ImageRecognition/ClassifyDectect.cpp

#include"ClassifyDectect.h"
#include "YunDaISASImageRecognitionService.h"
//using namespace cv::dnn;

//int img;
cv::Mat img;
cv::Mat imageWarp;
bool ClassifyDectect::Init(bool isCuda) {
	try {
		string model_path = "models/classify-sim.onnx";
		net = cv::dnn::readNet(model_path);
	}
	catch (const std::exception& ex) 
	{
		YunDaISASImageRecognitionService::ConsoleLog(ex.what());
		return false;
	}
	//cuda
	if (isCuda) {
		net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
		net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA_FP16);
	}
	//cpu
	else {
		net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
		net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
	}
	return true;
}
bool ClassifyDectect::Detect(cv::Mat& SrcImg, vector<Output>& output ) {
	cv::Mat blob;
	int col = SrcImg.cols;
	int row = SrcImg.rows;
	int maxLen = MAX(col, row);
	cv::Mat netInputImg = SrcImg.clone();
	if (maxLen > 1.2 * col || maxLen > 1.2 * row) {
		cv::Mat resizeImg = cv::Mat::zeros(maxLen, maxLen, CV_8UC3);
		SrcImg.copyTo(resizeImg(cv::Rect(0, 0, col, row)));
		netInputImg = resizeImg;
	}
	cv::dnn::blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(0, 0, 0), true, false);
	net.setInput(blob);
	std::vector<cv::Mat> netOutputImg;
	net.forward(netOutputImg, net.getUnconnectedOutLayersNames());
	std::vector<int> classIds;//结果id数组
	std::vector<float> confidences;//结果每个id对应置信度数组
	std::vector<cv::Rect> 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);
						cv::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;
							int widthBox = int(w * ratio_w);
							int heightBox = int(h * ratio_h);
							widthBox = widthBox > col ? col : widthBox;
							heightBox = heightBox > row ? row : heightBox;
							left = left < 0 ? 0 : left;
							top = top < 0 ? 0 : top;
							if (left < 0 || left>col || top < 0 || top>row || widthBox > col || heightBox > row)
							{
								continue;
							}
							classIds.push_back(classIdPoint.x);
							confidences.push_back(max_class_socre * box_score);
							boxes.push_back(cv::Rect(left, top, widthBox, heightBox));
						}
					}
					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<int> nms_result;
	cv::dnn::NMSBoxes(boxes, confidences, nmsScoreThreshold, nmsThreshold, nms_result);
	for (int i = 0; i < nms_result.size(); i++) {
		int idx = nms_result[i];
		Output result;
		int figure =0;
		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 ClassifyDectect::drawPred(cv::Mat& img, vector<Output> result)
{
	for (int i = 0; i < result.size(); i++) {
		try
		{
			int left = result[i].box.x;
			int top = result[i].box.y;
			int width = result[i].box.width;
			int height = result[i].box.height;
			int baseLine;

			//2022.10.17
			cv::Rect box = result[i].box;
			/*MeterRead meterRead;*/
			cv::Mat input_image_copy = img.clone();
			cv::Mat cutMat = input_image_copy(box);
			/*	auto resSum = meterRead.GetResult(cutMat, true);
				cout << "识别结果:" << resSum << endl;*/

				//string label = className[result[i].id] + ":" + to_string(result[i].confidence);
			string label = className[result[i].id]+ ": " + to_string(result[i].confidence);
			cv::Size labelSize = getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
			top = max(top, labelSize.height);
			cv::rectangle(img, cv::Point(left, top - int(2 * labelSize.height)), cv::Point(left + int(2 * labelSize.width), top + baseLine), cv::Scalar(0, 0, 255), cv::FILLED);
			cv::rectangle(img, cv::Point(left, top), cv::Point(left + width, top + height), cv::Scalar(0, 0, 255), 3);
			cv::putText(img, label, cv::Point(left, top), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(255, 255, 255), 3);
		}
		catch (const std::exception& ex)
		{
			YunDaISASImageRecognitionService::ConsoleLog(ex.what());
		}
		
	}

	/*cv::namedWindow("result", CV_WINDOW_NORMAL);
	cv::imshow("result", img);
	cv::waitKey();
	cv::destroyAllWindows();*/
}

void ClassifyDectect::modifyConfidenceParameter(float boxThresholdPara, float classThresholdPara, float nmsThresholdPara)
{
	boxThreshold = boxThresholdPara;
	classThreshold = classThresholdPara;
	nmsThreshold = nmsThresholdPara;
}