111 lines
3.1 KiB
Python
111 lines
3.1 KiB
Python
from .imagefunc import *
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class EncodeBlindWaterMark:
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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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return {
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"required": {
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"image": ("IMAGE", ), #
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"watermark_image": ("IMAGE",), #
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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 = 'watermark_encode'
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CATEGORY = '😺dzNodes/LayerUtility/SystemIO'
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def watermark_encode(self, image, watermark_image):
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NODE_NAME = 'Add BlindWaterMark'
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l_images = []
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w_images = []
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ret_images = []
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for l in image:
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l_images.append(torch.unsqueeze(l, 0))
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for w in watermark_image:
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w_images.append(torch.unsqueeze(w, 0))
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for i in range(len(l_images)):
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_image = tensor2pil(l_images[i])
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wm_size = watermark_image_size(_image)
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_wm_image = w_images[i] if i < len(w_images) else w_images[-1]
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_wm_image = tensor2pil(_wm_image)
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_wm_image = _wm_image.resize((wm_size, wm_size), Image.LANCZOS)
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_wm_image = _wm_image.convert("L")
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y, u, v, _ = image_channel_split(_image, mode='YCbCr')
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_u = add_invisibal_watermark(u, _wm_image)
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ret_image = image_channel_merge((y, _u, v), mode='YCbCr')
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if _image.mode == "RGBA":
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ret_image = RGB2RGBA(ret_image, _image.split()[-1])
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ret_images.append(pil2tensor(ret_image))
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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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class DecodeBlindWaterMark:
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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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return {
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"required": {
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"image": ("IMAGE",), #
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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 = ("watermark_image",)
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FUNCTION = 'watermark_decode'
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CATEGORY = '😺dzNodes/LayerUtility/SystemIO'
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def watermark_decode(self, image):
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NODE_NAME = 'Decode BlindWaterMark'
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ret_images = []
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for i in image:
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_image = torch.unsqueeze(i,0)
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_image = tensor2pil(_image)
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wm_size = watermark_image_size(_image)
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y, u, v, _ = image_channel_split(_image, mode='YCbCr')
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ret_image = decode_watermark(u, wm_size)
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ret_image = ret_image.resize((512, 512), Image.LANCZOS)
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ret_image = normalize_gray(ret_image)
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ret_images.append(pil2tensor(ret_image.convert('RGB')))
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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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"LayerUtility: AddBlindWaterMark": EncodeBlindWaterMark,
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"LayerUtility: ShowBlindWaterMark": DecodeBlindWaterMark
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: AddBlindWaterMark": "LayerUtility: Add BlindWaterMark",
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"LayerUtility: ShowBlindWaterMark": "LayerUtility: Show BlindWaterMark"
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} |