700 lines
22 KiB
Python
700 lines
22 KiB
Python
import numpy as np
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import torch
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import cv2
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import torch.nn.functional as F
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from PIL import Image, ImageEnhance
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class CannyEdgeDetection:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"lower_threshold": ("INT", {
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"default": 100,
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"min": 0,
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"max": 500,
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"step": 10
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}),
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"upper_threshold": ("INT", {
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"default": 200,
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"min": 0,
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"max": 500,
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"step": 10
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "canny"
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CATEGORY = "postprocessing"
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def canny(self, image: torch.Tensor, lower_threshold: int, upper_threshold: int):
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batch_size, height, width, _ = image.shape
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result = torch.zeros(batch_size, height, width)
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for b in range(batch_size):
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tensor_image = image[b].numpy().copy()
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gray_image = (cv2.cvtColor(tensor_image, cv2.COLOR_RGB2GRAY) * 255).astype(np.uint8)
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canny = cv2.Canny(gray_image, lower_threshold, upper_threshold)
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tensor = torch.from_numpy(canny)
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result[b] = tensor
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return (result,)
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class Dither:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"bits": ("INT", {
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"default": 4,
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"min": 1,
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"max": 8,
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"step": 1
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "dither"
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CATEGORY = "postprocessing"
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def dither(self, image: torch.Tensor, bits: int):
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batch_size, height, width, _ = image.shape
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result = torch.zeros_like(image)
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for b in range(batch_size):
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tensor_image = image[b]
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img = (tensor_image * 255)
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height, width, _ = img.shape
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scale = 255 / (2**bits - 1)
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for y in range(height):
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for x in range(width):
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old_pixel = img[y, x].clone()
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new_pixel = torch.round(old_pixel / scale) * scale
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img[y, x] = new_pixel
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quant_error = old_pixel - new_pixel
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if x + 1 < width:
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img[y, x + 1] += quant_error * 7 / 16
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if y + 1 < height:
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if x - 1 >= 0:
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img[y + 1, x - 1] += quant_error * 3 / 16
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img[y + 1, x] += quant_error * 5 / 16
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if x + 1 < width:
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img[y + 1, x + 1] += quant_error * 1 / 16
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dithered = img / 255
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tensor = dithered.unsqueeze(0)
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result[b] = tensor
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return (result,)
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class FilmGrain:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"intensity": ("FLOAT", {
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"default": 0.2,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01
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}),
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"scale": ("FLOAT", {
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"default": 10,
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"min": 1,
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"max": 100,
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"step": 1
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}),
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"temperature": ("FLOAT", {
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"default": 0.0,
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"min": -100,
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"max": 100,
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"step": 1
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}),
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"vignette": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 10.0,
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"step": 1.0
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "film_grain"
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CATEGORY = "postprocessing"
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def film_grain(self, image: torch.Tensor, intensity: float, scale: float, temperature: float, vignette: float):
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batch_size, height, width, _ = image.shape
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result = torch.zeros_like(image)
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for b in range(batch_size):
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tensor_image = image[b].numpy()
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# Generate Perlin noise with shape (height, width) and scale
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noise = self.generate_perlin_noise((height, width), scale)
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noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise))
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# Apply grain intensity
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noise = (noise * 2 - 1) * intensity
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# Blend the noise with the image
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grain_image = np.clip(tensor_image + noise[:, :, np.newaxis], 0, 1)
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# Apply temperature
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grain_image = self.apply_temperature(grain_image, temperature)
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# Apply vignette
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grain_image = self.apply_vignette(grain_image, vignette)
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tensor = torch.from_numpy(grain_image).unsqueeze(0)
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result[b] = tensor
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return (result,)
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def generate_perlin_noise(self, shape, scale, octaves=4, persistence=0.5, lacunarity=2):
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def smoothstep(t):
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return t * t * (3.0 - 2.0 * t)
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def lerp(t, a, b):
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return a + t * (b - a)
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def gradient(h, x, y):
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vectors = np.array([[1, 1], [-1, 1], [1, -1], [-1, -1]])
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g = vectors[h % 4]
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return g[:, :, 0] * x + g[:, :, 1] * y
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height, width = shape
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noise = np.zeros(shape)
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for octave in range(octaves):
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octave_scale = scale * lacunarity ** octave
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x = np.linspace(0, 1, width, endpoint=False)
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y = np.linspace(0, 1, height, endpoint=False)
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X, Y = np.meshgrid(x, y)
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X, Y = X * octave_scale, Y * octave_scale
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xi = X.astype(int)
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yi = Y.astype(int)
