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colors.py
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colors.py
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"""
@author: Chris Freilich
@title: Virtuoso Pack - Color Nodes
@nickname: Virtuoso Pack - Color Nodes
@description: This extension provides a solid color node, Color Balance Node, Color Balance Advanced Node,
SplitRGB and MergeRGB nodes, Hue/Saturation, Hue/Saturation Advanced,
SolidColorRGB, SolidColorHSV, and Black and White nodes.
"""
import torch
from scipy.interpolate import CubicSpline
import colorsys
class SolidColorRGB():
NAME = "Solid Color RGB"
CATEGORY = "Virtuoso"
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ('solid color image',)
FUNCTION = "get_solid_color"
@classmethod
def INPUT_TYPES(s) -> dict:
return {
"required": {
"red": ("FLOAT", {
"default": 0,
"min": 0,
"max": 255.0,
"step": 1,
"round": 0.1,
"display": "number"}),
"green": ("FLOAT", {
"default": 0,
"min": 0,
"max": 255.0,
"step": 1,
"round": 0.1,
"display": "number"}),
"blue": ("FLOAT", {
"default": 0,
"min": 0,
"max": 255.0,
"step": 1,
"round": 0.1,
"display": "number"}),
"height": ("INT", {
"default": 1024,
"min": 1,
"max": 4096,
"step": 1,
"round": 1,
"display": "number"}),
"width": ("INT", {
"default": 1024,
"min": 1,
"max": 4096,
"step": 1,
"round": 1,
"display": "number"}),
},
"optional":{
"hex": ("STRING", {
"default": ""
}),
}
}
def get_solid_color(self, red, green, blue, height, width, hex=""):
validated_hex = validate_hex_code(hex)
if validated_hex == "":
return (create_solid_rgb(red, green, blue, height, width), )
else:
return (create_solid_hex(validated_hex, height, width), )
class SolidColorHSV():
NAME = "Solid Color HSV"
CATEGORY = "Virtuoso"
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ('solid color image',)
FUNCTION = "get_solid_color"
@classmethod
def INPUT_TYPES(s) -> dict:
return {
"required": {
"hue": ("FLOAT", {
"default": 0,
"min": 0,
"max": 360.0,
"step": 1,
"round": 0.1,
"display": "number"}),
"saturation": ("FLOAT", {
"default": 50,
"min": 0,
"max": 100.0,
"step": 1,
"round": 0.1,
"display": "number"}),
"value": ("FLOAT", {
"default": 100,
"min": 0,
"max": 100.0,
"step": 1,
"round": 0.1,
"display": "number"}),
"height": ("INT", {
"default": 1024,
"min": 1,
"max": 4096,
"step": 1,
"round": 1,
"display": "number"}),
"width": ("INT", {
"default": 1024,
"min": 1,
"max": 4096,
"step": 1,
"round": 1,
"display": "number"}),
}
}
def get_solid_color(self, hue, saturation, value, height, width):
return (create_solid_hsv(hue, saturation, value, height, width), )
class SolidColor():
NAME = "Solid Color"
CATEGORY = "Virtuoso"
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ('solid color image',)
FUNCTION = "get_solid_color"
@classmethod
def INPUT_TYPES(s) -> dict:
return {
"required": {
"color": (["black", "silver", "gray", "white","maroon","red",
"purple", "fuchsia", "green", "lime", "olive",
"yellow", "navy", "blue", "teal", "aqua"],),
"height": ("INT", {
"default": 1024,
"min": 1,
"max": 4096,
"step": 1,
"round": 1,
"display": "number"}),
"width": ("INT", {
"default": 1024,
"min": 1,
"max": 4096,
"step": 1,
"round": 1,
"display": "number"}),
}
}
def get_solid_color(self, color, height, width):
colors = {"black": "#000000",
"silver": "#c0c0c0",
"gray": "#808080",
"white": "#ffffff",
"maroon": "#800000",
"red": "#ff0000",
"purple": "#800080",
"fuchsia": "#ff00ff",
"green": "#008000",
"lime": "#00ff00",
"olive": "#808000",
"yellow": "#ffff00",
"navy": "#000080",
"blue": "#0000ff",
"teal": "#008080",
"aqua": "#00ffff",
}
return (create_solid_hex(colors[color], height, width), )
class SplitRGB():
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE","IMAGE","IMAGE")
RETURN_NAMES = ('red', 'green', 'blue',)
FUNCTION = "do_split"
CATEGORY = "Virtuoso"
def do_split(self, image):
# Create tensors for red, green, and blue channels
red = torch.zeros_like(image)
green = torch.zeros_like(image)
blue = torch.zeros_like(image)
# Assign the corresponding color channels from the input image to all images in the batch
red[:, :, :, 0] = image[:, :, :, 0]
green[:, :, :, 1] = image[:, :, :, 1]
blue[:, :, :, 2] = image[:, :, :, 2]
return (red, green, blue)
class MergeRGB():
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"red": ("IMAGE",),
"green": ("IMAGE",),
"blue": ("IMAGE",)
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "do_merge"
CATEGORY = "Virtuoso"
def do_merge(self, red, green, blue):
# Create a tensor for the new image
img = torch.zeros_like(red)
# Assign the corresponding color channels from the input images
img[0, :, :, 0] = red[0, :, :, 0]
img[0, :, :, 1] = green[0, :, :, 1]
img[0, :, :, 2] = blue[0, :, :, 2]
return (img,)
