nodes/chflame163-ComfyUI_LayerStyle
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Add GradientOverlay, ColorOverlay nodes and add invalid mask input judgment.

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chflame163
2024-01-21 22:42:01 +08:00
committed by GitHub
parent 35ef662ae1
commit 4ec4a9672c
21 changed files with 825 additions and 64 deletions
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py/color_overlay.py
+66
View File
@@ -0,0 +1,66 @@
from .imagefunc import *
class ColorOverlay:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
chop_mode = ['normal','multply','screen','add','subtract','difference','darker','lighter']
return {
"required": {
"background_image": ("IMAGE", ), #
"layer_image": ("IMAGE",), #
"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
"blend_mode": (chop_mode,), # 混合模式
"opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}), # 透明度
"color": ("STRING", {"default": "#FFBF30"}), # 渐变开始颜色
},
"optional": {
"layer_mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'color_overlay'
CATEGORY = '😺dzNodes/LayerStyle'
OUTPUT_NODE = True
def color_overlay(self, background_image, layer_image,
invert_mask, blend_mode, opacity, color,
layer_mask=None
):
# preprocess
_canvas = tensor2pil(background_image).convert('RGB')
_layer = tensor2pil(layer_image)
if _layer.mode == 'RGBA':
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
else:
_mask = Image.new('L', _layer.size, 'white')
_layer = _layer.convert('RGB')
if layer_mask is not None:
if invert_mask:
layer_mask = 1 - layer_mask
_mask = mask2image(layer_mask).convert('L')
if _mask.size != _layer.size:
_mask = Image.new('L', _layer.size, 'white')
log('Warning: mask mismatch, droped!')
_color = Image.new('RGB', size=_layer.size, color=color)
# 合成layer
_comp = chop_image(_layer, _color, blend_mode, opacity)
_canvas.paste(_comp, mask=_mask)
ret_image = _canvas
log('ColorOverlay Processed.')
return (pil2tensor(ret_image),)
NODE_CLASS_MAPPINGS = {
"LayerStyle: ColorOverlay": ColorOverlay
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerStyle: ColorOverlay": "LayerStyle: ColorOverlay"
}
py/drop_shadow.py
+13 -5
View File
@@ -38,16 +38,24 @@ class DropShadow:
layer_mask=None
):
distance_x = -distance_x
distance_y = -distance_y
# preprocess
_canvas = tensor2pil(background_image).convert('RGB')
_layer = tensor2pil(layer_image).convert('RGB')
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
# 处理mask
_layer = tensor2pil(layer_image)
if _layer.mode == 'RGBA':
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
else:
_mask = Image.new('L', _layer.size, 'white')
_layer = _layer.convert('RGB')
if layer_mask is not None:
if invert_mask:
layer_mask = 1 - layer_mask
_mask = mask2image(layer_mask).convert('L')
if _mask.size != _layer.size:
_mask = Image.new('L', _layer.size, 'white')
log('Warning: mask mismatch, droped!')
distance_x = -distance_x
distance_y = -distance_y
if distance_x != 0 or distance_y != 0:
__mask = shift_image(_mask, distance_x, distance_y) # 位移
shadow_mask = expand_mask(image2mask(__mask), grow, blur) #扩张，模糊
py/gradient_overlay.py
+79
View File
@@ -0,0 +1,79 @@
from .imagefunc import *
class GradientOverlay:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
chop_mode = ['normal','multply','screen','add','subtract','difference','darker','lighter']
return {
"required": {
"background_image": ("IMAGE", ), #
"layer_image": ("IMAGE",), #
"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
"blend_mode": (chop_mode,), # 混合模式
"opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}), # 透明度
"start_color": ("STRING", {"default": "#FFBF30"}), # 渐变开始颜色
"start_alpha": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
"end_color": ("STRING", {"default": "#FE0000"}), # 渐变结束颜色
"end_alpha": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
"angle": ("INT", {"default": 0, "min": -180, "max": 180, "step": 1}), # 渐变角度
},
"optional": {
"layer_mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'gradient_overlay'
CATEGORY = '😺dzNodes/LayerStyle'
OUTPUT_NODE = True
def gradient_overlay(self, background_image, layer_image,
invert_mask, blend_mode, opacity,
start_color, start_alpha, end_color, end_alpha, angle,
layer_mask=None
):
# preprocess
_canvas = tensor2pil(background_image).convert('RGB')
_layer = tensor2pil(layer_image)
if _layer.mode == 'RGBA':
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
else:
_mask = Image.new('L', _layer.size, 'white')
_layer = _layer.convert('RGB')
if layer_mask is not None:
if invert_mask:
layer_mask = 1 - layer_mask
_mask = mask2image(layer_mask).convert('L')
if _mask.size != _layer.size:
_mask = Image.new('L', _layer.size, 'white')
log('Warning: mask mismatch, droped!')
