def dynamic_preprocess()

in optimum/intel/openvino/modeling_visual_language.py [0:0]

• 33 lines of code
• 10 McCabe index (conditional complexity)

        def dynamic_preprocess(image, min_num=1, max_num=12, image_size=28, use_thumbnail=False):
            orig_width, orig_height = image.size
            aspect_ratio = orig_width / orig_height

            # calculate the existing image aspect ratio
            target_ratios = {
                (i, j)
                for n in range(min_num, max_num + 1)
                for i in range(1, n + 1)
                for j in range(1, n + 1)
                if i * j <= max_num and i * j >= min_num
            }
            target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

            # find the closest aspect ratio to the target
            target_aspect_ratio = find_closest_aspect_ratio(
                aspect_ratio, target_ratios, orig_width, orig_height, image_size
            )

            # calculate the target width and height
            target_width = image_size * target_aspect_ratio[0]
            target_height = image_size * target_aspect_ratio[1]
            blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

            # resize the image
            resized_img = image.resize((target_width, target_height))
            processed_images = []
            for i in range(blocks):
                box = (
                    (i % (target_width // image_size)) * image_size,
                    (i // (target_width // image_size)) * image_size,
                    ((i % (target_width // image_size)) + 1) * image_size,
                    ((i // (target_width // image_size)) + 1) * image_size,
                )
                # split the image
                split_img = resized_img.crop(box)
                processed_images.append(split_img)
            assert len(processed_images) == blocks
            if use_thumbnail and len(processed_images) != 1:
                thumbnail_img = image.resize((image_size, image_size))
                processed_images.append(thumbnail_img)
            return processed_images