C++ opencv(Yolov4-tiny)

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 
#include 
#include 
#include 
#include 
#include 
#include 

#include 
#include 
#include 

#include 
#include 

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 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 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 indices[NUM_CLASSES];
		std::vector boxes[NUM_CLASSES];
		std::vector 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(i, 0) * frame.cols;
				auto y = output.at(i, 1) * frame.rows;
				auto width = output.at(i, 2) * frame.cols;
				auto height = output.at(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(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(dnn_end - dnn_start).count();
		float total_fps = 1000.0 / std::chrono::duration_cast(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;
}