目標(biāo)

• 識(shí)別進(jìn)入攝像頭的人是誰(shuí)

face_recognition

face_recognition 是github上一個(gè)非常有名氣的人臉識(shí)別開源工具包，我們可以通過(guò)以下指令安裝到python環(huán)境內(nèi)

$ pip install face_recognition

代碼的設(shè)計(jì)思路

加載認(rèn)識(shí)的人臉圖

ray_image = face_recognition.load_image_file("ray.jpg")
ray_face_encoding = face_recognition.face_encodings(ray_image)[0]

將認(rèn)識(shí)的人臉變量加到數(shù)組內(nèi)

known_face_encodings = [ ray_face_encoding ]
known_face_names = [ "Ray" ]

全部代碼如下所示：

import face_recognition
import cv2

video_capture = cv2.VideoCapture(0)

ray_image = face_recognition.load_image_file("ray.jpg")
ray_face_encoding = face_recognition.face_encodings(ray_image)[0]

pinky_image = face_recognition.load_image_file("pinky.jpg")
pinky_face_encoding = face_recognition.face_encodings(ray_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    ray_face_encoding,
    pinky_face_encoding
]
known_face_names = [
    "Ray",
    "Pinky"
]

# 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, 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()