commit GetMainColors and ColorName node, Duplicate Brightness & Contr…

…ast node as BrightnessContrastV2, and Color of Shadow & Highlight node as ColorofShadowHighlightV2

README.MD

@@ -116,7 +116,8 @@ When this error has occurred, please check the network environment.
 ## Update
 <font size="4">**If the dependency package error after updating, please double clicking ```repair_dependency.bat``` (for Official ComfyUI Protable) or ```repair_dependency_aki.bat``` (for ComfyUI-aki-v1.x) in the plugin folder to reinstall the dependency packages. </font><br /> 
 
-
+* Commit [GetMainColors](#GetMainColors) node, it can obtained 5 main colors of image. Commit [ColorName](#ColorName) node, it can obtain the color name of input color value.
+* Duplicate the [Brightness & Contrast](#Brightness) node as [BrightnessContrastV2](#BrightnessContrastV2), and the [Color of Shadow & Highlight](#Highlight) node as [ColorofShadowHighlight](#HighlightV2) to avoid errors in ComfyUI workflow parsing caused by the "&" character in the node name.
 * Commit [VQAPrompt](#VQAPrompt) and [LoadVQAModel](#LoadVQAModel) nodes. 
 Download the model from [BaiduNetdisk](https://pan.baidu.com/s/1ILREVgM0eFJlkWaYlKsR0g?pwd=yw75) or [huggingface.co/Salesforce/blip-vqa-capfilt-large](https://huggingface.co/Salesforce/blip-vqa-capfilt-large/tree/main) and [huggingface.co/Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base/tree/main) and copy to ```ComfyUI\models\VQA``` folder.
 * [Florence2Ultra](#Florence2Ultra), [Florence2Image2Prompt](#Florence2Image2Prompt) 和 [LoadFlorence2Model](#LoadFlorence2Model) nodes support the MiaoshouAI/Florence-2-large-PromptGen-v1.5 and MiaoshouAI/Florence-2-base-PromptGen-v1.5 model. 
@@ -503,6 +504,8 @@ Node options:
 Node option: 
 * exposure: Exposure value. Higher values indicate brighter image.
 
+### Color of Shadow <a id="table1">HighlightV2</a>
+A replica of the ```Color of Shadow & Highlight``` node, with the "&" character removed from the node name to avoid ComfyUI workflow parsing errors.
 
 ### <a id="table1">ColorTemperature</a>
 ![image](image/color_temperature_example.jpg) 
@@ -554,6 +557,9 @@ Node options:
 * contrast: Value of contrast.
 * saturation: Value of saturation.
 
+### <a id="table1">BrightnessContrastV2</a>
+A replica of the ```Brightness & Contrast``` node, with the "&" character removed from the node name to avoid ComfyUI workflow parsing errors.
+
 ### <a id="table1">RGB</a>
 Adjust the RGB channels of the image.
 
@@ -971,6 +977,32 @@ Output:
 * image: Solid color picture output, the size is the same as the input picture.
 * mask: Mask output.
 
+### <a id="table1">GetMainColors</a>
+Obtain the main color of the image. You can obtain 5 colors.
+![image](image/get_main_color_and_color_name_example.jpg)
+![image](image/get_main_colors_example.jpg)
+
+Node Options:
+![image](image/get_main_color_node.jpg) 
+* image: The image input.
+* k_means_algorithm:K-Means algorithm options. "lloyd" is the standard K-Means algorithm, while "elkan" is the triangle inequality algorithm, suitable for larger images. 
+
+Outputs:
+* preview_image: 5 main color preview images.
+* color_1~color_5: Color value output. Output an RGB string in HEX format.
+
+### <a id="table1">ColorName</a>
+Output the most similar color name in the color palette based on the color value.
+![image](image/color_name_example.jpg)
+
+Node Options:
+![image](image/color_name_node.jpg) 
+* color: Color value input, in HEX format RGB string format.
+* palette: Color palette. ```xkcd``` includes 949 colors, ```css3``` includes 147 colors, and ```html4``` includes 16 colors.
+
+Output:
+* color_name: Color name in string.
+
 ### <a id="table1">ExtendCanvas</a>
 Extend the canvas
 ![image](image/extend_canvas_example.jpg) 

README_CN.MD

@@ -116,6 +116,8 @@ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
 ## 更新说明
 <font size="4">**如果本插件更新后出现依赖包错误，请双击运行插件目录下的```install_requirements.bat```(官方便携包)，或 ```install_requirements_aki.bat```(秋叶整合包) 重新安装依赖包。
 
