Improvements and notebooks
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34 changed files with 2668 additions and 1373 deletions
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@ -1,563 +0,0 @@
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"""
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Dithering algorithms for 6-color e-ink display testing.
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Includes multiple error-diffusion and ordered dithering algorithms
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for comparison testing.
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"""
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import numpy as np
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from PIL import Image
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from typing import Tuple, List
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from numba import jit
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# 6-color ACeP palette (RGB format)
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PALETTE_RGB = [
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(0, 0, 0), # BLACK
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(255, 255, 255), # WHITE
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(255, 255, 0), # YELLOW
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(255, 0, 0), # RED
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(0, 0, 255), # BLUE
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(0, 255, 0), # GREEN
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]
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PALETTE_NAMES = ['Black', 'White', 'Yellow', 'Red', 'Blue', 'Green']
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def create_pil_palette_image() -> Image.Image:
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"""Create a PIL palette image for quantization."""
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pal_image = Image.new("P", (1, 1))
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flat_palette = []
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for color in PALETTE_RGB:
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flat_palette.extend(color)
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# Pad to 256 colors (768 values)
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flat_palette.extend([0] * (768 - len(flat_palette)))
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pal_image.putpalette(flat_palette)
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return pal_image
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def find_nearest_color(pixel: np.ndarray, palette: np.ndarray) -> Tuple[int, np.ndarray]:
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"""Find the nearest palette color using Euclidean distance."""
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distances = np.sqrt(np.sum((palette - pixel) ** 2, axis=1))
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idx = np.argmin(distances)
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return idx, palette[idx]
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def find_nearest_color_weighted(pixel: np.ndarray, palette: np.ndarray) -> Tuple[int, np.ndarray]:
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"""Find nearest color using perceptually-weighted distance (human eye sensitivity)."""
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# Weights based on human perception: Green > Red > Blue
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weights = np.array([0.299, 0.587, 0.114])
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diff = palette - pixel
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weighted_diff = diff * weights
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distances = np.sqrt(np.sum(weighted_diff ** 2, axis=1))
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idx = np.argmin(distances)
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return idx, palette[idx]
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# =============================================================================
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# Error Diffusion Dithering Algorithms
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# =============================================================================
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def dither_floyd_steinberg(image: Image.Image, weighted: bool = False) -> Image.Image:
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"""
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Floyd-Steinberg dithering (1976).
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Classic error diffusion algorithm with distribution:
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* 7/16
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3/16 5/16 1/16
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"""
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img = np.array(image.convert('RGB'), dtype=np.float64)
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height, width = img.shape[:2]
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palette = np.array(PALETTE_RGB, dtype=np.float64)
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find_fn = find_nearest_color_weighted if weighted else find_nearest_color
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].copy()
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_, new_pixel = find_fn(old_pixel, palette)
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img[y, x] = new_pixel
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error = old_pixel - new_pixel
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if x + 1 < width:
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img[y, x + 1] += error * 7 / 16
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if y + 1 < height:
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if x > 0:
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img[y + 1, x - 1] += error * 3 / 16
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img[y + 1, x] += error * 5 / 16
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if x + 1 < width:
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img[y + 1, x + 1] += error * 1 / 16
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img = np.clip(img, 0, 255).astype(np.uint8)
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return Image.fromarray(img, 'RGB')
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def dither_jarvis_judice_ninke(image: Image.Image, weighted: bool = False) -> Image.Image:
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"""
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Jarvis, Judice, and Ninke dithering (1976).
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Spreads error over a larger area (12 pixels):
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* 7 5
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3 5 7 5 3
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1 3 5 3 1
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All divided by 48.
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"""
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img = np.array(image.convert('RGB'), dtype=np.float64)
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height, width = img.shape[:2]
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palette = np.array(PALETTE_RGB, dtype=np.float64)
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find_fn = find_nearest_color_weighted if weighted else find_nearest_color
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].copy()
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_, new_pixel = find_fn(old_pixel, palette)
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img[y, x] = new_pixel
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error = old_pixel - new_pixel
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# Row 0 (current row)
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if x + 1 < width:
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img[y, x + 1] += error * 7 / 48
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if x + 2 < width:
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img[y, x + 2] += error * 5 / 48
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# Row 1
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if y + 1 < height:
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for dx, w in [(-2, 3), (-1, 5), (0, 7), (1, 5), (2, 3)]:
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if 0 <= x + dx < width:
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img[y + 1, x + dx] += error * w / 48
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# Row 2
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if y + 2 < height:
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for dx, w in [(-2, 1), (-1, 3), (0, 5), (1, 3), (2, 1)]:
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if 0 <= x + dx < width:
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img[y + 2, x + dx] += error * w / 48
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img = np.clip(img, 0, 255).astype(np.uint8)
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return Image.fromarray(img, 'RGB')
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def dither_stucki(image: Image.Image, weighted: bool = False) -> Image.Image:
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"""
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Stucki dithering (1981).
