第7次实践作业

目录

• （1） 在树莓派中安装opencv库
  • • （1）安装依赖
    • （2）下载OpenCV源码
    • （3）安装pip
    • （4）安装Python虚拟机
    • （5）编译OpenCV
    • （6）安装opencv
    • （7）验证安装
• （2） 使用opencv和python控制树莓派的摄像头
• （3） 利用树莓派的摄像头实现人脸识别
  • • （1）基于picamera的人脸识别
    • （2）基于opencv的人脸识别
• （4） 结合微服务的进阶任务
  • • （1）使用微服务，部署opencv的docker容器（要能够支持arm），并在opencv的docker容器中跑通（3）的示例代码facerec_on_raspberry_pi.py
    • （2）选做：在opencv的docker容器中跑通步骤（3）的示例代码facerec_from_webcam_faster.py
• （5） 以小组为单位，发表一篇博客，记录遇到的问题和解决方法，提供小组成员名单以及在线协作的图片
  • • （1）踩过的坑
    • （2）小组成员名单
    • （3）在线协作图片

（1） 在树莓派中安装opencv库

参考1 参考2

（1）安装依赖

sudo apt-get update && sudo apt-get upgrade
sudo apt-get install build-essential cmake pkg-config
sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev
sudo apt-get install libgtk2.0-dev libgtk-3-dev
sudo apt-get install libatlas-base-dev gfortran
sudo apt-get install python2.7-dev python3-dev

偶尔有几个卡壳的就加上--fix-missing再执行几次

（2）下载OpenCV源码

cd ~
wget -O opencv.zip https://github.com/Itseez/opencv/archive/4.1.2.zip
unzip opencv.zip
wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/4.1.2.zip
unzip opencv_contrib.zip
#最好挂tz加快速度

（3）安装pip

wget https://bootstrap.pypa.io/get-pip.py
sudo python get-pip.py
sudo python3 get-pip.py

（4）安装Python虚拟机

sudo pip install virtualenv virtualenvwrapper
sudo rm -rf ~/.cache/pip

• 配置~/.profile ，添加如下，并使用source ~/.profile生效

export WORKON_HOME=$HOME/.virtualenvs
export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
export VIRTUALENVWRAPPER_VIRTUALENV=/usr/local/bin/virtualenv
source /usr/local/bin/virtualenvwrapper.sh
export VIRTUALENVWRAPPER_ENV_BIN_DIR=bin

• 使用Python3安装虚拟机

 mkvirtualenv cv -p python3

• 进入虚拟机

source ~/.profile && workon cv

​ 可以看到前面有cv虚拟机的标识

• 安装numpy

pip install numpy

（5）编译OpenCV

cd ~/opencv-4.1.2/
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D INSTALL_PYTHON_EXAMPLES=ON \
    -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-4.1.2/modules \
    -D BUILD_EXAMPLES=ON ..

要确保路径齐全

• 增大交换区内存到1024

• 重启swap服务并开始编译

  sudo /etc/init.d/dphys-swapfile stop && sudo /etc/init.d/dphys-swapfile start
  make -j4
  

• 1小时+后看到编译完成

（6）安装opencv

sudo make install
sudo ldconfig

• 检查安装位置

  

（7）验证安装

• 退出python虚拟机命令deactivate

  

（2） 使用opencv和python控制树莓派的摄像头

参考1 参考2

• 安装picamera（虚拟机环境下）

  source ~/.profile
  workon cv
  pip install "picamera[array]"
  

  

• 使用python和opencv控制摄像头

  # import the necessary packages
  from picamera.array import PiRGBArray
  from picamera import PiCamera
  import time
  import cv2
  # initialize the camera and grab a reference to the raw camera capture
  camera = PiCamera()
  rawCapture = PiRGBArray(camera)
  # allow the camera to warmup
  time.sleep(1)
  # grab an image from the camera
  camera.capture(rawCapture, format="bgr")
  image = rawCapture.array
  # display the image on screen and wait for a keypress
  cv2.imshow("Image", image)
  cv2.waitKey(0)
  

  

（3） 利用树莓派的摄像头实现人脸识别

​ 参考1

• 安装所需库

  pip install dlib &&
  pip install face_recognition &&
  pip install numpy	#前面安装过就不用了
  

  

• 同目录下放置一张用于识别test.jpg

  

（1）基于picamera的人脸识别

isLeiJun.py

import face_recognition
import picamera
import numpy as np

# Get a reference to the Raspberry Pi camera.
# If this fails, make sure you have a camera connected to the RPi and that you
# enabled your camera in raspi-config and rebooted first.
camera = picamera.PiCamera()
camera.resolution = (320, 240)
output = np.empty((240, 320, 3), dtype=np.uint8)

# Load a sample picture and learn how to recognize it.
print("Loading known face image(s)")
obama_image = face_recognition.load_image_file("test.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

