【ONNX】opencv450加载 yolov5 ONNX

main

#include "yolo.h"
#include 
#include
#include

using namespace std;
using namespace cv;
using namespace dnn;

int main()
{

	string model_path = "./weights/bestGPU.onnx";
	//int num_devices = cv::cuda::getCudaEnabledDeviceCount();
	//if (num_devices <= 0) {
		//cerr << "There is no cuda." << endl;
		//return -1;
	//}
	//else {
		//cout << num_devices << endl;
	//}

	Yolo test;
	Net net;
	if (test.readModel(net, model_path, true)) {
		cout << "read net ok!" << endl;
	}
	else {
		return -1;
	}

	//生成随机颜色
	vector color;
	srand(time(0));
	for (int i = 0; i < 80; i++) {
		int b = rand() % 256;
		int g = rand() % 256;
		int r = rand() % 256;
		color.push_back(Scalar(b, g, r));
	}
	cv::Mat frame;
	cv::VideoCapture capture("1.mp4");
	if (!capture.isOpened())
	{
		std::cerr << "Error opening video file\n";
		return -1;
	}

	int frame_count = 0;
	float fps = -1;
	int total_frames = 0;
	auto start = std::chrono::high_resolution_clock::now();
	while (1)
	{
		capture.read(frame);
		if (frame.empty())
		{
			std::cout << "End of stream\n";
			break;
		}
		vector
 result;
		if (test.Detect(frame, net, result)) {
			test.drawPred(frame, result, color);

		}
		else {
			cout << "Detect Failed!" << endl;
		}

		if (frame_count >= 30)
		{

			auto end = std::chrono::high_resolution_clock::now();
			fps = frame_count * 1000.0 / std::chrono::duration_cast(end - start).count();

			frame_count = 0;
			start = std::chrono::high_resolution_clock::now();
		}

		if (fps > 0)
		{

			std::ostringstream fps_label;
			fps_label << std::fixed << std::setprecision(2);
			fps_label << "FPS: " << fps;
			std::string fps_label_str = fps_label.str();

			cv::putText(frame, fps_label_str.c_str(), cv::Point(10, 25), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 0, 255), 2);
		}

		cv::imshow("output", frame);
		int keyboard = waitKey(1);
		if (keyboard == 'q' || keyboard == 27)
		{
			capture.release();
			std::cout << "finished by user\n";
			//break;
		}
	}

	//string img_path = "./images/1.bmp";
	//Mat img = imread(img_path);
	/*if (test.Detect(img, net, result)) {
		test.drawPred(img, result, color);

	}
	else {
		cout << "Detect Failed!" << endl;
	}*/

	system("pause");
	return 0;
}

yolo.h

#pragma once
#include
#include

#define YOLO_P6 false //是否使用P6模型//

struct Output {
	int id;             //结果类别id//
	float confidence;   //结果置信度//
	cv::Rect box;       //矩形框//
};

class Yolo {
public:
	Yolo() {
	}
	~Yolo() {}
	bool readModel(cv::dnn::Net& net, std::string& netPath, bool isCuda);
	bool Detect(cv::Mat& SrcImg, cv::dnn::Net& net, std::vector
& output);
	void drawPred(cv::Mat& img, std::vector
 result, std::vector color);

private:
#if(defined YOLO_P6 && YOLO_P6==true)
	const float netAnchors[4][6] = { { 19,27, 44,40, 38,94 },{ 96,68, 86,152, 180,137 },{ 140,301, 303,264, 238,542 },{ 436,615, 739,380, 925,792 } };

	const int netWidth = 1280;  //ONNX图片输入宽度
	const int netHeight = 1280; //ONNX图片输入高度

	const int strideSize = 4;  //stride size
#else
	const float netAnchors[3][6] = { { 10,13, 16,30, 33,23 },{ 30,61, 62,45, 59,119 },{ 116,90, 156,198, 373,326 } };

	const int netWidth = 416;   //ONNX图片输入宽度
	const int netHeight = 416;  //ONNX图片输入高度

	const int strideSize = 3;   //stride size
#endif // YOLO_P6

	const float netStride[4] = { 8, 16.0,32,64 };

	float boxThreshold = 0.25;
	float classThreshold = 0.25;

	float nmsThreshold = 0.45;
	float nmsScoreThreshold = boxThreshold * classThreshold;

	std::vector className = { "block" };
};

yolo.cpp

#include"yolo.h"
using namespace std;
using namespace cv;
using namespace cv::dnn;

