c/c++语言开发共享c++调用实现yolov5转onnx介绍

目录

• 介绍
• 训练模型.pt转onnx
• c++代码解析
• main函数部分
• 推理部分讲解
• darpred部分
• sigmod部分
• 结尾

  void postprocess(cv::mat& cv_src, std::vector<cv::mat>& outs, const std::vector<std::string>& classes, int net_size)  {  	float confthreshold = 0.1f;  	float nmsthreshold = 0.1f;  	std::vector<int> classids;  	std::vector<float> confidences;  	std::vector<cv::rect> boxes;  	int strides[] = { 8, 16, 32 };  	std::vector<std::vector<int> > anchors =  	{  		{ 10,13, 16,30, 33,23 },  		{ 30,61, 62,45, 59,119 },  		{ 116,90, 156,198, 373,326 }  	};  	for (size_t k = 0; k < outs.size(); k++)  	{  		float* data = outs[k].ptr<float>();  		int stride = strides[k];  		int num_classes = outs[k].size[4] - 5;  		for (int i = 0; i < outs[k].size[2]; i++)  		{  			for (int j = 0; j < outs[k].size[3]; j++)  			{  				for (int a = 0; a < outs[k].size[1]; ++a)  				{  					float* record = data + a * outs[k].size[2] * outs[k].size[3] * outs[k].size[4] +  						i * outs[k].size[3] * outs[k].size[4] + j * outs[k].size[4];  					float* cls_ptr = record + 5;  					for (int cls = 0; cls < num_classes; cls++)  					{  						float score = sigmoid(cls_ptr[cls]) * sigmoid(record[4]);  						if (score > confthreshold)  						{  							float cx = (sigmoid(record[0]) * 2.f - 0.5f + (float)j) * (float)stride;  							float cy = (sigmoid(record[1]) * 2.f - 0.5f + (float)i) * (float)stride;  							float w = pow(sigmoid(record[2]) * 2.f, 2) * anchors[k][2 * a];  							float h = pow(sigmoid(record[3]) * 2.f, 2) * anchors[k][2 * a + 1];  							float x1 = std::max(0, std::min(cv_src.cols, int((cx - w / 2.f) * (float)cv_src.cols / (float)net_size)));  							float y1 = std::max(0, std::min(cv_src.rows, int((cy - h / 2.f) * (float)cv_src.rows / (float)net_size)));  							float x2 = std::max(0, std::min(cv_src.cols, int((cx + w / 2.f) * (float)cv_src.cols / (float)net_size)));  							float y2 = std::max(0, std::min(cv_src.rows, int((cy + h / 2.f) * (float)cv_src.rows / (float)net_size)));  							classids.push_back(cls);  							confidences.push_back(score);  							boxes.push_back(cv::rect(cv::point(x1, y1), cv::point(x2, y2)));  						}  					}  				}  			}  		}  	}  	std::vector<int> indices;  	cv::dnn::nmsboxes(boxes, confidences, confthreshold, nmsthreshold, indices);  	for (size_t i = 0; i < indices.size(); i++)  	{  		int idx = indices[i];  		cv::rect box = boxes[idx];  		drawpred(classids[idx], confidences[idx], box.x, box.y,  			box.x + box.width, box.y + box.height, cv_src, classes);  	}  }  

  void drawpred(int classid, float conf, int left, int top, int right, int bottom, cv::mat& frame,  	const std::vector<std::string>& classes)  {  	cv::rectangle(frame, cv::point(left, top), cv::point(right, bottom), cv::scalar(0, 255, 0), 3);  	std::string label = cv::format("%.2f", conf);  	if (!classes.empty()) {  		cv_assert(classid < (int)classes.size());  		label = classes[classid] + ": " + label;  	}  	int baseline;  	cv::size labelsize = cv::gettextsize(label, cv::font_hershey_simplex, 0.5, 1, &baseline);  	top = std::max(top, labelsize.height);  	cv::rectangle(frame, cv::point(left, top - round(1.5 * labelsize.height)), cv::point(left + round(1.5 * labelsize.width), top + baseline), cv::scalar(0, 255, 0), cv::filled);  	cv::puttext(frame, label, cv::point(left, top), cv::font_hershey_simplex, 0.75, cv::scalar(), 2);  }    

  inline float sigmoid(float x)  {  	return 1.f / (1.f + exp(-x));  }