Update detect_faces.py

data/1.OpenCV初阶/7.OpenCV中的深度学习/3.人脸检测/detect_faces.py

 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,
+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]):
+    # 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]
@@ -36,7 +37,7 @@ for i in range(0, detections.shape[2]):
                           (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)
+    # 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