72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
import torch
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from PIL import Image
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from .imagefunc import log, tensor2pil, pil2tensor, image_channel_merge
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class ImageChannelMerge:
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def __init__(self):
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self.NODE_NAME = 'ImageChannelMerge'
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@classmethod
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def INPUT_TYPES(self):
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channel_mode = ['RGBA', 'YCbCr', 'LAB', 'HSV']
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return {
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"required": {
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"channel_1": ("IMAGE", ), #
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"channel_2": ("IMAGE",), #
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"channel_3": ("IMAGE",), #
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"mode": (channel_mode,), # 通道设置
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},
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"optional": {
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"channel_4": ("IMAGE",), #
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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 = 'image_channel_merge'
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CATEGORY = '😺dzNodes/LayerUtility'
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def image_channel_merge(self, channel_1, channel_2, channel_3, mode, channel_4=None):
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c1_images = []
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c2_images = []
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c3_images = []
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c4_images = []
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ret_images = []
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width, height = tensor2pil(torch.unsqueeze(channel_1[0], 0)).size
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for c in channel_1:
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c1_images.append(torch.unsqueeze(c, 0))
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for c in channel_2:
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c2_images.append(torch.unsqueeze(c, 0))
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for c in channel_3:
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c3_images.append(torch.unsqueeze(c, 0))
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if channel_4 is not None:
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for c in channel_4:
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c4_images.append(torch.unsqueeze(c, 0))
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else:
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c4_images.append(pil2tensor(Image.new('L', size=(width, height), color='white')))
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max_batch = max(len(c1_images), len(c2_images), len(c3_images), len(c4_images))
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for i in range(max_batch):
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c_1 = c1_images[i] if i < len(c1_images) else c1_images[-1]
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c_2 = c2_images[i] if i < len(c2_images) else c2_images[-1]
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c_3 = c3_images[i] if i < len(c3_images) else c3_images[-1]
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c_4 = c4_images[i] if i < len(c4_images) else c4_images[-1]
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ret_image = image_channel_merge((tensor2pil(c_1), tensor2pil(c_2), tensor2pil(c_3), tensor2pil(c_4)), mode)
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ret_images.append(pil2tensor(ret_image))
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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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"LayerUtility: ImageChannelMerge": ImageChannelMerge
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: ImageChannelMerge": "LayerUtility: ImageChannelMerge"
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} |