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C++Yolov4目标检测实战_c++ yolo
作者：Monodyee | 2024-03-02 01:18:32
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c++ yolo
Introduction
今年2月份，Yolo之父Joseph Redmon由于Yolo被用于军事和隐私窥探退出CV界表示抗议，就当我们以为Yolo系列就此终结的时候，4月24日，Yolov4横空出世，新的接棒者出现，而一作正是赫赫有名的AB大神。
paper github
在本篇文章里，我们先不急去探究Yolov4的原理，而是从工程的角度来使用Yolov4。首先我们来看一下，Yolov4的性能有多么强劲，下面是使用不同显卡的时候，主流目标检测器的性能，从下图我们发现，Yolov4真的比自己的前辈Yolov3强劲了很多。
官方darknet
darknetAB
官方工程的使用在AB大神的github里面已经讲的非常清楚，可以实现Yolov4网络的训练，检测等功能。但是使用官方darknet项目我们很难直接进行定制化的项目开发，因此本文利用官方项目提供的C++接口进行目标检测实战。
Yolov4实战
首先我们从github下载yolov4.weights,不能上外网的同学下载应该很慢，我上传到百度云盘里，方便大家下载，链接如下：
链接: https://pan.baidu.com/s/14U9pkxJE3MHYj7KCWHnkNw 提取码: 54v4
工程包括两个文件main.cpp, yolo_v2_class.hpp.我把自己的整个工程上传到github上，同学们可以直接去git clone下来，觉得有用的同学麻烦star一下，谢谢。
main.cpp
#include <iostream> #include <iomanip> #include <string> #include <vector> #include <queue> #include <fstream> #include <thread> #include <future> #include <atomic> #include <mutex> // std::mutex, std::unique_lock #include <cmath> // It makes sense only for video-Camera (not for video-File) // To use - uncomment the following line. Optical-flow is supported only by OpenCV 3.x - 4.x //#define TRACK_OPTFLOW //#define GPU // To use 3D-stereo camera ZED - uncomment the following line. ZED_SDK should be installed. //#define ZED_STEREO #include "yolo_v2_class.hpp" // imported functions from DLL #ifdef OPENCV #ifdef ZED_STEREO #include <sl/Camera.hpp> #if ZED_SDK_MAJOR_VERSION == 2 #define ZED_STEREO_2_COMPAT_MODE #endif #undef GPU // avoid conflict with sl::MEM::GPU #ifdef ZED_STEREO_2_COMPAT_MODE #pragma comment(lib, "sl_core64.lib") #pragma comment(lib, "sl_input64.lib") #endif #pragma comment(lib, "sl_zed64.lib") float getMedian(std::vector<float> &v) { size_t n = v.size() / 2; std::nth_element(v.begin(), v.begin() + n, v.end()); return v[n]; } std::vector<bbox_t> get_3d_coordinates(std::vector<bbox_t> bbox_vect, cv::Mat xyzrgba) { bool valid_measure; int i, j; const unsigned int R_max_global = 10; std::vector<bbox_t> bbox3d_vect; for (auto &cur_box : bbox_vect) { const unsigned int obj_size = std::min(cur_box.w, cur_box.h); const unsigned int R_max = std::min(R_max_global, obj_size / 2); int center_i = cur_box.x + cur_box.w * 0.5f, center_j = cur_box.y + cur_box.h * 0.5f; std::vector<float> x_vect, y_vect, z_vect; for (int R = 0; R < R_max; R++) { for (int y = -R; y <= R; y++) { for (int x = -R; x <= R; x++) { i = center_i + x; j = center_j + y; sl::float4 out(NAN, NAN, NAN, NAN); if (i >= 0 && i < xyzrgba.cols && j >= 0 && j < xyzrgba.rows) { cv::Vec4f &elem = xyzrgba.at<cv::Vec4f>(j, i); // x,y,z,w out.x = elem[0]; out.y = elem[1]; out.z = elem[2]; out.w = elem[3]; } valid_measure = std::isfinite(out.z); if (valid_measure) { x_vect.push_back(out.x); y_vect.push_back(out.y); z_vect.push_back(out.z); } } } } if (x_vect.size() * y_vect.size() * z_vect.size() > 0) { cur_box.x_3d = getMedian(x_vect); cur_box.y_3d = getMedian(y_vect); cur_box.z_3d = getMedian(z_vect); } else { cur_box.x_3d = NAN; cur_box.y_3d = NAN; cur_box.z_3d = NAN; } bbox3d_vect.emplace_back(cur_box); } return bbox3d_vect; } cv::Mat slMat2cvMat(sl::Mat &input) { int cv_type = -1; // Mapping between MAT_TYPE and CV_TYPE if(input.getDataType() == #ifdef ZED_STEREO_2_COMPAT_MODE sl::MAT_TYPE_32F_C4 #else sl::MAT_TYPE::F32_C4 #endif ) { cv_type = CV_32FC4; } else cv_type = CV_8UC4; // sl::Mat used are either RGBA images or XYZ (4C) point clouds return cv::Mat(input.getHeight(), input.getWidth(), cv_type, input.getPtr<sl::uchar1>( #ifdef ZED_STEREO_2_COMPAT_MODE sl::MEM::MEM_CPU #else sl::MEM::CPU #endif )); } cv::Mat zed_capture_rgb(sl::Camera &zed) { sl::Mat left; zed.retrieveImage(left); cv::Mat left_rgb; cv::cvtColor(slMat2cvMat(left), left_rgb, CV_RGBA2RGB); return left_rgb; } cv::Mat zed_capture_3d(sl::Camera &zed) { sl::Mat cur_cloud; zed.retrieveMeasure(cur_cloud, #ifdef ZED_STEREO_2_COMPAT_MODE sl::MEASURE_XYZ #else sl::MEASURE::XYZ #endif ); return slMat2cvMat(cur_cloud).clone(); } static sl::Camera zed; // ZED-camera #else // ZED_STEREO std::vector<bbox_t> get_3d_coordinates(std::vector<bbox_t> bbox_vect, cv::Mat xyzrgba) { return bbox_vect; } #endif // ZED_STEREO #include <opencv2/opencv.hpp> // C++ #include <opencv2/core/version.hpp> #ifndef CV_VERSION_EPOCH // OpenCV 3.x and 4.x #include <opencv2/videoio/videoio.hpp> #define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) #ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib") #ifdef TRACK_OPTFLOW /* #pragma comment(lib, "opencv_cudaoptflow" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_cudaimgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") */ #endif // TRACK_OPTFLOW #endif // USE_CMAKE_LIBS #else // OpenCV 2.x #define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR) #ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_video" OPENCV_VERSION ".lib") #endif // USE_CMAKE_LIBS #endif // CV_VERSION_EPOCH using namespace std; vector<string> split(const string&s,char sepeartor) { vector<string> split_vector; int subinit=0; for (int id=0;id!=s.length();id++) { if (s[id]==sepeartor) { split_vector.push_back(s.substr(subinit,id-subinit)); subinit=id+1; } } split_vector.push_back(s.substr(subinit,s.length()-subinit)); return split_vector; } void draw_boxes(cv::Mat mat_img, std::vector<bbox_t> result_vec, std::vector<std::string> obj_names, int current_det_fps = -1, int current_cap_fps = -1) { int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } }; for (auto &i : result_vec) { cv::Scalar color = obj_id_to_color(i.obj_id); cv::rectangle(mat_img, cv::Rect(i.x, i.y, i.w, i.h), color, 2); if (obj_names.size() > i.obj_id) { std::string obj_name = obj_names[i.obj_id]; if (i.track_id > 0) obj_name += " - " + std::to_string(i.track_id); cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0); int max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2); max_width = std::max(max_width, (int)i.w + 2); //max_width = std::max(max_width, 283); std::string coords_3d; if (!std::isnan(i.z_3d)) { std::stringstream ss; ss << std::fixed << std::setprecision(2) << "x:" << i.x_3d << "m y:" << i.y_3d << "m z:" << i.z_3d << "m "; coords_3d = ss.str(); cv::Size const text_size_3d = getTextSize(ss.str(), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, 1, 0); int const max_width_3d = (text_size_3d.width > i.w + 2) ? text_size_3d.width : (i.w + 2); if (max_width_3d > max_width) max_width = max_width_3d; } cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 35, 0)), cv::Point2f(std::min((int)i.x + max_width, mat_img.cols - 1), std::min((int)i.y, mat_img.rows - 1)), color, CV_FILLED, 8, 0); putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 16), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2); if(!coords_3d.empty()) putText(mat_img, coords_3d, cv::Point2f(i.x, i.y-1), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1); } } if (current_det_fps >= 0 && current_cap_fps >= 0) { std::string fps_str = "FPS detection: " + std::to_string(current_det_fps) + " FPS capture: " + std::to_string(current_cap_fps); putText(mat_img, fps_str, cv::Point2f(10, 20), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(50, 255, 0), 2); } } #endif // OPENCV void show_console_result(std::vector<bbox_t> const result_vec, std::vector<std::string> const obj_names, int frame_id = -1) { if (frame_id >= 0) std::cout << " Frame: " << frame_id << std::endl; for (auto &i : result_vec) { if (obj_names.size() > i.obj_id) std::cout << obj_names[i.obj_id] << " - "; std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y << ", w = " << i.w << ", h = " << i.h << std::setprecision(3) << ", prob = " << i.prob << std::endl; } } std::vector<std::string> objects_names_from_file(std::string const filename) { std::ifstream file(filename); std::vector<std::string> file_lines; if (!file.is_open()) return file_lines; for(std::string line; getline(file, line);) file_lines.push_back(line); std::cout << "object names loaded \n"; return file_lines; } template<typename T> class send_one_replaceable_object_t { const bool sync; std::atomic<T *> a_ptr; public: void send(T const& _obj) { T *new_ptr = new T; *new_ptr = _obj; if (sync) { while (a_ptr.load()) std::this_thread::sleep_for(std::chrono::milliseconds(3)); } std::unique_ptr<T> old_ptr(a_ptr.exchange(new_ptr)); } T receive() { std::unique_ptr<T> ptr; do { while(!a_ptr.load()) std::this_thread::sleep_for(std::chrono::milliseconds(3)); ptr.reset(a_ptr.exchange(NULL)); } while (!ptr); T obj = *ptr; return obj; } bool is_object_present() { return (a_ptr.load() != NULL); } send_one_replaceable_object_t(bool _sync) : sync(_sync), a_ptr(NULL) {} }; int main(int argc, char *argv[]) { std::string