2020系统综合实践第7次实践作业 10组

1 、在树莓派中安装opencv库

（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

（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

（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 ..

根据教程增大交换区内存到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

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

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ChengLong.jpg

（1）基于picamera的人脸识别

isChengLong.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)")
image = face_recognition.load_image_file("ChengLong.jpg")
face_encoding = face_recognition.face_encodings(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([face_encoding], face_encoding)
        name = "<Unknown Person>"

        if match[0]:
            name = "ChengLong"

摄像头对准后：

（2）基于opencv的人脸识别

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.
chenglong_image = face_recognition.load_image_file("ChengLong.jpg")
chenglong_face_encoding = face_recognition.face_encodings(chenglong_image)[0]

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

# Create arrays of known face encodings and their names
known_face_encodings = [
    chenglong_face_encoding,
    gutianle_face_encoding
]
known_face_names = [
    "ChengLong",
    "GuTianLe"
]

# 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

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 isChengLong.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

Windows系统安装XMing
检测ssh配置文件中X11是否开启 cat /etc/ssh/sshd_config
启动putty
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

（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、问题

（1）用python3安装虚拟机报错

解决办法：重新配置~/.profile ，添加以下内容

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

（2）编译opencv出错