使用OpenCV进行深度学习：YOLO、SSD

Myth丶恋晨 2023-07-12 03:42 9阅读 0赞

# ![20191009191333910.png][][日萌社][Link 1] #

[人工智能AI：Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战（不定时更新）][AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]

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**[yoloV3模型][yoloV3]**

**[目标检测：YOLO V1、YOLO V2、YOLO V3 算法][YOLO V1_YOLO V2_YOLO V3]**

**[KITTI自动驾驶数据集的训练和检测过程(人、车检测案例)、KITTI数据集的TFRecord格式存储、YOLO V3/Yolo V3 Tiny 迁移学习][KITTI_KITTI_TFRecord_YOLO V3_Yolo V3 Tiny]**

**[使用OpenCV进行深度学习：YOLO、SSD][OpenCV_YOLO_SSD]**

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# **YOLO v3 代码** #

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// This code is based on LearnOpenCV code writtend at BigVision LLC. It is based on the OpenCV project. It is subject to the license terms in the LICENSE file found in this distribution and at http://opencv.org/license.html
    
    // Usage example:  ./DeepLearning_ch12 image.jpg
    #include <fstream>
    #include <sstream>
    #include <iostream>
    
    #include <opencv2/core.hpp>
    #include <opencv2/dnn.hpp>
    #include <opencv2/imgproc.hpp>
    #include <opencv2/highgui.hpp>
    
    using namespace cv;
    using namespace dnn;
    using namespace std;
    
    // Initialize the parameters
    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;
    
    
    // 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, 255, 255), 1);
        
        //Get the label for the class name and its confidence
        string conf_label = format("%.2f", conf);
        string label="";
        if (!classes.empty())
        {
            label = classes[classId] + ":" + conf_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 - labelSize.height), Point(left + labelSize.width, top + baseLine), Scalar(255, 255, 255), FILLED);
        putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0),1,LINE_AA);
    }
    
    // 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);
        }
    }
    
    // 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;
    }
    
    int main(int argc, char** argv)
    {
        // Load names of classes
        string classesFile = "coco.names";
        ifstream ifs(classesFile.c_str());
        string line;
        while (getline(ifs, line)) classes.push_back(line);
        
        // Give the configuration and weight files for the model
        String modelConfiguration = "yolov3.cfg";
        String modelWeights = "yolov3.weights";
    
        // Load the network
        Net net = readNetFromDarknet(modelConfiguration, modelWeights);
        net.setPreferableBackend(DNN_BACKEND_OPENCV);
        net.setPreferableTarget(DNN_TARGET_CPU);
        
        Mat input, blob;
        
        input= imread("G:\\VSdaima\\opencv_Chapter_12\\opencv_Chapter_12\\test.jpg");
    
        // Stop the program if reached end of video
        if (input.empty()) {
            cout << "No input image" << endl;
            return 0;
        }
        // Create a 4D blob from a frame.
        blobFromImage(input, blob, 1/255.0, Size(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(input, 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 compute the image : %.2f ms", t);
        cout << label << endl;
        
        imshow("Deep Learning. Chapter 12", input);
        imwrite("result.jpg", input);    
        waitKey(0);
        
        return 0;
    }

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[20191009191333910.png]: https://img-blog.csdnimg.cn/20191009191333910.png
[Link 1]: http://www.rimengshe.com/
[AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]: https://blog.csdn.net/zimiao552147572/article/details/88867161
[yoloV3]: https://blog.csdn.net/zimiao552147572/article/details/106009093
[YOLO V1_YOLO V2_YOLO V3]: https://azusa.blog.csdn.net/article/details/105159170
[KITTI_KITTI_TFRecord_YOLO V3_Yolo V3 Tiny]: https://azusa.blog.csdn.net/article/details/105195662
[OpenCV_YOLO_SSD]: https://azusa.blog.csdn.net/article/details/104701819
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