+* 添加 [GetMainColors](#GetMainColors) 节点，可获得图片的5个主要颜色。 添加 [ColorName](#ColorName) 节点，可获得颜色名称。
+* 复制 [Brightness & Contrast](#Brightness) 节点为 [BrightnessContrastV2](#BrightnessContrastV2), [Color of Shadow & Highlight](#Highlight) 节点为 [ColorofShadowHighlight](#HighlightV2), 避免节点名称中的"&"字符造成ComfyUI工作流解析错误。 
 * 添加 [VQAPrompt](#VQAPrompt) 和 [LoadVQAModel](#LoadVQAModel) 节点。 
 请从[百度网盘](https://pan.baidu.com/s/1ILREVgM0eFJlkWaYlKsR0g?pwd=yw75) 或者 [huggingface.co/Salesforce/blip-vqa-capfilt-large](https://huggingface.co/Salesforce/blip-vqa-capfilt-large/tree/main) 和 [huggingface.co/Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base/tree/main) 下载全部模型文件并放到 ```ComfyUI\models\VQA```文件夹。
 * [Florence2Ultra](#Florence2Ultra), [Florence2Image2Prompt](#Florence2Image2Prompt) 和 [LoadFlorence2Model](#LoadFlorence2Model) 节点支持MiaoshouAI/Florence-2-large-PromptGen-v1.5 和 MiaoshouAI/Florence-2-base-PromptGen-v1.5 模型。
@@ -497,6 +499,9 @@ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
 * highlight_level_offset: 亮部取值的偏移量，更小的数值使更多靠近阴暗的区域纳入亮部。
 * highlight_range: 亮部的过渡范围。
 
+### Color of Shadow <a id="table1">HighlightV2</a>
+Color of Shadow & Highlight 节点的复制品，去掉了节点名称中的"&"字符以避免ComfyUI工作流解析错误。
+
 ### <a id="table1">ColorTemperature</a>
 ![image](image/color_temperature_example.jpg) 
 改变图像的色温。
@@ -547,6 +552,8 @@ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
 * contrast: 图像的对比度。
 * saturation: 图像的色彩饱和度。
 
+### <a id="table1">BrightnessContrastV2</a>
+```Brightness & Contrast```节点的复制品，去掉了节点名称中的"&"字符以避免ComfyUI工作流解析错误。
 
 ### <a id="table1">RGB</a>
 对图像的RGB各通道进行调整。
@@ -956,6 +963,32 @@ GetColorTone的V2升级版。可以指定获取主体或背景的主色或平均
 * image: 纯色图片输出, 尺寸与输入的图片相同。
 * mask: 遮罩输出。
 
+### <a id="table1">GetMainColors</a>
+获得图片的主色。可获得5个颜色。
+![image](image/get_main_color_and_color_name_example.jpg)
+![image](image/get_main_colors_example.jpg)
+
+节点选项:
+![image](image/get_main_color_node.jpg) 
+* image: 图片输入。
+* k_means_algorithm: K-Means 算法选项。 "lloyd" 为标准K-Means算法， "elkan" 为三角不等式算法，适合更大的图片。
+
+输出:
+* preview_image: 5个主色预览图片。
+* color_1~color_5: 色值输出。输出格式为HEX格式的RGB字符串。
+
+### <a id="table1">ColorName</a>
+根据色值输出调色盘里最近似的颜色名称。
+![image](image/color_name_example.jpg)
+
+节点选项:
+![image](image/color_name_node.jpg) 
+* color: 颜色色值输入，格式为HEX格式的RGB字符串。
+* palette: 调色板。 ```xkcd```包括了949种颜色， ```css3```包括了147种颜色, ```html4```包括了16种颜色。
+
+输出:
+* color_name: 颜色名称，格式为字符串。
+
 ### <a id="table1">ExtendCanvas</a>
 扩展画布。
 ![image](image/extend_canvas_example.jpg) 

py/color_correct_brightness_and_contrast.py

@@ -1,11 +1,11 @@
 from .imagefunc import *
 
-NODE_NAME = 'Brightness & Contrast'
+
 
 class ColorCorrectBrightnessAndContrast:
 
  def __init__(self):
- pass
+ self.NODE_NAME = 'Brightness & Contrast'
 
