commit GetMainColorsV2 node
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chflame163
@@ -146,7 +146,7 @@ When this error has occurred, please check the network environment.
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<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 />
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* Commit [GetMainColorsV2](#GetMainColorsV2) node, add sorting by color area and output color values and proportions in the preview image. This part of the code was improved by @ HL, thanks.
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* Optimize dependency packages. Optimize some algorithms.
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* Split some nodes of the dependencies that are prone to problems into [ComfyUI_LayerStyle_Advance](#https://github.com/chflame163/ComfyUI_LayerStyle_Advance) repository. Including:
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LayerMask: BiRefNetUltra, LayerMask: BiRefNetUltraV2, LayerMask: LoadBiRefNetModel, LayerMask: LoadBiRefNetModelV2,
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@@ -1099,6 +1099,12 @@ Outputs:
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* preview_image: 5 main color preview images.
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* color_1~color_5: Color value output. Output an RGB string in HEX format.
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### <a id="table1">GetMainColorsV2</a>
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Add sorting by color area to the [GetMainColors](#GetMainColors) node and display color values and color areas in the preview image.
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This part of the code was improved by @ HL, thanks.
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### <a id="table1">ColorName</a>
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Output the most similar color name in the color palette based on the color value.
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@@ -127,6 +127,7 @@ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
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## 更新说明
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<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。
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* 添加 [GetMainColorsV2](#GetMainColorsV2) 节点，增加按颜色面积排序，并在预览图中输出色值和比例。这部分代码由@HL完善，感谢。
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* 优化依赖包。优化部分图形算法。
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* 分拆部分依赖易出问题的节点至[ComfyUI_LayerStyle_Advance](#https://github.com/chflame163/ComfyUI_LayerStyle_Advance)仓库。包括下列节点：
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LayerMask: BiRefNetUltra, LayerMask: BiRefNetUltraV2, LayerMask: LoadBiRefNetModel, LayerMask: LoadBiRefNetModelV2,
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@@ -973,6 +974,12 @@ ImageScaleByAspectRatio的V2升级版
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* preview_image: 5个主色预览图片。
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* color_1~color_5: 色值输出。输出格式为HEX格式的RGB字符串。
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### <a id="table1">GetMainColorsV2</a>
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在[GetMainColors](#GetMainColors)节点基础上增加按颜色面积排序，并在预览图片中显示色值和颜色面积。
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这部分代码由@HL完善，感谢。
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### <a id="table1">ColorName</a>
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根据色值输出调色盘里最近似的颜色名称。
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@@ -1,10 +1,152 @@
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import torch
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from PIL import Image, ImageDraw
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from PIL import Image, ImageDraw, ImageFont
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from collections import Counter
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import colorsys
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from .imagefunc import AnyType, log, tensor2pil, pil2tensor, load_custom_size, gaussian_blur
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from .imagefunc import RGB_to_Hex
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any = AnyType("*")
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class LS_GetMainColorsV2:
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def __init__(self):
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self.NODE_NAME = 'Get Main Colors V2'
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@classmethod
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def INPUT_TYPES(self):
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size_list = ['custom']
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size_list.extend(load_custom_size())
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k_means_algorithm_list = ["lloyd", "elkan"]
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return {
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"required": {
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"image": ("IMAGE",),
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"k_means_algorithm": (k_means_algorithm_list,),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING","STRING", "STRING", "STRING", "STRING",)
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RETURN_NAMES = ("preview_image", "color_1", "color_2", "color_3", "color_4", "color_5",)
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FUNCTION = 'get_main_colors_v2'
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CATEGORY = '😺dzNodes/LayerUtility'
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def get_main_colors_v2(self, image, k_means_algorithm):
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ret_images = []
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grid_width = 512
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grid_height = 64 # Reduced height to fit 10 colors
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for i in range(len(image)):
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pil_img = tensor2pil(torch.unsqueeze(image[i], 0)).convert("RGB")
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blured_image = gaussian_blur(pil_img, (pil_img.width + pil_img.height) // 400)
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accuracy = 60
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num_colors = 5 # Increased to 5 colors
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num_iterations = int(512 * (accuracy / 100))
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original_colors, color_percentages = self.interrogate_colors(
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pil2tensor(blured_image), num_colors=num_colors, algorithm=k_means_algorithm, mix_iter=num_iterations,
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random_state=0)
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main_colors = self.ndarrays_to_colorhex(original_colors)
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# Sort colors by percentage
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sorted_colors = sorted(zip(main_colors, color_percentages), key=lambda x: x[1], reverse=True)
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print(f"sorted_colors={sorted_colors},type={type(sorted_colors)}")
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# Create color info string with HSB values
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color_info = "\n".join([
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f"RGB {color[1:]} HSB {self.rgb_to_hsb(color)[0]:03.0f} {self.rgb_to_hsb(color)[1]:03.0f} {self.rgb_to_hsb(color)[2]:03.0f} 占比 {percentage:.2f}%"
