12、从摄像头进行实时人脸识别的简单Demo附源码

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12、从摄像头进行实时人脸识别的简单Demo附源码

1. # -*- coding: utf-8 -*-
   
2. import face_recognition
   
3. import cv2
   
4. import numpy as np
   
5. 
   
6. # 12、从摄像头进行实时人脸识别的简单Demo附源码
   
7. 
   
8. 
   
9. # Get a reference to webcam #0 (the default one)
   
10. video_capture = cv2.VideoCapture(0)
    
11. 
    
12. # Load a sample picture and learn how to recognize it.
    
13. obama_image = face_recognition.load_image_file("obama.jpg")
    
14. obama_face_loc = face_recognition.face_locations(obama_image)
    
15. # 只有一个人脸位置
    
16. obama_face_encoding = face_recognition.face_encodings(obama_image, obama_face_loc)[0]
    
17. 
    
18. dfy_image = face_recognition.load_image_file("dfy.jpg")
    
19. dfy_face_loc = face_recognition.face_locations(dfy_image)
    
20. # 问题：face_encodings 是对大图还是小的人脸图获取128维embedding
    
21. # face_image: The image that contains one or more faces 结论：大图 还有一个参数known_face_locations
    
22. dfy_face_encoding = face_recognition.face_encodings(face_image=dfy_image, known_face_locations=dfy_face_loc)[0]
    
23. 
    
24. # Load a second sample picture and learn how to recognize it.
    
25. biden_image = face_recognition.load_image_file("biden.jpg")
    
26. biden_face_loc = face_recognition.face_locations(biden_image)
    
27. biden_face_encoding = face_recognition.face_encodings(biden_image, biden_face_loc)[0]
    
28. 
    
29. # Create arrays of known face encodings and their names
    
30. # 知道的人脸编码 是唯一的每个人
    
31. known_face_encodings = [
    
32.     obama_face_encoding,
    
33.     dfy_face_encoding,
    
34.     biden_face_encoding
    
35. ]
    
36. known_face_names = [
    
37.     "Barack Obama",
    
38.     "Dfy",
    
39.     "Joe Biden"
    
40. ]
    
41. 
    
42. # Initialize some variables
    
43. face_locations = []
    
44. face_encodings = []
    
45. face_names = []
    
46. process_this_frame = True
    
47. 
    
48. while True:
    
49. 
    
50.     # Grab a single frame of video
    
51.     ret, frame = video_capture.read()
    
52. 
    
53.     # Resize frame of video to 1/4 size for faster face recognition processing
    
54.     # 问题：这里的（0,0）代表中心 缩放到原来的四分之一
    
55.     # 默认的插值方法为：双线性插值   cv2.INTER_NEAREST最近邻插值法
    
56.     small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25, interpolation=cv2.INTER_NEAREST)
    
57. 
    
58.     # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    
59.     rgb_small_frame = small_frame[:, :, ::-1]
    
60. 
    
61.     # 仅每隔一帧处理一次视频以节省时间
    
62.     if process_this_frame:
    
63.         # Find all the faces and face encodings in the current frame of video
    
64.         face_locations = face_recognition.face_locations(rgb_small_frame)
    
65.         # 找到所有的人脸 和 人脸的特征编码
    
66.         face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
    
67. 
    
68.         face_names = []  # 这里并没有重复 为了下一次的清空
    
69.         for face_encoding in face_encodings:
    
70.             # See if the face is a match for the known face(s)
    
71.             matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
    
72.             name = "Unknown"
    
73. 
    
74.             # # If a match was found in known_face_encodings, just use the first one.
    
75.             # if True in matches:
    
76.             #     first_match_index = matches.index(True)
    
77.             #     name = known_face_names[first_match_index]
    
78. 
    
79.             # Or instead, use the known face with the smallest distance to the new face
    
80.             face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
    
81.             best_match_index = np.argmin(face_distances)
    
82.             # 同时考虑两个API：compare_faces  face_distance
    
83.             if matches[best_match_index]:
    
84.                 # 识别出来 人脸是谁了
    
85.                 name = known_face_names[best_match_index]
    
86. 
    
87.             face_names.append(name)
    
88. 
    
89.     # 这是为了降低处理速度的
    
90.     process_this_frame = not process_this_frame
    
91. 
    
92.     # Display the results   人脸位置的格式：(y1, x2, y2, x1)
    
93.     for (top, right, bottom, left), name in zip(face_locations, face_names):
    
94.         # Scale back up face locations since the frame we detected in was scaled to 1/4 size
    
95.         top *= 4
    
96.         right *= 4
    
97.         bottom *= 4
    
98.         left *= 4
    
99. 
    
100.         # Draw a box around the face  cv2里坐标必须都是整数
     
101.         cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
     
102. 
     
103.         # Draw a label with a name below the face
     
104.         # 也是画了一个矩形 但是有背景的 cv2.filled
     
105.         cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
     
106.         font = cv2.FONT_HERSHEY_DUPLEX
     
107.         cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
     
108. 
     
109.     # Display the resulting image
     
110.     cv2.imshow('Video', frame)
     
111. 
     
112.     # Hit 'q' on the keyboard to quit!
     
113.     if cv2.waitKey(1) & 0xFF == ord('q'):
     
114.         break
     
115. 
     
116. # Release handle to the webcam
     
117. video_capture.release()
     
118. cv2.destroyAllWindows()
     

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ghshow

您好，怎么提示没有  ModuleNotFoundError: No module named 'face_recognition'