  @classmethod
  def INPUT_TYPES(self):
@@ -48,13 +48,64 @@ def color_correct_brightness_and_contrast(self, image, brightness, contrast, sat
  ret_image = RGB2RGBA(ret_image, __image.split()[-1])
  ret_images.append(pil2tensor(ret_image))
 
- log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+ log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+ return (torch.cat(ret_images, dim=0),)
+
+
+# 节点名称去掉“&”
+class LS_ColorCorrect_Brightness_And_Contrast_V2:
+ def __init__(self):
+ self.NODE_NAME = 'Brightness Contrast V2'
+
+ @classmethod
+ def INPUT_TYPES(self):
+
+ return {
+ "required": {
+ "image": ("IMAGE", ), #
+ "brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+ "contrast": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+ "saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+ },
+ "optional": {
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = 'color_correct_brightness_contrast_v2'
+ CATEGORY = '😺dzNodes/LayerColor'
+
+ def color_correct_brightness_contrast_v2(self, image, brightness, contrast, saturation):
+
+ ret_images = []
+
+ for i in image:
+ i = torch.unsqueeze(i,0)
+ __image = tensor2pil(i)
+ ret_image = __image.convert('RGB')
+ if brightness != 1:
+ brightness_image = ImageEnhance.Brightness(ret_image)
+ ret_image = brightness_image.enhance(factor=brightness)
+ if contrast != 1:
+ contrast_image = ImageEnhance.Contrast(ret_image)
+ ret_image = contrast_image.enhance(factor=contrast)
+ if saturation != 1:
+ color_image = ImageEnhance.Color(ret_image)
+ ret_image = color_image.enhance(factor=saturation)
+
+ if __image.mode == 'RGBA':
+ ret_image = RGB2RGBA(ret_image, __image.split()[-1])
+ ret_images.append(pil2tensor(ret_image))
+
+ log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
  return (torch.cat(ret_images, dim=0),)
 
 NODE_CLASS_MAPPINGS = {
- "LayerColor: Brightness & Contrast": ColorCorrectBrightnessAndContrast
+ "LayerColor: Brightness & Contrast": ColorCorrectBrightnessAndContrast,
+ "LayerColor: BrightnessContrastV2": LS_ColorCorrect_Brightness_And_Contrast_V2
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
- "LayerColor: Brightness & Contrast": "LayerColor: Brightness & Contrast"
+ "LayerColor: BrightnessContrastV2": "LayerColor: Brightness Contrast V2"
 }

py/color_correct_shadow_and_highlight.py

@@ -1,17 +1,18 @@
 from .imagefunc import *
 
-NODE_NAME = 'Color of Shadow & Highlight'
+
 
 def norm_value(value):
  if value < 0.01:
  value = 0.01
  if value > 0.99:
  value = 0.99
  return value
+
 class ColorCorrectShadowAndHighlight:
 
  def __init__(self):
- pass
+ self.NODE_NAME = 'Color of Shadow & Highlight'
 
  @classmethod
  def INPUT_TYPES(self):
@@ -118,10 +119,125 @@ def color_shadow_and_highlight(self, image,
  log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
  return (torch.cat(ret_images, dim=0),)
 