_gradient = gradint(start_color, end_color, _layer.width, _layer.height, float(angle))
# 合成layer
_comp = chop_image(_layer, _gradient, blend_mode, opacity)
if start_alpha < 255 or end_alpha < 255:
#
start_color = RGB_to_Hex((start_alpha, start_alpha, start_alpha))
end_color = RGB_to_Hex((end_alpha, end_alpha, end_alpha))
comp_alpha = gradint(start_color, end_color, _layer.width, _layer.height, float(angle))
comp_alpha = ImageChops.invert(comp_alpha).convert('L')
_comp.paste(_layer, comp_alpha)
_canvas.paste(_comp, mask=_mask)
ret_image = _canvas
log('GradientOverlay Processed.')
return (pil2tensor(ret_image),)
NODE_CLASS_MAPPINGS = {
"LayerStyle: GradientOverlay": GradientOverlay
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerStyle: GradientOverlay": "LayerStyle: GradientOverlay"
}
py/imagefunc.py
+70 -12
View File
@@ -6,7 +6,7 @@ import torch
import scipy.ndimage
import cv2
from typing import Union, List
from PIL import Image, ImageFilter, ImageChops
from PIL import Image, ImageFilter, ImageChops, ImageDraw
def log(message):
name = 'LayerStyle'
@@ -119,9 +119,70 @@ def motion_blur(image:Image, angle:int, blur:int) -> Image:
ret_image = cv22pil(blurred)
return ret_image
def direction_blur(image:Image, angle:int, blur:int, color:str) -> Image:
def __rotate_expand(image:Image, angle:float, SSAA:int=0) -> Image:
images = pil2tensor(image)
expand = "true"
height, width = images[0, :, :, 0].shape
ret_image = image
def rotate_tensor(tensor):
resize_sampler = Image.LANCZOS
rotate_sampler = Image.BICUBIC
if SSAA > 1:
img = tensor.tensor_to_image()
img_us_scaled = img.resize((width * SSAA, height * SSAA), resize_sampler)
img_rotated = img_us_scaled.rotate(angle, rotate_sampler, expand == "true", fillcolor=(0, 0, 0, 0))
img_down_scaled = img_rotated.resize((img_rotated.width // SSAA, img_rotated.height // SSAA), resize_sampler)
result = img_down_scaled.image_to_tensor()
else:
img = tensor.tensor_to_image()
img_rotated = img.rotate(angle, rotate_sampler, expand == "true", fillcolor=(0, 0, 0, 0))
result = img_rotated.image_to_tensor()
return result
if angle == 0.0 or angle == 360.0:
return tensor2pil(images)
else:
rotated_tensor = torch.stack([rotate_tensor(images[i]) for i in range(len(images))])
return tensor2pil(rotated_tensor).convert('RGB')
def image_rotate_extend_with_alpha(image:Image, alpha:Image, angle:float, SSAA:int=0) -> tuple:
_image = __rotate_expand(image.convert('RGB'), angle, SSAA)
_alpha = __rotate_expand(alpha.convert('RGB'), angle, SSAA)
R, G, B = _image.split()
A = _alpha.convert('L')
ret_image = Image.merge('RGBA', (R, G, B, A))
return (_image, _alpha, ret_image)
def create_gradient(start_color_inhex:str, end_color_inhex:str, width:int, height:int) -> Image:
start_color = Hex_to_RGB(start_color_inhex)
end_color = Hex_to_RGB(end_color_inhex)
ret_image = Image.new("RGB", (width, height), start_color)
draw = ImageDraw.Draw(ret_image)
for i in range(height):
R = int(start_color[0] * (height - i) / height + end_color[0] * i / height)
G = int(start_color[1] * (height - i) / height + end_color[1] * i / height)
B = int(start_color[2] * (height - i) / height + end_color[2] * i / height)
color = (R, G, B)
draw.line((0, i, width, i), fill=color)
return ret_image
def gradint(start_color_inhex:str, end_color_inhex:str, width:int, height:int, angle:float, ) -> Image:
radius = int((width + height) / 4)
g = create_gradient(start_color_inhex, end_color_inhex, radius, radius)
_canvas = Image.new('RGB', size=(radius, radius*3), color=start_color_inhex)
top = Image.new('RGB', size=(radius, radius), color=start_color_inhex)
bottom = Image.new('RGB', size=(radius, radius),color=end_color_inhex)
_canvas.paste(top, box=(0, 0, radius, radius))
_canvas.paste(g, box=(0, radius, radius, radius * 2))
_canvas.paste(bottom,box=(0, radius * 2, radius, radius * 3))
_canvas = _canvas.resize((radius * 3, radius * 3))
_canvas = __rotate_expand(_canvas,angle)
center = int(_canvas.width / 2)
_x = int(width / 3)
_y = int(height / 3)
ret_image = _canvas.crop((center - _x, center - _y, center + _x, center + _y))
ret_image = ret_image.resize((width, height))
return ret_image
'''Mask Functions'''
@@ -180,18 +241,15 @@ def step_value(start_value, end_value, total_step, step) -> float: # 按当前
