yolov8-opencv-onnx/yolov8_obb.cpp

#include"yolov8_obb.h"

//using namespace std;
//using namespace cv;
//using namespace cv::dnn;

bool Yolov8Obb::ReadModel(cv::dnn::Net& net, std::string& netPath, bool isCuda = false) {
	try {
		if (!CheckModelPath(netPath))
			return false;
#if CV_VERSION_MAJOR==4 &&CV_VERSION_MINOR<7
		std::cout << "OBB Need OpenCV Version >=4.7.0" << std::endl; 
		return false;
#endif
		net = cv::dnn::readNetFromONNX(netPath);
#if CV_VERSION_MAJOR==4 &&CV_VERSION_MINOR==7&&CV_VERSION_REVISION==0
		net.enableWinograd(false);  //bug of opencv4.7.x in AVX only platform ,https://github.com/opencv/opencv/pull/23112 and https://github.com/opencv/opencv/issues/23080 
		//net.enableWinograd(true);		//If your CPU supports AVX2, you can set it true to speed up
#endif
	}
	catch (const std::exception&) {
		return false;
	}

	if (isCuda) {
		//cuda
		net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
		net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA); //or DNN_TARGET_CUDA_FP16
	}
	else {
		//cpu
		std::cout << "Inference device: CPU" << std::endl;
		net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
		net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
	}
	return true;
}


bool Yolov8Obb::Detect(cv::Mat& srcImg, cv::dnn::Net& net, std::vector<OutputParams>& output) {
	cv::Mat blob;
	output.clear();
	int col = srcImg.cols;
	int row = srcImg.rows;
	cv::Mat netInputImg;
	cv::Vec4d params;
	LetterBox(srcImg, netInputImg, params, cv::Size(_netWidth, _netHeight));
	cv::dnn::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
	// If there is no problem with other settings, but results are a lot different from  Python-onnx , you can try to use the following two sentences
	// 
	//$ cv::dnn::blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(104, 117, 123), true, false);
	//$ cv::dnn::blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(114, 114,114), true, false);
	//****************************************************************************************************************************************************/
	net.setInput(blob);
	std::vector<cv::Mat> net_output_img;

	net.forward(net_output_img, net.getUnconnectedOutLayersNames()); //get outputs
	std::vector<int> class_ids;// res-class_id
	std::vector<float> confidences;// res-conf 
	std::vector<cv::RotatedRect> boxes;// res-box
	cv::Mat output0 = cv::Mat(cv::Size(net_output_img[0].size[2], net_output_img[0].size[1]), CV_32F, (float*)net_output_img[0].data).t();  //[bs,20,21504]=>[bs,21504,20]
	int net_width = output0.cols;
	int rows = output0.rows;
	int class_score_length = net_width - 5;
	int angle_index = net_width - 1;
	float* pdata = (float*)output0.data;
	for (int r = 0; r < rows; ++r) {
		cv::Mat scores(1, class_score_length, CV_32FC1, pdata + 4);
		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] - params[2]) / params[0];
			float y = (pdata[1] - params[3]) / params[1];
			float w = pdata[2] / params[0];
			float h = pdata[3] / params[1];
			float angle = pdata[angle_index]  / CV_PI *180.0;
			class_ids.push_back(classIdPoint.x);
			confidences.push_back(max_class_socre);
			//cv::RotatedRect temp_rotated;
			//BBox2Obb(x, y, w, h, angle, temp_rotated);
			//boxes.push_back(temp_rotated);
			boxes.push_back(cv::RotatedRect(cv::Point2f(x, y), cv::Size(w, h), angle));
		}
		pdata += net_width;//next line
	}
	//NMS
	std::vector<int> nms_result;
	cv::dnn::NMSBoxes(boxes, confidences, _classThreshold, _nmsThreshold, nms_result);
	std::vector<std::vector<float>> temp_mask_proposals;
	//cv::Rect holeImgRect(0, 0, srcImg.cols, srcImg.rows);
	for (int i = 0; i < nms_result.size(); ++i) {
		int idx = nms_result[i];
		OutputParams result;
		result.id = class_ids[idx];
		result.confidence = confidences[idx];
		result.rotatedBox = boxes[idx];
		if (result.rotatedBox.size.width<1|| result.rotatedBox.size.height<1)
			continue;
		output.push_back(result);
	}
	if (output.size())
		return true;
	else
		return false;
}