vs2017环境opencv3中是svm所需要的特征数据类型和格式是什么

Question

我用gabor滤波器提取了人脸图片的特征该怎么整理数据呢，比如50张标签一样的图片是中每一张的gabor特征是要组成一个一维向量？然后一起成为一个50行的MAT数据吗?

下面是gabor滤波器函数，参数都是确定的，用5个频率8个方向得到40个小波函数对图像进行特征提取

Mat getMyGabor(int width, int height, int U, int V, double Kmax, double f,
    double sigma, int ktype, const string kernel_name)
{
    //CV_ASSERT(width % 2 == 0 && height % 2 == 0);
    //CV_ASSERT(ktype == CV_32F || ktype == CV_64F);

    int half_width = width / 2;
    int half_height = height / 2;
    double Qu = PI * U / 6;
    double sqsigma = sigma * sigma;
    double Kv = Kmax / pow(f, V);
    double postmean = exp(-sqsigma / 2);

    Mat kernel_re(width, height, ktype);
    Mat kernel_im(width, height, ktype);
    Mat kernel_mag(width, height, ktype);

    double tmp1, tmp2, tmp3;
    for (int j = -half_height; j <= half_height; j++) {
        for (int i = -half_width; i <= half_width; i++) {
            tmp1 = exp(-(Kv*Kv*(j*j + i * i)) / (2 * sqsigma));
            tmp2 = cos(Kv*cos(Qu)*i + Kv * sin(Qu)*j) - postmean;
            tmp3 = sin(Kv*cos(Qu)*i + Kv * sin(Qu)*j);

            if (ktype == CV_32F)
                kernel_re.at<float>(j + half_height, i + half_width) =
                (float)(Kv*Kv*tmp1*tmp2 / sqsigma);
            else
                kernel_re.at<double>(j + half_height, i + half_width) =
                (double)(Kv*Kv*tmp1*tmp2 / sqsigma);

            if (ktype == CV_32F)
                kernel_im.at<float>(j + half_height, i + half_width) =
                (float)(Kv*Kv*tmp1*tmp3 / sqsigma);
            else
                kernel_im.at<double>(j + half_height, i + half_width) =
                (double)(Kv*Kv*tmp1*tmp3 / sqsigma);
        }
    }

    magnitude(kernel_re, kernel_im, kernel_mag);

    if (kernel_name.compare("real") == 0)
        return kernel_re;
    else if (kernel_name.compare("imag") == 0)
        return kernel_im;
    else {
        printf("Invalid kernel name!\n");
        return kernel_mag;
    }
}



Mat gabor_filter(Mat& img, int type)
{
    // Ref: Mian Zhou. Thesis. Gabor-Boosting Face Recognition.
    // https://code.google.com/p/gaborboosting/
    const int kernel_size = 69; // should be odd
    // variables for gabor filter
    double Kmax = PI/2 ;
    double f = sqrt(2);
    double sigma =2* PI;
    int U = 7;
    int V = 4;
    int GaborH = kernel_size;
    int GaborW = kernel_size;
    int UStart = 0, UEnd = 6;
    int VStart = -1, VEnd = 4;

    // 
    Mat kernel_re, kernel_im;
    Mat dst_re, dst_im, dst_mag;

    // variables for filter2D
    Point archor(-1, -1);
    int ddepth = CV_64F;//CV_64F
    double delta = 0;


    Mat totalMat, totalMat_re, totalMat_im;
    for (U = UStart; U < UEnd; U++) {
        Mat colMat, colMat_re, colMat_im;
        for (V = VStart; V < VEnd; V++) {
            kernel_re = getMyGabor(GaborW, GaborH, U, V,
                Kmax, f, sigma, CV_64F, "real");
            kernel_im = getMyGabor(GaborW, GaborH, U, V,
                Kmax, f, sigma, CV_64F, "imag");



            filter2D(img, dst_re, ddepth, kernel_re);
            filter2D(img, dst_im, ddepth, kernel_im);

            dst_mag.create(img.rows, img.cols, CV_64FC1);
            magnitude(Mat_<float>(dst_re), Mat_<float>(dst_im),
                dst_mag);

            //show gabor kernel
            normalize(dst_mag, dst_mag, 0, 1, CV_MINMAX);
            normalize(dst_re, dst_re, 0, 1, CV_MINMAX);
            normalize(dst_im, dst_im, 0, 1, CV_MINMAX);


            if (V == VStart) {
                colMat = dst_mag;
                colMat_re = dst_re;
                colMat_im = dst_im;
            }
            else {
                vconcat(colMat, dst_mag, colMat);
                vconcat(colMat_re, dst_re, colMat_re);
                vconcat(colMat_im, dst_im, colMat_im);
            }
        }
        if (U == UStart) {
            totalMat = colMat;
            totalMat_re = colMat_re;
            totalMat_im = colMat_im;
        }
        else {
            hconcat(totalMat, colMat, totalMat);
            hconcat(totalMat_re, colMat_re, totalMat_re);
            hconcat(totalMat_im, colMat_im, totalMat_im);
        }
    }

    // return 
    switch (type) {
    case 0:
        return totalMat;
    case 1:
        return totalMat_re;
    case 2:
        return totalMat_im;
    default:
        return totalMat;
    }
}