63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
import torch
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from PIL import Image
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from .imagefunc import log, tensor2pil, pil2tensor, gaussian_blur, chop_image
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from .imagefunc import gray_threshold, remove_background, get_image_bright_average
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class SoftLight:
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def __init__(self):
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self.NODE_NAME = 'SoftLight'
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE", ), #
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"soft": ("FLOAT", {"default": 1, "min": 0.2, "max": 10, "step": 0.01}), # 模糊
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"threshold": ("INT", {"default": -10, "min": -255, "max": 255, "step": 1}), # 高光阈值
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"opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}), # 透明度
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},
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"optional": {
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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 = 'soft_light'
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CATEGORY = '😺dzNodes/LayerFilter'
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def soft_light(self, image, soft, threshold, opacity,):
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ret_images = []
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for i in image:
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i = torch.unsqueeze(i, 0)
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blend_mode = 'screen'
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_canvas = tensor2pil(i).convert('RGB')
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blur = int((_canvas.width + _canvas.height) / 200 * soft)
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_otsumask = gray_threshold(_canvas, otsu=True)
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_removebkgd = remove_background(_canvas, _otsumask, '#000000').convert('L')
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auto_threshold = get_image_bright_average(_removebkgd)
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light_mask = gray_threshold(_canvas, auto_threshold + threshold)
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highlight_mask = gray_threshold(_canvas, auto_threshold + (255 - auto_threshold) // 2 + threshold // 2)
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blurimage = gaussian_blur(_canvas, soft).convert('RGB')
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light = chop_image(_canvas, blurimage, blend_mode=blend_mode, opacity=opacity)
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highlight = chop_image(light, blurimage, blend_mode=blend_mode, opacity=opacity)
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_canvas.paste(highlight, mask=gaussian_blur(light_mask, blur * 2).convert('L'))
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_canvas.paste(highlight, mask=gaussian_blur(highlight_mask, blur).convert('L'))
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ret_images.append(pil2tensor(_canvas))
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log(f"{self.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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"LayerFilter: SoftLight": SoftLight
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerFilter: SoftLight": "LayerFilter: SoftLight"
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} |