2020系统综合实践 第7次实践作业 15组

实验一、在树莓派中安装opencv库

步骤1：扩展文件系统。第一件事就是扩展文件系统，以包括microSD卡上的所有可用空间：

 sudo raspi-config

完成以上操作后，执行下面命令来重新启动pi:

sudo reboot 

重新启动后，文件系统应已扩展为包括micro-SD卡上的所有可用空间。可以通过执行以下命令来验证磁盘是否已扩展并检查输出：

df -h

但是，即使扩展了文件系统，我也已经使用了15GB卡中的46％。因此，可以删除LibreOffice和Wolfram引擎以释放Pi上的一些空间：

sudo apt-get purge wolfram-engine
sudo apt-get purge libreoffice*
sudo apt-get clean
sudo apt-get autoremove

前后对比，多了2GB。做到这里基本没有什么问题。

步骤2：安装依赖项

在安装依赖包之前，建议先进行换清华源。不然很容易下着下着就崩了，很影响实验心情说实话。
注意：在更新前一定要安装 aptitude ，不然后面你可能会被“无法修正错误，因为您要求某些软件包保持现状，就是它们破坏了软件包间的依赖关系”这样的错误折磨发疯，aptitude 与 apt-get 一样，是 Debian 及其衍生系统中功能极其强大的包管理工具。与 apt-get 不同的是，aptitude 在处理依赖问题上更佳一些。举例来说，aptitude 在删除一个包时，会同时删除本身所依赖的包。这样，系统中不会残留无用的包，整个系统更为干净。树莓派在换源后，apt-get install 安装软件， 会报“无法修正错误，因为您要求某些软件包保持现状，就是它们破坏了软件包间的依赖关系”。这个错误出现的主要原因是，系统中已经安装了被依赖的包的高版本，并且版本高于所要求的这个版本，不匹配导致安装失败。虽然这种报错可以手动安装指定版本的软件包，但是各种互相依赖的一环套一环，会出现各种新的报错。

sudo apt-get install aptitude

下载完aptitude之后，准备换源。执行以下命令，进行编辑系统源文件：

sudo nano /etc/apt/sources.list

将初始的源使用#注释掉，添加如下两行清华的镜像源。【注意】这里的树莓派系统是Raspbian-buster系统，在写系统源链接时要注意是buster，网上很多教程都是之前stretch版本，容易出错！

deb http://mirrors.tuna.tsinghua.edu.cn/raspbian/raspbian/ buster main contrib non-free rpi
deb-src http://mirrors.tuna.tsinghua.edu.cn/raspbian/raspbian/ buster main contrib non-free rpi

再更改系统源：

sudo nano /etc/apt/sources.list.d/raspi.list

用#注释掉原文件内容，用以下内容取代：

deb http://mirrors.tuna.tsinghua.edu.cn/raspberrypi/ buster main ui
deb-src http://mirrors.tuna.tsinghua.edu.cn/raspberrypi/ buster main ui

然后执行以下操作完成换源：

sudo apt-get update
sudo apt-get upgrade

可能会出现以下情况：

我直接跳过了，不理它。

这个部分参考这篇文章树莓派4B（Raspbian-buster系统）换清华源并更换系统源

然后安装开发工具CMake，帮助我们配置OpenCV构建过程

sudo apt-get install build-essential cmake pkg-config

接着安装图像I/O包，允许我们从磁盘加载各种图像文件格式。这种文件格式的例子包括JPEG，PNG，TIFF等

sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev

视频I/O包。这些库允许我们从磁盘读取各种视频文件格式，并直接处理视频流

sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev

OpenCV库附带一个名为highgui的子模块 ，用于在我们的屏幕上显示图像并构建基本的GUI。为了编译 highgui模块，我们需要安装GTK开发库

sudo apt-get install libgtk2.0-dev libgtk-3-dev

OpenCV中的许多操作（即矩阵操作）可以通过安装一些额外的依赖关系进一步优化

sudo apt-get install libatlas-base-dev gfortran

安装Python 2.7和Python 3头文件，以便我们可以用Python绑定来编译OpenCV

sudo apt-get install python2.7-dev python3-dev

做到这里，这一部分遇到问题如下：
问题一：在参考Raspbian Stretch: Install OpenCV 3 + Python on your Raspberry Pi这篇文档，执行sudo apt-get update && sudo apt-get upgrade 来更新所有软件包时，下载速度贼慢，最后挂掉。（在没换清华源之前，我是用了阿里源，不知道是不是我阿里源没配置好的问题）。

