第7次实践作业

一、 在树莓派中安装opencv库

（1）安装依赖

# 更新和升级任何现有的软件包
sudo apt-get update && sudo apt-get upgrade 
# 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
# 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

image.png

（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

image.png

（3）安装pip

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

image.png

（4）安装python虚拟机

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

image.png

  • 配置~/.profile ，添加如下，
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

image.png

  • 并使之生效
source ~/.profile

image.png

  • 使用Python3安装虚拟机
 mkvirtualenv cv -p python3

image.png

提醒：后续所有操作均在虚拟机中

  • 安装numpy
pip install numpy

image.png

（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 ..

image.png

  • 增大交换空间 CONF_SWAPSIZE=1024
sudo nano /etc/dphys-swapfile  #虚拟机中sudo才可以修改

image.png

  • 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

image.png

（6）安装opencv

sudo make install
sudo ldconfig

image.png

  • 检查安装位置
ls -l /usr/local/lib/python3.7/site-packages
ls -l /usr/local/lib/python3.7/dist-packages

image.png

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

image.png

（7）验证安装

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

image.png

提示：退出python虚拟机命令deactivate

二、 使用opencv和python控制树莓派的摄像头

（1）picamera模块安装

  • 开启虚拟机
source ~/.profile
workon cv

再次提醒：后续所有操作均在虚拟机中

  • 安装picamera
pip install "picamera[array]"

image.png

（2）在Python代码中导入OpenCV控制摄像头

  • Python代码
# 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)

image.png

三、 利用树莓派的摄像头实现人脸识别

（1）安装所需库

pip install dlib
pip install face_recognition

image.png

（2）准备好需要用到的图片和代码文件

image.png

  • facerec_on_raspberry_pi.py代码
# 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)")
obama_image = face_recognition.load_image_file("obama_small.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 = "Barack Obama"

        print("I see someone named {}!".format(name))
  • facerec_from_webcam_faster.py代码
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.
obama_image = face_recognition.load_image_file("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("biden.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",
    "Joe Biden"
]

# 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()

（3）人脸识别

image.png
image.png

四、 结合微服务的进阶任务

（1）安装Docker

  • 下载安装脚本
curl -fsSL https://get.docker.com -o get-docker.sh

image.png

  • 执行安装脚本(使用阿里云镜像)
sh get-docker.sh --mirror Aliyun

image.png

  • 将当前用户加入docker用户组
sudo usermod -aG docker $USER
  • 查看docker版本，验证是否安装成功
docker --version

image.png

(2).配置docker的镜像加速

  • 写加速器地址
sudo nano /etc/docker/daemon.json

image.png

  • 重启docker
service docker restart

(3）定制opencv镜像

  • 拉取镜像
sudo docker pull sixsq/opencv-python

image.png

  • 创建并运行容器
sudo docker run -it sixsq/opencv-python /bin/bash
  • pip安装 "picamera[array]" dlib face_recognition
pip install "picamera[array]" 
pip install dlib 
pip install face_recognition

image.png
（face_recognition可离线安装）

sudo docker cp face_recognition_models-0.3.0-py2.py3-none-any.whl e8e0d5cfb823:/home
sudo docker cp face_recognition-1.3.0-py2.py3-none-any.whl e8e0d5cfb823:/home

image.png

  • commit镜像
sudo docker commit priceless_chatelet gkd

image.png

（4）自定义镜像

  • 创建文件
FROM gkd

MAINTAINER GROUP08

RUN mkdir /myapp

WORKDIR /myapp

COPY myapp .

image.png

  • 生成镜像
sudo docker build -t myopencv .

image.png

  • 运行代码
sudo docker run -it --device=/dev/vchiq --device=/dev/video0 --name face myopencv
python3 facerec_on_raspberry_pi.py

image.png
image.png

（5）opencv的docker容器中运行facerec_from_webcam_faster.py

  • 在Windows系统中安装Xming
  • 检查树莓派的ssh配置中的X11是否开启
cat /etc/ssh/sshd_config

image.png

  • 启动putty
    image.png

  • 查看DISPLAY环境变量值

printenv

image.png

  • 编写run.sh
#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 \
        myopencv7 \
	python3 facerec_from_webcam_faster.py
  • 启动
sh run.sh

