feb 13

Author: chenyu76

img/hotori_pyramid.png

@@ -1,67 +1,116 @@
-from PIL import Image
+import argparse
+
 import numpy as np
+from PIL import Image, ImageFilter
 from sklearn.cluster import KMeans
 
-def reduce_colors(input_path, output_path, num_colors):
+
+def reduce_colors(input_path, output_path, num_colors, mode="hsv", smooth=0):
     """
-    减少图片颜色数量
-    
+    减少图片颜色数量并进行平滑处理
+
     :param input_path: 输入文件路径
     :param output_path: 输出文件路径
     :param num_colors: 目标颜色数量
+    :param mode: 颜色模式 'rgb' 或 'hsv'
+    :param smooth: 平滑窗口大小 (0表示不平滑，建议3或5)
     """
-    # 打开图片并保留原始模式（RGB/RGBA）
     img = Image.open(input_path)
     original_mode = img.mode
-    has_alpha = 'A' in original_mode
-    
-    # 转换到RGB/RGBA并提取像素数据
-    if original_mode not in ('RGB', 'RGBA'):
-        img = img.convert('RGBA' if has_alpha else 'RGB')
-    
-    pixels = np.array(img)
-    height, width = img.size[1], img.size[0]
-    
-    # 分离颜色通道和透明度通道
-    if has_alpha:
-        alpha_channel = pixels[:, :, 3]
-        color_pixels = pixels[:, :, :3]
+
+    if original_mode != "RGBA":
+        img = img.convert("RGBA")
+
+    # 分离通道
+    r, g, b, alpha = img.split()
+    rgb_img = Image.merge("RGB", (r, g, b))
+
+    # 颜色空间转换
+    process_mode = mode.upper()
+    if process_mode == "HSV":
+        work_img = rgb_img.convert("HSV")
     else:
-        color_pixels = pixels
-    
-    # 准备K-Means输入数据
-    color_data = color_pixels.reshape(-1, 3)
-    
-    # 执行K-Means聚类
+        work_img = rgb_img
+
+    pixels = np.array(work_img)
+    height, width = pixels.shape[:2]
+    color_data = pixels.reshape(-1, 3)
+
+    # K-Means聚类
     kmeans = KMeans(n_clusters=num_colors, random_state=0, n_init=10)
     kmeans.fit(color_data)
-    
-    # 生成新颜色数据
-    new_colors = kmeans.cluster_centers_[kmeans.labels_]
-    new_colors = new_colors.reshape(color_pixels.shape).astype(np.uint8)
-    
-    # 合并透明度通道
-    if has_alpha:
-        output_pixels = np.dstack((new_colors, alpha_channel))
+
+    # 获取原始标签矩阵
+    labels = kmeans.labels_.reshape(height, width)
+
+    # --- 核心修改：对标签进行平滑处理 ---
+    if smooth > 0:
+        # 将标签转为图像以便使用PIL的滤波器
+        # 标签范围是0 ~ num_colors-1，可以直接存为L模式(8-bit)
+        label_img = Image.fromarray(labels.astype(np.uint8), mode="L")
+
+        # 使用 ModeFilter (众数滤波)
+        # 它会将像素替换为邻域内出现频率最高的标签，非常适合去除噪点且不引入新颜色
+        label_img = label_img.filter(ImageFilter.ModeFilter(size=smooth))
+
+        # 转回numpy数组
+        labels = np.array(label_img)
+    # ---------------------------------
+
+    # 根据（可能平滑过的）标签重构图像
+    # labels.flatten() 将二维标签展平以索引 cluster_centers_
+    new_colors = kmeans.cluster_centers_[labels.flatten()]
+    new_colors = new_colors.reshape(pixels.shape).astype(np.uint8)
+
+    # 重建图像
+    output_img_no_alpha = Image.fromarray(new_colors, mode=process_mode)
+
+    if process_mode == "HSV":
+        output_img_no_alpha = output_img_no_alpha.convert("RGB")
+
+    # 合并Alpha通道
+    r_new, g_new, b_new = output_img_no_alpha.split()
+    if "A" in original_mode:
+        final_output = Image.merge("RGBA", (r_new, g_new, b_new, alpha))
     else:
-        output_pixels = new_colors
-    
-    # 创建并保存图片
-    output_img = Image.fromarray(output_pixels, mode=original_mode)
-    output_img.save(output_path)
-
-if __name__ == '__main__':
-    import argparse
-    
-    parser = argparse.ArgumentParser(description='图片颜色量化工具')
-    parser.add_argument('input', help='输入图片路径')
-    parser.add_argument('output', help='输出图片路径')
-    parser.add_argument('-n', '--num-colors', type=int, required=True,
-                       help='目标颜色数量（2-256）')
-    
+        final_output = Image.merge("RGB", (r_new, g_new, b_new))
+
+    final_output.save(output_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="图片颜色聚类工具")
+    parser.add_argument("input", help="输入图片路径")
+    parser.add_argument("-o", "--output", help="输出图片路径")
+    parser.add_argument(
+        "-n", "--num-colors", type=int, required=True, help="目标颜色数量（2-256）"
+    )
+    parser.add_argument(
+        "-m",
+        "--mode",
+        choices=["rgb", "hsv"],
+        default="hsv",
+        help="颜色聚类模式：rgb 或 hsv (默认: hsv)",
+    )
+    parser.add_argument('-s', '--smooth', type=int, default=0,
+                       help='平滑窗口大小，推荐3或5，0为不平滑 (默认: 0)')
+
     args = parser.parse_args()
-    
+
+    if args.output is None:
+        from pathlib import Path
+
+        smooth_str = ''
+        if args.smooth is not None:
+            smooth_str = f"_smooth_{args.smooth}"
+
+
+        input_path = Path(args.input)
+        # 在文件名末尾添加 color_num_{颜色数量}_{模式}
+        new_filename = f"{input_path.stem}_color_num_{args.num_colors}_{args.mode}{smooth_str}{input_path.suffix}"
+        args.output = str(input_path.parent / new_filename)
+
     if not 2 <= args.num_colors <= 256:
         raise ValueError("颜色数量必须在2到256之间")
-    
-    reduce_colors(args.input, args.output, args.num_colors)
+
+    reduce_colors(args.input, args.output, args.num_colors, args.mode, args.smooth)

