opencv4 mask_rcnn模型调(c++)
昨天有人问我关于调用mask_rcnn模型的问题,忽然想到最近三个月都没用opencv调用训练好的mask_rcnn模型了,今晚做个尝试,所以重新编译了 opencv4,跑个案例试试 #include <fstream> #include <sstream> #include <iostream> #include <string.h> #include <opencv2/dnn.hpp> #include <opencv2/imgproc.hpp> #include <opencv2/highgui.hpp> using namespace cv; using namespace dnn; using namespace std; RNG rng1; // Initialize the parameters float confThreshold = 0.5; // Confidence threshold float maskThreshold = 0.3; // Mask threshold //vector<string> classes; //vector<Scalar> colors; // Draw the predicted bounding box void drawBox(Mat& frame,int classId,float conf,Rect box,Mat& objectMask); // Postprocess the neural network‘s output for each frame void postprocess(Mat& frame,const vector<Mat>& outs); int main() { // Give the configuration and weight files for the model //String textGraph = "./mask_rcnn_inception_v2_coco_2018_01_28/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt"; //String modelWeights = "./mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb"; String modelWeights = "E:Opencvmodel_1mask_rcnn_inception_v2_coco_2018_01_28frozen_inference_graph.pb"; String textGraph = "E:Opencvmodel_1mask_rcnn_inception_v2_coco_2018_01_28mask_rcnn_inception_v2_coco_2018_01_28.pbtxt"; // Load the network Net net = readNetFromTensorflow(modelWeights,textGraph); net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setPreferableTarget(DNN_TARGET_CPU); // Open a video file or an image file or a camera stream. string str,outputFile; VideoCapture cap(0);//根据摄像头端口id不同,修改下即可 //VideoWriter video; Mat frame,blob; // Create a window static const string kWinName = "Deep learning object detection in OpenCV"; namedWindow(kWinName,WINDOW_NORMAL); // Process frames. if (1>0) { // get frame from the video //cap >> frame; frame = cv::imread("D:image5.png"); // Stop the program if reached end of video if (frame.empty()) { cout << "Done processing !!!" << endl; cout << "Output file is stored as " << outputFile << endl; } // Create a 4D blob from a frame. blobFromImage(frame,blob,1.0,Size(frame.cols,frame.rows),Scalar(),true,false); //blobFromImage(frame,blob); //Sets the input to the network net.setInput(blob); // Runs the forward pass to get output from the output layers std::vector<String> outNames(2); outNames[0] = "detection_out_final"; outNames[1] = "detection_masks"; vector<Mat> outs; net.forward(outs,outNames); // Extract the bounding box and mask for each of the detected objects postprocess(frame,outs); // Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes) vector<double> layersTimes; double freq = getTickFrequency() / 1000; double t = net.getPerfProfile(layersTimes) / freq; string label = format("Mask-RCNN on 2.5 GHz Intel Core i7 CPU,Inference time for a frame : %0.0f ms",t); putText(frame,label,Point(0,15),FONT_HERSHEY_SIMPLEX,0.5,Scalar(0,0,0)); // Write the frame with the detection boxes Mat detectedFrame; frame.convertTo(detectedFrame,CV_8U); imshow(kWinName,frame); } //cap.release(); waitKey(0); return 0; } // For each frame,extract the bounding box and mask for each detected object void postprocess(Mat& frame,const vector<Mat>& outs) { Mat outDetections = outs[0]; Mat outMasks = outs[1]; // Output size of masks is NxCxHxW where // N - number of detected boxes // C - number of classes (excluding background) // HxW - segmentation shape const int numDetections = outDetections.size[2]; const int numClasses = outMasks.size[1]; outDetections = outDetections.reshape(1,outDetections.total() / 7); for (int i = 0; i < numDetections; ++i) { float score = outDetections.at<float>(i,2); if (score > confThreshold) { // Extract the bounding box int classId = static_cast<int>(outDetections.at<float>(i,1)); int left = static_cast<int>(frame.cols * outDetections.at<float>(i,3)); int top = static_cast<int>(frame.rows * outDetections.at<float>(i,4)); int right = static_cast<int>(frame.cols * outDetections.at<float>(i,5)); int bottom = static_cast<int>(frame.rows * outDetections.at<float>(i,6)); left = max(0,min(left,frame.cols - 1)); top = max(0,min(top,frame.rows - 1)); right = max(0,min(right,frame.cols - 1)); bottom = max(0,min(bottom,frame.rows - 1)); Rect box = Rect(left,top,right - left + 1,bottom - top + 1); // Extract the mask for the object Mat objectMask(outMasks.size[2],outMasks.size[3],CV_32F,outMasks.ptr<float>(i,classId)); // Draw bounding box,colorize and show the mask on the image drawBox(frame,classId,score,box,objectMask); } } } // Draw the predicted bounding box,colorize and show the mask on the image void drawBox(Mat& frame,Mat& objectMask) { //Draw a rectangle displaying the bounding box rectangle(frame,Point(box.x,box.y),Point(box.x + box.width,box.y + box.height),Scalar(255,178,50),3); //Get the label for the class name and its confidence /*string label = format("%.2f",conf); 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,0.5,1,&baseLine); box.y = max(box.y,labelSize.height); rectangle(frame,box.y - round(1.5*labelSize.height)),Point(box.x + round(1.5*labelSize.width),box.y + baseLine),Scalar(255,255,255),FILLED); putText(frame,0.75,Scalar(0,0),1); */ //Scalar color = colors[classId%colors.size()]; Scalar color = Scalar(rng1.uniform(0,255),rng1.uniform(0,255)); // Resize the mask,threshold,color and apply it on the image resize(objectMask,objectMask,Size(box.width,box.height)); Mat mask = (objectMask > maskThreshold); Mat coloredRoi = (0.3 * color + 0.7 * frame(box)); coloredRoi.convertTo(coloredRoi,CV_8UC3); // Draw the contours on the image vector<Mat> contours; Mat hierarchy; mask.convertTo(mask,CV_8U); findContours(mask,contours,hierarchy,RETR_CCOMP,CHAIN_APPROX_SIMPLE); drawContours(coloredRoi,-1,color,5,LINE_8,100); coloredRoi.copyTo(frame(box),mask); } ? 检测速度和python比起来偏慢 运行日志: [ INFO:0] global E:Opencvopencv-4.1.1modulesvideoiosrcvideoio_registry.cpp (187) cv::`anonymous-namespace‘::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Enabled backends(7,sorted by priority): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940)[ INFO:0] global E:Opencvopencv-4.1.1modulesvideoiosrcbackend_plugin.cpp (340) cv::impl::getPluginCandidates Found 2 plugin(s) for GSTREAMER[ INFO:0] global E:Opencvopencv-4.1.1modulesvideoiosrcbackend_plugin.cpp (172) cv::impl::DynamicLib::libraryLoad load E:Opencvopencv_4_1_1_installbinopencv_videoio_gstreamer411_64.dll => FAILED[ INFO:0] global E:Opencvopencv-4.1.1modulesvideoiosrcbackend_plugin.cpp (172) cv::impl::DynamicLib::libraryLoad load opencv_videoio_gstreamer411_64.dll => FAILED[ INFO:0] global E:Opencvopencv-4.1.1modulescoresrcocl.cpp (888) cv::ocl::haveOpenCL Initialize OpenCL runtime... 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