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eyemLib-Sharp/EyemLib.cs

@@ -787,7 +787,7 @@ namespace eyemLib_Sharp
         #region 测试接口
         //测试接口
         [DllImport("eyemLib.dll", CharSet = CharSet.None, CallingConvention = CallingConvention.Cdecl)]
-        private static extern int eyemAllMindImpl(EyemImage tpImage);
+        private static extern int eyemLibImpl(EyemImage tpImage, out EyemImage tpDstImg);
         #endregion
 
         #region 日志功能
@@ -861,7 +861,7 @@ namespace eyemLib_Sharp
 
             //flag = eyemImageAbs(image1, ref tpDstImg);
 
-            flag = eyemAllMindImpl(image);
+            flag = eyemLibImpl(image, out tpDstImg);
 
 
             #region Test Blob
@@ -1017,12 +1017,12 @@ namespace eyemLib_Sharp
             //获取用于制作模板的图像
             //flag = eyemAchvTemplateImage(image, tpRoi, out tpDstImg);
 
-            //Bitmap bitmap = eyemCvtToBitmap(tpDstImg);
+            Bitmap bitmap = eyemCvtToBitmap(tpDstImg);
 
-            //if (bitmap != null)
-            //{
-            //    bitmap.Save(System.Windows.Forms.Application.StartupPath + "\\ResOut\\" + file);
-            //}
+            if (bitmap != null)
+            {
+                bitmap.Save(System.Windows.Forms.Application.StartupPath + "\\ResOut\\" + file);
+            }
 
             //创建模板匹配模型
             EyemRect tpRoi2 = new EyemRect();

eyemLib/eyemLib.h

@@ -859,7 +859,7 @@ extern "C" {
 	EXPORTS int				eyemReleaseModel(IntPtr &hModelID);
 	EXPORTS int				eyemTrackFeature(EyemImage tpPrevImg, EyemImage tpNextImg, EyemRect3 *tpRois, int iRoiNum, int *ipResults, EyemImage *tpDstImg);
 	EXPORTS int				eyemAOIForTSAV(EyemImage tpRefImg, EyemImage tpNextImg, EyemRect3 *tpRois, int iRoiNum);
-	EXPORTS int				eyemAllMindImpl(EyemImage tpImage);
+	EXPORTS int				eyemLibImpl(EyemImage tpImage, EyemImage *tpDstImg);
 
 #ifdef __cplusplus
 }

eyemLib/eyemMisc.cpp

@@ -5989,62 +5989,17 @@ int eyemAOIForTSAV(EyemImage tpRefImg, EyemImage tpNextImg, EyemRect3 *tpRois, i
 //}
 
 
-//移除低置信度边界框
-static void postprocess(cv::Mat& frame, const std::vector<cv::Mat>& outs) {
-	std::vector<int> classIds;//储存识别类的索引
-	std::vector<float> confidences;//储存置信度
-	std::vector<cv::Rect> boxes;//储存边框
-
-	for (size_t i = 0; i < outs.size(); i++) {
-		//从网络输出中扫描所有边界框
-		//保留高置信度选框
-		cv::Mat tm = outs[i];
-		//目标数据data:x,y,w,h为百分比，x,y为目标中心点坐标
-		float* data = (float*)outs[i].data;
-		for (int j = 0; j < outs[i].rows; j++, data += outs[i].cols) {
-			cv::Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
-			cv::Point classIdPoint;
-			double confidence;//置信度
-							  //取得最大分数值与索引
-			cv::minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
-			if (confidence > 0.5) {
-				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(cv::Rect(left, top, width, height));
-			}
-
-		}
-
-	}
-
-	//低置信度
-	std::vector<int> indices;//保存没有重叠边框的索引
-
-	//该函数用于抑制重叠边框
-	cv::dnn::NMSBoxes(boxes, confidences, 0.5, 0.4, 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, frame);
-		cv::rectangle(frame, box, cv::Scalar(0, 0, 255));
-	}
-}
-
-int eyemAllMindImpl(EyemImage tpImage)
+int eyemLibImpl(EyemImage tpImage, EyemImage *tpDstImg)
 {
 	cv::Mat image = cv::Mat(tpImage.iHeight, tpImage.iWidth, MAKETYPE(tpImage.iDepth, tpImage.iChannels), tpImage.vpImage).clone();
 
 	if (image.empty())
 		return FUNC_IMAGE_NOT_EXIST;
 
+	if (image.channels() != 3) {
+		cv::cvtColor(image, image, cv::COLOR_BGRA2BGR);
+	}
+
 #pragma region wechat_qrcode
 
 	//cv::Ptr<wechat_qrcode::WeChatQRCode> detector;
@@ -6065,6 +6020,15 @@ int eyemAllMindImpl(EyemImage tpImage)
 
