Detecting a pixelated image in python

Question

I\'m trying to determine if an image is squared(pixelated).

I\'ve heard of 2D fourrier transform with numpy or scipy but it is a bit complicated.

The goal is

Answer 1

I have no idea if this would work - but, something you could try is to get the nearest neighbors around a pixel. The pixellated squares will be a visible jump in RGB values around a region.

You can find the nearest neighbors for every pixel in an image with something like

def get_neighbors(x,y, img):
    ops = [-1, 0, +1]
    pixels = []
    for opy in ops:
        for opx in ops:
            try:
                pixels.append(img[x+opx][y+opy])
            except:
                pass
    return pixels

This will give you the nearest pixels in a region of your source image.

To use it, you'd do something like

def detect_pixellated(fp):
    img = misc.imread(fp)
    width, height = np.shape(img)[0:2]

    # Pixel change to detect edge
    threshold = 20

    for x in range(width):
        for y in range(height):
            neighbors = get_neighbors(x, y, img)

            # Neighbors come in this order:
            #  6   7   8
            #  3   4   5
            #  0   1   2

            center = neighbor[4]
            del neighbor[4]

            for neighbor in neighbors:
                diffs = map(operator.abs, map(operator.sub, neighbor, center))
                possibleEdge = all(diff > threshold for diff in diffs)

After further thought though, use OpenCV and do edge detection and get contour sizes. That would be significantly easier and more robust.