不久乘高铁出行，看见高铁火车站已经实现了“刷脸进站”，而且效率很高，很感兴趣，今天抽时间研究一下，其实没那么复杂。
我基本上是基于https://github.com/ageitgey/face_recognition上的资料和源码做一些尝试和试验。
首先，需要配置我们的python环境，我悬着的python27(比较稳定)，具体过程不多说了。
然后，需要安装这次的主角face_recognition库，这个的安装花了我不少时间，需要注意一下几点(按照本人的环境)：
1，首先，安装visual studio 2015，因为vs2015默认只安装c#相关组件，所以需要安装c++相关组件。
ps:vs2015安装c++相关组件的方法：在vs2015中新建c++项目，出现下面场景
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选择第二项，确定后就会自动安装。
为什么需要安装c++,因为安装face_recognition时会先安装dlib,dlib是基于c++的一个库。
2，安装cmake(一个跨平台编译工具),然后需要将cmake的安装路径加入到系统环境变量path中去。
最后，就可以直接在dos中执行安装命令了(需要切换到python目录下的Script目录下)：pip install face_recognition,命令会自动帮你安装好需要的dlib库。
到此为止，我们完成了face_recognition安装工作。
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下面给出几个实例来逐步了解“人脸识别”：
1.一行代码实现“人脸识别”
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在Python目录中新建两个文件夹：分别表示“已知姓名的人”和“未知姓名的人”，图片以额、人名命名，如下：
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接下来，我们通过“认识的人”来识别“不认识的人”：
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结果表明：1.jpg不认识，3.jpg是obama,unkown.jpg中有两个人，一个是obama,另一个不认识
结果还挺准确的！很给力！！
2.识别图片中所有的人脸，并显示出来
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import Image
import face_recognition
image = face_recognition.load_image_file('F:/Python27/Scripts/all.jpg')
face_locations = face_recognition.face_locations(image)
#face_locations =face_recognition.
#face_locations(image,number_of_times_to_upsample=0,model='cnn')
print('i found {} face(s) in this photograph.'.format(len(face_locations)))
for face_location in face_locations:
top,right,bottom,left = face_location
print('A face is located at pixel location Top:{},Left:{},Bottom:{},Right:{}'.format(top,right,bottom,left))
face_image = image[top:bottom,left:right]
pil_image=Image.fromarray(face_image)
pil_image.show()
View Code
避坑指南：import Image需要先安装PIL库，在pycharm中安装的时候会报错（因为pil没有64位的版本），这时我们安装Pillow-PIL就好了。
我们的all.jpg如下：
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执行以下，看看结果：
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没有错，总共12个人脸都被识别出来了！！！
3.给照片“美颜”
face_recognition可以识别人像的下巴，眼睛，鼻子，嘴唇，眼球等区域，包含以下这些个特征:
facial_features = [ 'chin', 'left_eyebrow', 'right_eyebrow', 'nose_bridge', 'nose_tip', 'left_eye', 'right_eye', 'top_lip', 'bottom_lip' ]
利用这些特征属性，可以轻松的给人像“美颜”
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from PIL import Image, ImageDraw
face_recognition
import face_recognition
image = face_recognition.load_image_file("F:/Python27/Scripts/known_people/obama.jpg")
#查找图像中所有面部的所有面部特征
face_landmarks_list = face_recognition.face_landmarks(image)
for face_landmarks in face_landmarks_list:
pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image, 'RGBA')
#让眉毛变成了一场噩梦
d.polygon(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 128))
d.polygon(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 128))
d.line(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 150), width=5)
d.line(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 150), width=5)
#光泽的嘴唇
d.polygon(face_landmarks['top_lip'], fill=(150, 0, 0, 128))
d.polygon(face_landmarks['bottom_lip'], fill=(150, 0, 0, 128))
d.line(face_landmarks['top_lip'], fill=(150, 0, 0, 64), width=8)
d.line(face_landmarks['bottom_lip'], fill=(150, 0, 0, 64), width=8)
#闪耀眼睛
d.polygon(face_landmarks['left_eye'], fill=(255, 255, 255, 30))
d.polygon(face_landmarks['right_eye'], fill=(255, 255, 255, 30))
#涂一些眼线
d.line(face_landmarks['left_eye'] + [face_landmarks['left_eye'][0]], fill=(0, 0, 0, 110), width=6)
d.line(face_landmarks['right_eye'] + [face_landmarks['right_eye'][0]], fill=(0, 0, 0, 110), width=6)
pil_image.show()
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执行下看看结果：
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有点辣眼睛！！！！
4.利用笔记本摄像头识别人像
回到前面说的高铁站的“刷脸”，其实就是基于摄像头的“人像识别”。
这里要调用电脑的摄像头，而且涉及一些计算机视觉系统的计算，所以我们要先安装opencv库，
安装方法：
pip install --upgrade setuptools
pip install numpy Matplotlib
pip install opencv-python
ps:如果报错：EnvironmentError: [Errno 13] Permission denied: 在install后加上--user即可
小技巧：可以在python命令行中用 import site; site.getsitepackages()来确定当前的python环境的site-packages目录的位置
目的：这里我们需要用摄像头识别自己，那么首先需要有一张自己的照片，我将我的照片命名为mike.jpg,然后使用摄像头来识别我自己。
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看看代码：
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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.
obama_image = face_recognition.load_image_file("F:/Python27/Scripts/known_people/obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("F:/Python27/Scripts/known_people/mike.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
obama_face_encoding,
biden_face_encoding
]
known_face_names = [
"Barack Obama",
"mike"
]
# 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()
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只想看看结果：
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看来，我被识别成功了。看起来有点小激动呢。
通过上面四个小例子基本了解face_recognition的用法，这只是小试牛刀，具体在现实中的应用要复杂很多，
我们需要大量的人脸数据，会涉及到机器学习和数学算法等等，而且根据应用场景的不同也会出现很多不同的要求。
这里只是一起学习分享，期待后续关于"人工智能"的内容。
