64 lines
2.3 KiB
Python
64 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, image2mask, gaussian_blur, chop_image
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from .imagefunc import image_channel_split, gray_threshold, remove_background, get_image_bright_average, image_beauty
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class SkinBeauty:
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def __init__(self):
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self.NODE_NAME = 'SkinBeauty'
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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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"smooth": ("INT", {"default": 20, "min": 1, "max": 64, "step": 1}), # 磨皮程度
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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", "MASK")
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RETURN_NAMES = ("image", "beauty_mask")
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FUNCTION = 'skin_beauty'
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CATEGORY = '😺dzNodes/LayerFilter'
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def skin_beauty(self, image, smooth, threshold, opacity
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):
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ret_images = []
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ret_masks = []
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for i in image:
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i = torch.unsqueeze(i, 0)
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_canvas = tensor2pil(i).convert('RGB')
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_R, _, _, _ = image_channel_split(_canvas, mode='RGB')
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_otsumask = gray_threshold(_R, otsu=True)
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_removebkgd = remove_background(_R, _otsumask, '#000000')
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auto_threshold = get_image_bright_average(_removebkgd) - 16
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light_mask = gray_threshold(_canvas, auto_threshold + threshold)
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blur = int((_canvas.width + _canvas.height) / 2000 * smooth)
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_image = image_beauty(_canvas, level=smooth)
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_image = gaussian_blur(_image, blur)
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_image = chop_image(_canvas, _image, 'normal', opacity)
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light_mask = gaussian_blur(light_mask, blur).convert('L')
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_canvas.paste(_image, mask=light_mask)
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ret_images.append(pil2tensor(_canvas))
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ret_masks.append(image2mask(light_mask))
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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), torch.cat(ret_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerFilter: SkinBeauty": SkinBeauty
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerFilter: SkinBeauty": "LayerFilter: SkinBeauty"
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} |