methods.py 文件源码

def binary_morph(img, thresh=50, min_size=None, mask_only=True):

    if min_size is None:  # default to 10% of largest image dimension
        min_size = float(max(img.shape)) * .1

    if len(img.shape) == 3:  # flatten if RGB image
        img = np.mean(img, 2).astype(np.uint8)

    # Apply binary threshold and erode
    ret, thresh_im = cv2.threshold(img, thresh, 255, cv2.THRESH_BINARY)

    # Connected component labelling
    n, labels, stats, centroids = cv2.connectedComponentsWithStats(thresh_im)
    mask = np.zeros_like(labels)

    # Loop through areas in order of size
    areas = [s[4] for s in stats]
    sorted_idx = np.argsort(areas)

    for lidx, cc in zip(sorted_idx, [areas[s] for s in sorted_idx][:-1]):

        if cc > min_size:
            mask[labels == lidx] = 1

    if mask_only:
        return mask * 255
    return np.dstack([img * mask] * 3).astype(np.uint8)