names_file = "coco.names"; std::string cfg_file = "cfg/yolov4.cfg"; std::string weights_file = "yolov4.weights"; std::string filename; if (argc > 4) { //voc.names yolo-voc.cfg yolo-voc.weights test.mp4 names_file = argv[1]; cfg_file = argv[2]; weights_file = argv[3]; filename = argv[4]; } else if (argc > 1) filename = argv[1]; float const thresh = (argc > 5) ? std::stof(argv[5]) : 0.2; Detector detector(cfg_file, weights_file); auto obj_names = objects_names_from_file(names_file); std::string out_videofile = "result.avi"; bool const save_output_videofile = false; // true - for history bool const send_network = false; // true - for remote detection bool const use_kalman_filter = false; // true - for stationary camera bool detection_sync = true; // true - for video-file #ifdef TRACK_OPTFLOW // for slow GPU detection_sync = false; Tracker_optflow tracker_flow; //detector.wait_stream = true; #endif // TRACK_OPTFLOW while (true) { std::cout << "input image or video filename: "; if(filename.size() == 0) std::cin >> filename; if (filename.size() == 0) break; try { #ifdef OPENCV preview_boxes_t large_preview(100, 150, false), small_preview(50, 50, true); bool show_small_boxes = false; std::string const file_ext = filename.substr(filename.find_last_of(".") + 1); std::string const protocol = filename.substr(0, 7); if (file_ext == "avi" || file_ext == "mp4" || file_ext == "mjpg" || file_ext == "mov" || // video file protocol == "rtmp://" || protocol == "rtsp://" || protocol == "http://" || protocol == "https:/" || // video network stream filename == "zed_camera" || file_ext == "svo" || filename == "web_camera") // ZED stereo camera { if (protocol == "rtsp://" || protocol == "http://" || protocol == "https:/" || filename == "zed_camera" || filename == "web_camera") detection_sync = false; cv::Mat cur_frame; std::atomic<int> fps_cap_counter(0), fps_det_counter(0); std::atomic<int> current_fps_cap(0), current_fps_det(0); std::atomic<bool> exit_flag(false); std::chrono::steady_clock::time_point steady_start, steady_end; int video_fps = 25; bool use_zed_camera = false; track_kalman_t track_kalman; #ifdef ZED_STEREO sl::InitParameters init_params; init_params.depth_minimum_distance = 0.5; #ifdef ZED_STEREO_2_COMPAT_MODE init_params.depth_mode = sl::DEPTH_MODE_ULTRA; init_params.camera_resolution = sl::RESOLUTION_HD720;// sl::RESOLUTION_HD1080, sl::RESOLUTION_HD720 init_params.coordinate_units = sl::UNIT_METER; init_params.camera_buffer_count_linux = 2; if (file_ext == "svo") init_params.svo_input_filename.set(filename.c_str()); #else init_params.depth_mode = sl::DEPTH_MODE::ULTRA; init_params.camera_resolution = sl::RESOLUTION::HD720;// sl::RESOLUTION::HD1080, sl::RESOLUTION::HD720 init_params.coordinate_units = sl::UNIT::METER; if (file_ext == "svo") init_params.input.setFromSVOFile(filename.c_str()); #endif //init_params.sdk_cuda_ctx = (CUcontext)detector.get_cuda_context(); init_params.sdk_gpu_id = detector.cur_gpu_id; if (filename == "zed_camera" || file_ext == "svo") { std::cout << "ZED 3D Camera " << zed.open(init_params) << std::endl; if (!zed.isOpened()) { std::cout << " Error: ZED Camera should be connected to USB 3.0. And ZED_SDK should be installed. \n"; getchar(); return 0; } cur_frame = zed_capture_rgb(zed); use_zed_camera = true; } #endif // ZED_STEREO cv::VideoCapture cap; if (filename == "web_camera") { cap.open(0); cap >> cur_frame; } else if (!use_zed_camera) { cap.open(filename); cap >> cur_frame; } #ifdef CV_VERSION_EPOCH // OpenCV 2.x video_fps = cap.get(CV_CAP_PROP_FPS); #else video_fps = cap.get(cv::CAP_PROP_FPS); #endif cv::Size const frame_size = cur_frame.size(); //cv::Size const frame_size(cap.get(CV_CAP_PROP_FRAME_WIDTH), cap.get(CV_CAP_PROP_FRAME_HEIGHT)); std::cout << "\n Video size: " << frame_size << std::endl; cv::VideoWriter output_video; if (save_output_videofile) #ifdef CV_VERSION_EPOCH // OpenCV 2.x output_video.open(out_videofile, CV_FOURCC('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true); #else output_video.open(out_videofile, cv::VideoWriter::fourcc('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true); #endif struct detection_data_t { cv::Mat cap_frame; std::shared_ptr<image_t> det_image; std::vector<bbox_t> result_vec; cv::Mat draw_frame; bool new_detection; uint64_t frame_id; bool exit_flag; cv::Mat