feat(nodes): 集成 ImitationHueNode 追色节点 (v1.0.8)
基于 ComfyUI-MingNodes 项目集成追色功能 - 支持图像色彩迁移和追色 - 支持皮肤保护参数 - 支持自动亮度、对比度、饱和度调节 - 支持影调模仿功能 - 支持区域色彩迁移（通过掩码） - 添加 opencv-python 依赖支持
This commit is contained in:
Cyber Dick Lang
+15
-2
@@ -8,13 +8,23 @@ A comprehensive toolkit for ComfyUI that provides various utility nodes for imag
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:license: MIT, see LICENSE for more details.
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"""
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__version__ = "1.0.7"
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__version__ = "1.0.8"
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__author__ = "CyberDickLang"
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__email__ = "286878701@qq.com"
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__url__ = "https://github.com/whmc76"
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# 更新日志
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CHANGELOG = {
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"1.0.8": [
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"新增 ImitationHueNode_UTK 节点（追色节点）:",
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"- 基于 ComfyUI-MingNodes 项目集成",
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"- 支持图像色彩迁移和追色功能",
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"- 支持皮肤保护参数，避免肤色失真",
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"- 支持自动亮度、对比度、饱和度调节",
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"- 支持影调模仿功能",
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"- 支持区域色彩迁移（通过掩码）",
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"- 添加 opencv-python 依赖支持",
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],
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"1.0.7": [
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"改进 ImagePadForOutpaintMasked (UTK) 节点:",
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"- 新增数据模式（data_mode）参数，支持 'pixel' 和 'percent' 两种模式",
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@@ -74,7 +84,7 @@ CHANGELOG = {
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]
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}
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from .nodes.image_nodes_utk import EmptyUnitGenerator_UTK, ImageRatioDetector_UTK, DepthMapBlur_UTK, ImageConcatenate_UTK, ImageConcatenateMulti_UTK, ImagePadForOutpaintMasked_UTK, ImageAndMaskPreview_UTK
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from .nodes.image_nodes_utk import EmptyUnitGenerator_UTK, ImageRatioDetector_UTK, DepthMapBlur_UTK, ImageConcatenate_UTK, ImageConcatenateMulti_UTK, ImagePadForOutpaintMasked_UTK, ImageAndMaskPreview_UTK, ImitationHueNode_UTK
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from .nodes.tool_nodes_utk import ShowInt_UTK, ShowFloat_UTK, ShowList_UTK, ShowText_UTK, PreviewMask_UTK, FillMaskedArea_UTK
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from .nodes.audio_nodes_utk import LoadAudioPlusFromPath_UTK, AudioCropProcessUTK
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from .nodes.mask_nodes_utk import MaskAnd_UTK, MaskSub_UTK, MaskAdd_UTK
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@@ -98,6 +108,7 @@ NODE_CLASS_MAPPINGS = {
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"MaskAnd_UTK": MaskAnd_UTK,
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"MaskSub_UTK": MaskSub_UTK,
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"MaskAdd_UTK": MaskAdd_UTK,
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"ImitationHueNode_UTK": ImitationHueNode_UTK,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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@@ -119,6 +130,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"MaskAnd_UTK": "Mask And (UTK)",
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"MaskSub_UTK": "Mask Sub (UTK)",
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"MaskAdd_UTK": "Mask Add (UTK)",
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"ImitationHueNode_UTK": "Imitation Hue Node (UTK)",
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}
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NODE_CATEGORIES = {
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@@ -136,6 +148,7 @@ NODE_CATEGORIES = {
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"MaskAnd_UTK",
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"MaskSub_UTK",
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"MaskAdd_UTK",
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"ImitationHueNode_UTK",
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]
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}
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+249
-1
@@ -6,6 +6,7 @@ import math
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import random
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import os
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import json
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import cv2
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from comfy.utils import ProgressBar, common_upscale
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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@@ -754,4 +755,251 @@ nodes for example.
