通过图片哈希值计算汉明距离来匹配人脸

opencv通过图片哈希值计算汉明距离来匹配人脸

string xmlPath = "/data/data/org.opencv.samples.tutorial2/cache/lbpcascade_frontalface.xml";
//识别并截取人脸
Mat detectAndDisplay(Mat image) {
    CascadeClassifier ccf;
    ccf.load(xmlPath);
    vector<Rect> faces;
    Mat gray;
    cvtColor(image, gray, CV_BGR2GRAY);
    equalizeHist(gray, gray);
    ccf.detectMultiScale(gray, faces, 1.1, 3, 0, Size(50, 50), Size(500, 500));
    for (vector<Rect>::const_iterator iter = faces.begin(); iter != faces.end(); iter++) {
        rectangle(image, *iter, Scalar(0, 0, 255), 2, 8); //画出脸部矩形
    }
    for (size_t i = 0; i < faces.size(); i++) {
        image = image(Rect(faces[i].x, faces[i].y, faces[i].width, faces[i].height));
    }
    return image;
}


//得到图片的哈希值
string HashValue(Mat &src) {
    string rst(64, '\0');
    Mat img;
    cvtColor(src, img, CV_BGR2GRAY);
    resize(img, img, Size(8, 8));
    uchar *pData;
    for (int i = 0; i < img.rows; i++) {
        pData = img.ptr<uchar>(i);
        for (int j = 0; j < img.cols; j++) {
            pData[j] = pData[j] / 4;
        }
    }


    int average = mean(img).val[0];
    Mat mask = (img >= (uchar) average);
    int index = 0;
    for (int i = 0; i < mask.rows; i++) {
        pData = mask.ptr<uchar>(i);
        for (int j = 0; j < mask.cols; j++) {
            if (pData[j] == 0)
                rst[index++] = '0';
            else
                rst[index++] = '1';
        }
    }
    return rst;
}
//求两张图片的汉明距离
int HanmingDistance(string &str1, string &str2) {
    if ((str1.size() != 64) || (str2.size() != 64))
        return -1;
    int diff = 0;
    for (int i = 0; i < 64; i++) {
        if (str1[i] != str2[i])
            diff++;
    }
    return diff;
}


void main(){
        //从数据库选择，实际上如果2张判断更简单，只是一次比对而已。我数据库的是多次循环，取最优而已
        Mat image1, image2;
        image1 = oneMat;
        string str1, str2, path2;


        image1 = detectAndDisplay(image1);
        str1 = HashValue(image1);


        image2 = twoMat;
        image2 = detectAndDisplay(image2);
        image2.convertTo(dstMat, CV_8UC4);
        str2 = HashValue(image2);
        int dif = HanmingDistance(str1, str2);

        LOGI("jason db %d %s %s", dif, str1.c_str(), str2.c_str());
}