class ColorBalance():
NAME = "Color Balance"
CATEGORY = "Virtuoso"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "do_color_balance"
@classmethod
def INPUT_TYPES(s) -> dict:
return {
"required": {
"image": ("IMAGE",),
"lows_cyan_red": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"lows_magenta_green": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"lows_yellow_blue": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"mids_cyan_red": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"mids_magenta_green": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"mids_yellow_blue": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"highs_cyan_red": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"highs_magenta_green": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"highs_yellow_blue": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"preserve_luminosity": ("BOOLEAN", {"default": True})
}
}
def do_color_balance(self, image, lows_cyan_red, lows_magenta_green, lows_yellow_blue,
mids_cyan_red, mids_magenta_green, mids_yellow_blue,
highs_cyan_red, highs_magenta_green, highs_yellow_blue, preserve_luminosity):
return (color_balance(image,
[lows_cyan_red, lows_magenta_green, lows_yellow_blue],
[mids_cyan_red, mids_magenta_green, mids_yellow_blue],
[highs_cyan_red, highs_magenta_green, highs_yellow_blue], preserve_luminosity=preserve_luminosity), )
class ColorBalanceAdvanced():
NAME = "Color Balance Advanced"
CATEGORY = "Virtuoso"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "do_color_balance"
@classmethod
def INPUT_TYPES(s) -> dict:
return {
"required": {
"image": ("IMAGE",),
"brightness_target": ("FLOAT", {
"default": 0.5,
"min": 0.001,
"max": 0.999,
"step": 0.001,
"round": 0.001,
"display": "number"}),
"cyan_red": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"magenta_green": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"yellow_blue": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"preserve_luminosity": ("BOOLEAN", {"default": True})
}
}
def do_color_balance(self, image, brightness_target, cyan_red, magenta_green, yellow_blue, preserve_luminosity):
return (color_balance(image,
[0, 0, 0],
[cyan_red, magenta_green,yellow_blue],
[0, 0, 0], 0.15, brightness_target, midtone_max=1, preserve_luminosity=preserve_luminosity), )
def color_balance(img, shadows, midtones, highlights, shadow_center=0.15, midtone_center=0.5, highlight_center=0.8, shadow_max=0.1, midtone_max=0.3, highlight_max=0.2, preserve_luminosity=False):
# Create a copy of the img tensor
img_copy = img.clone()
# Calculate the original luminance if preserve_luminosity is True
if preserve_luminosity:
original_luminance = 0.2126 * img_copy[..., 0] + 0.7152 * img_copy[..., 1] + 0.0722 * img_copy[..., 2]
# Define the adjustment curves
def adjust(x, center, value, max_adjustment):
# Scale the adjustment value
value = value * max_adjustment
# Define control points
points = torch.tensor([[0, 0], [center, center + value], [1, 1]])
# Create cubic spline
cs = CubicSpline(points[:, 0], points[:, 1])
# Apply the cubic spline to the color channel
return torch.clamp(torch.from_numpy(cs(x)), 0, 1)
# Apply the adjustments to each color channel
# shadows, midtones, highlights are lists of length 3 (for R, G, B channels) with values between -1 and 1
for i, (s, m, h) in enumerate(zip(shadows, midtones, highlights)):
img_copy[..., i] = adjust(img_copy[..., i], shadow_center, s, shadow_max)
img_copy[..., i] = adjust(img_copy[..., i], midtone_center, m, midtone_max)
img_copy[..., i] = adjust(img_copy[..., i], highlight_center, h, highlight_max)
# If preserve_luminosity is True, adjust the RGB values to match the original luminance
if preserve_luminosity:
current_luminance = 0.2126 * img_copy[..., 0] + 0.7152 * img_copy[..., 1] + 0.0722 * img_copy[..., 2]
img_copy *= (original_luminance / current_luminance).unsqueeze(-1)
return img_copy
class BlackAndWhite():
NAME = "Black and White"
CATEGORY = "Virtuoso"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "do_black_and_white"
@classmethod
def INPUT_TYPES(s) -> dict:
return {
"required": {
"image": ("IMAGE",),
"red": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"green": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"blue": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"cyan": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"magenta": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
"yellow": ("FLOAT", {
"default": 0,
"min": -1.0,
"max": 1.0,
"step": 0.01,
"round": 0.001,
"display": "number"}),
}
}
def do_black_and_white(self, image, red, green, blue, cyan, magenta, yellow):
"""
Convert a color image to black and white with adjustable color weights.