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Similar to JJN but with different weights:
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* 8 4
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2 4 8 4 2
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1 2 4 2 1
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All divided by 42.
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"""
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img = np.array(image.convert('RGB'), dtype=np.float64)
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height, width = img.shape[:2]
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palette = np.array(PALETTE_RGB, dtype=np.float64)
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find_fn = find_nearest_color_weighted if weighted else find_nearest_color
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].copy()
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_, new_pixel = find_fn(old_pixel, palette)
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img[y, x] = new_pixel
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error = old_pixel - new_pixel
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if x + 1 < width:
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img[y, x + 1] += error * 8 / 42
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if x + 2 < width:
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img[y, x + 2] += error * 4 / 42
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if y + 1 < height:
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for dx, w in [(-2, 2), (-1, 4), (0, 8), (1, 4), (2, 2)]:
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if 0 <= x + dx < width:
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img[y + 1, x + dx] += error * w / 42
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if y + 2 < height:
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for dx, w in [(-2, 1), (-1, 2), (0, 4), (1, 2), (2, 1)]:
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if 0 <= x + dx < width:
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img[y + 2, x + dx] += error * w / 42
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img = np.clip(img, 0, 255).astype(np.uint8)
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return Image.fromarray(img, 'RGB')
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def dither_atkinson(image: Image.Image, weighted: bool = False) -> Image.Image:
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"""
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Atkinson dithering (Bill Atkinson, Apple).
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Only diffuses 6/8 of the error (loses some detail but reduces noise):
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* 1 1
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1 1 1
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1
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All divided by 8 (but only 6/8 total error diffused).
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"""
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img = np.array(image.convert('RGB'), dtype=np.float64)
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height, width = img.shape[:2]
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palette = np.array(PALETTE_RGB, dtype=np.float64)
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find_fn = find_nearest_color_weighted if weighted else find_nearest_color
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].copy()
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_, new_pixel = find_fn(old_pixel, palette)
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img[y, x] = new_pixel
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error = old_pixel - new_pixel
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# Distribute 1/8 to each of 6 neighbors
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if x + 1 < width:
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img[y, x + 1] += error / 8
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if x + 2 < width:
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img[y, x + 2] += error / 8
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if y + 1 < height:
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if x > 0:
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img[y + 1, x - 1] += error / 8
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img[y + 1, x] += error / 8
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if x + 1 < width:
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img[y + 1, x + 1] += error / 8
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if y + 2 < height:
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img[y + 2, x] += error / 8
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img = np.clip(img, 0, 255).astype(np.uint8)
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return Image.fromarray(img, 'RGB')
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def dither_sierra(image: Image.Image, weighted: bool = False) -> Image.Image:
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"""
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Sierra dithering (Frankie Sierra).
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Full Sierra (Sierra-3):
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* 5 3
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2 4 5 4 2
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2 3 2
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All divided by 32.
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"""
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img = np.array(image.convert('RGB'), dtype=np.float64)
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height, width = img.shape[:2]
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palette = np.array(PALETTE_RGB, dtype=np.float64)
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find_fn = find_nearest_color_weighted if weighted else find_nearest_color
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].copy()
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_, new_pixel = find_fn(old_pixel, palette)
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img[y, x] = new_pixel
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error = old_pixel - new_pixel
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if x + 1 < width:
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img[y, x + 1] += error * 5 / 32
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if x + 2 < width:
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img[y, x + 2] += error * 3 / 32
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if y + 1 < height:
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for dx, w in [(-2, 2), (-1, 4), (0, 5), (1, 4), (2, 2)]:
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if 0 <= x + dx < width:
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img[y + 1, x + dx] += error * w / 32
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if y + 2 < height:
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for dx, w in [(-1, 2), (0, 3), (1, 2)]:
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if 0 <= x + dx < width:
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img[y + 2, x + dx] += error * w / 32
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img = np.clip(img, 0, 255).astype(np.uint8)
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return Image.fromarray(img, 'RGB')
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def dither_sierra_lite(image: Image.Image, weighted: bool = False) -> Image.Image:
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"""
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Sierra Lite (Sierra-2-4A) - faster variant.
