• 尝鲜opencv4.0 运行yolo


      opencv自3.4版本以后就添加了对darknet的支持,用opencv来运行yolo模型,同样在cpu上跑是darknet性能的十倍以上,具体可以去看opencv官网。

      本文主要测试了yolov2模型,需要将文件yolov2-tiny-voc.cfg最后region中的thresh设置得小一点,否则小于此threshold的目标将被过滤掉,这时再在代码里设置threshold就徒劳了

      当然yolov3模型当然也是能跑的,只是测试中没有发现cfg文件中关于confidence threshold的设置。暂时也用不到yolov3,罢了,望有心路人指点一二。

      测试结果发现,框的分数和darknet跑出来的不一致,也不清楚是什么原因,具体可以看opencv问答社区问题

      网上的教程比较多了,在此给出一个简单的demo,代码都是从github上copy过来的,只是整理了一下, 配好环境就可以跑了。


    运行环境:

    1.   win10
    2.   opencv4.0预编译版
    3.   vs2015

    #include <fstream>
    #include <sstream>
    #include <iostream>
    #include <io.h>
    #include <opencv2/dnn.hpp>
    #include <opencv2/imgproc.hpp>
    #include <opencv2/highgui.hpp>
    #include<vector>
    
    using namespace std;
    using namespace cv;
    using namespace dnn;
    
    vector<string> classes;
    
    vector<String> getOutputsNames(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;
    }
    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("%.5f", 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);
    }
    void postprocess(Mat& frame, const vector<Mat>& outs, float confThreshold, float nmsThreshold)
    {
        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);
        }
    }
    
    int main()
    {
        string names_file = "E:\programing\VSProject\object_detect_opencv\model\yolov2_tiny\voc.names";
        String model_def = "E:\programing\VSProject\object_detect_opencv\model\yolov2_tiny\yolov2-tiny-voc.cfg";
        String weights = "E:\programing\VSProject\object_detect_opencv\model\yolov2_tiny\yolov2-tiny-voc.weights";
    
        int in_w, in_h;
        double thresh = 0.35;
        double nms_thresh = 0.25;
        in_w = in_h = 416;
    
        string img_path = "E:\programing\VSProject\object_detect_opencv\test_images\dog.jpg";
    
        //read names
    
        ifstream ifs(names_file.c_str());
        string line;
        while (getline(ifs, line)) classes.push_back(line);
    
        //init model
        Net net = readNetFromDarknet(model_def, weights);
        net.setPreferableBackend(DNN_BACKEND_OPENCV);
        net.setPreferableTarget(DNN_TARGET_CPU);
    
        //read image and forward
        
        Mat frame, blob;
        if ((_access(img_path.c_str(), 0)) == -1)
        {
            cerr << "file: " << img_path.c_str() << " not exist" << endl;
            return -1;
        }
        frame = imread(img_path);
        // Create a 4D blob from a frame.
    
        blobFromImage(frame, blob, 1 / 255.0, Size(in_w, in_h), Scalar(), true, false);
    
        vector<Mat> mat_blob;
        imagesFromBlob(blob, mat_blob);
    
        //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));
    
        postprocess(frame, outs, thresh, nms_thresh);
    
        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));
    
        imshow("res", frame);
    
        waitKey(0);
    }

     

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  • 原文地址:https://www.cnblogs.com/walter-xh/p/10027479.html
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