convariance.cpp

void train(cv::Mat &image) {
    cv::cvtColor(image, image, CV_BGR2YCrCb);
    const size_t w = image.cols;
    const size_t h = image.rows;

    double mean_cb = 0.0;
    double mean_cr = 0.0;
    double mean_cb2 = 0.0;
    double mean_cr2 = 0.0;
    double mean_crcb = 0.0;

    double i = 1.0;
    //TODO: #pragma omp parallel for
    for (size_t x = 0; x < h; x++) {
        const uchar* const row = image.ptr(x);
        for (size_t y = 0; y < w; y++) {
            const uchar cb = row[y*NUM_CHANNELS + 1];
            const uchar cr = row[y*NUM_CHANNELS + 2];
            // QUESTION: better not to play with inline functions here, yeah?
            mean_cb = ((i-1.0)/i) * mean_cb + cb/i;
            mean_cr = ((i-1.0)/i) * mean_cr + cr/i;
            mean_cb2 = ((i-1.0)/i) * mean_cb2 + cb*cb/i;
            mean_cr2 = ((i-1.0)/i) * mean_cr2 + cr*cr/i;
            mean_crcb = ((i-1.0)/i) * mean_crcb + cr*cb/i;
            i += 1.0;
        }
    }
  
    // TODO: Mat cameraMatrix = (Mat_<double>(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1);
    cv::Mat cov(2, 2, CV_64F);
    cov.at<double>(0, 0) = mean_cb2;
    cov.at<double>(0, 1) = cov.at<double>(1, 0) = mean_crcb - mean_cb*mean_cr;
    cov.at<double>(1, 1) = mean_cr2;
    cov = cov.inv();

    cv::Mat means(1, 2, CV_64F);
    means.at<double>(0, 0) = mean_cr;
    means.at<double>(0, 1) = mean_cb;

    write_model(MODEL_FILE_NAME, cov, means);
}