 #pragma endregion
 
+	//加载类名
+	std::vector<std::string> classes;
+	std::string classFile = ".\\darknet\\detect.names";
+
+	std::ifstream ifs(classFile.c_str());
+
+	std::string line;
+	while (std::getline(ifs, line)) classes.push_back(line);
+
 	//加载网络
 	cv::dnn::Net net = cv::dnn::readNet(".\\darknet\\detect.cfg", ".\\darknet\\detect.weights");
 	net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
@@ -6087,9 +6051,15 @@ int eyemAllMindImpl(EyemImage tpImage)
 	//为网络输入新值
 	net.setInput(blob);
 
+	double begin0 = (double)cv::getTickCount();
 	//获取预测结果
 	std::vector<cv::Mat> outputBlobs;
 	net.forward(outputBlobs, outBlobNames);
+	std::cout << ((double)cv::getTickCount() - begin0) / cv::getTickFrequency() << std::endl;
+
+	std::vector<int> classIds;//储存识别类的索引
+	std::vector<float> confidences;//储存置信度
+	std::vector<cv::Rect> bboxes;//储存边框
 
 	for (int n = 0; n < outputBlobs.size(); n++) {
 		for (int row = 0; row < outputBlobs[n].rows; row++) {
@@ -6101,31 +6071,71 @@ int eyemAllMindImpl(EyemImage tpImage)
 
 			//取得最大分数值与索引
 			cv::minMaxLoc(prob, 0, &confidence, 0, &classIdPoint);
+			//如果置信度大于阈值
+			if (confidence > 0.35) {
 
-			cv::Mat dt = outputBlobs[n];
+				cv::Mat dt = outputBlobs[n];
 
-			cv::Mat x= outputBlobs[n](cv::Range(row, row + 1), cv::Range(0, 5));
+				cv::Mat x = outputBlobs[n](cv::Range(row, row + 1), cv::Range(0, 5));
 
-			//如果置信度大于阈值
-			if (confidence > 0.5) {
-				/*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(cv::Rect(left, top, width, height));
+				int cx = cvRound(x.ptr<float>(0)[0] * (float)image.cols);
+				int cy = cvRound(x.ptr<float>(0)[1] * (float)image.rows);
+				int w = cvRound(x.ptr<float>(0)[2] * (float)image.cols);
+				int h = cvRound(x.ptr<float>(0)[3] * (float)image.rows);
+
+				int left = cx - w / 2;
+				int top = cy - h / 2;
+
+				classIds.push_back(classIdPoint.x);
+				confidences.push_back((float)confidence);
+				bboxes.push_back(cv::Rect(left, top, w, h));
 			}
 		}
 	}
 
 	//cv::Mat showMat;
 	//cv::cvtColor(image)
-	//绘制结果
-	postprocess(image, outputBlobs);
+
+	std::vector<int> indices;
+	//非极大值抑制
+	cv::dnn::NMSBoxes(bboxes, confidences, 0.5, 0.4, indices);
+
+	for (int i = 0; i < indices.size(); i++) {
+		int idx = indices[i];
+		cv::Rect bbox = bboxes[idx] & cv::Rect(0, 0, image.cols, image.rows);
+		//标签
+		std::string label = cv::format("%.2f", confidences[idx]);
+
+		if (!classes.empty()) {
+			label = classes[classIds[idx]] + ":" + label;
+		}
+
+		int baseLine;
+		cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
+
+		cv::Scalar pal(0, 255, 0);
+
+		cv::rectangle(image, bbox, pal);
+		cv::rectangle(image, cv::Point(bbox.x, bbox.y - labelSize.height - baseLine), cv::Point(bbox.x + labelSize.width, bbox.y), pal, cv::FILLED);
+		cv::putText(image, label, cv::Point(bbox.tl().x, bbox.tl().y - baseLine), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0), 1);
+	}
+
+	///<输出计数结果标记图像
+	{
+		tpDstImg->iWidth = image.cols; tpDstImg->iHeight = image.rows; tpDstImg->iDepth = image.depth(); tpDstImg->iChannels = image.channels();
+
+		//内存尺寸
+		int _Size = tpDstImg->iWidth*tpDstImg->iHeight*tpDstImg->iChannels * sizeof(uint8_t);
+
+		//分配初始化内存
+		tpDstImg->vpImage = (uint8_t *)malloc(_Size);
+		if (NULL == tpDstImg->vpImage)
+			return FUNC_NOT_ENOUGH_MEM;
+		memset(tpDstImg->vpImage, 0, _Size);
+
+		//拷贝数据
+		memcpy(tpDstImg->vpImage, image.data, _Size);
+	}
 
 	return FUNC_OK;
 }
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