人脸识别神经网络实现

前提
安装face_recognition 库 可参考这里
opencv 4.2
numpy

#人脸识别的神经网络实现
import face_recognition
import cv2
import numpy as np


#调取摄像头
video_capture = cv2.VideoCapture(0)


#导入已知图片
obama_image = face_recognition.load_image_file("lif.jpg")
#获得已知图片的面部编码信息
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("9k_.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    obama_face_encoding,
    biden_face_encoding
]
known_face_names = [
    "liu yi fei",
    "liu yi fei"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
fps =30
size = (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)),int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
videoWriter = cv2.VideoWriter('chen.avi', cv2.VideoWriter_fourcc('I','4','2','0'), fps, size)
num=10*fps-1
            #ret, frame = video_capture.read()
while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

   

    # R将视频帧调整为1/4尺寸以加快人脸识别处理
 

    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)
    #将BGR转化为RGB格式
    rgb_small_frame= cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)

   # 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
        #返回脸位置
        face_locations = face_recognition.face_locations(rgb_small_frame)
        #返回面部编码
        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)
            #比较
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "chen dong xiang"

            # # 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]

            # Or instead, use the known face with the smallest distance to the new face
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]
                if num>0:
                    videoWriter.write(frame)
                #success, frame = video_capture.read()
                    num-=1
 

            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, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), 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', 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()