import numpy as np
import scipy.ndimage
import torch
import comfy.utils

from nodes import MAX_RESOLUTION

def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
    if resize_source:
        source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")

    source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])

    x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
    y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))

    left, top = (x // multiplier, y // multiplier)
    right, bottom = (left + source.shape[3], top + source.shape[2],)

    if mask is None:
        mask = torch.ones_like(source)
    else:
        mask = mask.clone()
        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
        mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])

    # calculate the bounds of the source that will be overlapping the destination
    # this prevents the source trying to overwrite latent pixels that are out of bounds
    # of the destination
    visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)

    mask = mask[:, :, :visible_height, :visible_width]
    inverse_mask = torch.ones_like(mask) - mask

    source_portion = mask * source[:, :, :visible_height, :visible_width]
    destination_portion = inverse_mask  * destination[:, :, top:bottom, left:right]

    destination[:, :, top:bottom, left:right] = source_portion + destination_portion
    return destination

class LatentCompositeMasked:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "destination": ("LATENT",),
                "source": ("LATENT",),
                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                "resize_source": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                "mask": ("MASK",),
            }
        }
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "composite"

    CATEGORY = "latent"

    def composite(self, destination, source, x, y, resize_source, mask = None):
        output = destination.copy()
        destination = destination["samples"].clone()
        source = source["samples"]
        output["samples"] = composite(destination, source, x, y, mask, 8, resize_source)
        return (output,)

class ImageCompositeMasked:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "destination": ("IMAGE",),
                "source": ("IMAGE",),
                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "resize_source": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                "mask": ("MASK",),
            }
        }
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "composite"

    CATEGORY = "image"

    def composite(self, destination, source, x, y, resize_source, mask = None):
        destination = destination.clone().movedim(-1, 1)
        output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
        return (output,)

class MaskToImage:
    @classmethod
    def INPUT_TYPES(s):
        return {
                "required": {
                    "mask": ("MASK",),
                }
        }

    CATEGORY = "mask"

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "mask_to_image"

    def mask_to_image(self, mask):
        result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
        return (result,)

class ImageToMask:
    @classmethod
    def INPUT_TYPES(s):
        return {
                "required": {
                    "image": ("IMAGE",),
                    "channel": (["red", "green", "blue"],),
                }
        }

    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)
    FUNCTION = "image_to_mask"

    def image_to_mask(self, image, channel):
        channels = ["red", "green", "blue"]
        mask = image[:, :, :, channels.index(channel)]
        return (mask,)

class ImageColorToMask:
    @classmethod
    def INPUT_TYPES(s):
        return {
                "required": {
                    "image": ("IMAGE",),
                    "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}),
                }
        }

    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)
    FUNCTION = "image_to_mask"

    def image_to_mask(self, image, color):
        temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
        temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2]
        mask = torch.where(temp == color, 255, 0).float()
        return (mask,)

class SolidMask:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
                "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
            }
        }

    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)

    FUNCTION = "solid"

    def solid(self, value, width, height):
        out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu")
        return (out,)

class InvertMask:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "mask": ("MASK",),
            }
        }

    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)

    FUNCTION = "invert"

    def invert(self, mask):
        out = 1.0 - mask
        return (out,)

class CropMask:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "mask": ("MASK",),
                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
                "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
            }
        }

    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)

    FUNCTION = "crop"

    def crop(self, mask, x, y, width, height):
        mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
        out = mask[:, y:y + height, x:x + width]
        return (out,)

class MaskComposite:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "destination": ("MASK",),
                "source": ("MASK",),
                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "operation": (["multiply", "add", "subtract", "and", "or", "xor"],),
            }
        }

    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)

    FUNCTION = "combine"

    def combine(self, destination, source, x, y, operation):
        output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
        source = source.reshape((-1, source.shape[-2], source.shape[-1]))

        left, top = (x, y,)
        right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
        visible_width, visible_height = (right - left, bottom - top,)

        source_portion = source[:visible_height, :visible_width]
        destination_portion = destination[top:bottom, left:right]

        if operation == "multiply":
            output[:, top:bottom, left:right] = destination_portion * source_portion
        elif operation == "add":
            output[:, top:bottom, left:right] = destination_portion + source_portion
        elif operation == "subtract":
            output[:, top:bottom, left:right] = destination_portion - source_portion
        elif operation == "and":
            output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()
        elif operation == "or":
            output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float()
        elif operation == "xor":
            output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()

        output = torch.clamp(output, 0.0, 1.0)

        return (output,)

class FeatherMask:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "mask": ("MASK",),
                "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
            }
        }

    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)

    FUNCTION = "feather"

    def feather(self, mask, left, top, right, bottom):
        output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()

        left = min(left, output.shape[1])
        right = min(right, output.shape[1])
        top = min(top, output.shape[0])
        bottom = min(bottom, output.shape[0])

        for x in range(left):
            feather_rate = (x + 1.0) / left
            output[:, :, x] *= feather_rate

        for x in range(right):
            feather_rate = (x + 1) / right
            output[:, :, -x] *= feather_rate

        for y in range(top):
            feather_rate = (y + 1) / top
            output[:, y, :] *= feather_rate

        for y in range(bottom):
            feather_rate = (y + 1) / bottom
            output[:, -y, :] *= feather_rate

        return (output,)
    
class GrowMask:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "mask": ("MASK",),
                "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}),
                "tapered_corners": ("BOOLEAN", {"default": True}),
            },
        }
    
    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)

    FUNCTION = "expand_mask"

    def expand_mask(self, mask, expand, tapered_corners):
        c = 0 if tapered_corners else 1
        kernel = np.array([[c, 1, c],
                           [1, 1, 1],
                           [c, 1, c]])
        output = mask.numpy().copy()
        while expand < 0:
            output = scipy.ndimage.grey_erosion(output, footprint=kernel)
            expand += 1
        while expand > 0:
            output = scipy.ndimage.grey_dilation(output, footprint=kernel)
            expand -= 1
        output = torch.from_numpy(output)
        return (output,)



NODE_CLASS_MAPPINGS = {
    "LatentCompositeMasked": LatentCompositeMasked,
    "ImageCompositeMasked": ImageCompositeMasked,
    "MaskToImage": MaskToImage,
    "ImageToMask": ImageToMask,
    "ImageColorToMask": ImageColorToMask,
    "SolidMask": SolidMask,
    "InvertMask": InvertMask,
    "CropMask": CropMask,
    "MaskComposite": MaskComposite,
    "FeatherMask": FeatherMask,
    "GrowMask": GrowMask,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "ImageToMask": "Convert Image to Mask",
    "MaskToImage": "Convert Mask to Image",
}