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for color, percentage in sorted_colors
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])
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# draw colors image
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ret_image = Image.new('RGB', size=(grid_width, grid_height * len(main_colors)), color="white")
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draw = ImageDraw.Draw(ret_image)
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# Use default font with size 20
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font = ImageFont.load_default().font_variant(size=20)
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for j, (color, percentage) in enumerate(sorted_colors):
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x1 = 0
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y1 = grid_height * j
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draw.rectangle((x1, y1, x1 + grid_width, y1 + grid_height), fill=color, outline=color)
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# Calculate contrast color
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contrast_color = self.get_contrast_color(color)
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# Add text with contrast color and HSB values
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h, s, b = self.rgb_to_hsb(color)
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text = f"RGB {color[1:]} HSB {h:03.0f} {s:03.0f} {b:03.0f} {percentage:.2f}%"
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# 使用 font.getbbox() 来获取文本的边界框
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bbox = font.getbbox(text)
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text_height = bbox[3] - bbox[1]
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# 计算文本的垂直位置，使其在色块中垂直居中
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text_x = 10 # 固定左边距为10像素
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text_y = y1 + (grid_height - text_height) // 2
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# 绘制文本
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draw.text((text_x, text_y), text, fill=contrast_color, font=font)
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ret_images.append(pil2tensor(ret_image))
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log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0), sorted_colors[0][0], sorted_colors[1][0], sorted_colors[2][0], sorted_colors[3][0], sorted_colors[4][0],)
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def ndarrays_to_colorhex(self, colors: list) -> list:
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return [RGB_to_Hex((int(color[0]), int(color[1]), int(color[2]))) for color in colors]
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def interrogate_colors(self, image: torch.Tensor, num_colors: int, algorithm: str, mix_iter: int,
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random_state: int) -> tuple:
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from sklearn.cluster import KMeans
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pixels = image.view(-1, image.shape[-1]).numpy()
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kmeans = KMeans(
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n_clusters=num_colors,
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algorithm=algorithm,
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max_iter=mix_iter,
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random_state=random_state,
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).fit(pixels)
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colors = kmeans.cluster_centers_ * 255
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# Count pixels in each cluster
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labels = kmeans.labels_
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label_counts = Counter(labels)
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total_pixels = len(labels)
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# Calculate percentages
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color_percentages = [label_counts[i] / total_pixels * 100 for i in range(num_colors)]
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return colors, color_percentages
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def get_contrast_color(self, hex_color):
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# Convert hex to RGB
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rgb = tuple(int(hex_color[i:i + 2], 16) for i in (1, 3, 5))
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# Calculate luminance
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luminance = (0.299 * rgb[0] + 0.587 * rgb[1] + 0.114 * rgb[2]) / 255
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# Choose black or white based on luminance
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if luminance > 0.5:
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return "#000000" # Black for light backgrounds
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else:
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return "#FFFFFF" # White for dark backgrounds
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def rgb_to_hsb(self, hex_color):
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# Convert hex to RGB
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rgb = tuple(int(hex_color[i:i + 2], 16) for i in (1, 3, 5))
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# Convert RGB to HSB
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h, s, v = colorsys.rgb_to_hsv(rgb[0] / 255, rgb[1] / 255, rgb[2] / 255)
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# Convert to degrees and percentages
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h = h * 360
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s = s * 100
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b = v * 100
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return h, s, b
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class LS_GetMainColors:
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def __init__(self):
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@@ -83,9 +225,12 @@ class LS_GetMainColors:
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: GetMainColors": LS_GetMainColors
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"LayerUtility: GetMainColors": LS_GetMainColors,
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"LayerUtility: GetMainColorsV2": LS_GetMainColorsV2,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: GetMainColors": "LayerUtility: Get Main Colors"
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"LayerUtility: GetMainColors": "LayerUtility: Get Main Colors",
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"LayerUtility: GetMainColorsV2": "LayerUtility: Get Main Colors V2",
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}
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+1
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[project]
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name = "comfyui_layerstyle"
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description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress."
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version = "2.0.5"
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version = "2.0.6"
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license = "MIT"
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dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "timm", "colour-science", "transformers", "blend_modes", "huggingface_hub", "loguru"]
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