fix entropy point calculation

    points = cv2.goodFeaturesToTrack(

        np_im,

        maxCorners=50,

        maxCorners=100,

        qualityLevel=0.04,

        minDistance=min(grayscale.width, grayscale.height)*0.05,

        minDistance=min(grayscale.width, grayscale.height)*0.07,

        useHarrisDetector=False,

    )

def image_entropy_point(im, crop_width, crop_height):

    img = im.copy()

    # just make it easier to slide the test crop with images oriented the same way

    if (img.size[0] < img.size[1]):

        portrait = True

        img = img.rotate(90, expand=1)

    landscape = im.height < im.width

    portrait = im.height > im.width

    if landscape:

      move_idx = [0, 2]

      move_max = im.size[0]

    elif portrait:

      move_idx = [1, 3]

      move_max = im.size[1]

    e_max = 0

    crop_current = [0, 0, crop_width, crop_height]

    crop_best = crop_current

    while crop_current[2] < img.size[0]:

        crop = img.crop(tuple(crop_current))

    while crop_current[move_idx[1]] < move_max:

        crop = im.crop(tuple(crop_current))

        e = image_entropy(crop)

        if (e_max < e):

        if (e > e_max):

          e_max = e

          crop_best = list(crop_current)

        crop_current[0] += 4

        crop_current[2] += 4

        crop_current[move_idx[0]] += 4

        crop_current[move_idx[1]] += 4

    x_mid = int(crop_best[0] + crop_width/2)

    y_mid = int(crop_best[1] + crop_height/2)

    x_mid = int((crop_best[2] - crop_best[0])/2)

    y_mid = int((crop_best[3] - crop_best[1])/2)

    return {

        'x': x_mid,

def image_entropy(im):

    # greyscale image entropy

    band = np.asarray(im.convert("L"))

    band = np.asarray(im.convert("1"))

    hist, _ = np.histogram(band, bins=range(0, 256))

    hist = hist[hist > 0]

    return -np.log2(hist / hist.sum()).sum()