C ++ OpenCV使用向量 <Point> 作为矩阵的索引

Question

I have a matrix img (480*640 pixel, float 64 bits) on which I apply a complex mask. After this, I need to multiply my matrix by a value but in order to win time I want to do this multiplication only on the non-zero elements because for now the multiplication is too long because I have to iterate the operation 2000 times on 2000 different matrix but with the same mask. So I found the index (on x/y axes) of the nonzero pixels which I keep in a vector of Point. But I don't succeed to use this vector to do the multplication only on the pixels indexed in this same vector.

Here is an example (with a simple mask) to understand my problem :

Mat img_temp(480, 640, CV_64FC1);
Mat img = img_temp.clone();
Mat mask = Mat::ones(img.size(), CV_8UC1);
double value = 3.56;

// Apply mask
img_temp.copyTo(img, mask);

// Finding non zero elements
vector<Point> nonZero;
findNonZero(img, nonZero);

// Previous multiplication (long because on all pixels)
Mat result = img.clone()*value;

// What I wish to do : multiplication only on non-zero pixels (not functional)
Mat result = Mat::zeros(img.size(), CV_64FC1);
result.at<int>(nonZero) = img.at(nonZero).clone() * value

What is tricky is that my pixels are not on a range (for example pixels 3, 4 and 50, 51 on a line).

Thank you in advance.

Answer 1

Constructing vector of points will also increase computation time. I think you should consider iterating over all pixels and multiply if the pixel is not equal to zero.

Iterating will be faster if you have the matrix as raw data.

Answer 2

I would suggest using Mat.convertTo . Basically, for the parameter alpha , which is the scaling factor, use the value of the mask (3.56 in your case). Make sure that the Mat is of type CV_32 or CV_64 .

This will be faster than finding all non-zero pixels, saving their coordinates in a Vector and iterating (it was faster for me in Java).

Hope it helps!

Answer 3

If you do

Mat result = img*value;

Instead of

Mat result = img.clone()*value;

The speed will be almost 10 times as fast

I have also tested your suggestion with vector but this is even slower than your first solution. Below the code I used to test your firs suggestion

cv::Mat multMask(cv::Mat &img, std::vector<cv::Point> mask, double fact)
{
    if (img.type() != CV_64FC1) throw "invalid format";
    cv::Mat res = cv::Mat::zeros(img.size(), img.type());
    int iLen = (int)mask.size();
    for (int i = 0; i < iLen; i++)
    {
        cv::Point &p = mask[i];
        ((double*)(res.data + res.step.p[0] * p.y))[p.x] = ((double*)(img.data + img.step.p[0] * p.y))[p.x] * fact;
    }
    return res;
}