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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虚拟环境并激活，可参考Anaconda使用笔记
conda create -n face_python python=3.6
conda env list
conda activate face_python
创建环境
务必选择python3.6版本
2.安装dlib
安装包链接：https://pypi.org/simple/dlib/
dlib
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，安装时候可能需要翻墙...
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()
testAPI
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")
picture
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()
video
参考链接
face_recognition
https://github.com/ageitgey/face_recognition
从零开始人脸识别：face-recognition库
https://blog.csdn.net/qq_34374211/article/details/81481298
Face_ recognition+openCV人脸检测以及识别，附源码
https://blog.csdn.net/Free_FFF/article/details/88674500
Anaconda使用笔记
https://zhuanlan.zhihu.com/p/57612562
Pycharm简易使用教程
https://zhuanlan.zhihu.com/p/52470112
