70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
import torch
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import math
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from PIL import Image
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from .imagefunc import log, tensor2pil, pil2tensor
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from .imagefunc import shift_image
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class ChannelShake:
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def __init__(self):
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self.NODE_NAME = 'ChannelShake'
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@classmethod
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def INPUT_TYPES(self):
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channel_mode = ['RGB', 'RBG', 'BGR', 'BRG', 'GBR', 'GRB']
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return {
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"required": {
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"image": ("IMAGE", ), #
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"distance": ("INT", {"default": 20, "min": 1, "max": 999, "step": 1}), # 距离
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"angle": ("FLOAT", {"default": 40, "min": -360, "max": 360, "step": 0.1}), # 角度
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"mode": (channel_mode,), # 模式
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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 = 'channel_shake'
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CATEGORY = '😺dzNodes/LayerFilter'
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def channel_shake(self, image, distance, angle, mode, ):
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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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_canvas = tensor2pil(i).convert('RGB')
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R, G, B = _canvas.split()
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x = int(math.cos(angle) * distance)
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y = int(math.sin(angle) * distance)
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if mode.startswith('R'):
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R = shift_image(R.convert('RGB'), -x, -y).convert('L')
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if mode.startswith('G'):
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G = shift_image(G.convert('RGB'), -x, -y).convert('L')
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if mode.startswith('B'):
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B = shift_image(B.convert('RGB'), -x, -y).convert('L')
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if mode.endswith('R'):
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R = shift_image(R.convert('RGB'), x, y).convert('L')
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if mode.endswith('G'):
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G = shift_image(G.convert('RGB'), x, y).convert('L')
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if mode.endswith('B'):
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B = shift_image(B.convert('RGB'), x, y).convert('L')
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ret_image = Image.merge('RGB', [R, G, B])
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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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"LayerFilter: ChannelShake": ChannelShake
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerFilter: ChannelShake": "LayerFilter: ChannelShake"
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} |