+
+# 名称去掉“&”
+class LS_ColorCorrectShadow_And_Highlight_V2:
+
+ def __init__(self):
+ self.NODE_NAME = 'Color of Shadow & Highlight V2'
+
+ @classmethod
+ def INPUT_TYPES(self):
+
+ return {
+ "required": {
+ "image": ("IMAGE", ),
+ "shadow_brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+ "shadow_saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+ "shadow_hue": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
+ "shadow_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
+ "shadow_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
+ "highlight_brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+ "highlight_saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+ "highlight_hue": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
+ "highlight_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
+ "highlight_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
+ },
+ "optional": {
+ "mask": ("MASK",), #
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ RETURN_NAMES = ("image",)
+ FUNCTION = 'color_shadow_and_highlight_v2'
+ CATEGORY = '😺dzNodes/LayerColor'
+
+ def color_shadow_and_highlight_v2(self, image,
+ shadow_brightness, shadow_saturation,
+ shadow_level_offset, shadow_range, shadow_hue,
+ highlight_brightness, highlight_saturation, highlight_hue,
+ highlight_level_offset, highlight_range,
+ mask=None
+ ):
+
+ ret_images = []
+ input_images = []
+ input_masks = []
+
+ for i in image:
+ input_images.append(torch.unsqueeze(i, 0))
+ m = tensor2pil(i)
+ if m.mode == 'RGBA':
+ input_masks.append(m.split()[-1])
+ else:
+ input_masks.append(Image.new('L', size=m.size, color='white'))
+ if mask is not None:
+ if mask.dim() == 2:
+ mask = torch.unsqueeze(mask, 0)
+ input_masks = []
+ for m in mask:
+ input_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
+ max_batch = max(len(input_images), len(input_masks))
+
+ for i in range(max_batch):
+ _image = input_images[i] if i < len(input_images) else input_images[-1]
+ _image = tensor2pil(_image).convert('RGB')
+ _mask = input_masks[i] if i < len(input_masks) else input_masks[-1]
+
+ avg_gray = get_gray_average(_image, _mask)
+ shadow_level, highlight_level = calculate_shadow_highlight_level(avg_gray)
+ _canvas = _image.copy()
+ if shadow_saturation !=1 or shadow_brightness !=1 or shadow_hue:
+ shadow_low_threshold = (shadow_level + shadow_level_offset) / 100 + shadow_range / 2
+ shadow_low_threshold = norm_value(shadow_low_threshold)
+ shadow_high_threshold = (shadow_level + shadow_level_offset) / 100 - shadow_range / 2
+ shadow_high_threshold = norm_value(shadow_high_threshold)
+ _shadow_mask = luminance_keyer(_image, shadow_low_threshold, shadow_high_threshold)
+ _shadow = _image.copy()
+ if shadow_brightness != 1:
+ brightness_image = ImageEnhance.Brightness(_shadow)
+ _shadow = brightness_image.enhance(factor=shadow_brightness)
+ if shadow_saturation != 1:
+ color_image = ImageEnhance.Color(_shadow)
+ _shadow = color_image.enhance(factor=shadow_saturation)
+ if shadow_hue:
+ _h, _s, _v = _shadow.convert('HSV').split()
+ _h = image_hue_offset(_h, shadow_hue)
+ _shadow = image_channel_merge((_h, _s, _v), 'HSV')
+ _canvas.paste(_shadow, mask=gaussian_blur(_shadow_mask,(_shadow_mask.width + _shadow_mask.height)//800))
+ _canvas.paste(_image, mask=ImageChops.invert(_mask))
+ if highlight_saturation != 1 or highlight_brightness != 1 or highlight_hue:
+ highlight_low_threshold = (highlight_level + highlight_level_offset) / 100 - highlight_range / 2
+ highlight_low_threshold = norm_value(highlight_low_threshold)
+ highlight_high_threshold = (highlight_level + highlight_level_offset) / 100 + highlight_range / 2
+ highlight_high_threshold = norm_value(highlight_high_threshold)
+ _highlight_mask = luminance_keyer(_image, highlight_low_threshold, highlight_high_threshold)
+ _highlight = _image.copy()
+ if highlight_brightness != 1:
+ brightness_image = ImageEnhance.Brightness(_highlight)
+ _highlight = brightness_image.enhance(factor=highlight_brightness)
+ if highlight_saturation != 1:
+ color_image = ImageEnhance.Color(_highlight)
+ _highlight = color_image.enhance(factor=highlight_saturation)
+ if highlight_hue:
+ _h, _s, _v = _highlight.convert('HSV').split()
+ _h = image_hue_offset(_h, highlight_hue)
+ _highlight = image_channel_merge((_h, _s, _v), 'HSV')
+ _canvas.paste(_highlight, mask=gaussian_blur(_highlight_mask, (_highlight_mask.width + _highlight_mask.height)//800))
+ _canvas.paste(_image, mask=ImageChops.invert(_mask))
+ ret_images.append(pil2tensor(_canvas))
+
+ log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+ return (torch.cat(ret_images, dim=0),)
+
+
 NODE_CLASS_MAPPINGS = {
- "LayerColor: Color of Shadow & Highlight": ColorCorrectShadowAndHighlight
+ "LayerColor: Color of Shadow & Highlight": ColorCorrectShadowAndHighlight,
+ "LayerColor: ColorofShadowHighlightV2": LS_ColorCorrectShadow_And_Highlight_V2
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
- "LayerColor: Color of Shadow & Highlight": "LayerColor: Color of Shadow & Highlight"
+ "LayerColor: Color of Shadow & Highlight": "LayerColor: Color of Shadow & Highlight",
+ "LayerColor: ColorofShadowHighlightV2": "LayerColor: Colorof Shadow Highlight V2"
 }