Parameters:
img (torch.Tensor): Input image tensor with shape [batch size, height, width, number of channels]
red (float): Weight for red, range -1.0 to 1.0
green (float): Weight for green, range -1.0 to 1.0
blue (float): Weight for blue, range -1.0 to 1.0
cyan (float): Weight for cyan, range -1.0 to 1.0
magenta (float): Weight for magenta, range -1.0 to 1.0
yellow (float): Weight for yellow, range -1.0 to 1.0
Returns:
torch.Tensor: Black and white image tensor with values in range 0-1
"""
# Calculate minimum color value across all color channels for each pixel
min_c, _ = image.min(dim=-1)
# Calculate differences between color channels and minimum color value
diff = image - min_c.unsqueeze(-1)
# Create masks for red, green, and blue pixels
red_mask = (diff[:, :, :, 0] == 0)
green_mask = torch.logical_and((diff[:, :, :, 1] == 0), ~red_mask)
blue_mask = ~torch.logical_or(red_mask, green_mask)
# Calculate c, m, and yel values
c, _ = diff[:, :, :, 1:].min(dim=-1)
m, _ = diff[:, :, :, [0, 2]].min(dim=-1)
yel, _ = diff[:, :, :, :2].min(dim=-1)
# Calculate luminance using vectorized operations
luminance = min_c + red_mask * (c * cyan + (diff[:, :, :, 1] - c) * green + (diff[:, :, :, 2] - c) * blue)
luminance += green_mask * (m * magenta + (diff[:, :, :, 0] - m) * red + (diff[:, :, :, 2] - m) * blue)
luminance += blue_mask * (yel * yellow + (diff[:, :, :, 0] - yel) * red + (diff[:, :, :, 1] - yel) * green)
# Clip luminance values to be between 0 and 1
luminance = luminance.clamp(0, 1)
# Add an extra dimension for color channels
luminance = luminance.unsqueeze(-1)
# Convert grayscale to RGB by duplicating the grayscale channel
rgb_image = luminance.expand(-1, -1, -1, 3)
# If the original image had an alpha channel, append it back
if image.shape[-1] == 4:
alpha_channel = image[:, :, :, 3:]
rgb_image = torch.cat((rgb_image, alpha_channel), dim=-1)
return (rgb_image,)
class HueSatAdvanced():
NAME = "Hue/Saturation Advanced"
CATEGORY = "Virtuoso"
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "do_hue_sat"
@classmethod
def INPUT_TYPES(s) -> dict:
return {
"required": {
"image": ("IMAGE",),
"hue_low": ("FLOAT", {
"default": 0,
"min": 0,
"max": 360.0,
"step": 1,
"round": 0.1,
"display": "number"}),
"hue_low_feather": ("FLOAT", {
"default": 0,
"min": 0,
"max": 180,
"step": 1,
"round": 0.1,
"display": "number"}),
"hue_high": ("FLOAT", {
"default": 360,
"min": 0,
"max": 360.0,
"step": 1,
"round": 0.1,
"display": "number"}),
"hue_high_feather": ("FLOAT", {
"default": 0,
"min": 0,
"max": 180,
"step": 1,
"round": 0.1,
"display": "number"}),
"hue_offset": ("FLOAT", {
"default": 0,
"min": -180.0,
"max": 180.0,
"step": 0.1,
"round": 0.01,
"display": "number"}),
"sat_offset": ("FLOAT", {
"default": 0,
"min": -100,
"max": 100,
"step": 0.1,
"round": 0.01,
"display": "number"}),
"lightness_offset": ("FLOAT", {
"default": 0,
"min": -100.0,
"max": 100.0,
"step": 0.1,
"round": 0.01,