zed_cloud; std::queue<cv::Mat> track_optflow_queue; detection_data_t() : exit_flag(false), new_detection(false) {} }; const bool sync = detection_sync; // sync data exchange send_one_replaceable_object_t<detection_data_t> cap2prepare(sync), cap2draw(sync), prepare2detect(sync), detect2draw(sync), draw2show(sync), draw2write(sync), draw2net(sync); std::thread t_cap, t_prepare, t_detect, t_post, t_draw, t_write, t_network; // capture new video-frame if (t_cap.joinable()) t_cap.join(); t_cap = std::thread([&]() { uint64_t frame_id = 0; detection_data_t detection_data; do { detection_data = detection_data_t(); #ifdef ZED_STEREO if (use_zed_camera) { while (zed.grab() != #ifdef ZED_STEREO_2_COMPAT_MODE sl::SUCCESS #else sl::ERROR_CODE::SUCCESS #endif ) std::this_thread::sleep_for(std::chrono::milliseconds(2)); detection_data.cap_frame = zed_capture_rgb(zed); detection_data.zed_cloud = zed_capture_3d(zed); } else #endif // ZED_STEREO { cap >> detection_data.cap_frame; } fps_cap_counter++; detection_data.frame_id = frame_id++; if (detection_data.cap_frame.empty() || exit_flag) { std::cout << " exit_flag: detection_data.cap_frame.size = " << detection_data.cap_frame.size() << std::endl; detection_data.exit_flag = true; detection_data.cap_frame = cv::Mat(frame_size, CV_8UC3); } if (!detection_sync) { cap2draw.send(detection_data); // skip detection } cap2prepare.send(detection_data); } while (!detection_data.exit_flag); std::cout << " t_cap exit \n"; }); // pre-processing video frame (resize, convertion) t_prepare = std::thread([&]() { std::shared_ptr<image_t> det_image; detection_data_t detection_data; do { detection_data = cap2prepare.receive(); det_image = detector.mat_to_image_resize(detection_data.cap_frame); detection_data.det_image = det_image; prepare2detect.send(detection_data); // detection } while (!detection_data.exit_flag); std::cout << " t_prepare exit \n"; }); // detection by Yolo if (t_detect.joinable()) t_detect.join(); t_detect = std::thread([&]() { std::shared_ptr<image_t> det_image; detection_data_t detection_data; do { detection_data = prepare2detect.receive(); det_image = detection_data.det_image; std::vector<bbox_t> result_vec; if(det_image) result_vec = detector.detect_resized(*det_image, frame_size.width, frame_size.height, thresh, true); // true fps_det_counter++; //std::this_thread::sleep_for(std::chrono::milliseconds(150)); detection_data.new_detection = true; detection_data.result_vec = result_vec; detect2draw.send(detection_data); } while (!detection_data.exit_flag); std::cout << " t_detect exit \n"; }); // draw rectangles (and track objects) t_draw = std::thread([&]() { std::queue<cv::Mat> track_optflow_queue; detection_data_t detection_data; do { // for Video-file if (detection_sync) { detection_data = detect2draw.receive(); } // for Video-camera else { // get new Detection result if present if (detect2draw.is_object_present()) { cv::Mat old_cap_frame = detection_data.cap_frame; // use old captured frame detection_data = detect2draw.receive(); if (!old_cap_frame.empty()) detection_data.cap_frame = old_cap_frame; } // get new Captured frame else { std::vector<bbox_t> old_result_vec = detection_data.result_vec; // use old detections detection_data = cap2draw.receive(); detection_data.result_vec = old_result_vec; } } cv::Mat cap_frame = detection_data.cap_frame; cv::Mat draw_frame = detection_data.cap_frame.clone(); std::vector<bbox_t> result_vec = detection_data.result_vec; #ifdef TRACK_OPTFLOW if (detection_data.new_detection) { tracker_flow.update_tracking_flow(detection_data.cap_frame, detection_data.result_vec); while (track_optflow_queue.size() > 0) { draw_frame = track_optflow_queue.back(); result_vec = tracker_flow.tracking_flow(track_optflow_queue.front(), false); track_optflow_queue.pop(); } } else { track_optflow_queue.push(cap_frame); result_vec = tracker_flow.tracking_flow(cap_frame, false); } detection_data.new_detection = true; // to correct kalman filter #endif //TRACK_OPTFLOW // track ID by using kalman filter if (use_kalman_filter) { if (detection_data.new_detection) { result_vec = track_kalman.correct(result_vec); } else { result_vec = track_kalman.predict(); } } // track ID by using custom function else { int frame_story = std::max(5, current_fps_cap.load()); result_vec = detector.tracking_id(result_vec, true, frame_story, 40); } if (use_zed_camera && !detection_data.zed_cloud.empty()) { result_vec = get_3d_coordinates(result_vec, detection_data.zed_cloud); } //small_preview.set(draw_frame, result_vec); //large_preview.set(draw_frame, result_vec); draw_boxes(draw_frame, result_vec, obj_names, current_fps_det, current_fps_cap); //show_console_result(result_vec, obj_names, detection_data.frame_id); //large_preview.draw(draw_frame); //small_preview.draw(draw_frame, true); detection_data.result_vec = result_vec; detection_data.draw_frame = draw_frame; draw2show.send(detection_data); if (send_network) draw2net.send(detection_data); if (output_video.isOpened()) draw2write.send(detection_data); } while (!detection_data.exit_flag); std::cout << " t_draw exit \n"; }); // write frame to videofile t_write = std::thread([&]() { if (output_video.isOpened()) { detection_data_t detection_data; cv::Mat output_frame; do { detection_data = draw2write.receive(); if(detection_data.draw_frame.channels() == 4) cv::cvtColor(detection_data.draw_frame, output_frame, CV_RGBA2RGB); else output_frame = detection_data.draw_frame; output_video << output_frame; } while (!detection_data.exit_flag); output_video.release(); } std::cout << " t_write exit \n"; }); // send detection to the network t_network = std::thread([&]() { if (send_network) { detection_data_t detection_data; do { detection_data = draw2net.receive(); detector.send_json_http(detection_data.result_vec, obj_names, detection_data.frame_id, filename); } while (!detection_data.exit_flag); } std::cout << " t_network exit \n"; }); // show detection detection_data_t detection_data; do { steady_end = std::chrono::steady_clock::now(); float time_sec = std::chrono::duration<double>(steady_end - steady_start).count(); if (time_sec >= 1) { current_fps_det = fps_det_counter.load() / time_sec; current_fps_cap = fps_cap_counter.load() / time_sec; steady_start = steady_end; fps_det_counter = 0; fps_cap_counter = 0; } detection_data = draw2show.receive(); cv::Mat draw_frame = detection_data.draw_frame; //if (extrapolate_flag) { // cv::putText(draw_frame, "extrapolate", cv::Point2f(10, 40), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.0, cv::Scalar(50, 50, 0), 2); //} cv::imshow("window name", draw_frame); filename.replace(filename.end()-4, filename.end(), "_yolov4_out.jpg"); int key = cv::waitKey(3); // 3 or 16ms if (key == 'f') show_small_boxes = !show_small_boxes; if (key == 'p') while (true) if (cv::waitKey(100) == 'p') break; //if (key == 'e') extrapolate_flag = !extrapolate_flag; if (key == 27) { exit_flag = true;} //std::cout << " current_fps_det = " << current_fps_det << ", current_fps_cap = " << current_fps_cap << std::endl; } while (!detection_data.exit_flag); std::cout << " show detection exit \n"; cv::destroyWindow("window name"); // wait for all threads if (t_cap.joinable()) t_cap.join(); if (t_prepare.joinable()) t_prepare.join(); if (t_detect.joinable()) t_detect.join(); if (t_post.joinable()) t_post.join(); if (t_draw.joinable()) t_draw.join(); if (t_write.joinable()) t_write.join(); if (t_network.joinable()) t_network.join(); break; } else if (file_ext == "txt") { // list of image files std::ifstream file(filename); if (!file.is_open()) std::cout << "File not found! \n"; else for (std::string line; file >> line;) { std::cout << line << std::endl; cv::Mat mat_img = cv::imread(line); std::vector<bbox_t> result_vec = detector.detect(mat_img); show_console_result(result_vec, obj_names); //draw_boxes(mat_img, result_vec, obj_names); //cv::imwrite("res_" + line, mat_img); } } else { // image file // to achive high performance for multiple images do these 2 lines in another thread cv::Mat mat_img = cv::imread(filename); auto det_image = detector.mat_to_image_resize(mat_img); auto start = std::chrono::steady_clock::now(); std::vector<bbox_t> result_vec = detector.detect_resized(*det_image, mat_img.size().width, mat_img.size().height); auto end = std::chrono::steady_clock::now(); std::chrono::duration<double> spent = end - start; std::cout << " Time: " << spent.count() << " sec \n"; //result_vec = detector.tracking_id(result_vec); // comment it - if track_id is not required draw_boxes(mat_img, result_vec, obj_names); cv::imshow("window name", mat_img); vector<string> filenamesplit=split(filename,'/'); string endname=filenamesplit[filenamesplit.size()-1]; endname.replace(endname.end()-4,endname.end(),"_yolov4_out.jpg"); std::string outputfile="detect_result/"+endname; imwrite(outputfile, mat_img); show_console_result(result_vec, obj_names); cv::waitKey(0); } #else // OPENCV //std::vector<bbox_t> result_vec = detector.detect(filename); auto img = detector.load_image(filename); std::vector<bbox_t> result_vec = detector.detect(img); detector.free_image(img); show_console_result(result_vec, obj_names); #endif // OPENCV } catch (std::exception &e) { std::cerr << "exception: " << e.what() << "\n"; getchar(); } catch (...) { std::cerr << "unknown exception \n"; getchar(); } filename.clear(); } return 0; }
