预训练权重文件： https://pjreddie.com/media/files/yolov3.weights
网络配置文件： https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg
coco.names： https://github.com/pjreddie/darknet/blob/master/data/coco.names
下面展示 代码。
#include <fstream> #include <sstream> #include <iostream> #include <opencv2/dnn.hpp> #include <opencv2/imgproc.hpp> #include <opencv2/highgui.hpp> using namespace cv; using namespace dnn; using namespace std; string pro_dir = "E:/process/VSproject/"; //项目根目录 float confThreshold = 0.5; // Confidence threshold float nmsThreshold = 0.4; // Non-maximum suppression threshold int inpWidth = 416; // Width of network's input image int inpHeight = 416; // Height of network's input image vector<string> classes; // Remove the bounding boxes with low confidence using non-maxima suppression void postprocess(Mat& frame, const vector<Mat>& out); // Draw the predicted bounding box void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame); // Get the names of the output layers vector<String> getOutputsNames(const Net& net); void detect_image(string image_path, string modelWeights, string modelConfiguration, string classesFile); void detect_video(string video_path, string modelWeights, string modelConfiguration, string classesFile); int main(int argc, char** argv) { // Give the configuration and weight files for the model String modelConfiguration = pro_dir + "yolov3/yolov3.cfg"; String modelWeights = pro_dir + "yolov3/yolov3.weights"; string image_path = pro_dir + "yolov3/dog.jpg"; string classesFile = pro_dir + "yolov3/coco.names";// "coco.names"; //detect_image(image_path, modelWeights, modelConfiguration, classesFile); string video_path = pro_dir + "yolov3/movie.avi"; detect_video(video_path, modelWeights, modelConfiguration, classesFile); cv::waitKey(0); return 0; } void detect_image(string image_path, string modelWeights, string modelConfiguration, string classesFile) { // Load names of classes ifstream ifs(classesFile.c_str()); string line; while (getline(ifs, line)) classes.push_back(line); // Load the network Net net = readNetFromDarknet(modelConfiguration, modelWeights); net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setPreferableTarget(DNN_TARGET_OPENCL); // Open a video file or an image file or a camera stream. string str, outputFile; cv::Mat frame = cv::imread(image_path); // Create a window static const string kWinName = "Deep learning object detection in OpenCV"; namedWindow(kWinName, WINDOW_NORMAL); // Stop the program if reached end of video // Create a 4D blob from a frame. Mat blob; blobFromImage(frame, blob, 1 / 255.0, cvSize(inpWidth, inpHeight), Scalar(0, 0, 0), true, false); //Sets the input to the network net.setInput(blob); // Runs the forward pass to get output of the output layers vector<Mat> outs; net.forward(outs, getOutputsNames(net)); // Remove the bounding boxes with low confidence 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("Inference time for a frame : %.2f ms", t); putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255)); // Write the frame with the detection boxes imshow(kWinName, frame); cv::waitKey(30); } void detect_video(string video_path, string modelWeights, string modelConfiguration, string classesFile) { string outputFile = "./yolo_out_cpp.avi";; // Load names of classes ifstream ifs(classesFile.c_str()); string line; while (getline(ifs, line)) classes.push_back(line); // Load the network Net net = readNetFromDarknet(modelConfiguration, modelWeights); net.setPreferableBackend(DNN_BACKEND_OPENCV); net.setPreferableTarget(DNN_TARGET_CPU); // Open a video file or an image file or a camera stream. VideoCapture cap; //VideoWriter video; Mat frame, blob; try { // Open the video file ifstream ifile(video_path); if (!ifile) throw("error"); cap.open(video_path); } catch (...) { cout << "Could not open the input image/video stream" << endl; return; } // Create a window static const string kWinName = "Deep learning object detection in OpenCV"; namedWindow(kWinName, WINDOW_NORMAL); // Process frames. while (waitKey(1) < 0) { // get frame from the video cap >> frame; // Stop the program if reached end of video if (frame.empty()) { cout << "Done processing !!!" << endl; cout << "Output file is stored as " << outputFile << endl; waitKey(3000); break; } // Create a 4D blob from a frame. blobFromImage(frame, blob, 1 / 255.0, cvSize(inpWidth, inpHeight), Scalar(0, 0, 0), true, false); //Sets the input to the network net.setInput(blob); // Runs the forward pass to get output of the output layers vector<Mat> outs; net.forward(outs, getOutputsNames(net)); // Remove the bounding boxes with low confidence 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("Inference time for a frame : %.2f ms", t); putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255)); // Write the frame with the detection boxes Mat detectedFrame; frame.convertTo(detectedFrame, CV_8U); //video.write(detectedFrame); imshow(kWinName, frame); } cap.release(); //video.release(); } // Remove the bounding boxes with low confidence using non-maxima suppression void postprocess(Mat& frame, const vector<Mat>& outs) { vector<int> classIds; vector<float> confidences; vector<Rect> boxes; for (size_t i = 0; i < outs.size(); ++i) { // Scan through all the bounding boxes output from the network and keep only the // ones with high confidence scores. Assign the box's class label as the class // with the highest score for the box. float* data = (float*)outs[i].data; for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols) { Mat scores = outs[i].row(j).colRange(5, outs[i].cols); Point classIdPoint; double confidence; // Get the value and location of the maximum score minMaxLoc(scores, 0, &confidence, 0, &classIdPoint); if (confidence > confThreshold) { 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(Rect(left, top, width, height)); } } } // Perform non maximum suppression to eliminate redundant overlapping boxes with // lower confidences vector<int> indices; NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices); for (size_t i = 0; i < indices.size(); ++i) { int idx = indices[i]; Rect box = boxes[idx]; drawPred(classIds[idx], confidences[idx], box.x, box.y, box.x + box.width, box.y + box.height, frame); } } // Draw the predicted bounding box void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame) { //Draw a rectangle displaying the bounding box rectangle(frame, Point(left, top), Point(right, bottom), 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, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); top = max(top, labelSize.height); rectangle(frame, Point(left, top - round(1.5*labelSize.height)), Point(left + round(1.5*labelSize.width), top + baseLine), Scalar(255, 255, 255), FILLED); putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 0, 0), 1); } // Get the names of the output layers vector<String> getOutputsNames(const Net& net) { static vector<String> names; if (names.empty()) { //Get the indices of the output layers, i.e. the layers with unconnected outputs vector<int> outLayers = net.getUnconnectedOutLayers(); //get the names of all the layers in the network vector<String> layersNames = net.getLayerNames(); // Get the names of the output layers in names names.resize(outLayers.size()); for (size_t i = 0; i < outLayers.size(); ++i) names[i] = layersNames[outLayers[i] - 1]; } return names; }
