c++调用tensorflow object detect api 生成的模型文件

#include<opencv2/opencv.hpp>
#include<opencv2/dnn.hpp>
#include <iostream>

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","face" };

int main() {

    String weights = "/root/ssd_mayun_mobilenet_v1_coco_2018_01_28/frozen_inference_graph.pb";
    //String prototxt = "/root/ssd_mayun_mobilenet_v1_coco_2018_01_28/mayun.pbtxt";

//ssd-v1.pbtxt 这个模型是通过opencv源码提供的转换工具转化的
    String prototxt = "/root/lvyunxiangoutput/ssd-v1.pbtxt";
    //String weights = "/root/my_tensorflow_object_detech_api/ssd_mobilenet_v1_coco_2018_01_28/frozen_inference_graph.pb";
    //String prototxt = "/root/my_tensorflow_object_detech_api/models-master/research/object_detection/data/mscoco_label_map.pbtxt";
    dnn::Net net = cv::dnn::readNetFromTensorflow(weights, prototxt);
    cout << "lvyunxiang test 1" << endl;
    Mat frame = cv::imread("/root/my_tensorflow_object_detech_api/test_mayun.jpg");
    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));
    }
    cout << "lvyunxiang test 2\n"<< endl;
    imshow("wwwww",frame);
    waitKey(0);
    Rect crop(Point((frame_size.width - cropSize.width) / 2,
        (frame_size.height - cropSize.height) / 2),
        cropSize);

    cout << "lvyunxiang test3\n"<< endl;
    cv::Mat blob = cv::dnn::blobFromImage(frame,1./255,Size(300,300));
    //imshow("bbbbb",blob);
    //cout << "lvyunxiang test 4\n" << endl;
    cout << "blob size: " << blob.size << endl;

    net.setInput(blob);
    cout <<"lvyunxiang test 4\n"<< endl;
    Mat output = net.forward();
    cout << "output size: " << output.size << endl;

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

    frame = frame(crop);
    float confidenceThreshold = 0.20;
    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),2);
            String label = String(classNames[objectClass]) + ": " + conf;
            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);
             rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
                Size(labelSize.width, labelSize.height + baseLine)),
                Scalar(0, 255, 0), -1);
            putText(frame, label, Point(xLeftBottom, yLeftBottom),
                FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
        }
    }
//    namedWindow("image", CV_WINDOW_NORMAL);
    imshow("image", frame);
    waitKey(0);
    return 0;
}