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yolo_v2_class.hpp直接使用github的代码
Cmakelist.txt如下：
cmake_minimum_required(VERSION 3.5)
project(yolov4)
find_package( OpenCV 3 REQUIRED )
set(CMAKE_CXX_STANDARD 14)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(yolov4 main.cpp yolo_v2_class.hpp)
target_link_libraries(yolov4 ${OpenCV_LIBS} libdarknet.so libpthread.so.0)
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注意project的yolov4改成自己工程的名字，另外需要链接两个动态库，第一个库是darknet的所有网络层的定义，第二个库是linux的多线程库。两个库都放在/usr/lib目录下，因为这个目录是可以被C++自动检索到的。
libpthread.so.0默认情况下在/lib/x86_64-linux-gnu下，因此可以自动被检索到，只需要把它写在Cmakelist里面就可以。
/usr/bin/ld: CMakeFiles/yolov4.dir/main.cpp.o: undefined reference to symbol 'pthread_create@@GLIBC_2.2.5'
//lib/x86_64-linux-gnu/libpthread.so.0: error adding symbols: DSO missing from command line
collect2: error: ld returned 1 exit status
CMakeFiles/yolov4.dir/build.make:111: recipe for target 'yolov4' failed
make[2]: *** [yolov4] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/yolov4.dir/all' failed
make[1]: *** [CMakeFiles/yolov4.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2
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libdarknet.so则可以通过编译AB大神的darknet工程得到，修改工程的makefile如下：
GPU=0
CUDNN=0
CUDNN_HALF=0
OPENCV=1
AVX=0
OPENMP=1
LIBSO=1
ZED_CAMERA=0 # ZED SDK 3.0 and above
ZED_CAMERA_v2_8=0 # ZED SDK 2.X
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然后make，编译成功以后，就在目录里就出现了libdarknet.so动态库，将其copy至/usr/lib文件夹。
切换到我们的Yolov4文件夹，然后make，通过编译以后，可以运行可执行程序。
./yolov4 image/kite.jpg //检测图片
./yolov4 test.mp4 //检测视频
./yolov4 web_camera //检测摄像头
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mini_batch = 1, batch = 1, time_steps = 1, train = 0 layer filters size/strd(dil) input output 0 conv 32 3 x 3/ 1 576 x 576 x 3 -> 576 x 576 x 32 0.573 BF 1 conv 64 3 x 3/ 2 576 x 576 x 32 -> 288 x 288 x 64 3.058 BF 2 conv 64 1 x 1/ 1 288 x 288 x 64 -> 288 x 288 x 64 0.679 BF 3 route 1 -> 288 x 288 x 64 4 conv 64 1 x 1/ 1 288 x 288 x 64 -> 288 x 288 x 64 0.679 BF 5 conv 32 1 x 1/ 1 288 x 288 x 64 -> 288 x 288 x 32 0.340 BF 6 conv 64 3 x 3/ 1 288 x 288 x 32 -> 288 x 288 x 64 3.058 BF 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 288 x 288 x 64 0.005 BF 8 conv 64 1 x 1/ 1 288 x 288 x 64 -> 288 x 288 x 64 0.679 BF 9 route 8 2 -> 288 x 288 x 128 10 conv 64 1 x 1/ 1 288 x 288 x 128 -> 288 x 288 x 64 1.359 BF 11 conv 128 3 x 3/ 2 288 x 288 x 64 -> 144 x 144 x 128 3.058 BF 12 conv 64 1 x 1/ 1 144 x 144 x 128 -> 144 x 144 x 64 0.340 BF 13 route 11 -> 144 x 144 x 128 14 conv 64 1 x 1/ 1 144 x 144 x 128 -> 144 x 144 x 64 0.340 BF 15 conv 64 1 x 1/ 1 144 x 144 x 64 -> 144 x 144 x 64 0.170 BF 16 conv 64 3 x 3/ 1 144 x 144 x 64 -> 144 x 144 x 64 1.529 BF 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 144 x 144 x 64 0.001 BF 18 conv 64 1 x 1/ 1 144 x 144 x 64 -> 144 x 144 x 64 0.170 BF 19 conv 64 3 x 3/ 1 144 x 144 x 64 -> 144 x 144 x 64 1.529 BF 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 144 x 144 x 64 0.001 BF 21 conv 64 1 x 1/ 1 144 x 144 x 64 -> 144 x 144 x 64 0.170 BF 22 route 21 12 -> 144 x 144 x 128 23 conv 128 1 x 1/ 1 144 x 144 x 128 -> 144 x 144 x 128 0.679 BF 24 conv 256 3 x 3/ 2 144 x 144 x 128 -> 72 x 72 x 256 3.058 BF 25 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF 26 route 24 -> 72 x 72 x 256 27 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF 28 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF 29 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF 31 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF 32 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF 34 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF 35 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF 37 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF 38 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF 40 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF 41 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF 43 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF 44 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF 46 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF 47 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF 49 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF 50 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF 52 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF 53 route 52 25 -> 72 x 72 x 256 54 conv 256 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 256 0.679 BF 55 conv 512 3 x 3/ 2 72 x 72 x 256 -> 36 x 36 x 512 3.058 BF 56 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF 57 route 55 -> 36 x 36 x 512 58 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF 59 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF 60 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF 62 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF 63 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF 65 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF 66 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF 68 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF 69 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF 71 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF 72 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF 74 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF 75 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF 77 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF 78 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF 80 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF 81 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF 83 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF 84 route 83 56 -> 36 x 36 x 512 85 conv 512 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 512 0.679 BF 86 conv 1024 3 x 3/ 2 36 x 36 x 512 -> 18 x 18 x1024 3.058 BF 87 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF 88 route 86 -> 18 x 18 x1024 89 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF 90 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF 91 conv 512 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x 512 1.529 BF 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 18 x 18 x 512 0.000 BF 93 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF 94 conv 512 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x 512 1.529 BF 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 18 x 18 x 512 0.000 BF 96 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF 97 conv 512 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x 512 1.529 BF 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 18 x 18 x 512 0.000 BF 99 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF 100 conv 512 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x 512 1.529 BF 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 18 x 18 x 512 0.000 BF 102 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF 103 route 102 87 -> 18 x 18 x1024 104 conv 1024 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x1024 0.679 BF 105 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF 106 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF 107 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF 108 max 5x 5/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.004 BF 109 route 107 -> 18 x 18 x 512 110 max 9x 9/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.013 BF 111 route 107 -> 18 x 18 x 512 112 max 13x13/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.028 BF 113 route 112 110 108 107 -> 18 x 18 x2048 114 conv 512 1 x 1/ 1 18 x 18 x2048 -> 18 x 18 x 512 0.679 BF 115 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF 116 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF 117 conv 256 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 256 0.085 BF 118 upsample 2x 18 x 18 x 256 -> 36 x 36 x 256 119 route 85 -> 36 x 36 x 512 120 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF 121 route 120 118 -> 36 x 36 x 512 122 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF 123 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF 124 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF 125 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF 126 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF 127 conv 128 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 128 0.085 BF 128 upsample 2x 36 x 36 x 128 -> 72 x 72 x 128 129 route 54 -> 72 x 72 x 256 130 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF 131 route 130 128 -> 72 x 72 x 256 132 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF 133 conv 256 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 256 3.058 BF 134 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF 135 conv 256 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 256 3.058 BF 136 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF 137 conv 256 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 256 3.058 BF 138 conv 255 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 255 0.677 BF 139 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.20 nms_kind: greedynms (1), beta = 0.600000 140 route 136 -> 72 x 72 x 128 141 conv 256 3 x 3/ 2 72 x 72 x 128 -> 36 x 36 x 256 0.764 BF 142 route 141 126 -> 36 x 36 x 512 143 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF 144 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF 145 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF 146 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF 147 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF 148 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF 149 conv 255 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 255 0.338 BF 150 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.10 nms_kind: greedynms (1), beta = 0.600000 151 route 147 -> 36 x 36 x 256 152 conv 512 3 x 3/ 2 36 x 36 x 256 -> 18 x 18 x 512 0.764 BF 153 route 152 116 -> 18 x 18 x1024 154 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF 155 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF 156 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF 157 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF 158 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF 159 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF 160 conv 255 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 255 0.169 BF 161 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05 nms_kind: greedynms (1), beta = 0.600000 Total BFLOPS 115.293 avg_outputs = 958892 Loading weights from yolov4.weights... seen 64, trained: 32032 K-images (500 Kilo-batches_64) Done! Loaded 162 layers from weights-file object names loaded input image or video filename: Time: 7.29136 sec surfboard - obj_id = 37, x = 521, y = 517, w = 32, h = 15, prob = 0.393 surfboard - obj_id = 37, x = 497, y = 521, w = 39, h = 10, prob = 0.255 surfboard - obj_id = 37, x = 814, y = 567, w = 30, h = 10, prob = 0.248 kite - obj_id = 33, x = 591, y = 79, w = 75, h = 71, prob = 0.994 kite - obj_id = 33, x = 279, y = 235, w = 23, h = 45, prob = 0.979 kite - obj_id = 33, x = 575, y = 343, w = 25, h = 25, prob = 0.951 kite - obj_id = 33, x = 1082, y = 393, w = 14, h = 28, prob = 0.943 kite - obj_id = 33, x = 464, y = 339, w = 16, h = 18, prob = 0.855 kite - obj_id = 33, x = 300, y = 375, w = 23, h = 32, prob = 0.68 kite - obj_id = 33, x = 760, y = 379, w = 7, h = 9, prob = 0.634 person - obj_id = 0, x = 110, y = 610, w = 51, h = 151, prob = 0.994 person - obj_id = 0, x = 213, y = 698, w = 53, h = 159, prob = 0.993 person - obj_id = 0, x = 1204, y = 450, w = 9, h = 12, prob = 0.872 person - obj_id = 0, x = 37, y = 509, w = 16, h = 51, prob = 0.871 person - obj_id = 0, x = 345, y = 487, w = 9, h = 14, prob = 0.866 person - obj_id = 0, x = 176, y = 539, w = 11, h = 32, prob = 0.832 person - obj_id = 0, x = 21, y = 529, w = 14, h = 26, prob = 0.801 person - obj_id = 0, x = 82, y = 506, w = 25, h = 57, prob = 0.697 person - obj_id = 0, x = 518, y = 506, w = 16, h = 18, prob = 0.606 person - obj_id = 0, x = 692, y = 462, w = 7, h = 6, prob = 0.552 person - obj_id = 0, x = 460, y = 471, w = 7, h = 6, prob = 0.394 person - obj_id = 0, x = 537, y = 514, w = 14, h = 17, prob = 0.381
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测试图片结果
总结
在本文中，我们使用C++调用了作者在COCO数据集上的训练结果进行了图片测试，并且可以进行视频测试和网络摄像头测试（自己笔记本显卡太弱，视频跑不起来），如果我们想要自己训练数据集，仍然可以参考github，可以通过训练得到weights文件，然后按照本文所讲的进行测试。
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