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Add drop shadow v3 and image blend advanced v3

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Alexis Rolland
2024-11-23 11:23:50 +08:00
parent 4505116302
commit 2c7a65f307
2 changed files with 249 additions and 0 deletions
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py/drop_shadow_v3.py
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from PIL import Image
from .imagefunc import *
NODE_NAME = 'DropShadowV3'
class DropShadowV3:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"layer_image": ("IMAGE",), #
"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
"blend_mode": (chop_mode_v2,), # 混合模式
"opacity": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), # 透明度
"distance_x": ("INT", {"default": 25, "min": -9999, "max": 9999, "step": 1}), # x_偏移
"distance_y": ("INT", {"default": 25, "min": -9999, "max": 9999, "step": 1}), # y_偏移
"grow": ("INT", {"default": 6, "min": -9999, "max": 9999, "step": 1}), # 扩张
"blur": ("INT", {"default": 18, "min": 0, "max": 1000, "step": 1}), # 模糊
"shadow_color": ("STRING", {"default": "#000000"}), # 背景颜色
},
"optional": {
"background_image": ("IMAGE", ), #
"layer_mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'drop_shadow_v2'
CATEGORY = '😺dzNodes/LayerStyle'
def drop_shadow_v2(self, layer_image, invert_mask, blend_mode, opacity,
distance_x, distance_y, grow, blur, shadow_color,
background_image=None, layer_mask=None
):
# If background image is empty, create transparent background image for each layer image
if background_image == None:
background_image = []
for l in layer_image:
m = tensor2pil(l)
background_image.append(pil2tensor(Image.new('RGBA', (m.width, m.height), (0, 0, 0, 0))))
b_images = []
l_images = []
l_masks = []
ret_images = []
for b in background_image:
b_images.append(torch.unsqueeze(b, 0))
for l in layer_image:
l_images.append(torch.unsqueeze(l, 0))
m = tensor2pil(l)
if m.mode == 'RGBA':
l_masks.append(m.split()[-1])
if layer_mask is not None:
if layer_mask.dim() == 2:
layer_mask = torch.unsqueeze(layer_mask, 0)
l_masks = []
for m in layer_mask:
if invert_mask:
m = 1 - m
l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
if len(l_masks) == 0:
log(f"Error: {NODE_NAME} skipped, because the available mask is not found.", message_type='error')
return (background_image,)
max_batch = max(len(b_images), len(l_images), len(l_masks))
distance_x = -distance_x
distance_y = -distance_y
shadow_color = Image.new("RGBA", tensor2pil(l_images[0]).size, color=shadow_color)
for i in range(max_batch):
background_image = b_images[i] if i < len(b_images) else b_images[-1]
layer_image = l_images[i] if i < len(l_images) else l_images[-1]
_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
# preprocess
_canvas = tensor2pil(background_image).convert('RGBA')
_layer = tensor2pil(layer_image)
if _mask.size != _layer.size:
_mask = Image.new('L', _layer.size, 'white')
log(f"Warning: {NODE_NAME} mask mismatch, dropped!", message_type='warning')
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
# 合成阴影
alpha = tensor2pil(shadow_mask).convert('L')
_shadow = chop_image_v2(_canvas, shadow_color, blend_mode, opacity)
_canvas.paste(_shadow, mask=alpha)
# 合成layer
_canvas.paste(_layer, mask=_mask)
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
"LayerStyle: DropShadow V3": DropShadowV3
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerStyle: DropShadow V3": "LayerStyle: DropShadow V3"
}
py/image_blend_advance_v3.py
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from .imagefunc import *
NODE_NAME = 'ImageBlendAdvanceV3'
class ImageBlendAdvanceV3:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
mirror_mode = ['None', 'horizontal', 'vertical']
method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
return {
"required": {
"layer_image": ("IMAGE",), #
"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
