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I've been working on BRISQUE IQA for Python and C++ for a while now. There is a set of code in the source code for C++ :

int scalenum = 2;
for (int itr_scale = 1; itr_scale<=scalenum; itr_scale++)
{
    Size dst_size(orig_bw.cols/cv::pow((double)2, itr_scale-1), orig_bw.rows/pow((double)2, itr_scale-1));
    Mat imdist_scaled;
    resize(orig_bw, imdist_scaled, dst_size, 0, 0, INTER_CUBIC); // INTER_CUBIC

    imdist_scaled.convertTo(imdist_scaled, CV_64FC1, 1.0/255.0);

    Mat mu(imdist_scaled.size(), CV_64FC1, 1);
    GaussianBlur(imdist_scaled, mu, Size(7, 7), 1.166);

    Mat mu_sq(imdist_scaled.size(), CV_64FC1, 1);

    mu_sq = mu.mul(mu);


    //compute sigma
    Mat sigma(imdist_scaled.size(), CV_64FC1, 1);
    sigma = imdist_scaled.mul(imdist_scaled);
    GaussianBlur(sigma, sigma, Size(7, 7), 1.166);
    subtract(sigma, mu_sq, sigma);
    cv::pow(sigma, double(0.5), sigma);
    //compute structdis = (x-mu)/sigma
    add(sigma, Scalar(1.0/255), sigma);

    //cvAddS(sigma, cvScalar(1.0/255), sigma);
    Mat structdis(imdist_scaled.size(), CV_64FC1, 1);
    subtract(imdist_scaled, mu, structdis);
    divide(structdis, sigma, structdis);

    //cvDiv(structdis, sigma, structdis);
    //Compute AGGD fit
    double lsigma_best, rsigma_best, gamma_best;

    structdis = AGGDfit(structdis, lsigma_best, rsigma_best, gamma_best);

So this is nothing major what's happening above, just gaussian blur, addition, division and multiplication operations. I tried to convert the above set to python as follows:

>

scalenum = 2
feat = []
im_original = im_.copy()
for itr_scale in range(scalenum):
    im = im_original.copy()
    im = im / 255.0
    mu = np.zeros((im.shape[0], im.shape[1]), dtype="float64")
    mu += 255.0
    mu_ = cv2.GaussianBlur(im, (7, 7), 1.166)
    mu = mu_.copy()
    mu_sq = mu * mu
    sigma = im * im
    sigma = cv2.GaussianBlur(sigma, (7, 7), 1.166)
    sigma = mu_sq - sigma
    sigma = abs(sigma) ** 0.5
    sigma = sigma + 1.0/255
    structdis = mu - im
    structdis /= sigma
    '''
    sigma = np.sqrt(abs(cv2.GaussianBlur(im*im, (7, 7), 1.166) - mu_sq))        
    structdis = (mu-im)/(sigma+(1.0/255))
    '''
    structdis = AGGDfit(structdis)

Now, the AGGDfit function has some operations where it finds the number of positive pixel points and the negative pixel points, so for both the total number of negative and positive pixel points differ by a small amount (around 300-400). Why would that be? Is there any difference possible in the outputs of GaussianBlur of C++ and Python APIs?

I've been working on BRISQUE IQA for Python and C++ for a while now. There is a set of code in the source code for C++ : Note: In the given code, orig_bw is the input image I've read using imread function (in grayscale).

int scalenum = 2;
for (int itr_scale = 1; itr_scale<=scalenum; itr_scale++)
{
    Size dst_size(orig_bw.cols/cv::pow((double)2, itr_scale-1), orig_bw.rows/pow((double)2, itr_scale-1));
    Mat imdist_scaled;
    resize(orig_bw, imdist_scaled, dst_size, 0, 0, INTER_CUBIC); // INTER_CUBIC

    imdist_scaled.convertTo(imdist_scaled, CV_64FC1, 1.0/255.0);

    Mat mu(imdist_scaled.size(), CV_64FC1, 1);
    GaussianBlur(imdist_scaled, mu, Size(7, 7), 1.166);

    Mat mu_sq(imdist_scaled.size(), CV_64FC1, 1);

    mu_sq = mu.mul(mu);


    //compute sigma
    Mat sigma(imdist_scaled.size(), CV_64FC1, 1);
    sigma = imdist_scaled.mul(imdist_scaled);
    GaussianBlur(sigma, sigma, Size(7, 7), 1.166);
    subtract(sigma, mu_sq, sigma);
    cv::pow(sigma, double(0.5), sigma);
    //compute structdis = (x-mu)/sigma
    add(sigma, Scalar(1.0/255), sigma);

    //cvAddS(sigma, cvScalar(1.0/255), sigma);
    Mat structdis(imdist_scaled.size(), CV_64FC1, 1);
    subtract(imdist_scaled, mu, structdis);
    divide(structdis, sigma, structdis);

    //cvDiv(structdis, sigma, structdis);
    //Compute AGGD fit
    double lsigma_best, rsigma_best, gamma_best;

    structdis = AGGDfit(structdis, lsigma_best, rsigma_best, gamma_best);

So this is nothing major what's happening above, just gaussian blur, addition, division and multiplication operations. I tried to convert the above set to python as follows:

Note: In the given code, im_ is the input image I'm taking using imread function. I'm just testing for one iteration, and there is some change in the amount of negative pixels + positive pixels in structdis. >

scalenum = 2
feat = []
im_original = im_.copy()
for itr_scale in range(scalenum):
    im = im_original.copy()
    im = im / 255.0
    mu = np.zeros((im.shape[0], im.shape[1]), dtype="float64")
    mu += 255.0
    mu_ = cv2.GaussianBlur(im, (7, 7), 1.166)
    mu = mu_.copy()
    mu_sq = mu * mu
    sigma = im * im
    sigma = cv2.GaussianBlur(sigma, (7, 7), 1.166)
    sigma = mu_sq - sigma
    sigma = abs(sigma) ** 0.5
    sigma = sigma + 1.0/255
    structdis = mu - im
    structdis /= sigma
    '''
    sigma = np.sqrt(abs(cv2.GaussianBlur(im*im, (7, 7), 1.166) - mu_sq))        
    structdis = (mu-im)/(sigma+(1.0/255))
    '''
    structdis = AGGDfit(structdis)

Now, the AGGDfit function has some operations where it finds the number of positive pixel points and the negative pixel points, so for both the total number of negative and positive pixel points differ by a small amount (around 300-400). 300-400) [here positive means > 0 and negative means < 0, and NOT >= or <=] . Why would that be? Is there any difference possible in the outputs of GaussianBlur of C++ and Python APIs? APIs?