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preview, = ImageCompositeMasked.composite(self, image, mask_image, 0, 0, True, mask_adjusted)
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if pass_through:
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return (preview, )
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return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo))
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return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo))
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# -----------------------------------------------------------------------------------
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# ComfyUI-MingNodes - ImitationHueNode
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# https://github.com/mingsky-ai/ComfyUI-MingNodes
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# -----------------------------------------------------------------------------------
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def image_stats(image):
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return np.mean(image[:, :, 1:], axis=(0, 1)), np.std(image[:, :, 1:], axis=(0, 1))
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def is_skin_or_lips(lab_image):
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l, a, b = lab_image[:, :, 0], lab_image[:, :, 1], lab_image[:, :, 2]
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skin = (l > 20) & (l < 250) & (a > 120) & (a < 180) & (b > 120) & (b < 190)
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lips = (l > 20) & (l < 200) & (a > 150) & (b > 140)
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return (skin | lips).astype(np.float32)
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def adjust_brightness(image, factor, mask=None):
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hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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v = hsv[:, :, 2].astype(np.float32)
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if mask is not None:
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mask = mask.squeeze()
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v = np.where(mask > 0, np.clip(v * factor, 0, 255), v)
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else:
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v = np.clip(v * factor, 0, 255)
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hsv[:, :, 2] = v.astype(np.uint8)
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return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
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def adjust_saturation(image, factor, mask=None):
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hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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s = hsv[:, :, 1].astype(np.float32)
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if mask is not None:
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mask = mask.squeeze()
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s = np.where(mask > 0, np.clip(s * factor, 0, 255), s)
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else:
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s = np.clip(s * factor, 0, 255)
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hsv[:, :, 1] = s.astype(np.uint8)
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return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
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def adjust_contrast(image, factor, mask=None):
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mean = np.mean(image)
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adjusted = image.astype(np.float32)
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if mask is not None:
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mask = mask.squeeze()
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mask = np.repeat(mask[:, :, np.newaxis], 3, axis=2)
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adjusted = np.where(mask > 0, np.clip((adjusted - mean) * factor + mean, 0, 255), adjusted)
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else:
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adjusted = np.clip((adjusted - mean) * factor + mean, 0, 255)
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return adjusted.astype(np.uint8)
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def adjust_tone(source, target, tone_strength=0.7, mask=None):
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h, w = target.shape[:2]
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source = cv2.resize(source, (w, h))
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lab_image = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype(np.float32)
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lab_source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype(np.float32)
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l_image = lab_image[:,:,0]
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l_source = lab_source[:,:,0]
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if mask is not None:
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mask = cv2.resize(mask, (w, h))
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mask = mask.astype(np.float32) / 255.0
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l_adjusted = np.copy(l_image)
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mean_source = np.mean(l_source[mask > 0])
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std_source = np.std(l_source[mask > 0])
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mean_target = np.mean(l_image[mask > 0])
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std_target = np.std(l_image[mask > 0])
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l_adjusted[mask > 0] = (l_image[mask > 0] - mean_target) * (std_source / (std_target + 1e-6)) * 0.7 + mean_source
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l_adjusted[mask > 0] = np.clip(l_adjusted[mask > 0], 0, 255)
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clahe = cv2.createCLAHE(clipLimit=2.5, tileGridSize=(8,8))
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l_enhanced = clahe.apply(l_adjusted.astype(np.uint8))
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l_final = cv2.addWeighted(l_adjusted, 0.7, l_enhanced.astype(np.float32), 0.3, 0)
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l_final = np.clip(l_final, 0, 255)
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l_contrast = cv2.addWeighted(l_final, 1.3, l_final, 0, -20)
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l_contrast = np.clip(l_contrast, 0, 255)
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l_image[mask > 0] = l_image[mask > 0] * (1 - tone_strength) + l_contrast[mask > 0] * tone_strength
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else:
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mean_source = np.mean(l_source)
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std_source = np.std(l_source)