结果和上面相同

五、 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、分工、各自贡献以及在线协作的图片

1.困难

（1）安装依赖时报错，提示要安装的xxx依赖于xxx，但是xxx不会被安装。

解决：重新烧录一个干净的系统

（2）系统重装导致ssh密匙重复

解决：删掉密匙

ssh-keygen -R 192.168.43.167
（3）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

解决：用aptitude install下载，可以在下载软件包的同时选择下载相关依赖

sudo apt-get install aptitude
sudo aptitude install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo aptitude install libxvidcore-dev libx264-dev
sudo aptitude install libgtk2.0-dev libgtk-3-dev
sudo aptitude install libatlas-base-dev gfortran
sudo aptitude install python2.7-dev python3-dev
（4）编译OpenCV出错

image.png
解决方案

cd /home/pi/opencv_contrib-4.1.2/modules/xfeatures2d/src
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/34e4206aef44d50e6bbcd0ab06354b52e7466d26/boostdesc_lbgm.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/34e4206aef44d50e6bbcd0ab06354b52e7466d26/boostdesc_binboost_256.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/34e4206aef44d50e6bbcd0ab06354b52e7466d26/boostdesc_binboost_128.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/34e4206aef44d50e6bbcd0ab06354b52e7466d26/boostdesc_binboost_064.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/34e4206aef44d50e6bbcd0ab06354b52e7466d26/boostdesc_bgm_hd.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/34e4206aef44d50e6bbcd0ab06354b52e7466d26/boostdesc_bgm_bi.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/34e4206aef44d50e6bbcd0ab06354b52e7466d26/boostdesc_bgm.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/fccf7cd6a4b12079f73bbfb21745f9babcd4eb1d/vgg_generated_120.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/fccf7cd6a4b12079f73bbfb21745f9babcd4eb1d/vgg_generated_64.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/fccf7cd6a4b12079f73bbfb21745f9babcd4eb1d/vgg_generated_48.i
wget https://raw.githubusercontent.com/opencv/opencv_3rdparty/fccf7cd6a4b12079f73bbfb21745f9babcd4eb1d/vgg_generated_80.i
（5）raw.githubusercontent.com (raw.githubusercontent.com)|0.0.0.0|:443... 失败：拒绝连接

解决方案

sudo nano /etc/hosts
151.101.76.133 raw.githubusercontent.com
（6）cv2库用不了

image.png
解决：依赖没下全，少libgtk2.0-dev，重新下载，再重新编译。

（7）libgtk2.0-dev下载不了
libgtk2.0-dev : 依赖: libgtk2.0-0 (= 2.24.32-3) 但是 2.24.32-3+rpt1 已安装
 libcairo2-dev : 依赖: libcairo2 (= 1.16.0-4) 但是 1.16.0-4+rpt1 已安装
                 依赖: libcairo-gobject2 (= 1.16.0-4) 但是 1.16.0-4+rpt1 已安装
 libpng-dev : 冲突: libpng12-dev 但是 1.2.54-6 已安装
 libpng12-dev : 冲突: libpng-dev 但是 1.6.36-6 将被安装
 libpixman-1-dev : 依赖: libpixman-1-0 (= 0.36.0-1) 但是 0.36.0-1+rpt1 已安装

解决：降级再下载

（8）face_recognition下载太慢

解决：先在本机下载whl文件，再移动到树莓派

2.分工

学号 名称 任务
031702606 余琳玲 查找资料、解决问题
111700306 陈佳雯 查找资料、解决问题
031702616 林涛 实际操作、编写博客

3.在线协作

  • 耗时：16h
    image.png

最后一次大作业博客

posted @ 2020-06-08 22:11  雨霖铃0000  阅读(164)  评论(0)  编辑  收藏  举报