107 lines
4.5 KiB
Python
107 lines
4.5 KiB
Python
from .imagefunc import *
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NODE_NAME = 'InnerGlow'
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class InnerGlow:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(self):
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chop_mode = ['screen', 'add', 'lighter', 'normal', 'multply', 'subtract','difference','darker',
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'color_burn', 'color_dodge', 'linear_burn', 'linear_dodge', 'overlay',
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'soft_light', 'hard_light', 'vivid_light', 'pin_light', 'linear_light', 'hard_mix']
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return {
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"required": {
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"background_image": ("IMAGE", ), #
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"layer_image": ("IMAGE",), #
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"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
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"blend_mode": (chop_mode,), # 混合模式
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"opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}), # 透明度
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"brightness": ("INT", {"default": 5, "min": 2, "max": 20, "step": 1}), # 迭代
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"glow_range": ("INT", {"default": 48, "min": -9999, "max": 9999, "step": 1}), # 扩张
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"blur": ("INT", {"default": 25, "min": 0, "max": 9999, "step": 1}), # 扩张
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"light_color": ("STRING", {"default": "#FFBF30"}), # 光源中心颜色
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"glow_color": ("STRING", {"default": "#FE0000"}), # 辉光外围颜色
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},
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"optional": {
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"layer_mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = 'inner_glow'
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CATEGORY = '😺dzNodes/LayerStyle'
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OUTPUT_NODE = True
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def inner_glow(self, background_image, layer_image,
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invert_mask, blend_mode, opacity,
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brightness, glow_range, blur, light_color, glow_color,
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layer_mask=None
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):
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b_images = []
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l_images = []
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l_masks = []
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ret_images = []
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for b in background_image:
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b_images.append(torch.unsqueeze(b, 0))
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for l in layer_image:
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l_images.append(torch.unsqueeze(l, 0))
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m = tensor2pil(l)
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if m.mode == 'RGBA':
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l_masks.append(m.split()[-1])
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if layer_mask is not None:
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if layer_mask.dim() == 2:
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layer_mask = torch.unsqueeze(layer_mask, 0)
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l_masks = []
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for m in layer_mask:
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if invert_mask:
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m = 1 - m
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l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
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if len(l_masks) == 0:
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log(f"Error: {NODE_NAME} skipped, because the available mask is not found.", message_type='error')
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return (background_image,)
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max_batch = max(len(b_images), len(l_images), len(l_masks))
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for i in range(max_batch):
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background_image = b_images[i] if i < len(b_images) else b_images[-1]
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layer_image = l_images[i] if i < len(l_images) else l_images[-1]
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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# preprocess
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_canvas = tensor2pil(background_image).convert('RGB')
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_layer = tensor2pil(layer_image).convert('RGB')
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if _mask.size != _layer.size:
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_mask = Image.new('L', _layer.size, 'white')
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log(f"Warning: {NODE_NAME} mask mismatch, dropped!", message_type='warning')
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blur_factor = blur / 20.0
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grow = glow_range
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inner_mask = _mask
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for x in range(brightness):
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blur = int(grow * blur_factor)
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_color = step_color(glow_color, light_color, brightness, x)
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glow_mask = expand_mask(image2mask(inner_mask), -grow, blur) #扩张，模糊
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# 合成glow
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color_image = Image.new("RGB", _layer.size, color=_color)
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alpha = tensor2pil(mask_invert(glow_mask)).convert('L')
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_glow = chop_image(_layer, color_image, blend_mode, int(step_value(1, opacity, brightness, x)))
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_layer.paste(_glow, mask=alpha)
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grow = grow - int(glow_range/brightness)
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# 合成layer
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_layer.paste(_canvas, mask=ImageChops.invert(_mask))
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ret_images.append(pil2tensor(_layer))
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log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerStyle: InnerGlow": InnerGlow
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerStyle: InnerGlow": "LayerStyle: InnerGlow"
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} |