# layerstyle advance

from .imagefunc import *


class EncodeBlindWaterMark:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):

        return {
            "required": {
                "image": ("IMAGE", ),  #
                "watermark_image": ("IMAGE",),  #
            },
            "optional": {
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = 'watermark_encode'
    CATEGORY = '😺dzNodes/LayerUtility/SystemIO'

    def watermark_encode(self, image, watermark_image):

        NODE_NAME = 'Add BlindWaterMark'

        l_images = []
        w_images = []
        ret_images = []

        for l in image:
            l_images.append(torch.unsqueeze(l, 0))
        for w in watermark_image:
            w_images.append(torch.unsqueeze(w, 0))

        for i in range(len(l_images)):
            _image = tensor2pil(l_images[i])
            wm_size = watermark_image_size(_image)
            _wm_image = w_images[i] if i < len(w_images) else w_images[-1]
            _wm_image = tensor2pil(_wm_image)
            _wm_image = _wm_image.resize((wm_size, wm_size), Image.LANCZOS)
            _wm_image = _wm_image.convert("L")

            y, u, v, _ = image_channel_split(_image, mode='YCbCr')
            _u = add_invisibal_watermark(u, _wm_image)
            ret_image = image_channel_merge((y, _u, v), mode='YCbCr')

            if _image.mode == "RGBA":
                ret_image = RGB2RGBA(ret_image, _image.split()[-1])
            ret_images.append(pil2tensor(ret_image))

        log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
        return (torch.cat(ret_images, dim=0),)



class DecodeBlindWaterMark:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):

        return {
            "required": {
                "image": ("IMAGE",),  #
            },
            "optional": {
            }
        }

    RETURN_TYPES = ("IMAGE", )
    RETURN_NAMES = ("watermark_image",)
    FUNCTION = 'watermark_decode'
    CATEGORY = '😺dzNodes/LayerUtility/SystemIO'

    def watermark_decode(self, image):

        NODE_NAME = 'Decode BlindWaterMark'

        ret_images = []

        for i in image:
            _image = torch.unsqueeze(i,0)
            _image = tensor2pil(_image)
            wm_size = watermark_image_size(_image)
            y, u, v, _ = image_channel_split(_image, mode='YCbCr')
            ret_image = decode_watermark(u, wm_size)
            ret_image = ret_image.resize((512, 512), Image.LANCZOS)
            ret_image = normalize_gray(ret_image)
            ret_images.append(pil2tensor(ret_image.convert('RGB')))

        log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
        return (torch.cat(ret_images, dim=0), )



NODE_CLASS_MAPPINGS = {
    "LayerUtility: AddBlindWaterMark": EncodeBlindWaterMark,
    "LayerUtility: ShowBlindWaterMark": DecodeBlindWaterMark
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "LayerUtility: AddBlindWaterMark": "LayerUtility: Add BlindWaterMark(Advance)",
    "LayerUtility: ShowBlindWaterMark": "LayerUtility: Show BlindWaterMark(Advance)"
}