import torch
import cv2
import numpy as np
from PIL import Image, ImageEnhance
import torch.nn.functional as F


class Blend:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image1": ("IMAGE",),
                "image2": ("IMAGE",),
                "blend_factor": ("FLOAT", {
                    "default": 0.5,
                    "min": 0.0,
                    "max": 1.0,
                    "step": 0.01
                }),
                "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light"],),
            },
        }

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

    CATEGORY = "postprocessing"

    def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
        blended_image = self.blend_mode(image1, image2, blend_mode)
        blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
        blended_image = torch.clamp(blended_image, 0, 1)
        return (blended_image,)

    def blend_mode(self, img1, img2, mode):
        if mode == "normal":
            return img2
        elif mode == "multiply":
            return img1 * img2
        elif mode == "screen":
            return 1 - (1 - img1) * (1 - img2)
        elif mode == "overlay":
            return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
        elif mode == "soft_light":
            return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
        else:
            raise ValueError(f"Unsupported blend mode: {mode}")

    def g(self, x):
        return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))

class CannyEdgeDetection:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "lower_threshold": ("INT", {
                    "default": 100,
                    "min": 0,
                    "max": 500,
                    "step": 10
                }),
                "upper_threshold": ("INT", {
                    "default": 200,
                    "min": 0,
                    "max": 500,
                    "step": 10
                }),
            },
        }

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

    CATEGORY = "postprocessing"

    def canny(self, image: torch.Tensor, lower_threshold: int, upper_threshold: int):
        batch_size, height, width, _ = image.shape
        result = torch.zeros(batch_size, height, width)

        for b in range(batch_size):
            tensor_image = image[b].numpy().copy()
            gray_image = (cv2.cvtColor(tensor_image, cv2.COLOR_RGB2GRAY) * 255).astype(np.uint8)
            canny = cv2.Canny(gray_image, lower_threshold, upper_threshold)
            tensor = torch.from_numpy(canny)
            result[b] = tensor

        return (result,)

class ColorCorrect:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "temperature": ("FLOAT", {
                    "default": 0,
                    "min": -100,
                    "max": 100,
                    "step": 5
                }),
                "hue": ("FLOAT", {
                    "default": 0,
                    "min": -90,
                    "max": 90,
                    "step": 5
                }),
                "brightness": ("FLOAT", {
                    "default": 0,
                    "min": -100,
                    "max": 100,
                    "step": 5
                }),
                "contrast": ("FLOAT", {
                    "default": 0,
                    "min": -100,
                    "max": 100,
                    "step": 5
                }),
                "saturation": ("FLOAT", {
                    "default": 0,
                    "min": -100,
                    "max": 100,
                    "step": 5
                }),
                "gamma": ("FLOAT", {
                    "default": 1,
                    "min": 0.2,
                    "max": 2.2,
                    "step": 0.1
                }),
            },
        }

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

    CATEGORY = "postprocessing"

    def color_correct(self, image: torch.Tensor, temperature: float, hue: float, brightness: float, contrast: float, saturation: float, gamma: float):
        batch_size, height, width, _ = image.shape
        result = torch.zeros_like(image)

        for b in range(batch_size):
            tensor_image = image[b].numpy()

            brightness /= 100
            contrast /= 100
            saturation /= 100
            temperature /= 100

            brightness = 1 + brightness
            contrast = 1 + contrast
            saturation = 1 + saturation

            modified_image = Image.fromarray((tensor_image * 255).astype(np.uint8))

            # brightness
            modified_image = ImageEnhance.Brightness(modified_image).enhance(brightness)

            # contrast
            modified_image = ImageEnhance.Contrast(modified_image).enhance(contrast)
            modified_image = np.array(modified_image).astype(np.float32)

            # temperature
            if temperature > 0:
                modified_image[:, :, 0] *= 1 + temperature
                modified_image[:, :, 1] *= 1 + temperature * 0.4
            elif temperature < 0:
                modified_image[:, :, 2] *= 1 - temperature
            modified_image = np.clip(modified_image, 0, 255)/255

            # gamma
            modified_image = np.clip(np.power(modified_image, gamma), 0, 1)

            # saturation
            hls_img = cv2.cvtColor(modified_image, cv2.COLOR_RGB2HLS)
            hls_img[:, :, 2] = np.clip(saturation*hls_img[:, :, 2], 0, 1)
            modified_image = cv2.cvtColor(hls_img, cv2.COLOR_HLS2RGB) * 255