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* 2
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1 1
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All divided by 4.
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"""
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img = np.array(image.convert('RGB'), dtype=np.float64)
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height, width = img.shape[:2]
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palette = np.array(PALETTE_RGB, dtype=np.float64)
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find_fn = find_nearest_color_weighted if weighted else find_nearest_color
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].copy()
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_, new_pixel = find_fn(old_pixel, palette)
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img[y, x] = new_pixel
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error = old_pixel - new_pixel
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if x + 1 < width:
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img[y, x + 1] += error * 2 / 4
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if y + 1 < height:
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if x > 0:
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img[y + 1, x - 1] += error * 1 / 4
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img[y + 1, x] += error * 1 / 4
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img = np.clip(img, 0, 255).astype(np.uint8)
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return Image.fromarray(img, 'RGB')
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def dither_burkes(image: Image.Image, weighted: bool = False) -> Image.Image:
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"""
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Burkes dithering.
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* 8 4
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2 4 8 4 2
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All divided by 32.
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"""
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img = np.array(image.convert('RGB'), dtype=np.float64)
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height, width = img.shape[:2]
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palette = np.array(PALETTE_RGB, dtype=np.float64)
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find_fn = find_nearest_color_weighted if weighted else find_nearest_color
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].copy()
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_, new_pixel = find_fn(old_pixel, palette)
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img[y, x] = new_pixel
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error = old_pixel - new_pixel
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if x + 1 < width:
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img[y, x + 1] += error * 8 / 32
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if x + 2 < width:
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img[y, x + 2] += error * 4 / 32
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if y + 1 < height:
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for dx, w in [(-2, 2), (-1, 4), (0, 8), (1, 4), (2, 2)]:
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if 0 <= x + dx < width:
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img[y + 1, x + dx] += error * w / 32
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img = np.clip(img, 0, 255).astype(np.uint8)
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return Image.fromarray(img, 'RGB')
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# =============================================================================
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# Ordered Dithering Algorithms
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# =============================================================================
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def get_bayer_matrix(n: int) -> np.ndarray:
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"""Generate a Bayer matrix of size 2^n x 2^n."""
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if n == 0:
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return np.array([[0]])
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smaller = get_bayer_matrix(n - 1)
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size = 2 ** (n - 1)
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result = np.zeros((2 ** n, 2 ** n))
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result[:size, :size] = 4 * smaller
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result[:size, size:] = 4 * smaller + 2
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result[size:, :size] = 4 * smaller + 3
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result[size:, size:] = 4 * smaller + 1
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return result
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def dither_ordered_bayer(image: Image.Image, matrix_size: int = 4, strength: float = 1.0) -> Image.Image:
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"""
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Ordered dithering using Bayer matrix.
