人工智能之基于face_recognition的人脸检测与识别

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

　　　　

　　　　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。

---------------------------------------------------------------分割线----------------------------------------------------------------------------------

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”

 

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：

 接下来，我们通过“认识的人”来识别“不认识的人”：

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

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()

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：

 执行以下，看看结果：

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”

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()

执行下看看结果：

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。

 看看代码：

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()

只想看看结果：

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。