Win10使用Face_ recognition+openCV人脸检测以及识别

程序员文章站 2022-07-14 15:15:07

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文章目录

Face_ recognition+openCV人脸识别

Face_ recognition的安装配置
配置Pycharm
Face_recognition API测试
照片中的人脸识别
视频中实时人脸识别
参考链接

Face_ recognition+openCV人脸识别

本章利用face-recognition库进行图片、视频端的人脸识别

环境：win10，python3.6，dlib19.7.0，face_recognition1.2.3

Face_ recognition的安装配置

1.Window下通过Anaconda创建face_python虚拟环境并**，可参考Anaconda使用笔记

conda create -n face_python python=3.6
conda env list
conda activate face_python

Win10使用Face_ recognition+openCV人脸检测以及识别

务必选择python3.6版本

2.安装dlib
安装包链接：https://pypi.org/simple/dlib/
Win10使用Face_ recognition+openCV人脸检测以及识别

下载完成之后，cmd到对应文件夹：

pip install dlib-19.7.0-cp36-cp36m-win_amd64.whl

安装Dlib库，这里Dlib一定要安装19.7.0以上的版本，不然会有很多麻烦。

3.安装face_python

使用pip指令安装face_recognition，安装时候可能需要*…

pip install face_recognition

4.安装opencv_python

pip install opencv_python

配置Pycharm

新建一个项目名为Face_python，把python环境更改到face_python。可参考Pycharm简易使用教程

Face_recognition API测试

import face_recognition
import cv2
image = face_recognition.load_image_file('dilireba_1.jpg')
face_locations = face_recognition.face_locations(image)
cv2.imshow('img',image)
cv2.waitKey()

Win10使用Face_ recognition+openCV人脸检测以及识别

会显示出背景发蓝的照片。

照片中的人脸识别

通过比较两张照片中的人脸特征来判断是否是同一个人
先读取一张照片作为数据训练，获取encoding
然后在读取一张照片获取encoding与之前的比较来判断

import face_recognition
import time

picture_of_me = face_recognition.load_image_file('dilireba_1.jpg')
my_face_encoding = face_recognition.face_encodings(picture_of_me)[0]

start = time.clock()
unknown_picture = face_recognition.load_image_file('E:\\PycharmProjects\\Face_python\\'
                                                   'data\\dataset\\images\\dilireba\\dilireba_4.jpg')
unknown_face_encoding = face_recognition.face_encodings(unknown_picture)[0]

results = face_recognition.compare_faces([my_face_encoding], unknown_face_encoding)
end = time.clock()
print(end - start)
if results[0]:
    print("迪丽热巴")

Win10使用Face_ recognition+openCV人脸检测以及识别

视频中实时人脸识别

通过openCV获取实时视频，从视频中抽帧获取照片来进行识别，识别以后再通过openCV显示。

import face_recognition
import cv2

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

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

# Load a sample picture and learn how to recognize it.
Chaowentao_image = face_recognition.load_image_file("E:\\PycharmProjects\\Face_python\\"
                                                 "data\\dataset\\images\\test\\ChaoWentao.jpg")
Chaowentao_face_encoding = face_recognition.face_encodings(Chaowentao_image)[0]

# Load a second sample picture and learn how to recognize it.
Tongliya_image = face_recognition.load_image_file("E:\\PycharmProjects\\Face_python\\"
                                                  "data\\dataset\\images\\test\\TongLiYa.jpg")
Tongliya_face_encoding = face_recognition.face_encodings(Tongliya_image)[0]

Tangyan_image = face_recognition.load_image_file("E:\\PycharmProjects\\Face_python\\"
                                                 "data\\dataset\\images\\test\\TangYan.jpg")
Tangyan_face_encoding = face_recognition.face_encodings(Tangyan_image)[0]

# Tangyan_image =


# Create arrays of known face encodings and their names
known_face_encodings = [
    Chaowentao_face_encoding,
    Tongliya_face_encoding,
    Tangyan_face_encoding
]
known_face_names = [
    "Chao Wentao",
    "Tong Liya",
    "Tang Yan"
]

# 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, 255), 2)

        # Draw a label with a name below the face
        # cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 255, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 0, 255), 2)

    # 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()

Win10使用Face_ recognition+openCV人脸检测以及识别