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xf = X - xi
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yf = Y - yi
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u = smoothstep(xf)
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v = smoothstep(yf)
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n00 = gradient(np.random.randint(0, 4, (height, width)), xf, yf)
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n01 = gradient(np.random.randint(0, 4, (height, width)), xf, yf - 1)
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n10 = gradient(np.random.randint(0, 4, (height, width)), xf - 1, yf)
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n11 = gradient(np.random.randint(0, 4, (height, width)), xf - 1, yf - 1)
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x1 = lerp(u, n00, n10)
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x2 = lerp(u, n01, n11)
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y1 = lerp(v, x1, x2)
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noise += y1 * persistence ** octave
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return noise / (1 - persistence ** octaves)
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def apply_temperature(self, image, temperature):
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if temperature == 0:
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return image
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temperature /= 100
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new_image = image.copy()
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if temperature > 0:
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new_image[:, :, 0] *= 1 + temperature
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new_image[:, :, 1] *= 1 + temperature * 0.4
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else:
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new_image[:, :, 2] *= 1 - temperature
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return np.clip(new_image, 0, 1)
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def apply_vignette(self, image, vignette_strength):
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if vignette_strength == 0:
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return image
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height, width, _ = image.shape
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x = np.linspace(-1, 1, width)
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y = np.linspace(-1, 1, height)
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X, Y = np.meshgrid(x, y)
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radius = np.sqrt(X ** 2 + Y ** 2)
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# Map vignette strength from 0-10 to 1.800-0.800
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mapped_vignette_strength = 1.8 - (vignette_strength - 1) * 0.1
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vignette = 1 - np.clip(radius / mapped_vignette_strength, 0, 1)
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return np.clip(image * vignette[..., np.newaxis], 0, 1)
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class GaussianBlur:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"kernel_size": ("INT", {
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"default": 5,
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"min": 1,
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"max": 31,
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"step": 1
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}),
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"sigma": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 10.0,
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"step": 0.1
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "blur"
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CATEGORY = "postprocessing"
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def gaussian_kernel(self, kernel_size: int, sigma: float):
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x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size))
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d = torch.sqrt(x * x + y * y)
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g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
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return g / g.sum()
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def blur(self, image: torch.Tensor, kernel_size: int, sigma: float):
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batch_size, height, width, channels = image.shape
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kernel = self.gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)
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image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
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blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)
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blurred = blurred.permute(0, 2, 3, 1)
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return (blurred,)
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class PixelSort:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"mask": ("IMAGE",),
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"direction": (["horizontal", "vertical"],),
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"span_limit": ("INT", {
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"default": None,
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"min": 0,
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"max": 100,
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"step": 5
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}),
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"sort_by": (["hue", "saturation", "value"],),
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"order": (["forward", "backward"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "sort_pixels"
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CATEGORY = "postprocessing"
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def sort_pixels(self, image: torch.Tensor, mask: torch.Tensor, direction: str, span_limit: int, sort_by: str, order: str):
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horizontal_sort = direction == "horizontal"
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reverse_sorting = order == "backward"
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sort_by = sort_by[0].upper()
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span_limit = span_limit if span_limit > 0 else None
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batch_size = image.shape[0]
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result = torch.zeros_like(image)
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for b in range(batch_size):
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tensor_img = image[b].numpy()
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tensor_mask = mask[b].numpy()
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sorted_image = pixel_sort(tensor_img, tensor_mask, horizontal_sort, span_limit, sort_by, reverse_sorting)
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result[b] = torch.from_numpy(sorted_image)
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return (result,)
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class Sharpen:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"kernel_size": ("INT", {
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"default": 5,
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"min": 1,
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"max": 31,
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"step": 1
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}),
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"alpha": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 5.0,
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"step": 0.1
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "sharpen"
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CATEGORY = "postprocessing"
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def sharpen(self, image: torch.Tensor, kernel_size: int, alpha: float):
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batch_size, height, width, channels = image.shape
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kernel = torch.ones((kernel_size, kernel_size), dtype=torch.float32) * -1
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center = kernel_size // 2
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kernel[center, center] = kernel_size**2
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kernel *= alpha
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kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)
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tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
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sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)
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sharpened = sharpened.permute(0, 2, 3, 1)
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result = torch.clamp(sharpened, 0, 1)
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return (result,)
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class ColorCorrect:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"temperature": ("FLOAT", {
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"default": 0,
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"min": -100,
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"max": 100,
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"step": 5
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}),