解决方案：其实在换清华源之前，我是使用的是阿里源，然后换成了中科大源，可是在接下来遇到了问题，不得不再一次换源。（我觉得应该是我换中科大源的时候没配置好）。所以建议，一步到位，按我上面的方法直接换成清华源。
问题二：在没换清华源以及下载aptitude之前，执行sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev命令后，出现包与包之间依赖的问题。

解决方案：先下载aptitude,再换成清华源，就解决了。清华源真香！

步骤＃3：下载OpenCV源代码

现在我们已经安装了依赖项，从官方的OpenCV仓库中获取OpenCV 的 4.1.2归档。

cd ~
wget -O opencv.zip https://github.com/Itseez/opencv/archive/4.1.2.zip
unzip opencv.zip

我们需要完整安装 OpenCV 3（例如，可以访问SIFT和SURF等功能），因此我们还需要获取opencv_contrib存储库。注意：确保 opencv和opencv_contrib版本相同。如果版本号不匹配，那么可能会遇到编译时错误或运行时错误。

wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/4.1.2.zip
unzip opencv_contrib.zip

这里我在执行unzip xxx.zip的时候出现问题
问题三：

解决方案：参考这篇博客在Linux中使用unzip打开zip文件失败。也有博客写着下载7zip来解压.zip文件，我这个方法试了，结果出现新的问题，所以不建议采用。

步骤4：Python 2.7或Python 3？

首先需要安装 Python包管理器pip：

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

接下来安装virtualenv和 virtualenvwrapper,这里与文档给出的命令有些不一样，因为按文档的命令执行会出现一些问题，建议改成下面命令。

sudo pip3 install virtualenv virtualenvwrapper=='4.8.4'
sudo rm -rf ~/.cache/pip 

配置~/.profile，添加内容：(这里建议下载一个VIM,方便编辑)

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

使之生效：

source ~/.profile

使用Python3 安装虚拟机:

mkvirtualenv cv -p python3

虚拟机完成安装之后，后续的所有操作全部在虚拟机中进行。按照教程的说明，一定要看清楚命令行前面是否有（cv），以此作为是否在虚拟机的判断！
需要重新进入虚拟机，可运行下面的命令:

source ~/.profile
workon cv

接下来的操作都是在虚拟机中进行
安装numpy：

pip install numpy

执行上面的命令，我又出问题了：
问题四：安装超时。

解决方案：参考博客python pip安装第三方库超时问题
更改命令：

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple numpy

步骤5：编译并安装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 ..

编译之前配置交换空间大小。这使OpenCV可以使用 Raspberry PI的所有四个内核进行编译，而不会由于内存问题而挂起编译。把交换空间交换空间增大到 CONF_SWAPSIZE=1024。

注意：vim保存修改只读文件命令为（:w !sudo tee %）。在终端输入的时候没有括号。

# 虚拟机中sudo才可以修改
sudo nano /etc/dphys-swapfile  
sudo /etc/init.d/dphys-swapfile stop
sudo /etc/init.d/dphys-swapfile start
# 开始编译（近2个小时吧）
make  

步骤6：完成在Pi上安装OpenCV

sudo make install
sudo ldconfig

检查OpenCV的安装位置

ls -l /usr/local/lib/python3.7/site-packages/
cd ~/.virtualenvs/cv/lib/python3.7/site-packages/
ln -s /usr/local/lib/python3.7/site-packages/cv2 cv2

