fix entropy point calculation
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087609ee18
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41e3877be2
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@ -196,9 +196,9 @@ def image_focal_points(im):
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points = cv2.goodFeaturesToTrack(
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np_im,
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maxCorners=50,
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maxCorners=100,
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qualityLevel=0.04,
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minDistance=min(grayscale.width, grayscale.height)*0.05,
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minDistance=min(grayscale.width, grayscale.height)*0.07,
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useHarrisDetector=False,
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)
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@ -218,28 +218,32 @@ def image_focal_points(im):
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def image_entropy_point(im, crop_width, crop_height):
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img = im.copy()
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# just make it easier to slide the test crop with images oriented the same way
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if (img.size[0] < img.size[1]):
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portrait = True
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img = img.rotate(90, expand=1)
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landscape = im.height < im.width
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portrait = im.height > im.width
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if landscape:
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move_idx = [0, 2]
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move_max = im.size[0]
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elif portrait:
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move_idx = [1, 3]
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move_max = im.size[1]
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e_max = 0
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crop_current = [0, 0, crop_width, crop_height]
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crop_best = crop_current
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while crop_current[2] < img.size[0]:
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crop = img.crop(tuple(crop_current))
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while crop_current[move_idx[1]] < move_max:
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crop = im.crop(tuple(crop_current))
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e = image_entropy(crop)
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if (e_max < e):
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if (e > e_max):
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e_max = e
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crop_best = list(crop_current)
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crop_current[0] += 4
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crop_current[2] += 4
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crop_current[move_idx[0]] += 4
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crop_current[move_idx[1]] += 4
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x_mid = int(crop_best[0] + crop_width/2)
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y_mid = int(crop_best[1] + crop_height/2)
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x_mid = int((crop_best[2] - crop_best[0])/2)
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y_mid = int((crop_best[3] - crop_best[1])/2)
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return {
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'x': x_mid,
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@ -250,7 +254,7 @@ def image_entropy_point(im, crop_width, crop_height):
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def image_entropy(im):
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# greyscale image entropy
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band = np.asarray(im.convert("L"))
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band = np.asarray(im.convert("1"))
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hist, _ = np.histogram(band, bins=range(0, 256))
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hist = hist[hist > 0]
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return -np.log2(hist / hist.sum()).sum()
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