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。

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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);
}