人工智能之基于face_recognition的人脸检测与识别-白红宇

人工智能之基于face_recognition的人脸检测与识别

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发布时间：2019-06-11

本文共 7611 字，大约阅读时间需要 25 分钟。

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。

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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”

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 Imageimport face_recognitionimage = 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()

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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, ImageDrawface_recognitionimport face_recognitionimage = 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：

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 setuptoolspip install numpy Matplotlibpip 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_recognitionimport 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 namesknown_face_encodings = [    obama_face_encoding,    biden_face_encoding]known_face_names = [    "Barack Obama",    "mike"]# Initialize some variablesface_locations = []face_encodings = []face_names = []process_this_frame = Truewhile 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 webcamvideo_capture.release()cv2.destroyAllWindows()

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只想看看结果：