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l_mean = np.mean(l_image)
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l_std = np.std(l_image)
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l_adjusted = (l_image - l_mean) * (std_source / (l_std + 1e-6)) * 0.7 + mean_source
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l_adjusted = np.clip(l_adjusted, 0, 255)
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clahe = cv2.createCLAHE(clipLimit=2.5, tileGridSize=(8,8))
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l_enhanced = clahe.apply(l_adjusted.astype(np.uint8))
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l_final = cv2.addWeighted(l_adjusted, 0.7, l_enhanced.astype(np.float32), 0.3, 0)
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l_final = np.clip(l_final, 0, 255)
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l_contrast = cv2.addWeighted(l_final, 1.3, l_final, 0, -20)
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l_contrast = np.clip(l_contrast, 0, 255)
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l_image = l_image * (1 - tone_strength) + l_contrast * tone_strength
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lab_image[:,:,0] = l_image
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return cv2.cvtColor(lab_image.astype(np.uint8), cv2.COLOR_LAB2BGR)
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def tensor2cv2(image: torch.Tensor) -> np.array:
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if image.dim() == 4:
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image = image.squeeze()
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npimage = image.numpy()
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cv2image = np.uint8(npimage * 255 / npimage.max())
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return cv2.cvtColor(cv2image, cv2.COLOR_RGB2BGR)
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def color_transfer(source, target, mask=None, strength=1.0, skin_protection=0.2, auto_brightness=True,
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brightness_range=0.5, auto_contrast=False, contrast_range=0.5,
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auto_saturation=False, saturation_range=0.5, auto_tone=False, tone_strength=0.7):
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source_lab = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype(np.float32)
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target_lab = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype(np.float32)
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src_means, src_stds = image_stats(source_lab)
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tar_means, tar_stds = image_stats(target_lab)
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skin_lips_mask = is_skin_or_lips(target_lab.astype(np.uint8))
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skin_lips_mask = cv2.GaussianBlur(skin_lips_mask, (5, 5), 0)
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if mask is not None:
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mask = cv2.resize(mask, (target.shape[1], target.shape[0]))
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mask = mask.astype(np.float32) / 255.0
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result_lab = target_lab.copy()
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for i in range(1, 3):
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adjusted_channel = (target_lab[:, :, i] - tar_means[i - 1]) * (src_stds[i - 1] / (tar_stds[i - 1] + 1e-6)) + \
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src_means[i - 1]
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adjusted_channel = np.clip(adjusted_channel, 0, 255)
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if mask is not None:
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result_lab[:, :, i] = target_lab[:, :, i] * (1 - mask) + \
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(target_lab[:, :, i] * skin_lips_mask * skin_protection + \
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adjusted_channel * skin_lips_mask * (1 - skin_protection) + \
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adjusted_channel * (1 - skin_lips_mask)) * mask
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else:
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result_lab[:, :, i] = target_lab[:, :, i] * skin_lips_mask * skin_protection + \
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adjusted_channel * skin_lips_mask * (1 - skin_protection) + \
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adjusted_channel * (1 - skin_lips_mask)
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result_bgr = cv2.cvtColor(result_lab.astype(np.uint8), cv2.COLOR_LAB2BGR)
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final_result = cv2.addWeighted(target, 1 - strength, result_bgr, strength, 0)
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if mask is not None:
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mask = cv2.resize(mask, (target.shape[1], target.shape[0]))
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mask = mask.astype(np.float32) / 255.0
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if auto_brightness:
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source_brightness = np.mean(cv2.cvtColor(source, cv2.COLOR_BGR2GRAY))
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target_brightness = np.mean(cv2.cvtColor(target, cv2.COLOR_BGR2GRAY))
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brightness_difference = source_brightness - target_brightness
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brightness_factor = 1.0 + np.clip(brightness_difference / 255 * brightness_range, brightness_range*-1, brightness_range)
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final_result = adjust_brightness(final_result, brightness_factor, mask)
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if auto_contrast:
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source_gray = cv2.cvtColor(source, cv2.COLOR_BGR2GRAY)
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target_gray = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)
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source_contrast = np.std(source_gray)
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target_contrast = np.std(target_gray)
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contrast_difference = source_contrast - target_contrast
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contrast_factor = 1.0 + np.clip(contrast_difference / 255, contrast_range*-1, contrast_range)
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final_result = adjust_contrast(final_result, contrast_factor, mask)
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if auto_saturation:
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source_hsv = cv2.cvtColor(source, cv2.COLOR_BGR2HSV)
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target_hsv = cv2.cvtColor(target, cv2.COLOR_BGR2HSV)
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source_saturation = np.mean(source_hsv[:, :, 1])