# Initialize some variables
face_locations = []
face_encodings = []

while True:
    print("Capturing image.")
    # Grab a single frame of video from the RPi camera as a numpy array
    camera.capture(output, format="rgb")

    # Find all the faces and face encodings in the current frame of video
    face_locations = face_recognition.face_locations(output)
    print("Found {} faces in image.".format(len(face_locations)))
    face_encodings = face_recognition.face_encodings(output, face_locations)

    # Loop over each face found in the frame to see if it's someone we know.
    for face_encoding in face_encodings:
        # See if the face is a match for the known face(s)
        match = face_recognition.compare_faces([obama_face_encoding], face_encoding)
        name = "<Unknown Person>"

        if match[0]:
            name = "雷军"

        print("I see someone named {}!".format(name))

（2）基于opencv的人脸识别

recognition_opcv.py

import face_recognition
import cv2
import numpy as np

# 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.
leijun_image = face_recognition.load_image_file("leijun.jpg")
leijun_face_encoding = face_recognition.face_encodings(leijun_image)[0]

# Load a second sample picture and learn how to recognize it.
liuzuohu_image = face_recognition.load_image_file("liuzuohu.jpg")
liuzuohu_face_encoding = face_recognition.face_encodings(liuzuohu_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    leijun_face_encoding,
    liuzuohu_face_encoding
]
known_face_names = [
    "LeiJun",
    "LiuZuoHu"
]

# 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]

            # Or instead, use the known face with the smallest distance to the new face
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_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()

（4） 结合微服务的进阶任务

（1）使用微服务，部署opencv的docker容器（要能够支持arm），并在opencv的docker容器中跑通（3）的示例代码facerec_on_raspberry_pi.py

• 准备操作

  #退出python虚拟机
  deactivate
  #脚本安装docker
  sudo curl -sSL https://get.docker.com | sh
  #填加用户到docker组
  sudo usermod -aG docker pi
  #重新登陆以用户组生效
  exit && ssh pi@raspiberry
  #验证docker版本
  docker --version
  

• 创建对应目录

  

• 拉取arm可用的docker镜像

  docker pull sixsq/opencv-python
  

• 进入容器并安装所需库

  docker run -it [imageid] /bin/bash
  pip install "picamera[array]" dlib face_recognition
  

  

• comiit镜像

  docker commit [containerid] my-opencv
  

• 自定义镜像

  • Dockerfile

    FROM my-opencv
    
    MAINTAINER GROUP13
    
    RUN mkdir /myapp
    
    WORKDIR /myapp
    
    ENTRYPOINT ["python3"]
    

  • 生成镜像

    docker build -t my-opencv-test .
    

  • 运行脚本

    docker run -it --rm --name my-running-py -v ${PWD}/workdir:/myapp --device=/dev/vchiq --device=/dev/video0 my-opencv-test isLeiJun.py
    

    

（2）选做：在opencv的docker容器中跑通步骤（3）的示例代码facerec_from_webcam_faster.py

采用的是在Windows上通过ssh连接树莓派并传送X11数据 参考1 参考2

• 环境准备

  • windows端安装XMing

  • 检测ssh配置文件中X11是否开启 cat /etc/ssh/sshd_config

  • 支持X11转发的ssh客户端

    

    由于安装XMing的时候创建了链接到putty，所以此处打个勾就行

  • 查看DISPLAY环境变量值printenv

    

• 编辑启动脚本 run.sh

  xhost +	#允许来自任何主机的连接
  docker run -it \
          --rm \
          -v ${PWD}/workdir:/myapp \
          --net=host \
          -v $HOME/.Xauthority:/root/.Xauthority \
          -e DISPLAY=:10.0  \	#此处填写上面查看到的变量值
          -e QT_X11_NO_MITSHM=1 \
          --device=/dev/vchiq \
          --device=/dev/video0 \
          --name my-running-py \
          my-opencv-test \
          recognition.py
  

• 执行脚本sh run.sh

  效果同上，不再录制视频了

  

（5） 以小组为单位，发表一篇博客，记录遇到的问题和解决方法，提供小组成员名单以及在线协作的图片

（1）踩过的坑

• 原本使用windows terminal ssh连接树莓派时找不到DISPLAY环境变量，即使使用了 ssh -X pi@raspi
  • 后面换了putty，打勾一下X11转发即可
• 期间各种下载和安装依赖实在慢
  • 直接在路由器端转发国外的流量到代理，也不用一直换源了
• 一开始插入视频标签无法显示
  • 

（2）小组成员名单

• 031702626杨世杰
• 031702625杨蓝宇
• 171709012沈鸿骁

（3）在线协作图片

• 采用的是群内分享屏幕，三位同学一起查找资料和解决困难，由杨世杰进行主要操作