Create image_processing.py

image_processing.py

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+from PIL import Image, ImageDither, ImageQuantize
+
+DITHER_METHODS = {
+    "None": ImageDither.NONE,
+    "Floyd-Steinberg": ImageDither.FLOYDSTEINBERG
+}
+
+QUANTIZATION_METHODS = {
+    "Median cut": ImageQuantize.MEDIANCUT,
+    "Maximum coverage": ImageQuantize.MAXCOVERAGE,
+    "Fast octree": ImageQuantize.FASTOCTREE,
+    "libimagequant": ImageQuantize.LIBIMAGEQUANT
+}
+
+def downscale_image(image: Image, scale: int) -> Image:
+    width, height = image.size
+    downscaled_image = image.resize((int(width / scale), int(height / scale)), Image.NEAREST)
+    return downscaled_image
+
+def limit_colors(
+        image,
+        limit: int=16,
+        palette=None,
+        palette_colors: int=256,
+        quantize: Image.Quantize=Image.Quantize.MEDIANCUT,
+        dither: Image.Dither=Image.Dither.NONE,
+        use_k_means: bool=False
+    ):
+    if use_k_means:
+        k_means_value = limit
+    else:
+        k_means_value = 0
+
+    if palette:
+        palette_image = palette
+        ppalette = palette.getcolors()
+        if ppalette:
+            color_palette = palette.quantize(colors=len(list(set(ppalette))))
+        else:
+            colors = len(palette_image.getcolors()) if palette_image.getcolors() else palette_colors
+            color_palette = palette_image.quantize(colors, kmeans=colors)
+    else:
+        # we need to get palette from image, because
+        # dither in quantize doesn't work without it
+        # https://pillow.readthedocs.io/en/stable/_modules/PIL/Image.html#Image.quantize
+        color_palette = image.quantize(colors=limit, kmeans=k_means_value, method=quantize, dither=Image.Dither.NONE)
+
+    new_image = image.quantize(palette=color_palette, dither=dither)
+
+    return new_image
+
+def convert_to_grayscale(image):
+    new_image = image.convert("L")
+    return new_image.convert("RGB")
+
+def convert_to_black_and_white(image: Image, threshold: int=128, is_inversed: bool=False):
+    if is_inversed:
+        apply_threshold = lambda x : 255 if x < threshold else 0
+    else:
+        apply_threshold = lambda x : 255 if x > threshold else 0
+
+    black_and_white_image = image.convert('L', dither=Image.Dither.NONE).point(apply_threshold, mode='1')
+    return black_and_white_image.convert("RGB")
+
+def resize_image(image: Image, size) -> Image:
+    width, height = size
+    resized_image = image.resize((width, height), Image.NEAREST)
+    return resized_image