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target_saturation = np.mean(target_hsv[:, :, 1])
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saturation_difference = source_saturation - target_saturation
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saturation_factor = 1.0 + np.clip(saturation_difference / 255, saturation_range*-1, saturation_range)
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final_result = adjust_saturation(final_result, saturation_factor, mask)
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if auto_tone:
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final_result = adjust_tone(source, final_result, tone_strength, mask)
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else:
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if auto_brightness:
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source_brightness = np.mean(cv2.cvtColor(source, cv2.COLOR_BGR2GRAY))
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target_brightness = np.mean(cv2.cvtColor(target, cv2.COLOR_BGR2GRAY))
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brightness_difference = source_brightness - target_brightness
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brightness_factor = 1.0 + np.clip(brightness_difference / 255 * brightness_range, brightness_range*-1, brightness_range)
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final_result = adjust_brightness(final_result, brightness_factor)
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if auto_contrast:
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source_gray = cv2.cvtColor(source, cv2.COLOR_BGR2GRAY)
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target_gray = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)
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source_contrast = np.std(source_gray)
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target_contrast = np.std(target_gray)
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contrast_difference = source_contrast - target_contrast
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contrast_factor = 1.0 + np.clip(contrast_difference / 255, contrast_range*-1, contrast_range)
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final_result = adjust_contrast(final_result, contrast_factor)
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if auto_saturation:
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source_hsv = cv2.cvtColor(source, cv2.COLOR_BGR2HSV)
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target_hsv = cv2.cvtColor(target, cv2.COLOR_BGR2HSV)
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source_saturation = np.mean(source_hsv[:, :, 1])
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target_saturation = np.mean(target_hsv[:, :, 1])
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saturation_difference = source_saturation - target_saturation
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saturation_factor = 1.0 + np.clip(saturation_difference / 255, saturation_range*-1, saturation_range)
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final_result = adjust_saturation(final_result, saturation_factor)
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if auto_tone:
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final_result = adjust_tone(source, final_result, tone_strength)
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return final_result
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class ImitationHueNode_UTK:
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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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"imitation_image": ("IMAGE",),
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"target_image": ("IMAGE",),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 1.0, "step": 0.1}),
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"skin_protection": ("FLOAT", {"default": 0.2, "min": 0, "max": 1.0, "step": 0.1}),
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"auto_brightness": ("BOOLEAN", {"default": True}),
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"brightness_range": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.1}),
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"auto_contrast": ("BOOLEAN", {"default": False}),
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"contrast_range": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.1}),
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"auto_saturation": ("BOOLEAN", {"default": False}),
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"saturation_range": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.1}),
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"auto_tone": ("BOOLEAN", {"default": False}),
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"tone_strength": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.1}),
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},
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"optional": {
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"mask": ("MASK", {"default": None}),
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},
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}
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CATEGORY = "UniversalToolkit"
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "imitation_hue"
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def imitation_hue(self, imitation_image, target_image, strength, skin_protection, auto_brightness, brightness_range,
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auto_contrast, contrast_range, auto_saturation, saturation_range, auto_tone, tone_strength,
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mask=None):
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for img in imitation_image:
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img_cv1 = tensor2cv2(img)
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for img in target_image:
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img_cv2 = tensor2cv2(img)
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img_cv3 = None
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if mask is not None:
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for img3 in mask:
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img_cv3 = img3.cpu().numpy()
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img_cv3 = (img_cv3 * 255).astype(np.uint8)
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result_img = color_transfer(img_cv1, img_cv2, img_cv3, strength, skin_protection, auto_brightness,
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brightness_range,auto_contrast, contrast_range, auto_saturation,
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saturation_range, auto_tone, tone_strength)
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result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
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rst = torch.from_numpy(result_img.astype(np.float32) / 255.0).unsqueeze(0)
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return (rst,)
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