            # hue
            hsv_img = cv2.cvtColor(modified_image, cv2.COLOR_RGB2HSV)
            hsv_img[:, :, 0] = (hsv_img[:, :, 0] + hue) % 360
            modified_image = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2RGB)

            modified_image = modified_image.astype(np.uint8)
            modified_image = modified_image / 255
            modified_image = torch.from_numpy(modified_image).unsqueeze(0)
            result[b] = modified_image

        return (result, )

class Dither:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "bits": ("INT", {
                    "default": 4,
                    "min": 1,
                    "max": 8,
                    "step": 1
                }),
            },
        }

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

    CATEGORY = "postprocessing"

    def dither(self, image: torch.Tensor, bits: int):
        batch_size, height, width, _ = image.shape
        result = torch.zeros_like(image)

        for b in range(batch_size):
            tensor_image = image[b]
            img = (tensor_image * 255)
            height, width, _ = img.shape

            scale = 255 / (2**bits - 1)

            for y in range(height):
                for x in range(width):
                    old_pixel = img[y, x].clone()
                    new_pixel = torch.round(old_pixel / scale) * scale
                    img[y, x] = new_pixel

                    quant_error = old_pixel - new_pixel

                    if x + 1 < width:
                        img[y, x + 1] += quant_error * 7 / 16
                    if y + 1 < height:
                        if x - 1 >= 0:
                            img[y + 1, x - 1] += quant_error * 3 / 16
                        img[y + 1, x] += quant_error * 5 / 16
                        if x + 1 < width:
                            img[y + 1, x + 1] += quant_error * 1 / 16

            dithered = img / 255
            tensor = dithered.unsqueeze(0)
            result[b] = tensor

        return (result,)

class FilmGrain:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "intensity": ("FLOAT", {
                    "default": 0.2,
                    "min": 0.0,
                    "max": 1.0,
                    "step": 0.01
                }),
                "scale": ("FLOAT", {
                    "default": 10,
                    "min": 1,
                    "max": 100,
                    "step": 1
                }),
                "temperature": ("FLOAT", {
                    "default": 0.0,
                    "min": -100,
                    "max": 100,
                    "step": 1
                }),
                "vignette": ("FLOAT", {
                    "default": 0.0,
                    "min": 0.0,
                    "max": 10.0,
                    "step": 1.0
                }),
            },
        }

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

    CATEGORY = "postprocessing"

    def film_grain(self, image: torch.Tensor, intensity: float, scale: float, temperature: float, vignette: float):
        batch_size, height, width, _ = image.shape
        result = torch.zeros_like(image)

        for b in range(batch_size):
            tensor_image = image[b].numpy()

            # Generate Perlin noise with shape (height, width) and scale
            noise = self.generate_perlin_noise((height, width), scale)
            noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise))

            # Apply grain intensity
            noise = (noise * 2 - 1) * intensity

            # Blend the noise with the image
            grain_image = np.clip(tensor_image + noise[:, :, np.newaxis], 0, 1)

            # Apply temperature
            grain_image = self.apply_temperature(grain_image, temperature)

            # Apply vignette
            grain_image = self.apply_vignette(grain_image, vignette)

            tensor = torch.from_numpy(grain_image).unsqueeze(0)
            result[b] = tensor

        return (result,)

    def generate_perlin_noise(self, shape, scale, octaves=4, persistence=0.5, lacunarity=2):
        def smoothstep(t):
            return t * t * (3.0 - 2.0 * t)

        def lerp(t, a, b):
            return a + t * (b - a)

        def gradient(h, x, y):
            vectors = np.array([[1, 1], [-1, 1], [1, -1], [-1, -1]])
            g = vectors[h % 4]
            return g[:, :, 0] * x + g[:, :, 1] * y

        height, width = shape
        noise = np.zeros(shape)

        for octave in range(octaves):
            octave_scale = scale * lacunarity ** octave
            x = np.linspace(0, 1, width, endpoint=False)
            y = np.linspace(0, 1, height, endpoint=False)
            X, Y = np.meshgrid(x, y)
            X, Y = X * octave_scale, Y * octave_scale

            xi = X.astype(int)
            yi = Y.astype(int)

            xf = X - xi
            yf = Y - yi

            u = smoothstep(xf)
            v = smoothstep(yf)

