实例介绍
【实例简介】
摄像头采集图像与库中图像分析,输出人脸姓名或unknown
【实例截图】
【核心代码】
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | #摄像头采集图像与库中图像分析,输出人脸姓名或unknown import face_recognition import cv2 video_capture = cv2.VideoCapture( 0 ) # Load a sample picture and learn how to recognize it. chenglong_image = face_recognition.load_image_file( "C:/Users/hp/Pictures/chenglong.jpg" ) chenglong_face_encoding = face_recognition.face_encodings(chenglong_image)[ 0 ] # Load a second sample picture and learn how to recognize it. zaixia_image = face_recognition.load_image_file( "C:/Users/hp/Pictures/zaixia.jpg" ) zaixia_face_encoding = face_recognition.face_encodings(zaixia_image)[ 0 ] # Create arrays of known face encodings and their names known_face_encodings = [ chenglong_face_encoding, zaixia_face_encoding ] known_face_names = [ "ChengLong" , "mike" ] # Initialize some variables face_locations = [] face_encodings = [] face_names = [] process_this_frame = True while True : # Grab a single frame of video ret, frame = video_capture.read() # Resize frame of video to 1/4 size for faster face recognition processing small_frame = cv2.resize(frame, ( 0 , 0 ), fx = 0.25 , fy = 0.25 ) # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) rgb_small_frame = small_frame[:, :, :: - 1 ] # Only process every other frame of video to save time if process_this_frame: # Find all the faces and face encodings in the current frame of video face_locations = face_recognition.face_locations(rgb_small_frame) face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations) face_names = [] for face_encoding in face_encodings: # See if the face is a match for the known face(s) matches = face_recognition.compare_faces(known_face_encodings, face_encoding) name = "Unknown" # If a match was found in known_face_encodings, just use the first one. if True in matches: first_match_index = matches.index( True ) name = known_face_names[first_match_index] face_names.append(name) process_this_frame = not process_this_frame # Display the results for (top, right, bottom, left), name in zip (face_locations, face_names): # Scale back up face locations since the frame we detected in was scaled to 1/4 size top * = 4 right * = 4 bottom * = 4 left * = 4 # Draw a box around the face cv2.rectangle(frame, (left, top), (right, bottom), ( 0 , 255 , 0 ), 2 ) # Draw a label with a name below the face cv2.rectangle(frame, (left, bottom - 35 ), (right, bottom), ( 0 , 255 , 0 ), cv2.FILLED) font = cv2.FONT_HERSHEY_DUPLEX cv2.putText(frame, name, (left 6 , bottom - 6 ), font, 1.0 , ( 255 , 255 , 255 ), 1 ) # Display the resulting image cv2.imshow( 'video-exist' , frame) # Hit 'q' on the keyboard to quit! if cv2.waitKey( 1 ) & 0xFF = = ord ( 'q' ): break # Release handle to the webcam video_capture.release() cv2.destroyAllWindows() |
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