验证安装:

source ~/.profile 
workon cv
python
import cv2
cv2.__version__

最后打开你的 /etc/dphys-swapfile 然后把 CONF_SWAPSIZE改回100。

实验二、使用opencv和python控制树莓派的摄像头

1.安装picreame

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

2.实例代码

test_image.py

# 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(2)

# 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)

实验三、利用树莓派的摄像头实现人脸识别

1、安装依赖库dlib，face_recognition

pip install dlib
pip install face_recognition

2、facerec_on_raspberry_pi.py

# This is a demo of running face recognition on a Raspberry Pi.
# This program will print out the names of anyone it recognizes to the console.
# To run this, you need a Raspberry Pi 2 (or greater) with face_recognition and
# the picamera[array] module installed.
# You can follow this installation instructions to get your RPi set up:
# https://gist.github.com/ageitgey/1ac8dbe8572f3f533df6269dab35df65

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)")
Lilianjie_image = face_recognition.load_image_file("Lilianjie.jpg")
Lilianjie_face_encoding = face_recognition.face_encodings(Lilianjie_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([Lilianjie_face_encoding], face_encoding)
        name = ""

        if match[0]:
            name = "Lilianjie"
        print("I see someone named {}!".format(name))

3、facerec_from_webcam_faster.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.
Lilianjie_image = face_recognition.load_image_file("Lilianjie.jpg")
Lilianjie_face_encoding = face_recognition.face_encodings(Lilianjie_image)[0]

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

# Create arrays of known face encodings and their names
known_face_encodings = [
    Lilianjie_face_encoding,
    Guanzhiling_face_encoding
]
known_face_names = [
    "Lilianjie",
    "Guanzhiling"
]

# 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()

实验四、结合微服务的进阶任务

安装Docker

下载安装脚本

curl -fsSL https://get.docker.com -o get-docker.sh

行安装脚本(阿里云镜像)

sh get-docker.sh --mirror Aliyun

查看docker版本，验证是否安装成功

添加用户到docker组，然后重新登陆让用户组生效

sudo usermod -aG docker pi

定制opencv镜像

拉取镜像

docker pull sixsq/opencv-python

创建并运行容器

docker run -it sixsq/opencv-python /bin/bash

在容器中，用pip3安装 "picamera[array]"，dlib和face_recognition

pip3 install "picamera[array]" 
pip3 install dlib
pip3 install face_recognition
exit

commit镜像

自定义镜像

Dockerfile

FROM opencv1
RUN mkdir /my
WORKDIR /my
COPY my .

构建镜像

docker build -t opencv2 .

运行容器执行facerec_on_raspberry_pi.py

docker run -it --device=/dev/vchiq --device=/dev/video0 --name myopencv opencv2
python3 facerec_on_raspberry_pi.py

选做：

环境准备

在Windows系统中安装Xming和Putty安装好后，先打开Xming，然后打开Putty，开启树莓派的ssh配置中的X11
打开Putty后，把树莓派的IP地址填在下面这一栏里，端口用默认的就行了

查看DISPLAY环境变量值

注意：这个查看是在用Putty打开的窗口上查看到的，若直接在树莓派里的终端看，看到就是"DISPLAY=localhost:10.0"

printenv

在用Putty打开的窗口上编写run.sh(路径在树莓派中的docker1文件夹

#sudo apt-get install x11-xserver-utils
xhost +
docker run -it \
        --net=host \
        -v $HOME/.Xauthority:/root/.Xauthority \
        -e DISPLAY=:10.0  \
        -e QT_X11_NO_MITSHM=1 \
        --device=/dev/vchiq \
        --device=/dev/video0 \
        --name facerecgui \
        opencv2 \
	python3 facerec_from_webcam_faster.py

打开终端，运行run.sh

sh run.sh

小组分工

学号	姓名	分工
031502115	黄 臻	查找资料，解决问题
031702243	杨成锦	动手操作，解决问题
031702218	覃鸿浩	查找资料，解决问题