            n00 = gradient(np.random.randint(0, 4, (height, width)), xf, yf)
            n01 = gradient(np.random.randint(0, 4, (height, width)), xf, yf - 1)
            n10 = gradient(np.random.randint(0, 4, (height, width)), xf - 1, yf)
            n11 = gradient(np.random.randint(0, 4, (height, width)), xf - 1, yf - 1)

            x1 = lerp(u, n00, n10)
            x2 = lerp(u, n01, n11)
            y1 = lerp(v, x1, x2)

            noise += y1 * persistence ** octave

        return noise / (1 - persistence ** octaves)

    def apply_temperature(self, image, temperature):
        if temperature == 0:
            return image

        temperature /= 100

        new_image = image.copy()

        if temperature > 0:
            new_image[:, :, 0] *= 1 + temperature
            new_image[:, :, 1] *= 1 + temperature * 0.4
        else:
            new_image[:, :, 2] *= 1 - temperature

        return np.clip(new_image, 0, 1)

    def apply_vignette(self, image, vignette_strength):
        if vignette_strength == 0:
            return image

        height, width, _ = image.shape
        x = np.linspace(-1, 1, width)
        y = np.linspace(-1, 1, height)
        X, Y = np.meshgrid(x, y)
        radius = np.sqrt(X ** 2 + Y ** 2)

        # Map vignette strength from 0-10 to 1.800-0.800
        mapped_vignette_strength = 1.8 - (vignette_strength - 1) * 0.1
        vignette = 1 - np.clip(radius / mapped_vignette_strength, 0, 1)

        return np.clip(image * vignette[..., np.newaxis], 0, 1)

class GaussianBlur:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "kernel_size": ("INT", {
                    "default": 5,
                    "min": 1,
                    "max": 31,
                    "step": 1
                }),
                "sigma": ("FLOAT", {
                    "default": 1.0,
                    "min": 0.1,
                    "max": 10.0,
                    "step": 0.1
                }),
            },
        }

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

    CATEGORY = "postprocessing"

    def gaussian_kernel(self, kernel_size: int, sigma: float):
        x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size))
        d = torch.sqrt(x * x + y * y)
        g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
        return g / g.sum()

    def blur(self, image: torch.Tensor, kernel_size: int, sigma: float):
        batch_size, height, width, channels = image.shape

        kernel = self.gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)

        image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
        blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)
        blurred = blurred.permute(0, 2, 3, 1)

        return (blurred,)

class KMeansQuantize:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "colors": ("INT", {
                    "default": 16,
                    "min": 1,
                    "max": 256,
                    "step": 1
                }),
                "precision": ("INT", {
                    "default": 10,
                    "min": 1,
                    "max": 100,
                    "step": 1
                }),
            },
        }

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

    CATEGORY = "postprocessing"

    def kmeans_quantize(self, image: torch.Tensor, colors: int, precision: int):
        batch_size, height, width, _ = image.shape
        result = torch.zeros_like(image)

        for b in range(batch_size):
            tensor_image = image[b].numpy().astype(np.float32)
            img = tensor_image

            height, width, c = img.shape

            criteria = (
                cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,
                precision * 5, 0.01
            )

            img_copy = img.reshape(-1, c)
            _, label, center = cv2.kmeans(
                img_copy, colors, None,
                criteria, 1, cv2.KMEANS_PP_CENTERS
            )

            img = center[label.flatten()].reshape(*img.shape)
            tensor = torch.from_numpy(img).unsqueeze(0)
            result[b] = tensor

        return (result,)

class PixelSort:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("IMAGE",),
                "mask": ("IMAGE",),
                "direction": (["horizontal", "vertical"],),
                "span_limit": ("INT", {
                    "default": None,
                    "min": 0,
                    "max": 100,
                    "step": 5
                }),
                "sort_by": (["hue", "saturation", "value"],),
                "order": (["forward", "backward"],),
            },
        }

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

    CATEGORY = "postprocessing"

    def sort_pixels(self, image: torch.Tensor, mask: torch.Tensor, direction: str, span_limit: int, sort_by: str, order: str):
        horizontal_sort = direction == "horizontal"
        reverse_sorting = order == "backward"
        sort_by = sort_by[0].upper()
        span_limit = span_limit if span_limit > 0 else None

        batch_size = image.shape[0]
        result = torch.zeros_like(image)

        for b in range(batch_size):
            tensor_img = image[b].numpy()
            tensor_mask = mask[b].numpy()
            sorted_image = pixel_sort(tensor_img, tensor_mask, horizontal_sort, span_limit, sort_by, reverse_sorting)
            result[b] = torch.from_numpy(sorted_image)

        return (result,)

class Sharpen:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "kernel_size": ("INT", {
                    "default": 5,
                    "min": 1,
                    "max": 31,
                    "step": 1
                }),
                "alpha": ("FLOAT", {
                    "default": 1.0,
                    "min": 0.1,
                    "max": 5.0,
                    "step": 0.1
                }),
            },
        }

