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Qt+opencv dnn模块调用tensorflow模型

发布时间 2023-07-06 22:28:32作者: blackstrom
参考网址(1条消息) Qt+opencv dnn模块调用tensorflow模型_vs qt 调用 tensorflow_街道口扛把子的博客-CSDN博客
代码地址：GitHub - Whu-wxy/Simple_Qt_opencv_dnn: Using deep learning model with opencv in Qt
修改运行后的代码如下：

#include <QCoreApplication>
#include<opencv2\opencv.hpp>
#include<opencv2\dnn.hpp>
#include <iostream>
#include<map>
#include<string>
#include<time.h>


using namespace std;


using namespace cv;




const size_t inWidth = 300;


const size_t inHeight = 300;


const float WHRatio = inWidth / (float)inHeight;


const char* classNames[]= {"background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",


"fire hydrant", "background", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "background", "backpack",


"umbrella", "background", "background", "handbag", "tie", "suitcase", "frisbee","skis", "snowboard", "sports ball", "kite", "baseball bat","baseball glove", "skateboard", "surfboard", "tennis racket",


"bottle", "background", "wine glass", "cup", "fork", "knife", "spoon","bowl", "banana",  "apple", "sandwich", "orange","broccoli", "carrot", "hot dog",  "pizza", "donut",


"cake", "chair", "couch", "potted plant", "bed", "background", "dining table", "background", "background", "toilet", "background","tv", "laptop", "mouse", "remote", "keyboard",


"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "background","book", "clock", "vase", "scissors","teddy bear", "hair drier", "toothbrush"};




int main(int argc, char *argv[])


{


    QCoreApplication a(argc, argv);






    clock_t start, finish;


    double totaltime;


    Mat frame;




    String weights = "C:/Users/Administrator/Downloads/Simple_Qt_opencv_dnn-master/Simple_Qt_opencv_dnn-master/ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb";


    String prototxt = "C:/Users/Administrator/Downloads/Simple_Qt_opencv_dnn-master/Simple_Qt_opencv_dnn-master/ssd_mobilenet_v1_coco_11_06_2017/ssd_mobilenet_v1_coco.pbtxt";


    dnn::Net net = cv::dnn::readNetFromTensorflow(weights, prototxt);


    frame = imread("E:/0.jpg");




    start = clock();


    Size frame_size = frame.size();




    Size cropSize;


    if (frame_size.width / (float)frame_size.height > WHRatio)


    {


        cropSize = Size(static_cast<int>(frame_size.height * WHRatio),


                        frame_size.height);


    }


    else


    {


        cropSize = Size(frame_size.width,


                        static_cast<int>(frame_size.width / WHRatio));


    }




    Rect crop(Point((frame_size.width - cropSize.width) / 2,


                    (frame_size.height - cropSize.height) / 2),


              cropSize);






    cv::Mat blob = cv::dnn::blobFromImage(frame, 1. / 255, Size(300, 300));


    // 1*3*300*300


    cout << "blob size: " << blob.size << endl;




    net.setInput(blob);


    Mat output = net.forward();


    // 1*1*100*7


    cout << "output size: " << output.size << endl;




    Mat detectionMat(output.size[2], output.size[3], CV_32F, output.ptr<float>());




    frame = frame(crop);


    float confidenceThreshold = 0.50;


    for (int i = 0; i < detectionMat.rows; i++)


    {


        float confidence = detectionMat.at<float>(i, 2);




        if (confidence > confidenceThreshold)


        {


            size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));




            int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);


            int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);


            int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);


            int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);




            ostringstream ss;


            ss << confidence;


            String conf(ss.str());




            Rect object((int)xLeftBottom, (int)yLeftBottom,


                        (int)(xRightTop - xLeftBottom),


                        (int)(yRightTop - yLeftBottom));




            rectangle(frame, object, Scalar(0, 255, 0), 1);


            //cout << "objectClass:" << objectClass << endl;


            String label = String(classNames[objectClass]) + ": " + conf;


            //cout << "label"<<label << endl;


            int baseLine = 0;


            Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);


            rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),


                                  Size(labelSize.width, labelSize.height + baseLine)),


                      Scalar(0, 255, 0), CV_FILLED);


            putText(frame, label, Point(xLeftBottom, yLeftBottom),


                    FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));


        }


    }


    finish = clock();


    totaltime = finish - start;


    cout << "识别图像所用的时间为：" << totaltime <<"ms"<< endl;


    namedWindow("result", 0);


    imshow("result", frame);




    waitKey(0);






    return a.exec();


}