"blend_mode": (chop_mode_v2,), # 混合模式
"opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}), # 透明度
"x_percent": ("FLOAT", {"default": 50, "min": -999, "max": 999, "step": 0.01}),
"y_percent": ("FLOAT", {"default": 50, "min": -999, "max": 999, "step": 0.01}),
"mirror": (mirror_mode,), # 镜像翻转
"scale": ("FLOAT", {"default": 1, "min": 0.01, "max": 100, "step": 0.01}),
"aspect_ratio": ("FLOAT", {"default": 1, "min": 0.01, "max": 100, "step": 0.01}),
"rotate": ("FLOAT", {"default": 0, "min": -999999, "max": 999999, "step": 0.01}),
"transform_method": (method_mode,),
"anti_aliasing": ("INT", {"default": 0, "min": 0, "max": 16, "step": 1}),
},
"optional": {
"background_image": ("IMAGE", ), #
"layer_mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("image", "mask")
FUNCTION = 'image_blend_advance_v2'
CATEGORY = '😺dzNodes/LayerUtility'
def image_blend_advance_v2(self, layer_image, invert_mask, blend_mode, opacity,
x_percent, y_percent, mirror, scale, aspect_ratio, rotate,
transform_method, anti_aliasing, background_image=None, layer_mask=None
):
# If background image is empty, create transparent background image for each layer image
if background_image == None:
background_image = []
for l in layer_image:
m = tensor2pil(l)
background_image.append(pil2tensor(Image.new('RGBA', (m.width, m.height), (0, 0, 0, 0))))
b_images = []
l_images = []
l_masks = []
ret_images = []
ret_masks = []
for b in background_image:
b_images.append(torch.unsqueeze(b, 0))
for l in layer_image:
l_images.append(torch.unsqueeze(l, 0))
m = tensor2pil(l)
if m.mode == 'RGBA':
l_masks.append(m.split()[-1])
else:
l_masks.append(Image.new('L', m.size, 'white'))
if layer_mask is not None:
if layer_mask.dim() == 2:
layer_mask = torch.unsqueeze(layer_mask, 0)
l_masks = []
for m in layer_mask:
if invert_mask:
m = 1 - m
l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
max_batch = max(len(b_images), len(l_images), len(l_masks))
for i in range(max_batch):
background_image = b_images[i] if i < len(b_images) else b_images[-1]
layer_image = l_images[i] if i < len(l_images) else l_images[-1]
_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
# preprocess
_canvas = tensor2pil(background_image).convert('RGBA')
_layer = tensor2pil(layer_image)
if _mask.size != _layer.size:
_mask = Image.new('L', _layer.size, 'white')
log(f"Warning: {NODE_NAME} mask mismatch, dropped!", message_type='warning')
orig_layer_width = _layer.width
orig_layer_height = _layer.height
_mask = _mask.convert("RGBA")
target_layer_width = int(orig_layer_width * scale)
target_layer_height = int(orig_layer_height * scale * aspect_ratio)
# mirror
if mirror == 'horizontal':
_layer = _layer.transpose(Image.FLIP_LEFT_RIGHT)
_mask = _mask.transpose(Image.FLIP_LEFT_RIGHT)
elif mirror == 'vertical':
_layer = _layer.transpose(Image.FLIP_TOP_BOTTOM)
_mask = _mask.transpose(Image.FLIP_TOP_BOTTOM)
# scale
_layer = _layer.resize((target_layer_width, target_layer_height))
_mask = _mask.resize((target_layer_width, target_layer_height))
# rotate
_layer, _mask, _ = image_rotate_extend_with_alpha(_layer, rotate, _mask, transform_method, anti_aliasing)
# 处理位置
x = int(_canvas.width * x_percent / 100 - _layer.width / 2)
y = int(_canvas.height * y_percent / 100 - _layer.height / 2)
# composit layer
_comp = copy.copy(_canvas)
_compmask = Image.new("RGBA", _comp.size, color='black')
_comp.paste(_layer, (x, y))
_compmask.paste(_mask, (x, y))
_compmask = _compmask.convert('L')
_comp = chop_image_v2(_canvas, _comp, blend_mode, opacity)
# composition background
_canvas.paste(_comp, mask=_compmask)
ret_images.append(pil2tensor(_canvas))
ret_masks.append(image2mask(_compmask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
"LayerUtility: ImageBlendAdvance V3": ImageBlendAdvanceV3
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerUtility: ImageBlendAdvance V3": "LayerUtility: ImageBlendAdvance V3"
}