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

    CATEGORY = "postprocessing"

    def sharpen(self, image: torch.Tensor, kernel_size: int, alpha: float):
        batch_size, height, width, channels = image.shape

        kernel = torch.ones((kernel_size, kernel_size), dtype=torch.float32) * -1
        center = kernel_size // 2
        kernel[center, center] = kernel_size**2
        kernel *= alpha
        kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)

        tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
        sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)
        sharpened = sharpened.permute(0, 2, 3, 1)

        result = torch.clamp(sharpened, 0, 1)

        return (result,)

def sort_span(span, sort_by, reverse_sorting):
    if sort_by == 'H':
        key = lambda x: x[1][0]
    elif sort_by == 'S':
        key = lambda x: x[1][1]
    else:
        key = lambda x: x[1][2]

    span = sorted(span, key=key, reverse=reverse_sorting)
    return [x[0] for x in span]


def find_spans(mask, span_limit=None):
    spans = []
    start = None
    for i, value in enumerate(mask):
        if value == 0 and start is None:
            start = i
        if value == 1 and start is not None:
            span_length = i - start
            if span_limit is None or span_length <= span_limit:
                spans.append((start, i))
            start = None
    if start is not None:
        span_length = len(mask) - start
        if span_limit is None or span_length <= span_limit:
            spans.append((start, len(mask)))

    return spans


def pixel_sort(img, mask, horizontal_sort=False, span_limit=None, sort_by='H', reverse_sorting=False):
    height, width, _ = img.shape
    hsv_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV).astype(np.float32)
    hsv_image[..., 0] /= 2.0  # Scale H channel to [0, 1] range

    mask = np.where(mask > 0, 1, 0).astype(np.uint8)

    # loop over the rows and replace contiguous bands of 1s
    for i in range(height if horizontal_sort else width):
        in_band = False
        start = None
        end = None
        for j in range(width if horizontal_sort else height):
            if (mask[i, j] if horizontal_sort else mask[j, i]) == 1:
                if not in_band:
                    in_band = True
                    start = j
                end = j
            else:
                if in_band:
                    for k in range(start+1, end):
                        if horizontal_sort:
                            mask[i, k] = 0
                        else:
                            mask[k, i] = 0
                    in_band = False

        if in_band:
            for k in range(start+1, end):
                if horizontal_sort:
                    mask[i, k] = 0
                else:
                    mask[k, i] = 0

    sorted_image = np.zeros_like(img)
    if horizontal_sort:
        for y in range(height):
            row_mask = mask[y]
            spans = find_spans(row_mask, span_limit)
            sorted_row = np.copy(img[y])
            for start, end in spans:
                span = [(img[y, x], hsv_image[y, x]) for x in range(start, end)]
                sorted_span = sort_span(span, sort_by, reverse_sorting)
                for i, pixel in enumerate(sorted_span):
                    sorted_row[start + i] = pixel
            sorted_image[y] = sorted_row
    else:
        for x in range(width):
            column_mask = mask[:, x]
            spans = find_spans(column_mask, span_limit)
            sorted_column = np.copy(img[:, x])
            for start, end in spans:
                span = [(img[y, x], hsv_image[y, x]) for y in range(start, end)]
                sorted_span = sort_span(span, sort_by, reverse_sorting)
                for i, pixel in enumerate(sorted_span):
                    sorted_column[start + i] = pixel
            sorted_image[:, x] = sorted_column

    return sorted_image

NODE_CLASS_MAPPINGS = {
    "Blend": Blend,
    "CannyEdgeDetection": CannyEdgeDetection,
    "ColorCorrect": ColorCorrect,
    "Dither": Dither,
    "FilmGrain": FilmGrain,
    "GaussianBlur": GaussianBlur,
    "KMeansQuantize": KMeansQuantize,
    "PixelSort": PixelSort,
    "Sharpen": Sharpen,
}