trim.py

def trim_border(base_img, th=255, m=(1, 1, 1, 1)):

    if not isinstance(m, tuple):
        m = (m, m, m, m)

    if base_img.size == 0:
        return base_img, [0,0,0,0]

    img = base_img

    # trim without threshold
    # white_rows = np.all(white_pixels, axis=1)
    # white_cols = np.all(white_pixels, axis=0)

    white_pixels = img >= th
    white_rows = np.count_nonzero(white_pixels, axis=1) >= img.shape[1]
    white_cols = np.count_nonzero(white_pixels, axis=0) >= img.shape[0]

    good_rows = np.where(white_rows == False)[0]
    good_cols = np.where(white_cols == False)[0]

    # empty result
    if len(good_rows) == 0 or len(good_cols) == 0:
        if len(base_img.shape) == 2:
            return base_img[0:1, 0:1]
        else:
            return base_img[0:1, 0:1, :]

    y_min = good_rows[0] if len(good_rows) > 0 else 0
    y_max = good_rows[-1] if len(good_rows) > 0 else 0

    x_min = good_cols[0] if len(good_cols) > 0 else 0
    x_max = good_cols[-1] if len(good_cols) > 0 else 0

    # add margin
    h, w, *_ = base_img.shape
    y_min = max(0, y_min - m[0])
    y_max = min(h, y_max + m[1] + 1)

    x_min = max(0, x_min - m[2])
    x_max = min(w, x_max + m[3] + 1)

    return base_img[y_min:y_max, x_min:x_max]

if __name__ == '__main__':

  img_name = sys.argv[1]
  img = cv2.imread(img_name, 0)

  trimmed = trim_border(img, th=250, m=2)
  cv2.imshow("in", img)
  cv2.imshow("aaa", trimmed)
  cv2.waitKey(0)