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dithering_methods.py
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dithering_methods.py
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import numpy as np
from PIL import Image
# Threshold Method
def threshold_dithering(input_image_path, output_image_path, threshold):
# Open the image file
image = Image.open(input_image_path)
# Convert the image to grayscale (0.299 R + 0.587 G + 0.114 B)
image = image.convert('L')
# Get the width and height of the image
width, height = image.size
# Loop through each pixel in the image
for y in range(height):
for x in range(width):
# Get the intensity value of the pixel
intensity = image.getpixel((x, y))
# Compare the intensity value to the threshold
if intensity >= threshold:
# Set the pixel to white
image.putpixel((x, y), 255)
else:
# Set the pixel to black
image.putpixel((x, y), 0)
# Save the dithered image
image.save(output_image_path)
def improved_threshold_dithering(input_image_path, output_image_path, threshold):
# Open the image file and convert it to grayscale (0.299 R + 0.587 G + 0.114 B)
image = Image.open(input_image_path).convert('L')
# Convert the image to a NumPy array (improving performance)
image_array = np.array(image)
# Perform the ordered dithering using the NumPy arrays
# Make an array of zeros with the same shape of (image_array), saving condition branch
output_array = np.zeros_like(image_array)
# Threshold the input image one time rather than looping through each pixel
output_array[image_array >= threshold] = 255
# Save the dithered image
output_image = Image.fromarray(output_array)
output_image.save(output_image_path)
# Floyd-Steinberg Error Diffusion Method
def floyd_steinberg(input_image_path, output_image_path):
# Open the image file
image = Image.open(input_image_path)
# Convert the image to grayscale (0.299 R + 0.587 G + 0.114 B)
image = image.convert('L')
# Get the width and height of the image
width, height = image.size
# Loop through each pixel
for y in range(height):
for x in range(width):
# Get the old intensity value of the pixel
old_pixel = image.getpixel((x, y))
# Compare to the threshold and set the new pixel value
new_pixel = 0 if old_pixel < 128 else 255
# Set the new pixel value
image.putpixel((x, y), new_pixel)
# Calculate the error
error = old_pixel - new_pixel
# Distribute the error over the adjacent pixels
if x + 1 < width:
# Get the pixel, add the error part and save the new value
pixel = image.getpixel((x + 1, y))
pixel += int(error * 7 / 16)
image.putpixel((x + 1, y), pixel)
if x + 1 < width and y + 1 < height:
# Get the pixel, add the error part and save the new value
pixel = image.getpixel((x + 1, y + 1))
pixel += int(error * 1 / 16)
image.putpixel((x + 1, y + 1), pixel)
if y + 1 < height:
# Get the pixel, add the error part and save the new value
pixel = image.getpixel((x, y + 1))
pixel += int(error * 5 / 16)
image.putpixel((x, y + 1), pixel)
if x > 0 and y + 1 < height:
# Get the pixel, add the error part and save the new value
pixel = image.getpixel((x - 1, y + 1))
pixel += int(error * 3 / 16)
image.putpixel((x - 1, y + 1), pixel)
# Save the dithered image
image.save(output_image_path)
# Ordered Dither Method
def ordered_dithering(input_image_path, output_image_path):
# Open the image file
image = Image.open(input_image_path)
# Convert the image to grayscale (0.299 R + 0.587 G + 0.114 B)
image = image.convert('L')
# Get the width and height of the image
width, height = image.size
# Define the dither matrix
dither_matrix = [(0, 128, 32, 160, 8, 136, 40, 168),
(192, 64, 224, 96, 200, 72, 232, 104),
(48, 176, 16, 144, 56, 184, 24, 152),
(240, 112, 208, 80, 248, 120, 216, 88),
(12, 140, 44, 172, 4, 132, 36, 164),
(204, 76, 236, 108, 196, 68, 228, 100),
(60, 188, 28, 156, 52, 180, 20, 148),
(252, 124, 220, 92, 244, 116, 212, 84)]
# Loop through each pixel
for y in range(height):
for x in range(width):
# Get the intensity value of the pixel
pixel = image.getpixel((x, y))
# Find corresponding dithering pixel
dx = x % 8 # 8 is the width of "dither_matrix"
