删除不需要的依赖

BK_Img2Color.py

@@ -4,18 +4,14 @@
 import sys
 import os
 import torch
-import webcolors
-from PIL import Image, ImageDraw, ImageFont
-from colornamer import get_color_from_rgb
-from numpy import ndarray
+
 from sklearn.cluster import KMeans
-import matplotlib.pyplot as plt
-import matplotlib
+
+from numpy import ndarray
 import numpy as np
 
 sys.path.append(os.path.dirname(os.path.dirname(__file__)))
 
-
 class BK_Img2Color:
 
     @classmethod
@@ -64,8 +60,7 @@ def INPUT_TYPES(cls):
     DESCRIPTION = "从输入图像中提取主要颜色，可指定颜色数量，支持排除特定颜色，并可选择生成互补色"
 
     def __init__(self):
-        self.webcolor_dict = {**webcolors.CSS3_HEX_TO_NAMES, **webcolors.CSS2_HEX_TO_NAMES,
-                              **webcolors.CSS21_HEX_TO_NAMES, **webcolors.HTML4_HEX_TO_NAMES}
+        pass
 
     def main(self, input_image: torch.Tensor, num_colors: int = 5, accuracy: int = 80,
              get_complementary_color: bool = False, exclude_colors: str = "", select_color: int = 1) -> Tuple[str, str]:
@@ -112,126 +107,10 @@ def interrogate_colors(self, image: torch.Tensor, num_colors: int) -> List[ndarr
         colors = kmeans.fit(pixels).cluster_centers_ * 255
         return colors
 
-    def generate_color_blocks(self, color_string: str) -> np.ndarray:
-        colors = color_string.split(', ')
-        fig, axs = plt.subplots(1, len(colors), figsize=(
-            len(colors)*2, 2), squeeze=False)
-        axs = axs.flatten()  # 将 axs 转换为一维数组，便于迭代
-
-        for ax, color in zip(axs, colors):
-            ax.imshow(np.full((10, 10, 3), np.array(
-                matplotlib.colors.to_rgb(color))))
-            ax.axis('off')
-
-        plt.tight_layout()
-        # plt.savefig('d:\\color_blocks.png')
-
-        return plt
-
-    def generate_palette(self, img: torch.Tensor, n_colors=16, cell_size=128, padding=0, font_path=None, font_size=15, mode='chart'):
-
-        img = img.resize((img.width // 2, img.height // 2),
-                         resample=Image.BILINEAR)
-        pixels = np.array(img)
-        pixels = pixels.reshape((-1, 3))
-        kmeans = KMeans(n_clusters=n_colors, random_state=0,
-                        n_init='auto').fit(pixels)
-        cluster_centers = np.uint8(kmeans.cluster_centers_)
-
-        # Get the sorted indices based on luminance
-        luminance = np.sqrt(np.dot(cluster_centers, [0.299, 0.587, 0.114]))
-        sorted_indices = np.argsort(luminance)
-
-        # Rearrange the cluster centers and luminance based on sorted indices
-        cluster_centers = cluster_centers[sorted_indices]
-        luminance = luminance[sorted_indices]
-
-        # Group colors by their individual types
-        reds = []
-        greens = []
-        blues = []
-        others = []
-
-        for i in range(n_colors):
-            color = cluster_centers[i]
-            color_type = np.argmax(color)  # Find the dominant color component
-
-            if color_type == 0:
-                reds.append((color, luminance[i]))
-            elif color_type == 1:
-                greens.append((color, luminance[i]))
-            elif color_type == 2:
-                blues.append((color, luminance[i]))
-            else:
-                others.append((color, luminance[i]))
-
-        # Sort each color group by luminance
-        reds.sort(key=lambda x: x[1])
-        greens.sort(key=lambda x: x[1])
-        blues.sort(key=lambda x: x[1])
-        others.sort(key=lambda x: x[1])
-
-        # Combine the sorted color groups
-        sorted_colors = reds + greens + blues + others
-
-        if mode == 'back_to_back':
-            # Calculate the size of the palette image based on the number of colors
-            palette_width = n_colors * cell_size
-            palette_height = cell_size
-        else:
-            # Calculate the number of rows and columns based on the number of colors
-            num_rows = int(np.sqrt(n_colors))
-            num_cols = int(np.ceil(n_colors / num_rows))
-
-            # Calculate the size of the palette image based on the number of rows and columns
-            palette_width = num_cols * cell_size
-            palette_height = num_rows * cell_size
-
-        palette_size = (palette_width, palette_height)
-
-        palette = Image.new('RGB', palette_size, color='white')
-        draw = ImageDraw.Draw(palette)
-        if font_path:
-            font = ImageFont.truetype(font_path, font_size)
-        else:
-            font = ImageFont.load_default()
-
-        hex_palette = []
-        for i, (color, _) in enumerate(sorted_colors):
-            if mode == 'back_to_back':
-                cell_x = i * cell_size
-                cell_y = 0
-            else:
-                row = i % num_rows
-                col = i // num_rows
-                cell_x = col * cell_size
-                cell_y = row * cell_size
-
-            cell_width = cell_size
-            cell_height = cell_size
-
-            color = tuple(color)
-
-            cell = Image.new('RGB', (cell_width, cell_height), color=color)
-            palette.paste(cell, (cell_x, cell_y))
-
-            if mode != 'back_to_back':
-                text_x = cell_x + (cell_width / 2)
-                text_y = cell_y + cell_height + padding
-
-                draw.text((text_x + 1, text_y + 1),
-                          f"R: {color[0]} G: {color[1]} B: {color[2]}", font=font, fill='black', anchor='ms')
-                draw.text(
-                    (text_x, text_y), f"R: {color[0]} G: {color[1]} B: {color[2]}", font=font, fill='white', anchor='ms')
-
-            hex_palette.append('#%02x%02x%02x' % color)
-
-        return palette, '\n'.join(hex_palette)
-
 
 if __name__ == "__main__":
     bk_img2color = BK_Img2Color()
-    input_path = r"E:\\ComfyUI\\input\\ice.png"
+    input_path = r"./GetImageColor.png"
     input_image = Image.open(input_path)
     input_tensor = torch.from_numpy(
         np.array(input_image)).permute(2, 0, 1).float() / 255.0

pyproject.toml

@@ -1,9 +1,9 @@
 [project]
 name = "comfyui_baikong_node"
-description = "Nodes:BK Img To Color, BK Color Selector"
+description = "Nodes for advanced color manipulation and image processing: BK Img To Color, BK Color Selector, BK Color Contrast, BK Color Limit, BK Color Luminance, BK Gradient Image, and BK Image Aspect Filter."
 version = "1.1.5"
 license = { file = "LICENSE" }
-dependencies = ["colornamer==0.2.4", "scikit_learn==1.4.0", "webcolors==1.11"]
+dependencies = ["scikit_learn>=1.4.0",]
 
 [project.urls]
 Repository = "https://github.com/JayLyu/ComfyUI_BaiKong_Node"

requirements.txt

@@ -1,4 +1,2 @@
-colornamer==0.2.4
-scikit_learn==1.4.0
-webcolors==1.11
+scikit_learn>=1.4.0
 matplotlib