这是我在 medium 上写的第一篇文章，主题是人脸识别。你可以点击 这里 从我的 Github 存储库中获取本文的代码。

导入

首先，导入 OpenCV （点击这里进行安装）、 numpy 和最重要的 face_recognition。

import face_recognition
import cv2
import numpy as np

访问网络摄像头并加载样例图像

首先，通过 openCV 的 VideoCapture(0) 来使用网络摄像头，然后，加载样例图像或其他想识别的图像，可以使用 face_recog lib 对其进行编码。

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.
pavan_image = face_recognition.load_image_file("Pavan-1.jpg")
pavan_face_encoding = face_recognition.face_encodings(pavan_image)[0]

# Load a second sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

为已知的编码创建一个数组

如标题所述，创建一个数组（就是这么简单）并初始化几个变量。

# Create arrays of known face encodings and their names
known_face_encodings = [
    obama_face_encoding,
    pavan_face_encoding
   
   
]
known_face_names = [
    "Barack Obama",
    "Pavan Kunchala"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

读取、调整大小和处理

在 while True 的状态下读取帧，调整大小（以便图片能实时工作），并处理该图片以便能比较脸部编码。我使用的是最小距离公式，所以能够轻松比较脸部。还有更好的方法，大家可以自己试验一下~

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]

            # 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]

            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()

以上就是所有内容~（截止目前~）

补充： 我知道还有很多需要改进的地方，尤其是代码，我会尽快更新代码。各位可以将建议发送到我的 电子邮件 （内容可以是改进建议或者感兴趣的主题），如果想跟我谈论本文这个主题或任何 ML（机器语言） 或计算机视觉主题，请通过 LinkedIn 私信我（ Linkedin ）~

原文作者：Pavan Kunchala
原文链接：https://medium.com/analytics-vidhya/real-time-face-recognition-using-opencv-face-recog-507d355e0018