我正在尝试确定图像是否被平方(像素化).

我听说过2D四重变换有numpy或scipy,但它有点复杂.

目标是确定由于压缩不良导致的平方区域数量(img a)：

解决方法:

我不知道这是否可行 – 但是,你可以尝试的是让一个像素周围的最近邻居.像素化的正方形将是区域周围RGB值的可见跳跃.

你可以找到图像中每个像素的最近邻居

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

这将为您提供源图像区域中最近的像素.

要使用它,你会做类似的事情

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)

经过进一步思考后,使用OpenCV进行边缘检测并获得轮廓尺寸.这将更加容易和强大.