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nodes_graphics_filter.py
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nodes_graphics_filter.py
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#---------------------------------------------------------------------------------------------------------------------#
# Comfyroll Studio custom nodes by RockOfFire and Akatsuzi https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes
# for ComfyUI https://github.com/comfyanonymous/ComfyUI
#---------------------------------------------------------------------------------------------------------------------#
import torch
import numpy as np
from PIL import Image, ImageDraw, ImageStat, ImageFilter
from .functions_graphics import get_color_values
from ..config import color_mapping, COLORS
from ..categories import icons
def tensor2pil(image):
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
def pil2tensor(image):
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
#---------------------------------------------------------------------------------------------------------------------#
# Based on Color Tint node by hnmr293
class CR_ColorTint:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
#tints = COLORS.append('sepia')
tints = ["custom", "white", "black", "sepia", "red", "green", "blue",
"cyan", "magenta", "yellow", "purple", "orange", "warm",
"cool", "lime", "navy", "vintage", "rose", "teal",
"maroon", "peach", "lavender", "olive"]
return {
"required": {"image": ("IMAGE",),
"strength": ("FLOAT", {"default": 1.0,"min": 0.1,"max": 1.0,"step": 0.1}),
"mode": (tints,),
},
"optional": {"tint_color_hex": ("STRING", {"multiline": False, "default": "#000000"}),}
}
RETURN_TYPES = ("IMAGE", "STRING", )
RETURN_NAMES = ("IMAGE", "show_help", )
FUNCTION = "color_tint"
CATEGORY = icons.get("Comfyroll/Graphics/Filter")
def color_tint(self, image: torch.Tensor, strength, mode: str="sepia", tint_color_hex='#000000'):
if strength == 0:
return (image,)
# Get RGB values for the tint color
tint_color = get_color_values(mode, tint_color_hex, color_mapping)
color_rgb = tuple([value / 255 for value in tint_color])
sepia_weights = torch.tensor([0.2989, 0.5870, 0.1140]).view(1, 1, 1, 3).to(image.device)
mode_filters = {
"custom": torch.tensor([color_rgb[0], color_rgb[1], color_rgb[2]]),
"white": torch.tensor([1, 1, 1]),
"black": torch.tensor([0, 0, 0]),
"sepia": torch.tensor([1.0, 0.8, 0.6]),
"red": torch.tensor([1.0, 0.6, 0.6]),
"green": torch.tensor([0.6, 1.0, 0.6]),
"blue": torch.tensor([0.6, 0.8, 1.0]),
"cyan": torch.tensor([0.6, 1.0, 1.0]),
"magenta": torch.tensor([1.0, 0.6, 1.0]),
"yellow": torch.tensor([1.0, 1.0, 0.6]),
"purple": torch.tensor([0.8, 0.6, 1.0]),
"orange": torch.tensor([1.0, 0.7, 0.3]),
"warm": torch.tensor([1.0, 0.9, 0.7]),
"cool": torch.tensor([0.7, 0.9, 1.0]),
"lime": torch.tensor([0.7, 1.0, 0.3]),
"navy": torch.tensor([0.3, 0.4, 0.7]),
"vintage": torch.tensor([0.9, 0.85, 0.7]),
"rose": torch.tensor([1.0, 0.8, 0.9]),
"teal": torch.tensor([0.3, 0.8, 0.8]),
"maroon": torch.tensor([0.7, 0.3, 0.5]),
"peach": torch.tensor([1.0, 0.8, 0.6]),
"lavender": torch.tensor([0.8, 0.6, 1.0]),
"olive": torch.tensor([0.6, 0.7, 0.4]),
}
scale_filter = mode_filters[mode].view(1, 1, 1, 3).to(image.device)
grayscale = torch.sum(image * sepia_weights, dim=-1, keepdim=True)
tinted = grayscale * scale_filter
result = tinted * strength + image * (1 - strength)
show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/Filter-Nodes#cr-color-tint"
return (result, show_help, )
#---------------------------------------------------------------------------------------------------------------------#
class CR_HalftoneFilter:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
shapes = ["ellipse", "rectangle"]