factor = step / total_step
return (end_value - start_value) * factor + start_value
def step_color(start_color, end_color, total_step, step): # 按当前步数在总步数中的位置返回比例颜色
if isinstance(start_color, str):
start_color = tuple(Hex_to_RGB(start_color))
if isinstance(end_color, str):
end_color = tuple(Hex_to_RGB(end_color))
def step_color(start_color_inhex:str, end_color_inhex:str, total_step:int, step:int) -> str: # 按当前步数在总步数中的位置返回比例颜色
start_color = tuple(Hex_to_RGB(start_color_inhex))
end_color = tuple(Hex_to_RGB(end_color_inhex))
start_R, start_G, start_B = start_color[0], start_color[1], start_color[2]
end_R, end_G, end_B = end_color[0], end_color[1], end_color[2]
ret_color = (int(step_value(start_R, end_R, total_step, step)),
int(step_value(start_G, end_G, total_step, step)),
int(step_value(start_B, end_B, total_step, step)),
)
if isinstance(start_color, str):
return RGB_to_Hex(ret_color)
else:
return ret_color
return RGB_to_Hex(ret_color)
py/inner_glow.py
+10 -3
View File
@@ -38,14 +38,21 @@ class InnerGlow:
layer_mask=None
):
# preprocess
_canvas = tensor2pil(background_image).convert('RGB')
_layer = tensor2pil(layer_image).convert('RGB')
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
# 处理mask
_layer = tensor2pil(layer_image)
if _layer.mode == 'RGBA':
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
else:
_mask = Image.new('L', _layer.size, 'white')
_layer = _layer.convert('RGB')
if layer_mask is not None:
if invert_mask:
layer_mask = 1 - layer_mask
_mask = mask2image(layer_mask).convert('L')
if _mask.size != _layer.size:
_mask = Image.new('L', _layer.size, 'white')
log('Warning: mask mismatch, droped!')
blur_factor = blur / 20.0
grow = glow_range
py/inner_shadow.py
+13 -5
View File
@@ -39,16 +39,24 @@ class InnerShadow:
layer_mask=None
):
distance_x = -distance_x
distance_y = -distance_y
# preprocess
_canvas = tensor2pil(background_image).convert('RGB')
_layer = tensor2pil(layer_image).convert('RGB')
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
# 处理mask
_layer = tensor2pil(layer_image)
if _layer.mode == 'RGBA':
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
else:
_mask = Image.new('L', _layer.size, 'white')
_layer = _layer.convert('RGB')
if layer_mask is not None:
if invert_mask:
layer_mask = 1 - layer_mask
_mask = mask2image(layer_mask).convert('L')
if _mask.size != _layer.size:
_mask = Image.new('L', _layer.size, 'white')
log('Warning: mask mismatch, droped!')
distance_x = -distance_x
distance_y = -distance_y
if distance_x != 0 or distance_y != 0:
__mask = shift_image(_mask, distance_x, distance_y) # 位移
shadow_mask = expand_mask(image2mask(__mask), grow, blur) #扩张，模糊
py/outer_glow.py
+11 -3
View File
@@ -38,14 +38,22 @@ class OuterGlow:
layer_mask=None
):
# preprocess
_canvas = tensor2pil(background_image).convert('RGB')
_layer = tensor2pil(layer_image).convert('RGB')
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
# 处理mask
_layer = tensor2pil(layer_image)
if _layer.mode == 'RGBA':
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
else:
_mask = Image.new('L', _layer.size, 'white')
_layer = _layer.convert('RGB')
if layer_mask is not None:
if invert_mask:
layer_mask = 1 - layer_mask
_mask = mask2image(layer_mask).convert('L')
if _mask.size != _layer.size:
_mask = Image.new('L', _layer.size, 'white')
log('Warning: mask mismatch, droped!')
blur_factor = blur / 20.0
grow = glow_range
for x in range(brightness):
py/stroke.py
+10 -2
View File
@@ -37,13 +37,21 @@ class Stroke:
layer_mask=None
):
# preprocess
_canvas = tensor2pil(background_image).convert('RGB')
_layer = tensor2pil(layer_image).convert('RGB')
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
_layer = tensor2pil(layer_image)
if _layer.mode == 'RGBA':
_mask = tensor2pil(layer_image).convert('RGBA').split()[-1]
else:
_mask = Image.new('L', _layer.size, 'white')
_layer = _layer.convert('RGB')
if layer_mask is not None:
if invert_mask:
layer_mask = 1 - layer_mask
_mask = mask2image(layer_mask).convert('L')
if _mask.size != _layer.size:
_mask = Image.new('L', _layer.size, 'white')
log('Warning: mask mismatch, droped!')
grow_offset = int(stroke_width / 2)
inner_stroke = stroke_grow - grow_offset