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prep_image.py

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+import numpy as np
+from torchvision import transforms
+from PIL import Image
+
+
+def transform_image(image: Image) -> Image:
+    im_shape = 64
+    resize_transforms = transforms.Compose([
+        transforms.Resize(size=im_shape),
+        transforms.CenterCrop(size=(im_shape, im_shape))
+    ])
+    return resize_transforms(image)
+
+
+def to_grayscale(pil_image: np.ndarray) -> np.ndarray:
+    if pil_image.ndim == 2:
+        return pil_image.copy()[None]
+    if pil_image.ndim != 3:
+        raise ValueError("image must have either shape (H, W) or (H, W, 3)")
+    if pil_image.shape[2] != 3:
+        raise ValueError(f"image has shape (H, W, {pil_image.shape[2]}), but it should have (H, W, 3)")
+
+    rgb = pil_image / 255
+    rgb_linear = np.where(
+        rgb < 0.04045,
+        rgb / 12.92,
+        ((rgb + 0.055) / 1.055) ** 2.4
+    )
+    grayscale_linear = 0.2126 * rgb_linear[..., 0] + 0.7152 * rgb_linear[..., 1] + 0.0722 * rgb_linear[..., 2]
+
+    grayscale = np.where(
+        grayscale_linear < 0.0031308,
+        12.92 * grayscale_linear,
+        1.055 * grayscale_linear ** (1 / 2.4) - 0.055
+    )
+    grayscale = grayscale * 255
+
+    if np.issubdtype(pil_image.dtype, np.integer):
+        grayscale = np.round(grayscale)
+    return grayscale.astype(pil_image.dtype)[None]
+
+
+def prepare_image(image: np.ndarray, x: int, y: int, width: int, height: int, size: int) -> \
+        tuple[np.ndarray, np.ndarray, np.ndarray]:
+    if image.ndim < 3 or image.shape[-3] != 1:
+        # This is actually more general than the assignment specification
+        raise ValueError("image must have shape (..., 1, H, W)")
+    if width < 2 or height < 2 or size < 2:
+        raise ValueError("width/height/size must be >= 2")
+    if x < 0 or (x + width) > image.shape[-1]:
+        raise ValueError(f"x={x} and width={width} do not fit into the image width={image.shape[-1]}")
+    if y < 0 or (y + height) > image.shape[-2]:
+        raise ValueError(f"y={y} and height={height} do not fit into the image height={image.shape[-2]}")
+
+    # The (height, width) slices to extract the area that should be pixelated. Since we
+    # need this multiple times, specify the slices explicitly instead of using [:] notation
+    area = (..., slice(y, y + height), slice(x, x + width))
+
+    # This returns already a copy, so we are independent of "image"
+    pixelated_image = pixelate(image, x, y, width, height, size)
+
+    known_array = np.full_like(image, fill_value=False, dtype=bool)
+    known_array[area] = True
+
+    # Create a copy to avoid that "target_array" and "image" point to the same array
+    # target_array = image[area].copy()
+    stacked_target_array = np.full((1, 64, 64), 0)
+    stacked_target_array[area] = image[area].copy()
+    target_array = stacked_target_array
+
+    return pixelated_image, known_array, target_array
+
+
+def pixelate(image: np.ndarray, x: int, y: int, width: int, height: int, size: int) -> np.ndarray:
+    # Need a copy since we overwrite data directly
+    image = image.copy()
+    curr_x = x
+
+    while curr_x < x + width:
+        curr_y = y
+        while curr_y < y + height:
+            block = (..., slice(curr_y, min(curr_y + size, y + height)), slice(curr_x, min(curr_x + size, x + width)))
+            image[block] = image[block].mean()
+            curr_y += size
+        curr_x += size
+
+    return image