rez = ["normal", "hi-res (2x output size)"]
return {
"required": {
"image": ("IMAGE",),
"dot_size": ("INT", {"default": 5, "min": 1, "max": 30, "step": 1}),
"dot_shape": (shapes, {"default": "ellipse"}),
#"scale": ("INT", {"default": 1, "min": 1, "max": 8, "step": 1}),
"resolution": (rez, {"default": "normal"}),
"angle_c": ("INT", {"default": 75, "min": 0, "max": 360, "step": 1}),
"angle_m": ("INT", {"default": 45, "min": 0, "max": 360, "step": 1}),
"angle_y": ("INT", {"default": 15, "min": 0, "max": 360, "step": 1}),
"angle_k": ("INT", {"default": 0, "min": 0, "max": 360, "step": 1}),
"greyscale": ("BOOLEAN", {"default": True}),
"antialias": ("BOOLEAN", {"default": True}),
"antialias_scale": ("INT", {"default": 2, "min": 1, "max": 4, "step": 1}),
"border_blending": ("BOOLEAN", {"default": False}),
},
}
RETURN_TYPES = ("IMAGE", "STRING", )
RETURN_NAMES = ("IMAGE", "show_help", )
FUNCTION = "halftone_effect"
CATEGORY = icons.get("Comfyroll/Graphics/Filter")
def tensor_to_pil(self, tensor):
if tensor.ndim == 4 and tensor.shape[0] == 1: # Check for batch dimension
tensor = tensor.squeeze(0) # Remove batch dimension
if tensor.dtype == torch.float32: # Check for float tensors
tensor = tensor.mul(255).byte() # Convert to range [0, 255] and change to byte type
elif tensor.dtype != torch.uint8: # If not float and not uint8, conversion is needed
tensor = tensor.byte() # Convert to byte type
numpy_image = tensor.cpu().numpy()
# Determine the correct mode based on the number of channels
if tensor.ndim == 3:
if tensor.shape[2] == 1:
mode = 'L' # Grayscale
elif tensor.shape[2] == 3:
mode = 'RGB' # RGB
elif tensor.shape[2] == 4:
mode = 'RGBA' # RGBA
else:
raise ValueError(f"Unsupported channel number: {tensor.shape[2]}")
else:
raise ValueError(f"Unexpected tensor shape: {tensor.shape}")
pil_image = Image.fromarray(numpy_image, mode)
return pil_image
def pil_to_tensor(self, pil_image):
numpy_image = np.array(pil_image)
tensor = torch.from_numpy(numpy_image).float().div(255) # Convert to range [0, 1]
tensor = tensor.unsqueeze(0) # Add batch dimension
return tensor
def halftone_effect(self, image, dot_size, dot_shape, resolution, angle_c, angle_m, angle_y, angle_k, greyscale, antialias, border_blending, antialias_scale):
sample = dot_size
shape = dot_shape
# Map resolution to scale
resolution_to_scale = {
"normal": 1,
"hi-res (2x output size)": 2,
}
scale = resolution_to_scale.get(resolution, 1) # Default to 1 if resolution is not recognized
# If the input is a PyTorch tensor, convert to PIL Image
if isinstance(image, torch.Tensor):
image = self.tensor_to_pil(image)
# Ensure the image is a PIL Image
if not isinstance(image, Image.Image):
raise TypeError("The provided image is neither a PIL Image nor a PyTorch tensor.")
pil_image = image # Now we are sure pil_image is defined
# Convert to greyscale or CMYK
if greyscale:
pil_image = pil_image.convert("L")
channel_images = [pil_image]
angles = [angle_k]
else:
pil_image = pil_image.convert("CMYK")
channel_images = list(pil_image.split())
angles = [angle_c, angle_m, angle_y, angle_k]
# Apply the halftone effect using PIL
halftone_images = self._halftone_pil(pil_image, channel_images, sample, scale, angles, antialias, border_blending, antialias_scale, shape)
# Merge channels and convert to RGB
if greyscale:
new_image = halftone_images[0].convert("RGB") # Convert the greyscale image to RGB
else:
new_image = Image.merge("CMYK", halftone_images).convert("RGB")
result_tensor = self.pil_to_tensor(new_image)
# Debug print to check the final tensor shape
print("Final tensor shape:", result_tensor.shape)
return (result_tensor, show_help, )
def _halftone_pil(self, im, cmyk, sample, scale, angles, antialias, border_blending, antialias_scale, shape):
