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https://github.com/comfyanonymous/ComfyUI.git
synced 2026-05-10 09:12:31 +08:00
adds dither sharpen blur canny color and kmeans
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EllangoK
parent
9a27030519
commit
406f2872db
330
comfy_extras/nodes_post_processing.py
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330
comfy_extras/nodes_post_processing.py
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@ -0,0 +1,330 @@
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import numpy as np
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import cv2
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import torch
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from PIL import Image, ImageEnhance
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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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tensor_image = image.numpy()[0]
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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].copy()
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new_pixel = np.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 = torch.from_numpy(dithered).unsqueeze(0)
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return (tensor,)
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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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tensor_image = image.numpy()[0].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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result = center[label.flatten()].reshape(*img.shape)
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tensor = torch.from_numpy(result).unsqueeze(0)
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return (tensor,)
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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 blur(self, image: torch.Tensor, kernel_size: int, sigma: float):
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tensor_image = image.numpy()[0]
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blurred = cv2.GaussianBlur(tensor_image, (kernel_size, kernel_size), sigma)
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tensor = torch.from_numpy(blurred).unsqueeze(0)
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return (tensor,)
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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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tensor_image = image.numpy()[0]
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kernel = np.ones((kernel_size, kernel_size), dtype=np.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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sharpened = cv2.filter2D(tensor_image, -1, kernel)
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tensor = torch.from_numpy(sharpened).unsqueeze(0)
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tensor = torch.clamp(tensor, 0, 1)
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return (tensor,)
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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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tensor_image = image.numpy()[0]
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gray_image = (cv2.cvtColor(tensor_image, cv2.COLOR_BGR2GRAY) * 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).unsqueeze(0)
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return (tensor,)
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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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tensor_image = image.numpy()[0]
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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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return (modified_image, )
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NODE_CLASS_MAPPINGS = {
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"Dither": Dither,
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"KMeansQuantize": KMeansQuantize,
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"GaussianBlur": GaussianBlur,
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"Sharpen": Sharpen,
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"CannyEdgeDetection": CannyEdgeDetection,
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"ColorCorrect": ColorCorrect,
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}
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3
nodes.py
3
nodes.py
@ -1052,4 +1052,5 @@ def load_custom_nodes():
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load_custom_nodes()
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
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load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
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