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from PIL import Image, ImageEnhance, ImageChops
import numpy as np
def normalize_gray(image: Image) -> Image:
"""Normalize a grayscale image using histogram equalization."""
if image.mode != 'L':
image = image.convert('L')
img = np.asarray(image)
balanced_img = img.copy()
hist, bins = np.histogram(img.reshape(-1), 256, (0, 256))
bmin = np.min(np.where(hist > (hist.sum() * 0.0005)))
bmax = np.max(np.where(hist > (hist.sum() * 0.0005)))
balanced_img = np.clip(img, bmin, bmax)
balanced_img = ((balanced_img - bmin) / (bmax - bmin) * 255)
return Image.fromarray(balanced_img).convert('L')
def image_channel_split(image: Image, mode: str = 'RGBA') -> tuple:
"""Split image into channels based on color mode."""
_image = image.convert('RGBA')
channel1 = Image.new('L', size=_image.size, color='black')
channel2 = Image.new('L', size=_image.size, color='black')
channel3 = Image.new('L', size=_image.size, color='black')
channel4 = Image.new('L', size=_image.size, color='black')
if mode == 'RGBA':
channel1, channel2, channel3, channel4 = _image.split()
elif mode == 'RGB':
channel1, channel2, channel3 = _image.convert('RGB').split()
elif mode == 'YCbCr':
channel1, channel2, channel3 = _image.convert('YCbCr').split()
elif mode == 'LAB':
channel1, channel2, channel3 = _image.convert('LAB').split()
elif mode == 'HSV':
channel1, channel2, channel3 = _image.convert('HSV').split()
return channel1, channel2, channel3, channel4
def image_channel_merge(channels: tuple, mode: str = 'RGB') -> Image:
"""Merge channels back into an image based on color mode."""
channel1 = channels[0].convert('L')
channel2 = channels[1].convert('L')
channel3 = channels[2].convert('L')
channel4 = Image.new('L', size=channel1.size, color='white')
if mode == 'RGBA':
if len(channels) > 3:
channel4 = channels[3].convert('L')
ret_image = Image.merge('RGBA', [channel1, channel2, channel3, channel4])
elif mode == 'RGB':
ret_image = Image.merge('RGB', [channel1, channel2, channel3])
elif mode == 'YCbCr':
ret_image = Image.merge('YCbCr', [channel1, channel2, channel3]).convert('RGB')
elif mode == 'LAB':
ret_image = Image.merge('LAB', [channel1, channel2, channel3]).convert('RGB')
elif mode == 'HSV':
ret_image = Image.merge('HSV', [channel1, channel2, channel3]).convert('RGB')
return ret_image
def balance_to_gamma(balance: int) -> float:
"""Convert color balance value to gamma value."""
return 0.00005 * balance * balance - 0.01 * balance + 1
def gamma_trans(image: Image, gamma: float) -> Image:
"""Apply gamma correction to an image."""
if gamma == 1.0:
return image
img_array = np.array(image)
img_array = np.power(img_array / 255.0, gamma) * 255.0
return Image.fromarray(img_array.astype(np.uint8))
def RGB2RGBA(image: Image, mask: Image) -> Image:
"""Convert RGB image to RGBA using provided mask."""
if image.mode != 'RGB':
image = image.convert('RGB')
if mask.mode != 'L':
mask = mask.convert('L')
return Image.merge('RGBA', (*image.split(), mask))
def chop_image_v2(background_image: Image, layer_image: Image, blend_mode: str, opacity: int) -> Image:
"""Blend two images together with specified blend mode and opacity."""
if background_image.mode != 'RGB':
background_image = background_image.convert('RGB')
if layer_image.mode != 'RGB':
layer_image = layer_image.convert('RGB')
# Convert opacity to float (0-1)
opacity = opacity / 100.0
# Create a copy of the background image
result = background_image.copy()
# Apply blend mode
if blend_mode == "normal":
result = Image.blend(background_image, layer_image, opacity)
elif blend_mode == "multiply":
result = ImageChops.multiply(background_image, layer_image)
result = Image.blend(background_image, result, opacity)
elif blend_mode == "screen":
result = ImageChops.screen(background_image, layer_image)
result = Image.blend(background_image, result, opacity)
elif blend_mode == "overlay":
result = ImageChops.overlay(background_image, layer_image)
result = Image.blend(background_image, result, opacity)
return result
def auto_adjust(image: Image, strength: int = 100, brightness: int = 0,
contrast: int = 0, saturation: int = 0,
red: int = 0, green: int = 0, blue: int = 0,
mode: str = 'RGB') -> Image:
"""
Apply automatic adjustments to an image.