# If we're antialiasing, we'll multiply the size of the image by this
# scale while drawing, and then scale it back down again afterwards.
antialias_res = antialias_scale if antialias else 1
scale = scale * antialias_res
dots = []
for channel_index, (channel, angle) in enumerate(zip(cmyk, angles)):
channel = channel.rotate(angle, expand=1)
size = channel.size[0] * scale, channel.size[1] * scale
half_tone = Image.new("L", size)
draw = ImageDraw.Draw(half_tone)
# Cycle through one sample point at a time, drawing a circle for
# each one:
for x in range(0, channel.size[0], sample):
for y in range(0, channel.size[1], sample):
# Adjust the sampling near the borders for non-square angles
if border_blending and angle % 90 != 0 and (x < sample or y < sample or x > channel.size[0] - sample or y > channel.size[1] - sample):
# Get a weighted average of the neighboring pixels
neighboring_pixels = channel.crop((max(x - 1, 0), max(y - 1, 0), min(x + 2, channel.size[0]), min(y + 2, channel.size[1])))
pixels = list(neighboring_pixels.getdata())
weights = [0.5 if i in [0, len(pixels)-1] else 1 for i in range(len(pixels))]
weighted_mean = sum(p * w for p, w in zip(pixels, weights)) / sum(weights)
mean = weighted_mean
else:
# Area we sample to get the level:
box = channel.crop((x, y, x + sample, y + sample))
# The average level for that box (0-255):
mean = ImageStat.Stat(box).mean[0]
# The diameter or side length of the shape to draw based on the mean (0-1):
size = (mean / 255) ** 0.5
# Size of the box we'll draw the circle in:
box_size = sample * scale
# Diameter or side length of shape we'll draw:
draw_size = size * box_size
# Position of top-left of box we'll draw the circle in:
box_x, box_y = (x * scale), (y * scale)
# Positioned of top-left and bottom-right of circle:
x1 = box_x + ((box_size - draw_size) / 2)
y1 = box_y + ((box_size - draw_size) / 2)
x2 = x1 + draw_size
y2 = y1 + draw_size
# Draw the shape based on the variable passed
draw_method = getattr(draw, shape, None)
if draw_method:
draw_method([(x1, y1), (x2, y2)], fill=255)
half_tone = half_tone.rotate(-angle, expand=1)
width_half, height_half = half_tone.size
# Top-left and bottom-right of the image to crop to:
xx1 = (width_half - im.size[0] * scale) / 2
yy1 = (height_half - im.size[1] * scale) / 2
xx2 = xx1 + im.size[0] * scale
yy2 = yy1 + im.size[1] * scale
half_tone = half_tone.crop((xx1, yy1, xx2, yy2))
if antialias:
# Scale it back down to antialias the image.
w = int((xx2 - xx1) / antialias_scale)
h = int((yy2 - yy1) / antialias_scale)
half_tone = half_tone.resize((w, h), resample=Image.LANCZOS)
dots.append(half_tone)
show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/Filter-Nodes#cr-halftone-filter"
return (dots, show_help, )
#---------------------------------------------------------------------------------------------------------------------#
class CR_VignetteFilter:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
"vignette_shape": (["circle","oval","square","diamond"],),
"feather_amount": ("INT", {"default": 100, "min": 0, "max": 1024}),
"x_offset": ("INT", {"default": 0, "min": -2048, "max": 2048}),
"y_offset": ("INT", {"default": 0, "min": -2048, "max": 2048}),
"zoom": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"reverse": (["no","yes"],),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "STRING", )
RETURN_NAMES = ("IMAGE", "MASK", "show_help", )
FUNCTION = "make_vignette"
CATEGORY = icons.get("Comfyroll/Graphics/Filter")
def make_vignette(self, image, feather_amount, reverse,
vignette_shape='circle',
x_offset=0, y_offset=0, zoom=1.0):
images = []
masks = []
vignette_color = "black"
for img in image:
im = tensor2pil(img)
RADIUS = feather_amount
# Create an alpha mask for the vignette effect
alpha_mask = Image.new('L', im.size, 255)
draw = ImageDraw.Draw(alpha_mask)
center_x = im.size[0] // 2 + x_offset
center_y = im.size[1] // 2 + y_offset
radius = min(center_x, center_y) * zoom
size_x = (im.size[0] - RADIUS) * zoom
size_y = (im.size[1] - RADIUS) * zoom
if vignette_shape == 'circle':
if reverse == 'no':
# Calculate the position to center the circular mask with offsets and zoom
draw.ellipse([(center_x - radius, center_y - radius), (center_x + radius, center_y + radius)], fill=0)
elif reverse == 'yes':
draw.rectangle([(0, 0), im.size], fill=0)
draw.ellipse([(center_x - radius, center_y - radius), (center_x + radius, center_y + radius)], fill=255)
else:
raise ValueError("Invalid value for reverse. Use 'yes' or 'no'.")
