diff --git "a/data/1.OpenCV\345\210\235\351\230\266/7.OpenCV\344\270\255\347\232\204\346\267\261\345\272\246\345\255\246\344\271\240/3.\344\272\272\350\204\270\346\243\200\346\265\213/detect_faces.py" "b/data/1.OpenCV\345\210\235\351\230\266/7.OpenCV\344\270\255\347\232\204\346\267\261\345\272\246\345\255\246\344\271\240/3.\344\272\272\350\204\270\346\243\200\346\265\213/detect_faces.py"
index 1114242223aacc60e4d4e23b966f420d4d4845cf..d655897b9dfdab888544f831cb7f5bde587ad982 100644
--- "a/data/1.OpenCV\345\210\235\351\230\266/7.OpenCV\344\270\255\347\232\204\346\267\261\345\272\246\345\255\246\344\271\240/3.\344\272\272\350\204\270\346\243\200\346\265\213/detect_faces.py"
+++ "b/data/1.OpenCV\345\210\235\351\230\266/7.OpenCV\344\270\255\347\232\204\346\267\261\345\272\246\345\255\246\344\271\240/3.\344\272\272\350\204\270\346\243\200\346\265\213/detect_faces.py"
@@ -1,42 +1,43 @@
 import numpy as np
 import cv2
 
-low_confidence=0.5
-image_path='2.jpg'
-proto_txt='deploy.proto.txt'
-model_path='res10_300x300_ssd_iter_140000_fp16.caffemodel'
-# 加载模型
-print("[INFO] loading model...")
-net = cv2.dnn.readNetFromCaffe(proto_txt, model_path)
-# 加载输入图像并为图像构建一个输入 blob
-# 将大小调整为固定的 300x300 像素，然后对其进行标准化
-image = cv2.imread(image_path)
-(h, w) = image.shape[:2]
-blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
-	(300, 300), (104.0, 177.0, 123.0))
-# 通过网络传递blob并获得检测和预测
-print("[INFO] computing object detections...")
-net.setInput(blob)
-detections = net.forward()
-# 循环检测
-for i in range(0, detections.shape[2]):
-	# 提取与相关的置信度（即概率）
-	# 预测
-	confidence = detections[0, 0, i, 2]
-	# 通过确保“置信度”来过滤掉弱检测
-	# 大于最小置信度
-	if confidence > low_confidence:
-		# 计算边界框的 (x, y) 坐标
-		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
-		(startX, startY, endX, endY) = box.astype("int")
-		# 绘制人脸的边界框以及概率
-		text = "{:.2f}%".format(confidence * 100)
-		y = startY - 10 if startY - 10 > 10 else startY + 10
-		cv2.rectangle(image, (startX, startY), (endX, endY),
-					  (0, 0, 255), 2)
-		cv2.putText(image, text, (startX, y),
-					cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
-# 展示图片并保存
-cv2.imshow("Output", image)
-cv2.imwrite("01.jpg",image)
-cv2.waitKey(0)
\ No newline at end of file
+if __name__ == '__main__':
+    low_confidence = 0.5
+    image_path = '2.jpg'
+    proto_txt = 'deploy.proto.txt'
+    model_path = 'res10_300x300_ssd_iter_140000_fp16.caffemodel'
+    # 加载模型
+    print("[INFO] loading model...")
+    net = cv2.dnn.readNetFromCaffe(proto_txt, model_path)
+    # 加载输入图像并为图像构建一个输入 blob
+    # 将大小调整为固定的 300x300 像素，然后对其进行标准化
+    image = cv2.imread(image_path)
+    (h, w) = image.shape[:2]
+    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
+                                 (300, 300), (104.0, 177.0, 123.0))
+    # 通过网络传递blob并获得检测和预测
+    print("[INFO] computing object detections...")
+    net.setInput(blob)
+    detections = net.forward()
+    # 循环检测
+    for i in range(0, detections.shape[2]):
+        # 提取与相关的置信度（即概率）
+        # 预测
+        confidence = detections[0, 0, i, 2]
+        # 通过确保“置信度”来过滤掉弱检测
+        # 大于最小置信度
+        if confidence > low_confidence:
+            # 计算边界框的 (x, y) 坐标
+            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+            (startX, startY, endX, endY) = box.astype("int")
+            # 绘制人脸的边界框以及概率
+            text = "{:.2f}%".format(confidence * 100)
+            y = startY - 10 if startY - 10 > 10 else startY + 10
+            cv2.rectangle(image, (startX, startY), (endX, endY),
+                          (0, 0, 255), 2)
+            cv2.putText(image, text, (startX, y),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
+    # 展示图片并保存
+    cv2.imshow("Output", image)
+    cv2.imwrite("01.jpg", image)
+    cv2.waitKey(0)