def threshold_simple (img_grayscale):         img_grayscale,         0,         255,-        cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU+        cv2.THRESH_BINARY + cv2.THRESH_OTSU     )[1].astype(np.uint8) -def make_bitmap (img, method):+def threshold (img_grayscale, method):+    if method == 'SIMPLE':+        return threshold_simple(img_grayscale)+    +    return threshold_adaptive(img_grayscale) -    # Grayscale conversion-    if not is_single_channel(img):+def rgba_on_white (img):+    r, g, b, a = cv2.split(img)+    n_alpha = a / 255 -        # If the image is in RGBA format, composite it on top of a white background-        # by combining linearly-        if is_RGBA(img):-            r, g, b, a = cv2.split(img)-            n_alpha = a / 255+    r = (255 * (1 - n_alpha) + r * n_alpha).astype(np.uint8)+    g = (255 * (1 - n_alpha) + g * n_alpha).astype(np.uint8)+    b = (255 * (1 - n_alpha) + b * n_alpha).astype(np.uint8)++    return cv2.merge((r, g, b)) -            r = (255 * (1 - n_alpha) + r * n_alpha).astype(np.uint8)-            g = (255 * (1 - n_alpha) + g * n_alpha).astype(np.uint8)-            b = (255 * (1 - n_alpha) + b * n_alpha).astype(np.uint8)+def invert (img):+    if is_RGBA(img):+        r, g, b, a = cv2.split(img)+        r = 255 - r+        g = 255 - g+        b = 255 - b+        return cv2.merge((r, g, b, a))+    +    return 255 - img -            img = cv2.merge((r, g, b))+def make_bitmap (img, method):+    inverted = invert(img)++    if not is_single_channel(img):+        +        if is_RGBA(img):+            img = rgba_on_white(img)+            inverted = rgba_on_white(inverted)                     -        img_grayscale = cv2.cvtColor(+        # Grayscale conversion+        grayscale = cv2.cvtColor(             img,              cv2.COLOR_RGB2GRAY         ) -    # If image is single channel, no conversion is needed+        inverted_grayscale = cv2.cvtColor(+            inverted,+            cv2.COLOR_RGB2GRAY+        )++        candidates = [grayscale, inverted_grayscale]+         else:-        img_grayscale = img+        candidates = [img, inverted] -    if method == 'SIMPLE':-        thresholded = threshold_simple(img_grayscale)+    candidates = [threshold(img, method) for img in candidates] -    else:-        thresholded = threshold_adaptive(img_grayscale)-    +    bitmap, inverted_bitmap = [+        np.where(candidate == 255, 0, 1).astype(np.uint32) +        for candidate in candidates+    ]++    pixel_count = get_pixel_count(img)+    foreground_area = np.sum(bitmap)+    inverted_foreground_area = np.sum(inverted_bitmap)++    if (foreground_area / pixel_count) < 0.001:

 def threshold_simple (img_grayscale):         img_grayscale,         0,         255,-        cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU+        cv2.THRESH_BINARY + cv2.THRESH_OTSU     )[1].astype(np.uint8) -def make_bitmap (img, method):+def threshold (img_grayscale, method):+    if method == 'SIMPLE':+        return threshold_simple(img_grayscale)+    +    return threshold_adaptive(img_grayscale) -    # Grayscale conversion-    if not is_single_channel(img):+def rgba_on_white (img):+    r, g, b, a = cv2.split(img)+    n_alpha = a / 255 -        # If the image is in RGBA format, composite it on top of a white background-        # by combining linearly-        if is_RGBA(img):-            r, g, b, a = cv2.split(img)-            n_alpha = a / 255+    r = (255 * (1 - n_alpha) + r * n_alpha).astype(np.uint8)+    g = (255 * (1 - n_alpha) + g * n_alpha).astype(np.uint8)+    b = (255 * (1 - n_alpha) + b * n_alpha).astype(np.uint8)++    return cv2.merge((r, g, b)) -            r = (255 * (1 - n_alpha) + r * n_alpha).astype(np.uint8)-            g = (255 * (1 - n_alpha) + g * n_alpha).astype(np.uint8)-            b = (255 * (1 - n_alpha) + b * n_alpha).astype(np.uint8)+def invert (img):+    if is_RGBA(img):+        r, g, b, a = cv2.split(img)+        r = 255 - r+        g = 255 - g+        b = 255 - b+        return cv2.merge((r, g, b, a))+    +    return 255 - img -            img = cv2.merge((r, g, b))+def make_bitmap (img, method):+    inverted = invert(img)++    if not is_single_channel(img):+        +        if is_RGBA(img):+            img = rgba_on_white(img)+            inverted = rgba_on_white(inverted)                     -        img_grayscale = cv2.cvtColor(+        # Grayscale conversion+        grayscale = cv2.cvtColor(             img,              cv2.COLOR_RGB2GRAY         ) -    # If image is single channel, no conversion is needed+        inverted_grayscale = cv2.cvtColor(+            inverted,+            cv2.COLOR_RGB2GRAY+        )++        candidates = [grayscale, inverted_grayscale]+         else:-        img_grayscale = img+        candidates = [img, inverted] -    if method == 'SIMPLE':-        thresholded = threshold_simple(img_grayscale)+    candidates = [threshold(img, method) for img in candidates] -    else:-        thresholded = threshold_adaptive(img_grayscale)-    +    bitmap, inverted_bitmap = [+        np.where(candidate == 255, 0, 1).astype(np.uint32) +        for candidate in candidates+    ]++    pixel_count = get_pixel_count(img)+    foreground_area = np.sum(bitmap)+    inverted_foreground_area = np.sum(inverted_bitmap)++    if (foreground_area / pixel_count) < 0.001:+        # If total area of the foreground is less than 1 percent of the image's++        # It's possible that the inverted image produced a better bitmap.+        # The main relevant case is an all-or-mostly white foreground with a transparent background.++        #NOTE: The inverted bitmap CAN be worse than the non-inverted one.+        # e.g A very small amount of black pixels on a transparent background++        if inverted_foreground_area > foreground_area:+            # Then the inverted bitmap is better. And its area is at least 1.