elif vignette_shape == 'oval':
if reverse == 'no':
draw.ellipse([(center_x - size_x / 2, center_y - size_y / 2),
(center_x + size_x / 2, center_y + size_y / 2)], fill=0)
elif reverse == 'yes':
draw.rectangle([(0, 0), im.size], fill=0)
draw.ellipse([(center_x - size_x / 2, center_y - size_y / 2),
(center_x + size_x / 2, center_y + size_y / 2)], fill=255)
elif vignette_shape == 'diamond':
if reverse == 'no':
# Calculate the position and size to center the diamond mask with offsets and zoom
size = min(im.size[0] - x_offset, im.size[1] - y_offset) * zoom
draw.polygon([(center_x, center_y - size / 2),
(center_x + size / 2, center_y),
(center_x, center_y + size / 2),
(center_x - size / 2, center_y)],
fill=0)
elif reverse == 'yes':
size = min(im.size[0] - x_offset, im.size[1] - y_offset) * zoom
draw.rectangle([(0, 0), im.size], fill=0)
draw.polygon([(center_x, center_y - size / 2),
(center_x + size / 2, center_y),
(center_x, center_y + size / 2),
(center_x - size / 2, center_y)],
fill=255)
elif vignette_shape == 'square':
if reverse == 'no':
# Calculate the position to center the square mask with offsets and zoom
size = min(im.size[0] - x_offset, im.size[1] - y_offset) * zoom
draw.rectangle([(center_x - size / 2, center_y - size / 2),
(center_x + size / 2, center_y + size / 2)], fill=0)
elif reverse == 'yes':
size = min(im.size[0] - x_offset, im.size[1] - y_offset) * zoom
draw.rectangle([(0, 0), im.size], fill=0)
draw.rectangle([(center_x - size / 2, center_y - size / 2),
(center_x + size / 2, center_y + size / 2)], fill=255)
else:
raise ValueError("Invalid value for reverse. Use 'yes' or 'no'.")
else:
raise ValueError("Invalid vignette_shape. Use 'circle', 'oval', or 'square'.")
# Apply GaussianBlur to the alpha mask for feathering
alpha_mask = alpha_mask.filter(ImageFilter.GaussianBlur(RADIUS))
# Append the alpha mask to the masks list
masks.append(pil2tensor(alpha_mask).unsqueeze(0))
# Create a new image with the vignette effect
vignette_img = Image.new('RGBA', im.size, vignette_color)
vignette_img.putalpha(alpha_mask)
# Composite the original image with the vignette effect
result_img = Image.alpha_composite(im.convert('RGBA'), vignette_img)
images.append(pil2tensor(result_img.convert("RGB")))
images = torch.cat(images, dim=0)
masks = torch.cat(masks, dim=0)
show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/Layout-Nodes#cr-vignette-filter"
return (images, masks, show_help, )
#---------------------------------------------------------------------------------------------------------------------#
# MAPPINGS
#---------------------------------------------------------------------------------------------------------------------#
# For reference only, actual mappings are in __init__.py
'''
NODE_CLASS_MAPPINGS = {
"CR Halftone Filter": "CR HalftoneFilter",
"CR Color Tint": CR_ColorTint,
"CR Vignette Filter": CR_VignetteFilter,
}
'''