse_network_cfg(cfgfile); if (weightfile) { load_weights(&net, weightfile); } set_batch_network(&net, 1); net.gpu_index = gpu_id; layer l = net.layers[net.n - 1]; int j; detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); detector_gpu.boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box)); detector_gpu.probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); for (j = 0; j < l.w*l.h*l.n; ++j) detector_gpu.probs[j] = (float *)calloc(l.classes, sizeof(float)); cudaSetDevice(old_gpu_index); } YOLODLL_API Detector::~Detector() { detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get()); layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; free(detector_gpu.boxes); free(detector_gpu.avg); free(detector_gpu.predictions); for (int j = 0; j < l.w*l.h*l.n; ++j) free(detector_gpu.probs[j]); free(detector_gpu.probs); } YOLODLL_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh) { char *input = const_cast<char *>(image_filename.data()); image im = load_image_color(input, 0, 0); image_t img; img.c = im.c; img.data = im.data; img.h = im.h; img.w = im.w; return detect(img, thresh); } YOLODLL_API std::vector<bbox_t> Detector::detect(image_t img, float thresh) { detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get()); network &net = detector_gpu.net; int old_gpu_index; cudaGetDevice(&old_gpu_index); cudaSetDevice(net.gpu_index); //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; float nms = .4; image im; im.c = img.c; im.data = img.data; im.h = img.h; im.w = img.w; image sized = resize_image(im, net.w, net.h); layer l = net.layers[net.n - 1]; //box *boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box)); //float **probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); // (int j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *)); float *X = sized.data; network_predict(net, X); get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); //draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes); std::vector<bbox_t> bbox_vec; for (size_t i = 0; i < (l.w*l.h*l.n); ++i) { box b = detector_gpu.boxes[i]; int const obj_id = max_index(detector_gpu.probs[i], l.classes); float const prob = detector_gpu.probs[i][obj_id]; if (prob > thresh) { bbox_t bbox; bbox.x = (b.x - b.w / 2.)*im.w; bbox.y = (b.y - b.h / 2.)*im.h; bbox.w = b.w*im.w; bbox.h = b.h*im.h; bbox.obj_id = obj_id; bbox.prob = prob; bbox_vec.push_back(bbox); } } cudaSetDevice(old_gpu_index); return bbox_vec; } src/yolo_v2_class.hpp
New file @@ -0,0 +1,120 @@ #pragma once #include <memory> #include <vector> #ifdef OPENCV #include <opencv2/opencv.hpp> // C++ #include "opencv2/highgui/highgui_c.h" // C #include "opencv2/imgproc/imgproc_c.h" // C #endif // OPENCV //extern "C" { //#include "image.h" //} #ifdef YOLODLL_EXPORTS #define YOLODLL_API __declspec(dllexport) #else #define YOLODLL_API __declspec(dllimport) #endif struct bbox_t { float x, y, w, h; float prob; unsigned int obj_id; }; typedef struct { int h; int w; int c; float *data; } image_t; class Detector { std::shared_ptr<void> detector_gpu_ptr; public: YOLODLL_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); YOLODLL_API ~Detector(); YOLODLL_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh = 0.2); YOLODLL_API std::vector<bbox_t> detect(image_t img, float thresh = 0.2); #ifdef OPENCV std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2) { std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { free_image(*img); } ); *image_ptr = mat_to_image(mat); return detect(*image_ptr, thresh); } private: static image_t mat_to_image(cv::Mat img) { std::shared_ptr<IplImage> ipl_small = std::make_shared<IplImage>(img); image_t im_small = ipl_to_image(ipl_small.get()); rgbgr_image(im_small); return im_small; } static image_t ipl_to_image(IplImage* src) { unsigned char *data = (unsigned char *)src->imageData; int h = src->height; int w = src->width; int c = src->nChannels; int step = src->widthStep; image_t out = make_image_custom(w, h, c); int i, j, k, count = 0;; for (k = 0; k < c; ++k) { for (i = 0; i < h; ++i) { for (j = 0; j < w; ++j) { out.data[count++] = data[i*step + j*c + k] / 255.; } } } return out; } static image_t make_empty_image(int w, int h, int c) { image_t out; out.data = 0; out.h = h; out.w = w; out.c = c; return out; } static image_t make_image_custom(int w, int h, int c) { image_t out = make_empty_image(w, h, c); out.data = (float *)calloc(h*w*c, sizeof(float)); return out; } static void rgbgr_image(image_t im) { int i; for (i = 0; i < im.w*im.h; ++i) { float swap = im.data[i]; im.data[i] = im.data[i + im.w*im.h * 2]; im.data[i + im.w*im.h * 2] = swap; } } static void free_image(image_t m) { if (m.data) { free(m.data); } } #endif // OPENCV };