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Args:
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image: Input image
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matrix_size: Size of Bayer matrix (2, 4, 8, or 16)
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strength: Dithering strength multiplier (0.0-2.0)
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"""
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img = np.array(image.convert('RGB'), dtype=np.float64)
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height, width = img.shape[:2]
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palette = np.array(PALETTE_RGB, dtype=np.float64)
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# Get appropriate Bayer matrix
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n = {2: 1, 4: 2, 8: 3, 16: 4}.get(matrix_size, 2)
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bayer = get_bayer_matrix(n)
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bayer_size = bayer.shape[0]
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# Normalize Bayer matrix to -0.5 to 0.5 range, then scale
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bayer_normalized = (bayer / (bayer_size ** 2) - 0.5) * strength * 128
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result = np.zeros_like(img)
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for y in range(height):
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for x in range(width):
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threshold = bayer_normalized[y % bayer_size, x % bayer_size]
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adjusted_pixel = img[y, x] + threshold
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adjusted_pixel = np.clip(adjusted_pixel, 0, 255)
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_, new_pixel = find_nearest_color(adjusted_pixel, palette)
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result[y, x] = new_pixel
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return Image.fromarray(result.astype(np.uint8), 'RGB')
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_NUMBA_PALETTE = np.array([
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[0, 0, 0], [255, 255, 255], [255, 255, 0],
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[255, 0, 0], [0, 0, 255], [0, 255, 0],
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], dtype=np.float64)
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_NUMBA_WEIGHTS = np.array([0.299, 0.587, 0.114], dtype=np.float64)
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@jit(nopython=True)
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def _numba_find_nearest(r, g, b, palette, weights):
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best_idx = 0
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best_dist = 1e10
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for i in range(palette.shape[0]):
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dr = (palette[i, 0] - r) * weights[0]
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dg = (palette[i, 1] - g) * weights[1]
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db = (palette[i, 2] - b) * weights[2]
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dist = dr * dr + dg * dg + db * db
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if dist < best_dist:
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best_dist = dist
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best_idx = i
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return best_idx
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@jit(nopython=True)
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def _numba_atkinson(img, palette, weights):
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height, width = img.shape[0], img.shape[1]
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for y in range(height):
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for x in range(width):
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old_r, old_g, old_b = img[y, x, 0], img[y, x, 1], img[y, x, 2]
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idx = _numba_find_nearest(old_r, old_g, old_b, palette, weights)
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new_r, new_g, new_b = palette[idx, 0], palette[idx, 1], palette[idx, 2]
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img[y, x, 0], img[y, x, 1], img[y, x, 2] = new_r, new_g, new_b
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err_r = (old_r - new_r) / 8.0
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err_g = (old_g - new_g) / 8.0
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err_b = (old_b - new_b) / 8.0
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if x + 1 < width:
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img[y, x + 1, 0] += err_r
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img[y, x + 1, 1] += err_g
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img[y, x + 1, 2] += err_b
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if x + 2 < width:
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img[y, x + 2, 0] += err_r
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img[y, x + 2, 1] += err_g
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img[y, x + 2, 2] += err_b
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if y + 1 < height:
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if x > 0:
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img[y + 1, x - 1, 0] += err_r
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img[y + 1, x - 1, 1] += err_g
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img[y + 1, x - 1, 2] += err_b
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img[y + 1, x, 0] += err_r
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img[y + 1, x, 1] += err_g
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img[y + 1, x, 2] += err_b
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if x + 1 < width:
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img[y + 1, x + 1, 0] += err_r
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img[y + 1, x + 1, 1] += err_g
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img[y + 1, x + 1, 2] += err_b
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if y + 2 < height:
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img[y + 2, x, 0] += err_r
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img[y + 2, x, 1] += err_g
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img[y + 2, x, 2] += err_b
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return img
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def dither_atkinson_numba(image: Image.Image) -> Image.Image:
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"""Numba-accelerated Atkinson dithering with perceptual weighting (~150x faster)."""
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img = np.array(image.convert('RGB'), dtype=np.float64)
|
||||
img = _numba_atkinson(img, _NUMBA_PALETTE, _NUMBA_WEIGHTS)
|
||||
img = np.clip(img, 0, 255).astype(np.uint8)
|
||||
return Image.fromarray(img, 'RGB')
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# PIL Built-in (for comparison)
|
||||
# =============================================================================
|
||||
|
||||
def dither_pil_floyd_steinberg(image: Image.Image) -> Image.Image:
|
||||
"""PIL's built-in Floyd-Steinberg dithering for comparison."""
|
||||
pal_image = create_pil_palette_image()
|
||||
img = image.convert('RGB')
|
||||
quantized = img.quantize(dither=Image.Dither.FLOYDSTEINBERG, palette=pal_image)
|
||||
return quantized.convert('RGB')
|
||||
|
||||
|
||||
def dither_pil_none(image: Image.Image) -> Image.Image:
|
||||
"""PIL quantization with no dithering (nearest color only)."""
