OpenCV4 DNN人脸检测

此文源于在opencv学堂上看到的一篇文章，自己尝试了下，

首先安装opencv4，在OpenCV的\sources\samples\dnn\face_detector目录下，有一个download_weights.py脚本文件，首先运行一下，下载模型文件。下载的模型文件分别为：

Caffe模型

• res10_300x300_ssd_iter_140000_fp16.caffemodel

• deploy.prototxt

tensorflow模型

• opencv_face_detector_uint8.pb

• opencv_face_detector.pbtxt

下面为自己在visual sutio2019中的测试代码，

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

using namespace cv;
using namespace cv::dnn;

#include <iostream>
#include <cstdlib>
using namespace std;

const size_t inWidth = 300;
const size_t inHeight = 300;
const double inScaleFactor = 1.0;
const Scalar meanVal(104.0, 177.0, 123.0);
const float confidenceThreshold = 0.6;
void face_detect_dnn();
void mtcnn_demo();
int main(int argc, char** argv)
{
    face_detect_dnn();
    waitKey(0);
    return 0;
}

void face_detect_dnn() {
    //String modelDesc = "D:/projects/opencv_tutorial/data/models/resnet/deploy.prototxt";
    // String modelBinary = "D:/projects/opencv_tutorial/data/models/resnet/res10_300x300_ssd_iter_140000.caffemodel";
    //String modelBinary = "D:/opencv-4.2.0/opencv/sources/samples/dnn/face_detector/opencv_face_detector_uint8.pb";
    //String modelDesc = "D:/opencv-4.2.0/opencv/sources/samples/dnn/face_detector/opencv_face_detector.pbtxt";
    String modelBinary = "E:/opencv_4_2_0_is_installed_here/opencv/sources/samples/dnn/face_detector/opencv_face_detector_uint8.pb";
    String modelDesc = "E:/opencv_4_2_0_is_installed_here/opencv/sources/samples/dnn/face_detector/opencv_face_detector.pbtxt";
    // 初始化网络
    // dnn::Net net = readNetFromCaffe(modelDesc, modelBinary);
    dnn::Net net = readNetFromTensorflow(modelBinary, modelDesc);

    net.setPreferableBackend(DNN_BACKEND_OPENCV);
    net.setPreferableTarget(DNN_TARGET_CPU);
    if (net.empty())
    {
        printf("could not load net...\n");
        return;
    }

#if 0
    // 打开摄像头
    // VideoCapture capture(0);
    VideoCapture capture("D:/images/video/Boogie_Up.mp4");
    if (!capture.isOpened()) {
        printf("could not load camera...\n");
        return;
    }
#endif

    Mat frame;
    int count = 0;
    char imagePath[100] = {};
    char outPath[100] = {};
    //while (capture.read(frame)) 
    for (int i = 0; i < 81; i++)
    {
        waitKey(100);
        sprintf_s(imagePath, "E:/opencv_4_2_0_is_installed_here/opencv/sources/samples/dnn/face_detector/%d.jpg", i);
        printf("imagePath:%s\n", imagePath);
        //frame = cv::imread("E:/opencv_4_2_0_is_installed_here/opencv/sources/samples/dnn/face_detector/0.jpg");
        frame = cv::imread(imagePath);
        if (frame.empty())
        {
            printf("read test jpg error\n");
        }
        else
        {
            int64 start = getTickCount();

#if 0
            if (frame.empty())
            {
                break;
        }
#endif

            // 水平镜像调整
            // flip(frame, frame, 1);
            imshow("input", frame);
            if (frame.channels() == 4)
                cvtColor(frame, frame, COLOR_BGRA2BGR);

            // 输入数据调整
            Mat inputBlob = blobFromImage(frame, inScaleFactor,
                Size(inWidth, inHeight), meanVal, false, false);
            net.setInput(inputBlob, "data");

            // 人脸检测
            Mat detection = net.forward("detection_out");
            vector<double> layersTimings;
            double freq = getTickFrequency() / 1000;
            double time = net.getPerfProfile(layersTimings) / freq;
            Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());

            ostringstream ss;
            for (int i = 0; i < detectionMat.rows; i++)
            {
                // 置信度 0～1之间
                float confidence = detectionMat.at<float>(i, 2);
                if (confidence > confidenceThreshold)
                {
                    count++;
                    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);

                    Rect object((int)xLeftBottom, (int)yLeftBottom,
                        (int)(xRightTop - xLeftBottom),
                        (int)(yRightTop - yLeftBottom));

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

                    ss << confidence;
                    String conf(ss.str());
                    String label = "Face: " + 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(255, 255, 255), FILLED);
                    putText(frame, label, Point(xLeftBottom, yLeftBottom),
                        FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
                }
            }
            float fps = getTickFrequency() / (getTickCount() - start);
            ss.str("");
            ss << "FPS: " << fps << " ; inference time: " << time << " ms";
            putText(frame, ss.str(), Point(20, 20), 0, 0.75, Scalar(0, 0, 255), 2, 8);
            imshow("dnn_face_detection", frame);
            sprintf_s(outPath, "E:/opencv_4_2_0_is_installed_here/opencv/sources/samples/dnn/face_detector/out%d.jpg", i);
            imwrite(outPath, frame);
            //if (waitKey(1) >= 0) break;
            if (waitKey(1) >= 0) return;
  
    }
        printf("total face: %d\n", count);
    }
    
}