猿人学第8题-验证码图文点选

猿人学Web端爬虫攻防刷题平台/猿人学第8题-验证码图文点选/gan_rao_xian.py

@@ -12,8 +12,8 @@ def turn_white(img, r1, g1, b1):
                 img.putpixel((i, j), (255, 255, 255))
 
 
-def processing_image(img_file, standard=205):
-    """ 1.将图片进行降噪处理, 通过二值化去掉后面的背景色并加深文字对比度 """
+def noise_image(img_file):
+    """选出rgp颜色最多2个，进行降噪处理"""
     img = Image.open(img_file)
     # colors所有像素rgb值，counts对应的数量
     colors, counts = np.unique(np.array(img).reshape(-1, 3), axis=0, return_counts=True)
@@ -31,13 +31,63 @@ def processing_image(img_file, standard=205):
         color = colors[subscript]
         # 去除颜色
         turn_white(img, color[0], color[1], color[2])
-
     return img
 
 
+def interference(img_file):
+    """移除干扰线条"""
+    img = Image.open(img_file)
+    # 读取图片高，宽
+    height, width = img.height, img.width
+    # 首先创建一个空列表,用来存放出现在间隔当中的像素点
+    line_list = []
+    # 两个for循环,遍历9000次
+    for x in range(width):
+        for y in range(height):
+            r, g, b = img.getpixel((x, y))
+            if 0 < y < 10 or 96 < y < 105 or 199 < y < 209 or 292 < y < 299:
+                line_list.append([r, g, b])
+            if 0 < x < 20 or 109 < x < 120 or 209 < x < 220:
+                line_list.append([r, g, b])
+
+    arr = np.array(line_list)
+    line_list = np.array(list(set([tuple(t) for t in arr])))
+    # 处理像素 RGB 值
+    for line in line_list:
+        r = line[0]
+        g = line[1]
+        b = line[2]
+        if not (r == 255 and g == 255 and b == 255):
+            print(r, g, b)
+            turn_white(img, r, g, b)
+    return img
+
+
+def binary(img_file, standard=205):
+    """灰度转换"""
+    img = Image.open(img_file)
+    img = img.convert('L')
+    pixels = img.load()
+    for x in range(img.width):
+        for y in range(img.height):
+            if pixels[x, y] > standard:
+                pixels[x, y] = 255
+            else:
+                pixels[x, y] = 0
+    return img
+
+
+def enhance(img_file):
+    """增强字体显示效果"""
+
+
 def run():
-    image_b = processing_image('./img/1.png')
-    image_b.save('./img/1-test.png')
+    image_a = noise_image('./img/1.png')
+    image_a.save('./img/1-test.png')
+    image_b = interference('./img/1-test.png')
+    image_b.save('./img/2-test.png')
+    image_c = binary('./img/2-test.png')
+    image_c.save('./img/3-test.png')
 
 
 if __name__ == '__main__':