Args:
image: PIL Image to adjust
strength: Overall strength of the adjustment (0-100)
brightness: Brightness adjustment (-100 to 100)
contrast: Contrast adjustment (-100 to 100)
saturation: Saturation adjustment (-100 to 100)
red: Red channel adjustment (-100 to 100)
green: Green channel adjustment (-100 to 100)
blue: Blue channel adjustment (-100 to 100)
mode: Color mode for processing ('RGB', 'lum + sat', 'luminance', 'saturation', 'mono')
Returns:
Adjusted PIL Image
"""
def auto_level_gray(image):
"""Apply auto levels to a grayscale image."""
gray_image = Image.new("L", image.size, color='gray')
gray_image.paste(image.convert('L'))
return normalize_gray(gray_image)
# Calculate adjustment factors
if brightness < 0:
brightness_offset = brightness / 100 + 1
else:
brightness_offset = brightness / 50 + 1
if contrast < 0:
contrast_offset = contrast / 100 + 1
else:
contrast_offset = contrast / 50 + 1
if saturation < 0:
saturation_offset = saturation / 100 + 1
else:
saturation_offset = saturation / 50 + 1
# Get color channel gammas
red_gamma = balance_to_gamma(red)
green_gamma = balance_to_gamma(green)
blue_gamma = balance_to_gamma(blue)
# Process image based on mode
if mode == 'RGB':
r, g, b, _ = image_channel_split(image, mode='RGB')
r = auto_level_gray(r)
g = auto_level_gray(g)
b = auto_level_gray(b)
ret_image = image_channel_merge((r, g, b), 'RGB')
elif mode == 'lum + sat':
h, s, v, _ = image_channel_split(image, mode='HSV')
s = auto_level_gray(s)
ret_image = image_channel_merge((h, s, v), 'HSV')
l, a, b, _ = image_channel_split(ret_image, mode='LAB')
l = auto_level_gray(l)
ret_image = image_channel_merge((l, a, b), 'LAB')
elif mode == 'luminance':
l, a, b, _ = image_channel_split(image, mode='LAB')
l = auto_level_gray(l)
ret_image = image_channel_merge((l, a, b), 'LAB')
elif mode == 'saturation':
h, s, v, _ = image_channel_split(image, mode='HSV')
s = auto_level_gray(s)
ret_image = image_channel_merge((h, s, v), 'HSV')
else: # mono
gray = image.convert('L')
ret_image = auto_level_gray(gray).convert('RGB')
# Apply color channel adjustments if not in mono mode
if (red or green or blue) and mode != "mono":
r, g, b, _ = image_channel_split(ret_image, mode='RGB')
if red:
r = gamma_trans(r, red_gamma).convert('L')
if green:
g = gamma_trans(g, green_gamma).convert('L')
if blue:
b = gamma_trans(b, blue_gamma).convert('L')
ret_image = image_channel_merge((r, g, b), 'RGB')
# Apply brightness, contrast, and saturation
if brightness:
brightness_image = ImageEnhance.Brightness(ret_image)
ret_image = brightness_image.enhance(factor=brightness_offset)
if contrast:
contrast_image = ImageEnhance.Contrast(ret_image)
ret_image = contrast_image.enhance(factor=contrast_offset)
if saturation:
color_image = ImageEnhance.Color(ret_image)
ret_image = color_image.enhance(factor=saturation_offset)
# Blend with original image based on strength
ret_image = chop_image_v2(image, ret_image, blend_mode="normal", opacity=strength)
# Handle RGBA mode
if image.mode == 'RGBA':
ret_image = RGB2RGBA(ret_image, image.split()[-1])
return ret_image |