"display": "number"}),
}
}
def do_hue_sat(self, image, hue_low, hue_low_feather, hue_high, hue_high_feather, hue_offset, sat_offset, lightness_offset):
# Convert image to HSV and build mask
image_hsv = rgb_to_hsv(image)
mask = create_mask(image_hsv[..., 0], image_hsv[..., 1], hue_low, hue_high, hue_low_feather, hue_high_feather)
# Adjust hue
image_hsv[..., 0] = adjust_hue(image_hsv[..., 0], hue_offset)
# Adjust saturation
image_hsv[..., 1] = adjust_saturation(image_hsv[..., 1], sat_offset)
# Adjust lightness
image_hsv = adjust_lightness(image_hsv, lightness_offset)
# Convert back to RGB
adjusted_image_rgb = hsv_to_rgb(image_hsv[..., :3])
# Blend the original and adjusted images based on the mask
blended_rgb = (adjusted_image_rgb * mask.unsqueeze(-1)) + (image[..., :3] * (1 - mask.unsqueeze(-1)))
# Include the alpha channel if present
if image.shape[-1] == 4:
blended_rgba = torch.cat((blended_rgb, image[..., 3:4]), dim=-1)
else:
blended_rgba = blended_rgb
return (blended_rgba, mask)
class HueSat():
NAME = "Hue/Saturation"
CATEGORY = "Virtuoso"
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "do_hue_sat"
@classmethod
def INPUT_TYPES(s) -> dict:
return {
"required": {
"image": ("IMAGE",),
"hue": (["red", "yellow", "green", "cyan", "blue", "magenta"],),
"hue_width": (["normal","narrow", "wide"],),
"feather": (["normal", "none", "wide"],),
"hue_offset": ("FLOAT", {
"default": 0,
"min": -180.0,
"max": 180.0,
"step": 0.1,
"round": 0.01,
"display": "number"}),
"sat_offset": ("FLOAT", {
"default": 0,
"min": -100,
"max": 100,
"step": 0.1,
"round": 0.01,
"display": "number"}),
"lightness_offset": ("FLOAT", {
"default": 0,
"min": -100.0,
"max": 100.0,
"step": 0.1,
"round": 0.01,
"display": "number"}),
}
}
def do_hue_sat(self, image, hue, hue_width, feather, hue_offset, sat_offset, lightness_offset):
# Convert image to HSV
image_hsv = rgb_to_hsv(image)
# Calculate ranges from parameters
hues = {"red": 0, "yellow": 60, "green": 120, "cyan": 180, "blue": 240, "magenta": 300}
widths = {"narrow": 15, "normal": 30, "wide": 60}
feathers = {"none": 0, "normal": 25, "wide": 50}
base_hue = hues[hue]
hue_low = base_hue - (widths[hue_width]/2)
if hue_low < 0:
hue_low = 360 - hue_low
hue_high = base_hue + (widths[hue_width]/2)
mask = create_mask(image_hsv[..., 0], image_hsv[..., 1], hue_low, hue_high, feathers[feather]/2, feathers[feather]/2)
# Adjust hue
image_hsv[..., 0] = adjust_hue(image_hsv[..., 0], hue_offset)
# Adjust saturation
image_hsv[..., 1] = adjust_saturation(image_hsv[..., 1], sat_offset)
# Adjust lightness
image_hsv = adjust_lightness(image_hsv, lightness_offset)
# Convert back to RGB
adjusted_image_rgb = hsv_to_rgb(image_hsv[..., :3])
# Blend the original and adjusted images based on the mask
blended_rgb = (adjusted_image_rgb * mask.unsqueeze(-1)) + (image[..., :3] * (1 - mask.unsqueeze(-1)))
# Include the alpha channel if present
if image.shape[-1] == 4:
blended_rgba = torch.cat((blended_rgb, image[..., 3:4]), dim=-1)
else:
blended_rgba = blended_rgb
return (blended_rgba, mask)
## Support functions
def create_solid_rgb(r, g, b, h, w):
return torch.zeros(1, h, w, 3) + torch.tensor([r/255.0, g/255.0, b/255.0])
def create_solid_hsv(h, s, v, h_img, w_img):
r, g, b = colorsys.hsv_to_rgb(h/360.0, s/100.0, v/100.0)
return torch.zeros(1, h_img, w_img, 3) + torch.tensor([r, g, b])
def create_solid_hex(hex, h, w):
if hex[0] == '#':
hex = hex[1:]
r, g, b = tuple(int(hex[i:i+2], 16)/255.0 for i in (0, 2, 4))
return torch.zeros(1, h, w, 3) + torch.tensor([r, g, b])
def validate_hex_code(hex_code):
hex_code = hex_code.lstrip('#')
if len(hex_code) == 3 and all(c in '0123456789ABCDEFabcdef' for c in hex_code):
return ''.join([c*2 for c in hex_code]).upper()
elif len(hex_code) == 6 and all(c in '0123456789ABCDEFabcdef' for c in hex_code):
return hex_code.upper()
else:
return ""
# Thanks to MA Lee for conversion code
def rgb_to_hsv(rgb: torch.Tensor) -> torch.Tensor:
input_tensor = rgb.clone()
# Check if there's an alpha channel
has_alpha = input_tensor.shape[-1] == 4
# Remove the alpha channel if it exists
if has_alpha:
alpha_channel = input_tensor[:, :, :, 3:4]
input_tensor = input_tensor[:, :, :, :3]
# Permute the dimensions from [B, H, W, 3] to [B, 3, H, W]
input_tensor = input_tensor.permute(0, 3, 1, 2)
# Convert RGB to HSV
cmax, cmax_idx = torch.max(input_tensor, dim=1, keepdim=True)
cmin = torch.min(input_tensor, dim=1, keepdim=True)[0]
delta = cmax - cmin
hsv_h = torch.empty_like(input_tensor[:, 0:1, :, :])
cmax_idx[delta == 0] = 3
hsv_h[cmax_idx == 0] = (((input_tensor[:, 1:2] - input_tensor[:, 2:3]) / delta) % 6)[cmax_idx == 0]
hsv_h[cmax_idx == 1] = (((input_tensor[:, 2:3] - input_tensor[:, 0:1]) / delta) + 2)[cmax_idx == 1]
hsv_h[cmax_idx == 2] = (((input_tensor[:, 0:1] - input_tensor[:, 1:2]) / delta) + 4)[cmax_idx == 2]
hsv_h[cmax_idx == 3] = 0.
hsv_h /= 6.
hsv_s = torch.where(cmax == 0, torch.tensor(0.).type_as(input_tensor), delta / cmax)
hsv_v = cmax
hsv_tensor = torch.cat([hsv_h, hsv_s, hsv_v], dim=1)
# Permute the dimensions back to [B, H, W, 3]
hsv_tensor = hsv_tensor.permute(0, 2, 3, 1)
# Add back the alpha channel if it was present
if has_alpha:
hsv_tensor = torch.cat([hsv_tensor, alpha_channel], dim=-1)
return hsv_tensor
def hsv_to_rgb(hsv: torch.Tensor) -> torch.Tensor:
input_tensor = hsv.clone()
# Check if there's an alpha channel
has_alpha = input_tensor.shape[-1] == 4
# Remove the alpha channel if it exists
if has_alpha:
alpha_channel = input_tensor[:, :, :, 3:4]
input_tensor = input_tensor[:, :, :, :3]
# Permute the dimensions from [B, H, W, 3] to [B, 3, H, W]
input_tensor = input_tensor.permute(0, 3, 1, 2)
# Extract HSV components
hsv_h, hsv_s, hsv_v = input_tensor[:, 0:1], input_tensor[:, 1:2], input_tensor[:, 2:3]
_c = hsv_v * hsv_s
_x = _c * (- torch.abs(hsv_h * 6. % 2. - 1) + 1.)
