基本配置：VS+Opencv+摄像头（本地图片）
模型文件：包含Yolov4和Yolov4-tiny两个版本。链接：https://pan.baidu.com/s/1dXiRWDwZcRf1ckM1T8avmA 提取码：dxqu
代码：
#include <iostream>
#include <queue>
#include <iterator>
#include <sstream>
#include <fstream>
#include <iomanip>
#include <chrono>#include <opencv2/core.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/dnn/all_layers.hpp>#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>constexpr float CONFIDENCE_THRESHOLD = 0;
constexpr float NMS_THRESHOLD = 0.4;
constexpr int NUM_CLASSES = 80;// colors for bounding boxes
const cv::Scalar colors[] = {{0, 255, 255},{255, 255, 0},{0, 255, 0},{255, 0, 0}
};
const auto NUM_COLORS = sizeof(colors) / sizeof(colors[0]);int main()
{std::vector<std::string> class_names;{std::ifstream class_file("coco.names");if (!class_file){std::cerr << "failed to open classes.txt\n";return 0;}std::string line;while (std::getline(class_file, line))class_names.push_back(line);}cv::VideoCapture source(0);auto net = cv::dnn::readNetFromDarknet("yolov4-tiny.cfg", "yolov4-tiny.weights");//net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);//net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);auto output_names = net.getUnconnectedOutLayersNames();cv::Mat frame, blob;std::vector<cv::Mat> detections;while (cv::waitKey(1) < 1){source >> frame;if (frame.empty()){cv::waitKey();break;}auto total_start = std::chrono::steady_clock::now();cv::dnn::blobFromImage(frame, blob, 0.00392, cv::Size(416, 416), cv::Scalar(), true, false, CV_32F);net.setInput(blob);auto dnn_start = std::chrono::steady_clock::now();net.forward(detections, output_names);auto dnn_end = std::chrono::steady_clock::now();std::vector<int> indices[NUM_CLASSES];std::vector<cv::Rect> boxes[NUM_CLASSES];std::vector<float> scores[NUM_CLASSES];for (auto& output : detections){const auto num_boxes = output.rows;for (int i = 0; i < num_boxes; i++){auto x = output.at<float>(i, 0) * frame.cols;auto y = output.at<float>(i, 1) * frame.rows;auto width = output.at<float>(i, 2) * frame.cols;auto height = output.at<float>(i, 3) * frame.rows;cv::Rect rect(x - width / 2, y - height / 2, width, height);for (int c = 0; c < NUM_CLASSES; c++){auto confidence = *output.ptr<float>(i, 5 + c);if (confidence >= CONFIDENCE_THRESHOLD){boxes[c].push_back(rect);scores[c].push_back(confidence);}}}}for (int c = 0; c < NUM_CLASSES; c++)cv::dnn::NMSBoxes(boxes[c], scores[c], 0.0, NMS_THRESHOLD, indices[c]);for (int c = 0; c < NUM_CLASSES; c++){for (size_t i = 0; i < indices[c].size(); ++i){const auto color = colors[c % NUM_COLORS];auto idx = indices[c][i];const auto& rect = boxes[c][idx];cv::rectangle(frame, cv::Point(rect.x, rect.y), cv::Point(rect.x + rect.width, rect.y + rect.height), color, 3);std::ostringstream label_ss;label_ss << class_names[c] << ": " << std::fixed << std::setprecision(2) << scores[c][idx];auto label = label_ss.str();int baseline;auto label_bg_sz = cv::getTextSize(label.c_str(), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, 1, &baseline);cv::rectangle(frame, cv::Point(rect.x, rect.y - label_bg_sz.height - baseline - 10), cv::Point(rect.x + label_bg_sz.width, rect.y), color, cv::FILLED);cv::putText(frame, label.c_str(), cv::Point(rect.x, rect.y - baseline - 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(0, 0, 0));}}auto total_end = std::chrono::steady_clock::now();float inference_fps = 1000.0 / std::chrono::duration_cast<std::chrono::milliseconds>(dnn_end - dnn_start).count();float total_fps = 1000.0 / std::chrono::duration_cast<std::chrono::milliseconds>(total_end - total_start).count();std::ostringstream stats_ss;stats_ss << std::fixed << std::setprecision(2);stats_ss << "Inference FPS: " << inference_fps << ", Total FPS: " << total_fps;auto stats = stats_ss.str();int baseline;auto stats_bg_sz = cv::getTextSize(stats.c_str(), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, 1, &baseline);cv::rectangle(frame, cv::Point(0, 0), cv::Point(stats_bg_sz.width, stats_bg_sz.height + 10), cv::Scalar(0, 0, 0), cv::FILLED);cv::putText(frame, stats.c_str(), cv::Point(0, stats_bg_sz.height + 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(255, 255, 255));cv::namedWindow("output");cv::imshow("output", frame);}return 0;
}