Python distort image with repeating edges using numpy.roll()

Question

I have an image as a numpy array with (w,h,3) which I want to distort along the y axis using some values that were generated using overlaying sine waves.

Below is the code of the method and 3 images (input, current output, desired output). The desired output was done using the Photoshop Wave Distort filter which has an option to repeat the edge pixels. Basicly I want to implement that option to repeat the edge pixels instead of wrapping the pixels around so that the image looks more like a path.

Another thing is that I don't think np.roll() is the right method for the job but I'm a total noob with anything numpy-related.

def distortH(data, distortions, w, h):
    imgdata = np.zeros((h, w, 3), dtype=np.uint8)
    for y in range(h):
        imgdata[y] = np.roll(data[y], distortions[y], axis=0)
    return imgdata

• data: numpy array with w,h,3

• distortions: array that has the length of h with the distortion values between -500 and 500 (in this case; distortion values will have different scalings dependent on the image size)

• w: width of the image

• h: height of the image

Thanks in advance!

Input:

Current Output

Desired Output

Answer 1

Solved!

numpy.roll() was indeed not the right method.

Solved it using numpy.pad()

def distortH(data, distortions, w, h):
    imgdata = np.zeros((h, w, 3), dtype=np.uint8)
    for y in range(h):
        if distortions[y] == 0:
            imgdata[y] = data[y]
        elif distortions[y] > 0:
            ndata = np.pad(data[y], ((distortions[y], 0),(0,0)), mode='edge')
            imgdata[y] = ndata[:len(ndata)-distortions[y], :]
        else:
            ndata = np.pad(data[y], ((0, -distortions[y]),(0,0)), mode='edge')
            imgdata[y] = ndata[-distortions[y]:, :]
    return imgdata

Might not be the most optimal way of solving it. But it's way faster than going through all the lines manually and swapping the pixels one by one.