bool Yolo::readModel(Net& net, string& netPath, bool isCuda = false) {
	try {
		net = readNet(netPath);
	}
	catch (const std::exception&) {
		return false;
	}
	//cuda
	if (isCuda) {
		net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
		net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
	}
	//cpu
	else {
		net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
		net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
	}
	return true;
}
bool Yolo::Detect(Mat& SrcImg, Net& net, vector
& output) {
	Mat blob;
	int col = SrcImg.cols;
	int row = SrcImg.rows;
	int maxLen = MAX(col, row);
	Mat netInputImg = SrcImg.clone();
	if (maxLen > 1.2 * col || maxLen > 1.2 * row) {
		Mat resizeImg = Mat::zeros(maxLen, maxLen, CV_8UC3);
		SrcImg.copyTo(resizeImg(Rect(0, 0, col, row)));
		netInputImg = resizeImg;
	}
	blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(0, 0, 0), true, false);
	//如果在其他设置没有问题的情况下但是结果偏差很大，可以尝试下用下面两句语句
	//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(104, 117, 123), true, false);
	//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(114, 114,114), true, false);
	net.setInput(blob);
	std::vector netOutputImg;
	//vector outputLayerName{"345","403", "461","output" };
	//net.forward(netOutputImg, outputLayerName[3]); //获取output的输出//
	net.forward(netOutputImg, net.getUnconnectedOutLayersNames());//release GPU OK   debug GPU ->back to CPU
	std::vector classIds;//结果id数组//
	std::vector confidences;//结果每个id对应置信度数组//
	std::vector boxes;//每个id矩形框//

	float ratio_h = (float)netInputImg.rows / netHeight;
	float ratio_w = (float)netInputImg.cols / netWidth;
	int net_width = className.size() + 5;  //输出的网络宽度是类别数+5//
	float* pdata = (float*)netOutputImg[0].data;
	for (int stride = 0; stride < strideSize; stride++) {    //stride
		int grid_x = (int)(netWidth / netStride[stride]);
		int grid_y = (int)(netHeight / netStride[stride]);
		for (int anchor = 0; anchor < 3; anchor++) {	//anchors
			const float anchor_w = netAnchors[stride][anchor * 2];
			const float anchor_h = netAnchors[stride][anchor * 2 + 1];
			for (int i = 0; i < grid_y; i++) {
				for (int j = 0; j < grid_x; j++) {
					float box_score = pdata[4]; ;//获取每一行的box框中含有某个物体的概率//
					if (box_score >= boxThreshold) {
						cv::Mat scores(1, className.size(), CV_32FC1, pdata + 5);
						Point classIdPoint;
						double max_class_socre;
						minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
						max_class_socre = (float)max_class_socre;
						if (max_class_socre >= classThreshold) {
							//rect [x,y,w,h]
							float x = pdata[0];  //x//
							float y = pdata[1];  //y//
							float w = pdata[2];  //w//
							float h = pdata[3];  //h//
							int left = (x - 0.5 * w) * ratio_w;
							int top = (y - 0.5 * h) * ratio_h;
							classIds.push_back(classIdPoint.x);
							confidences.push_back(max_class_socre * box_score);
							boxes.push_back(Rect(left, top, int(w * ratio_w), int(h * ratio_h)));
						}
					}
					pdata += net_width;//下一行//
				}
			}
		}
	}
	//执行非最大抑制以消除具有较低置信度的冗余重叠框（NMS）//
	vector nms_result;
	NMSBoxes(boxes, confidences, nmsScoreThreshold, nmsThreshold, nms_result);
	for (int i = 0; i < nms_result.size(); i++) {
		int idx = nms_result[i];
		Output result;
		result.id = classIds[idx];
		result.confidence = confidences[idx];
		result.box = boxes[idx];
		output.push_back(result);
	}
	if (output.size())
		return true;
	else
		return false;
}

void Yolo::drawPred(Mat& img, vector
 result, vector color) {
	for (int i = 0; i < result.size(); i++) {
		int left, top;
		left = result[i].box.x;
		top = result[i].box.y;
		int color_num = i;
		rectangle(img, result[i].box, color[result[i].id], 2, 8);

		string label = className[result[i].id] + ":" + to_string(result[i].confidence);

		int baseLine;
		Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
		top = max(top, labelSize.height);
		//rectangle(frame, Point(left, top - int(1.5 * labelSize.height)), Point(left + int(1.5 * labelSize.width), top + baseLine), Scalar(0, 255, 0), FILLED);
		putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, color[result[i].id], 2);
	}
	//imshow("1", img);
	//imwrite("out.bmp", img);
	//waitKey();
	//destroyAllWindows();

}