import torch
from PIL import Image, ImageChops
from .imagefunc import log, tensor2pil, pil2tensor, image2mask
from .imagefunc import get_gray_average, calculate_shadow_highlight_level, luminance_keyer


def norm_value(value):
    if value < 0.01:
        value = 0.01
    if value > 0.99:
        value = 0.99
    return value

class ShadowAndHighlightMask:

    def __init__(self):
        self.NODE_NAME = 'Shadow & Highlight Mask'

    @classmethod
    def INPUT_TYPES(self):

        return {
            "required": {
                "image": ("IMAGE", ),
                "shadow_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
                "shadow_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
                "highlight_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
                "highlight_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
            },
            "optional": {
                "mask": ("MASK",),  #
            }
        }

    RETURN_TYPES = ("MASK", "MASK")
    RETURN_NAMES = ("shadow_mask", "highlight_mask")
    FUNCTION = 'shadow_and_highlight_mask'
    CATEGORY = '😺dzNodes/LayerMask'

    def shadow_and_highlight_mask(self, image,
                                  shadow_level_offset, shadow_range,
                                  highlight_level_offset, highlight_range,
                                  mask=None
                                  ):

        ret_shadow_masks = []
        ret_highlight_masks = []
        input_images = []
        input_masks = []

        for i in image:
            input_images.append(torch.unsqueeze(i, 0))
            m = tensor2pil(i)
            if m.mode == 'RGBA':
                input_masks.append(m.split()[-1])
            else:
                input_masks.append(Image.new('L', size=m.size, color='white'))
        if mask is not None:
            if mask.dim() == 2:
                mask = torch.unsqueeze(mask, 0)
            input_masks = []
            for m in mask:
                input_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
        max_batch = max(len(input_images), len(input_masks))

        for i in range(max_batch):
            _image = input_images[i] if i < len(input_images) else input_images[-1]
            _image = tensor2pil(_image).convert('RGB')
            _mask = input_masks[i] if i < len(input_masks) else input_masks[-1]

            avg_gray = get_gray_average(_image, _mask)
            shadow_level, highlight_level = calculate_shadow_highlight_level(avg_gray)
            shadow_low_threshold = (shadow_level + shadow_level_offset) / 100 + shadow_range / 2
            shadow_low_threshold = norm_value(shadow_low_threshold)
            shadow_high_threshold = (shadow_level + shadow_level_offset) / 100 - shadow_range / 2
            shadow_high_threshold = norm_value(shadow_high_threshold)
            _shadow_mask = luminance_keyer(_image, shadow_low_threshold, shadow_high_threshold)

            highlight_low_threshold = (highlight_level + highlight_level_offset) / 100 - highlight_range / 2
            highlight_low_threshold = norm_value(highlight_low_threshold)
            highlight_high_threshold = (highlight_level + highlight_level_offset) / 100 + highlight_range / 2
            highlight_high_threshold = norm_value(highlight_high_threshold)
            _highlight_mask = luminance_keyer(_image, highlight_low_threshold, highlight_high_threshold)

            black = Image.new('L', size=_image.size, color='black')
            _mask = ImageChops.invert(_mask)
            _shadow_mask.paste(black, mask=_mask)
            _highlight_mask.paste(black, mask=_mask)
            ret_shadow_masks.append(image2mask(_shadow_mask))
            ret_highlight_masks.append(image2mask(_highlight_mask))

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

class LS_ShadowAndHighlightMaskV2:

    def __init__(self):
        self.NODE_NAME = 'Shadow Highlight Mask V2'

    @classmethod
    def INPUT_TYPES(self):

        return {
            "required": {
                "image": ("IMAGE", ),
                "shadow_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
                "shadow_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
                "highlight_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
                "highlight_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
            },
            "optional": {
                "mask": ("MASK",),  #
            }
        }

    RETURN_TYPES = ("MASK", "MASK")
    RETURN_NAMES = ("shadow_mask", "highlight_mask")
    FUNCTION = 'shadow_and_highlight_mask_v2'
    CATEGORY = '😺dzNodes/LayerMask'

    def shadow_and_highlight_mask_v2(self, image,
                                  shadow_level_offset, shadow_range,
                                  highlight_level_offset, highlight_range,
                                  mask=None
                                  ):

        ret_shadow_masks = []
        ret_highlight_masks = []
        input_images = []
        input_masks = []

        for i in image:
            input_images.append(torch.unsqueeze(i, 0))
            m = tensor2pil(i)
            if m.mode == 'RGBA':
                input_masks.append(m.split()[-1])
            else:
                input_masks.append(Image.new('L', size=m.size, color='white'))
        if mask is not None:
            if mask.dim() == 2:
                mask = torch.unsqueeze(mask, 0)
            input_masks = []
            for m in mask:
                input_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
        max_batch = max(len(input_images), len(input_masks))

        for i in range(max_batch):
            _image = input_images[i] if i < len(input_images) else input_images[-1]
            _image = tensor2pil(_image).convert('RGB')
            _mask = input_masks[i] if i < len(input_masks) else input_masks[-1]


            avg_gray = get_gray_average(_image, _mask)
            shadow_level, highlight_level = calculate_shadow_highlight_level(avg_gray)
            shadow_low_threshold = (shadow_level + shadow_level_offset) / 100 + shadow_range / 2
            shadow_low_threshold = norm_value(shadow_low_threshold)
            shadow_high_threshold = (shadow_level + shadow_level_offset) / 100 - shadow_range / 2
            shadow_high_threshold = norm_value(shadow_high_threshold)
            _shadow_mask = luminance_keyer(_image, shadow_low_threshold, shadow_high_threshold)

            highlight_low_threshold = (highlight_level + highlight_level_offset) / 100 - highlight_range / 2
            highlight_low_threshold = norm_value(highlight_low_threshold)
            highlight_high_threshold = (highlight_level + highlight_level_offset) / 100 + highlight_range / 2
            highlight_high_threshold = norm_value(highlight_high_threshold)
            _highlight_mask = luminance_keyer(_image, highlight_low_threshold, highlight_high_threshold)

            black = Image.new('L', size=_image.size, color='black')
            _mask = ImageChops.invert(_mask)
            _shadow_mask.paste(black, mask=_mask)
            _highlight_mask.paste(black, mask=_mask)
            ret_shadow_masks.append(image2mask(_shadow_mask))
            ret_highlight_masks.append(image2mask(_highlight_mask))

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

NODE_CLASS_MAPPINGS = {
    "LayerMask: Shadow & Highlight Mask": ShadowAndHighlightMask,
    "LayerMask: ShadowHighlightMaskV2": LS_ShadowAndHighlightMaskV2
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "LayerMask: Shadow & Highlight Mask": "LayerMask: Shadow & Highlight Mask",
    "LayerMask: ShadowHighlightMaskV2": "LayerMask: Shadow Highlight Mask V2"
}