c++调用YOLOv3模型批量测试目标检测结果

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


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

vector<string> classes;

vector<String> getOutputsNames(Net&net)
{
	static vector<String> names;
	if (names.empty())
	{
		//Get the indices of the output layers, i.e. the layers with unconnected outputs
		vector<int> outLayers = net.getUnconnectedOutLayers();

		//get the names of all the layers in the network
		vector<String> layersNames = net.getLayerNames();

		// Get the names of the output layers in names
		names.resize(outLayers.size());
		for (size_t i = 0; i < outLayers.size(); ++i)
			names[i] = layersNames[outLayers[i] - 1];
	}
	return names;
}


void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
{
	//Draw a rectangle displaying the bounding box
	rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 0, 255), 1.5);//矩形框大小及颜色

	//Get the label for the class name and its confidence
	string label = format("%.3f", conf);   //预测值保留小数点后两位
	if (!classes.empty())
	{
		CV_Assert(classId < (int)classes.size());
		label = classes[classId] + ":" + label;
	}

	//Display the label at the top of the bounding box
	int baseLine;
	Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0, 1, &baseLine);   //0表示预测框上面的文本条大小，0表示无
	top = max(top, labelSize.height);
	rectangle(frame, Point(left, top - round(0.5*labelSize.height)), Point(left + round(0.5*labelSize.width), top + baseLine), Scalar(255, 255, 255), FILLED);
	//putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, Scalar(255, 0, 0), 3);  //0.4表示预测字体的大小，1表示字体的粗细
	putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.4, Scalar(255, 0, 0), 1.4);  //0.4表示预测字体的大小，1表示字体的粗细
}



void postprocess(Mat& frame, const vector<Mat>& outs, float confThreshold, float nmsThreshold)
{
	vector<int> classIds;
	vector<float> confidences;
	vector<Rect> boxes;

	for (size_t i = 0; i < outs.size(); ++i)
	{
		// Scan through all the bounding boxes output from the network and keep only the
		// ones with high confidence scores. Assign the box's class label as the class
		// with the highest score for the box.
		float* data = (float*)outs[i].data;
		for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
		{
			Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
			Point classIdPoint;
			double confidence;
			// Get the value and location of the maximum score
			minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
			if (confidence > confThreshold)
			{
				int centerX = (int)(data[0] * frame.cols);
				int centerY = (int)(data[1] * frame.rows);
				int width = (int)(data[2] * frame.cols);
				int height = (int)(data[3] * frame.rows);
				int left = centerX - width / 2;
				int top = centerY - height / 2;

				classIds.push_back(classIdPoint.x);
				confidences.push_back((float)confidence);
				boxes.push_back(Rect(left, top, width, height));
			}
		}
	}

	// Perform non maximum suppression to eliminate redundant overlapping boxes with
	// lower confidences
	vector<int> indices;
	NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);

	for (size_t i = 0; i < indices.size(); ++i)
	{
		int idx = indices[i];
		Rect box = boxes[idx];
		drawPred(classIds[idx], confidences[idx], box.x, box.y,
			box.x + box.width, box.y + box.height, frame);

	}
}

int main()
{

	string names_file = "D:\\PointerImg\\darknet-half-pointer\\data\\voc.names";
	String model_def = "D:\\PointerImg\\darknet-half-pointer\\cfg\\yolov3-voc.cfg";
	String weights = "D:\\PointerImg\\darknet-half-pointer\\backup\\tiny1\\yolov3-voc_last.weights";


	int in_w, in_h;
	double thresh = 0.5;
	double nms_thresh = 0.25;
	in_w = in_h = 416;


	string path = "D:/PointerImg/darknet-half-pointer/data/meter/reality/";
	String dest = "D:/PointerImg/darknet-half-pointer/data/predicts/pre2/";

	String savedfilename;
	int len = path.length();
	vector<cv::String> filenames;

	cv::glob(path, filenames);
	for (int i = 0; i < filenames.size(); i++) {


		//read names

		ifstream ifs(names_file.c_str());
		string line;
		while (getline(ifs, line)) classes.push_back(line);

		//init model
		Net net = readNetFromDarknet(model_def, weights);
		net.setPreferableBackend(DNN_BACKEND_OPENCV);
		net.setPreferableTarget(DNN_TARGET_CPU);

		//read image and forward
		VideoCapture capture(2);// VideoCapture:OENCV中新增的类，捕获视频并显示出来
		/*while (1)
		{*/
			Mat frame, blob;
			capture >> frame;

			frame = imread(filenames[i]);
			blobFromImage(frame, blob, 1 / 255.0, Size(in_w, in_h), Scalar(), true, false);

			vector<Mat> mat_blob;
			imagesFromBlob(blob, mat_blob);

			//Sets the input to the network
			net.setInput(blob);

			// Runs the forward pass to get output of the output layers
			vector<Mat> outs;
			net.forward(outs, getOutputsNames(net));

			postprocess(frame, outs, thresh, nms_thresh);

			vector<double> layersTimes;
			double freq = getTickFrequency() / 1000;
			double t = net.getPerfProfile(layersTimes) / freq;
			string label = format("Inference time for a frame : %.2f ms", t);
			putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
			printf("Inference time for a frame : %.2f ms", t);
			//savedfilename = dest + filenames[i].substr(54);   //path的字符串长度
			savedfilename = dest + filenames[i].substr(len);
			cout << savedfilename << endl;
			imwrite(savedfilename, frame);
			//imwrite("D:\\PointerImg\\darknet-master-meter_pointer\\data\\predicts\\1.jpg", frame);
			//imshow("res", frame);

			//waitKey(0);
		/*}*/
	}
	return 0;
}