2D Convolution Using Python and Numpy

Question

I am trying to perform a 2d convolution in python using numpy

I have a 2d array as follows with kernel H_r for the rows and H_c for the columns

data = np.zeros((nr, nc), dtype=np.float32)

#fill array with some data here then convolve

for r in range(nr):
    data[r,:] = np.convolve(data[r,:], H_r, 'same')

for c in range(nc):
    data[:,c] = np.convolve(data[:,c], H_c, 'same')

data = data.astype(np.uint8);

It does not produce the output that I was expecting, does this code look OK, I think the problem is with the casting from float32 to 8bit. Whats the best way to do this

Thanks

Accepted Answer

Maybe it is not the most optimized solution, but this is an implementation I used before with numpy library for Python:

def convolution2d(image, kernel, bias):
    m, n = kernel.shape
    if (m == n):
        y, x = image.shape
        y = y - m + 1
        x = x - m + 1
        new_image = np.zeros((y,x))
        for i in range(y):
            for j in range(x):
                new_image[i][j] = np.sum(image[i:i+m, j:j+m]*kernel) + bias
    return new_image

I hope this code helps other guys with the same doubt.

Regards.

Answer 2

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Edit [Jan 2019]

@Tashus comment bellow is correct, and @dudemeister's answer is thus probably more on the mark. The function he suggested is also more efficient, by avoiding a direct 2D convolution and the number of operations that would entail.

Robert Thorne

Automotive & Future Transportation Editor

Robert Thorne covers electric vehicle innovations, autonomous driving systems, global mobility trends, and automotive engineering developments.

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2D Convolution Using Python and Numpy

8 Answers

Must Read

Edit [Jan 2019]

Robert Thorne