|
||||
pal_image = create_pil_palette_image()
|
||||
img = image.convert('RGB')
|
||||
quantized = img.quantize(dither=Image.Dither.NONE, palette=pal_image)
|
||||
return quantized.convert('RGB')
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Algorithm Registry
|
||||
# =============================================================================
|
||||
|
||||
DITHER_ALGORITHMS = {
|
||||
'none': {
|
||||
'name': 'No Dithering (PIL)',
|
||||
'func': dither_pil_none,
|
||||
'description': 'Simple nearest-color quantization without error diffusion',
|
||||
},
|
||||
'pil_fs': {
|
||||
'name': 'Floyd-Steinberg (PIL)',
|
||||
'func': dither_pil_floyd_steinberg,
|
||||
'description': 'PIL built-in Floyd-Steinberg implementation',
|
||||
},
|
||||
'floyd_steinberg': {
|
||||
'name': 'Floyd-Steinberg',
|
||||
'func': dither_floyd_steinberg,
|
||||
'description': 'Classic error diffusion (1976), good balance of speed and quality',
|
||||
},
|
||||
'floyd_steinberg_weighted': {
|
||||
'name': 'Floyd-Steinberg (Weighted)',
|
||||
'func': lambda img: dither_floyd_steinberg(img, weighted=True),
|
||||
'description': 'Floyd-Steinberg with perceptual color weighting',
|
||||
},
|
||||
'atkinson': {
|
||||
'name': 'Atkinson',
|
||||
'func': dither_atkinson,
|
||||
'description': 'Bill Atkinson (Apple), diffuses only 75% of error for cleaner results',
|
||||
},
|
||||
'atkinson_weighted': {
|
||||
'name': 'Atkinson (Weighted)',
|
||||
'func': lambda img: dither_atkinson(img, weighted=True),
|
||||
'description': 'Atkinson with perceptual color weighting',
|
||||
},
|
||||
'atkinson_fast': {
|
||||
'name': 'Atkinson (Numba Fast)',
|
||||
'func': dither_atkinson_numba,
|
||||
'description': 'Numba-accelerated Atkinson (~150x faster, requires numba)',
|
||||
},
|
||||
'jarvis': {
|
||||
'name': 'Jarvis-Judice-Ninke',
|
||||
'func': dither_jarvis_judice_ninke,
|
||||
'description': 'Larger diffusion kernel (1976), smoother gradients but slower',
|
||||
},
|
||||
'stucki': {
|
||||
'name': 'Stucki',
|
||||
'func': dither_stucki,
|
||||
'description': 'Similar to JJN with modified weights (1981)',
|
||||
},
|
||||
'sierra': {
|
||||
'name': 'Sierra',
|
||||
'func': dither_sierra,
|
||||
'description': 'Full Sierra dithering, balanced results',
|
||||
},
|
||||
'sierra_lite': {
|
||||
'name': 'Sierra Lite',
|
||||
'func': dither_sierra_lite,
|
||||
'description': 'Faster Sierra variant with smaller kernel',
|
||||
},
|
||||
'burkes': {
|
||||
'name': 'Burkes',
|
||||
'func': dither_burkes,
|
||||
'description': 'Simplified two-row error diffusion',
|
||||
},
|
||||
'bayer2': {
|
||||
'name': 'Ordered (Bayer 2x2)',
|
||||
'func': lambda img: dither_ordered_bayer(img, matrix_size=2),
|
||||
'description': 'Ordered dithering with 2x2 Bayer matrix',
|
||||
},
|
||||
'bayer4': {
|
||||
'name': 'Ordered (Bayer 4x4)',
|
||||
'func': lambda img: dither_ordered_bayer(img, matrix_size=4),
|
||||
'description': 'Ordered dithering with 4x4 Bayer matrix',
|
||||
},
|
||||
'bayer8': {
|
||||
'name': 'Ordered (Bayer 8x8)',
|
||||
'func': lambda img: dither_ordered_bayer(img, matrix_size=8),
|
||||
'description': 'Ordered dithering with 8x8 Bayer matrix',
|
||||
},
|
||||
'bayer4_strong': {
|
||||
'name': 'Ordered (Bayer 4x4 Strong)',
|
||||
'func': lambda img: dither_ordered_bayer(img, matrix_size=4, strength=1.5),
|
||||
'description': 'Bayer 4x4 with increased dithering strength',
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def get_algorithm_names() -> List[str]:
|
||||
"""Return list of available algorithm names."""
|
||||
return list(DITHER_ALGORITHMS.keys())
|
||||
|
||||
|
||||
def apply_dithering(image: Image.Image, algorithm: str) -> Image.Image:
|
||||
"""Apply the specified dithering algorithm to an image."""
|
||||
if algorithm not in DITHER_ALGORITHMS:
|
||||
raise ValueError(f"Unknown algorithm: {algorithm}. Available: {get_algorithm_names()}")
|
||||
return DITHER_ALGORITHMS[algorithm]['func'](image)
|
||||
Loading…
Add table
Add a link
Reference in a new issue