_m = hsv_v - _c
_o = torch.zeros_like(_c)
idx = (hsv_h * 6.).type(torch.uint8)
idx = (idx % 6).expand(-1, 3, -1, -1)
rgb = torch.empty_like(input_tensor)
rgb[idx == 0] = torch.cat([_c, _x, _o], dim=1)[idx == 0]
rgb[idx == 1] = torch.cat([_x, _c, _o], dim=1)[idx == 1]
rgb[idx == 2] = torch.cat([_o, _c, _x], dim=1)[idx == 2]
rgb[idx == 3] = torch.cat([_o, _x, _c], dim=1)[idx == 3]
rgb[idx == 4] = torch.cat([_x, _o, _c], dim=1)[idx == 4]
rgb[idx == 5] = torch.cat([_c, _o, _x], dim=1)[idx == 5]
rgb += _m
# Permute the dimensions back to [B, H, W, 3]
rgb_tensor = rgb.permute(0, 2, 3, 1)
# Add back the alpha channel if it was present
if has_alpha:
rgb_tensor = torch.cat([rgb_tensor, alpha_channel], dim=-1)
return rgb_tensor
def create_mask(hue, saturation, hue_low, hue_high, hue_low_feather, hue_high_feather):
# Normalize the values to the range [0, 1]
hue_low_norm = hue_low / 360.0
hue_high_norm = hue_high / 360.0
hue_low_feather_norm = hue_low_feather / 360.0
hue_high_feather_norm = hue_high_feather / 360.0
# Calculate the mask
mask_low = linearstep(hue_low_norm - hue_low_feather_norm, hue_low_norm, hue, increasing=True)
mask_high = linearstep(hue_high_norm, hue_high_norm + hue_high_feather_norm, hue, increasing=False)
if hue_low_norm < hue_high_norm:
mask_middle = torch.where((hue >= hue_low_norm) & (hue <= hue_high_norm), torch.tensor(1.0), torch.tensor(0.0))
else:
mask_middle = torch.where((hue <= hue_low_norm) & (hue >= hue_high_norm), torch.tensor(0.0), torch.tensor(1.0))
# Calculate the final mask by taking the maximum value among the three masks
mask = torch.max(torch.max(mask_low, mask_middle), mask_high)
# Only select pixels with a saturation greater than 0
mask = torch.where(saturation > 0, mask, torch.tensor(0.0))
return mask
def linearstep(low_edge, high_edge, x, increasing=True):
# Handle the case where the gradient overflows past 0
if low_edge < 0:
# Define gradient function for low gradient
overflow_mask_low = torch.where((x >= 0) & (x <= high_edge), (x - low_edge) / (high_edge - low_edge), torch.tensor(0.0))
overflow_mask_high = torch.where((x >= (1 + low_edge)) & (x <= 1), (x - 1 - low_edge) / (high_edge - low_edge), torch.tensor(0.0))
mask = torch.max(overflow_mask_low, overflow_mask_high)
elif high_edge > 1:
overflow_mask_high = torch.where((x >= low_edge) & (x <= 1), 1 - ((x - low_edge) / (high_edge - low_edge)), torch.tensor(0.0))
overflow_mask_low = torch.where((x >= 0) & (x <= (high_edge - 1)), 1 - ((x + 1 - low_edge) / (high_edge - low_edge)), torch.tensor(0.0))
mask = torch.max(overflow_mask_low, overflow_mask_high)
else:
# Create a mask where values within the range are set to abs( (hue value - low_edge)/(high_edge - low_edge))
gradient = torch.abs((x - low_edge) / (high_edge - low_edge))
if not increasing:
gradient = 1 - gradient
# Return the mask where values within the range are set to the gradient, and 0 otherwise
mask = torch.where((x >= low_edge) & (x <= high_edge), gradient, torch.tensor(0.0))
return mask
def adjust_saturation(saturation, sat_offset):
# Calculate the change in saturation
if sat_offset < 0:
delta_saturation = (sat_offset / 100.0) * saturation
else:
delta_saturation = (sat_offset / 100.0) * (1 - saturation)
# Apply the change to the saturation channel
new_saturation = torch.clamp(saturation + delta_saturation, 0, 1)
return new_saturation
def adjust_hue(hue, hue_offset):
# Normalize hue_offset to the range [0, 1]
hue_offset_normalized = hue_offset / 360.0
# Apply the normalized hue_offset and ensure the result is within [0, 1]
new_hue = (hue + hue_offset_normalized) % 1.0
return new_hue
def adjust_lightness(image_hsv, lightness_offset):
# Map lightness_offset to [-1, 1]
offset = lightness_offset / 100.0
# If lightness_offset < 0, interpolate between the image and a black image
if lightness_offset < 0:
image_hsv[..., 2] = image_hsv[..., 2] * (1 + offset)
# If lightness_offset > 0, interpolate between the image and a white image
elif lightness_offset > 0:
image_hsv[..., 2] = image_hsv[..., 2] * (1 - offset) + offset
# Also reduce the saturation as the lightness increases
image_hsv[..., 1] = image_hsv[..., 1] * ((1 - offset) ** 0.45)
return image_hsv