Face_ recognition+openCV人脸识别

Face_ recognition的安装配置

Window下通过Anaconda安装

注意python版本一定选择3.6 ！！！
点击Create，然后等待一段时间虚拟环境创建完毕，再打开
通过指令 activate face_python进入到刚刚创建的虚拟环境

通过指令conda list看一下pip版本
由于安装Dlib库需要的最低pip版本需要>10
所以该Pip版本过低，先升级一下pip
python -m pip install --upgrade pip
然后pip install dlib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
如果电脑上没有配置好boost和cmake会报错
在这里我就直接把我编译好的Dlib.whl文件上传百度网盘了，自己编译报错的话可以直接下载，下载完可以直接pip install 安装
（百度搜索pip安装whl文件，超简单）

安装好Dlib之后
通过指令：pip install face_recognition 安装face_recognition
接下来安装opencv
直接指令：pip install opencv_python就OK啦
Pycharm配置方法：
打开pycharm
然后new一个Project




配置完成
接下来就来测试一下face_re的几个api
先检测一个图片

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()

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('E:\\data\\dataset\\images\\dilireba\\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:\\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] == True:
print("迪丽热巴")

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.
Zhanyan_image = face_recognition.load_image_file("E:\\data\\dataset\\images\\test\\ZhangYan.jpg")
Zhanyan_face_encoding = face_recognition.face_encodings(Zhanyan_image)[0]

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

uuu_image = face_recognition.load_image_file("E:\\data\\dataset\\images\\test\\WangYu.jpg")
uuu_face_encoding = face_recognition.face_encodings(uuu_image)[0]

# uuu_image =

# Create arrays of known face encodings and their names
known_face_encodings = [
Zhanyan_face_encoding,
tongliya_face_encoding,
uuu_face_encoding
]
known_face_names = [
"Zhang Yan",
"Tong Liya",
"Wang Yu"
]

# 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()