dy = y % 8 # 8 is the width of "dither_matrix"
# Compare image pixel with dither pixel
if pixel > dither_matrix[dx][dy]:
image.putpixel((x, y), 255)
else:
image.putpixel((x, y), 0)
# Save the dithered image
image.save(output_image_path)
# Ordered dithering function with NumPy arrays
def improved_ordered_dithering(input_image_path, output_image_path):
# Open the image file and convert it to grayscale
image = Image.open(input_image_path).convert('L')
# Convert the image to a NumPy array (improving performance)
image_array = np.array(image)
# Define the dither matrix
dither_matrix = np.array([[0, 128, 32, 160, 8, 136, 40, 168],
[192, 64, 224, 96, 200, 72, 232, 104],
[48, 176, 16, 144, 56, 184, 24, 152],
[240, 112, 208, 80, 248, 120, 216, 88],
[12, 140, 44, 172, 4, 132, 36, 164],
[204, 76, 236, 108, 196, 68, 228, 100],
[60, 188, 28, 156, 52, 180, 20, 148],
[252, 124, 220, 92, 244, 116, 212, 84]])
# Perform the ordered dithering using the NumPy arrays
# Make an array of zeros with the same shape of (image_array), saving condition branch
output_array = np.zeros_like(image_array)
# Loop through each pixel
for y in range(image_array.shape[0]):
for x in range(image_array.shape[1]):
# Find corresponding dithering pixel
dy = y % dither_matrix.shape[0]
dx = x % dither_matrix.shape[1]
# Compare the intensity value of the pixel with corresponding dither one
if image_array[y, x] > dither_matrix[dy, dx]:
output_array[y, x] = 255
# Convert the output array back to an image with the original size
output_image = Image.fromarray(output_array).resize(image.size)
# Save the output image
output_image.save(output_image_path)
# Generate a Bayer matrix of the specified size
def bayer_matrix(size):
matrix = [[0, 2], [3, 1]] # 2x2 Bayer matrix
while len(matrix) < size:
matrix = [x + [4 * x[-1] + i, 4 * x[-1] + 2 + i] for x in matrix for i in range(2)]
matrix += [x + [4 * x[-1] + 3 - i, 4 * x[-1] + 1 - i] for x in matrix for i in range(2)]
return matrix[:size][:size]
# Apply pattern dithering to the input image using the specified matrix
def pattern_dithering(input_image_path, output_image_path, matrix):
# Open the image file and convert it to grayscale
image = Image.open(input_image_path).convert('L')
width, height = image.size
output_image = Image.new('L', (width, height))
for y in range(0, height, len(matrix)):
for x in range(0, width, len(matrix[0])):
block = image.crop((x, y, x + len(matrix[0]), y + len(matrix)))
for j in range(len(matrix)):
for i in range(len(matrix[0])):
threshold = matrix[j][i] / 16.0
output_pixel = 255 if block.getpixel((i, j)) / 255.0 > threshold else 0
output_image.putpixel((x + i, y + j), output_pixel)
# Save output image
output_image.save(output_image_path)
# Generate a Bayer matrix of the specified size
def improved_bayer_matrix(size):
matrix = np.array([[0, 2], [3, 1]])
while matrix.shape[0] < size or matrix.shape[1] < size:
top = np.hstack((4 * matrix[:, -1][:, np.newaxis] + np.array([0, 1])[:, np.newaxis],
4 * matrix[:, -2][:, np.newaxis] + np.array([3, 2])[:, np.newaxis]))
bottom = np.hstack((4 * matrix[:, -2][:, np.newaxis] + np.array([2, 3])[:, np.newaxis],
4 * matrix[:, -1][:, np.newaxis] + np.array([1, 0])[:, np.newaxis]))
matrix = np.vstack((np.hstack((matrix, top)), np.hstack((bottom[::-1, :], np.rot90(matrix, 2)))))
return matrix[:size, :size]
# Apply pattern dithering to the input image using the specified matrix
def improved_pattern_dithering(input_image_path, output_image_path, matrix):
# Open the image file and convert it to grayscale
image = Image.open(input_image_path).convert('L')
width, height = image.size
output_image = Image.new('L', (width, height))
for y in range(0, height, len(matrix)):
for x in range(0, width, len(matrix[0])):
block = np.array(image.crop((x, y, x + len(matrix[0]), y + len(matrix)))) / 255.0
threshold = matrix / 16.0
output_block = np.where(block > threshold, 1.0, 0.0)
output_image.paste(Image.fromarray(np.uint8(output_block * 255)), (x, y))
# Save output image
output_image.save(output_image_path)