fix png

data/1.OpenCV初阶/2.二值图像处理/4.连通区域分析/connect.md

@@ -2,11 +2,11 @@
 
 OpenCV 里的连通区域分析可以将具有相同像素值且位置相邻的前景像素点组成的图像区域识别出来。有两种像素相邻的定义：
 
-![](./pixel_region.jpg)
+![](data/1.OpenCV初阶/2.二值图像处理/4.连通区域分析/pixel_region.jpg)
 
 通过OpenCV的连通区域分析算法，我们可以将下图的水鸭子的外框框出来：
 
-![](./duck_box.png)
+![](data/1.OpenCV初阶/2.二值图像处理/4.连通区域分析/duck_box.png)
 
 框架代码如下：
 

main.py

 # -*- coding: utf-8 -*-
 from src.tree import TreeWalker
 from src.doc import DocWalker
+from src.img import ImgWalker
 
 if __name__ == '__main__':
-    walker = TreeWalker("data", "opencv", "OpenCV")
-    walker.walk()
+    # walker = TreeWalker("data", "opencv", "OpenCV")
+    # walker.walk()
 
-    doc = DocWalker('doc')
-    doc.walk()
+    # doc = DocWalker('doc')
+    # doc.walk()
+
+    img = ImgWalker('data')
+    img.walk()

src/img.py

+import os
+from sys import path, version
+from types import new_class
+from .tree import load_json, dump_json
+
+
+def simple_list_md_load(p):
+    with open(p, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        result = []
+        for line in lines:
+            item = line.strip('\n')
+            result.append(item)
+        return result
+
+
+def simple_list_md_dump(p, lines):
+    with open(p, 'w', encoding='utf-8') as f:
+        f.write('\n'.join(lines))
+
+
+class ImgWalker():
+
+    def __init__(self, root) -> None:
+        self.root = root
+
+    def walk(self):
+        for base, dirs, files in os.walk(self.root):
+            for file in files:
+                if file[-3:] == '.md':
+                    md_file = os.path.join(base, file)
+                    md_lines = simple_list_md_load(md_file)
+                    md_new = []
+                    for line in md_lines:
+                        new_line = line.replace('![](./', f'![]({base}/')
+                        md_new.append(new_line)
+                    md_new.append('')
+                    simple_list_md_dump(md_file, md_new)
+                    import sys
+                    sys.exit(0)