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"hue": ("FLOAT", {
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"default": 0,
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"min": -90,
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"max": 90,
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"step": 5
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}),
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"brightness": ("FLOAT", {
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"default": 0,
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"min": -100,
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"max": 100,
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"step": 5
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}),
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"contrast": ("FLOAT", {
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"default": 0,
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"min": -100,
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"max": 100,
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"step": 5
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}),
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"saturation": ("FLOAT", {
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"default": 0,
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"min": -100,
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"max": 100,
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"step": 5
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}),
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"gamma": ("FLOAT", {
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"default": 1,
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"min": 0.2,
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"max": 2.2,
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"step": 0.1
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "color_correct"
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CATEGORY = "postprocessing"
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def color_correct(self, image: torch.Tensor, temperature: float, hue: float, brightness: float, contrast: float, saturation: float, gamma: float):
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batch_size, height, width, _ = image.shape
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result = torch.zeros_like(image)
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for b in range(batch_size):
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tensor_image = image[b].numpy()
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brightness /= 100
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contrast /= 100
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saturation /= 100
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temperature /= 100
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brightness = 1 + brightness
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contrast = 1 + contrast
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saturation = 1 + saturation
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modified_image = Image.fromarray((tensor_image * 255).astype(np.uint8))
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# brightness
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modified_image = ImageEnhance.Brightness(modified_image).enhance(brightness)
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# contrast
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modified_image = ImageEnhance.Contrast(modified_image).enhance(contrast)
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modified_image = np.array(modified_image).astype(np.float32)
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# temperature
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if temperature > 0:
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modified_image[:, :, 0] *= 1 + temperature
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modified_image[:, :, 1] *= 1 + temperature * 0.4
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elif temperature < 0:
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modified_image[:, :, 2] *= 1 - temperature
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modified_image = np.clip(modified_image, 0, 255)/255
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# gamma
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modified_image = np.clip(np.power(modified_image, gamma), 0, 1)
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# saturation
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hls_img = cv2.cvtColor(modified_image, cv2.COLOR_RGB2HLS)
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hls_img[:, :, 2] = np.clip(saturation*hls_img[:, :, 2], 0, 1)
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modified_image = cv2.cvtColor(hls_img, cv2.COLOR_HLS2RGB) * 255
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# hue
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hsv_img = cv2.cvtColor(modified_image, cv2.COLOR_RGB2HSV)
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hsv_img[:, :, 0] = (hsv_img[:, :, 0] + hue) % 360
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modified_image = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2RGB)
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modified_image = modified_image.astype(np.uint8)
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modified_image = modified_image / 255
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modified_image = torch.from_numpy(modified_image).unsqueeze(0)
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result[b] = modified_image
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return (result, )
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class KMeansQuantize:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"colors": ("INT", {
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"default": 16,
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"min": 1,
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"max": 256,
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"step": 1
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}),
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"precision": ("INT", {
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"default": 10,
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"min": 1,
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"max": 100,
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"step": 1
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "kmeans_quantize"
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CATEGORY = "postprocessing"
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def kmeans_quantize(self, image: torch.Tensor, colors: int, precision: int):
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batch_size, height, width, _ = image.shape
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result = torch.zeros_like(image)
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for b in range(batch_size):
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tensor_image = image[b].numpy().astype(np.float32)
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img = tensor_image
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height, width, c = img.shape
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criteria = (
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cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,
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precision * 5, 0.01
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)
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img_copy = img.reshape(-1, c)
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_, label, center = cv2.kmeans(
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img_copy, colors, None,
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criteria, 1, cv2.KMEANS_PP_CENTERS
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)
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img = center[label.flatten()].reshape(*img.shape)
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tensor = torch.from_numpy(img).unsqueeze(0)
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result[b] = tensor
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|
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return (result,)
|
|
|
|
class Blend:
|
|
def __init__(self):
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|
pass
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|
|
|
@classmethod
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|
def INPUT_TYPES(s):
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|
return {
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|
"required": {
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|
"image1": ("IMAGE",),
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|
"image2": ("IMAGE",),
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|
"blend_factor": ("FLOAT", {
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|
"default": 0.5,
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|
"min": 0.0,
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|
"max": 1.0,
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|
"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))
|
|
|
|
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
|
|
|
|
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
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"Blend": Blend,
|
|
"GaussianBlur": GaussianBlur,
|
|
"PixelSort": PixelSort,
|
|
"FilmGrain": FilmGrain,
|
|
"ColorCorrect": ColorCorrect,
|
|
"Sharpen": Sharpen,
|
|
"CannyEdgeDetection": CannyEdgeDetection,
|
|
"KMeansQuantize": KMeansQuantize,
|
|
"Dither": Dither,
|
|
}
|