img/pixelart/color_reducer.py

@@ -1,3 +1,4 @@
 #!/bin/bash
 DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+rm $DIR/scripts.js
 uglifyjs $DIR/*.js -c -o $DIR/scripts.js

img/pixelart/create_script.js.sh

@@ -56,6 +56,8 @@ A strange picture:
 
 ![what](/img/Tltamic.jpg)
 
+![hotiri](/img/hotori_pyramid.png)
+
 what?
 
 ## Alert Example

test-pages/equationtest.md

@@ -34,6 +34,20 @@ export const gitRepositories = {
  * 我希望能从生成的html文件里找到第一个<p>填进去，但目前还没实现）
  */
 export const recommend = [
+  {
+    date : 20250213,
+    link : "https://github.com/chenyu76/draftsman.nvim",
+    title : "Draftsman.nvim",
+    info :
+        "（Github链接）Neovim插件。<br>A feature-rich ASCII diagramming tool for Neovim. Draw boxes, arrows, lines, and text, and edit them easily. "
+  },
+  {
+    date : 20250126,
+    link : "https://github.com/chenyu76/github-latex-readme",
+    title : "在Github上用LaTeX写README",
+    info :
+        "（Github链接）使用 GitHub Actions 将 LaTeX 文件自动编译为 SVG，从而用 LaTeX 编写 Github 上的 README 文件。"
+  },
   {
     date : 20251221,
     link : "writings/college-reverse-proxy.html",