系统结构实践第七次作业——23组

在树莓派中安装opencv库

安装依赖

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
sudo apt install libqt4-test
sudo apt install libqtgui4

下载解压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

安装Python虚拟机

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

配置profile

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

安装numpy

pip3 install numpy

编译OpenCV

cd ~/opencv-4.1.2/
mkdir build
cd build
# 设置CMake构建选项
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 

教程中说这个需要1-2h来执行，我们组执行途中出现很多问题，索性放弃了这种方式，直接利用python安装
遇到的问题在最后

利用python3安装opencv

pip3 install opencv-python

安装好以后依次输入

python3
import cv2

如果没有报错则说明安装成功
我们安装时遇到了

Python 3.7.3 (default, Apr  3 2019, 05:39:12) 
[GCC 8.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import cv2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/pi/cv2/__init__.py", line 3, in <module>
    from .cv2 import *
ImportError: /home/pi/cv2/cv2.cpython-37m-arm-linux-gnueabihf.so: undefined symbol: __atomic_fetch_add_8

参考这里http://www.yoyojacky.com/?m=201911
得以解决，至此安装好了opencv

使用opencv和python控制树莓派的摄像头

拍照

# 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)

录像

import cv2

cap = cv2.VideoCapture(0)
while(1):
    ret, frame = cap.read()
    cv2.imshow("capture", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows() 

利用树莓派的摄像头实现人脸识别

安装face_recognition

pip3 install face_recognition

参考：https://blog.csdn.net/weixin_43106043/article/details/104962026
由于安装速度太慢，采用离线下载的办法，文件我上传到了百度云如下：
链接：https://pan.baidu.com/s/10WhHrz3yBJR2bBVt0xMNEQ
提取码：892w
下载完以后利用VNC文件传输传到/home/pi

python3 -m pip install face_recognition_models-0.3.0-py2.py3-none-any.whl
python3 -m pip install face_recognition-1.3.0-py2.py3-none-any.whl

完成后测试安装是否成功

python3
import face_recognition

示例

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

代码中的参数自行修改，需要在代码目录中放入相应图片，此处选用马云的图片，代码运行如下

示例2

代码如下：

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()

代码中的参数自行替换，此处用的马云以及马化腾的图片，需放在代码目录下，运行结果如图

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

执行安装脚本

sh get-docker.sh --mirror Aliyun

docker换源

cd /etc/docker/
sudo vi daemon.json

在文件中写入如下，此处用的中科大源，但后来发现中科大源一直timeout，遂换了网易源：http://hub-mirror.c.163.com

保存后重启docker service

service docker restart

拉取镜像

docker pull sixsq/opencv-python

运行镜像

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

进入镜像以后安装numpy dlib face_recognition,安装方法同上
由于安装速度太慢，一直timeout，选择把文件传进去再安装

cd +刚才传入的face_recognition的位置
sudo docker cp xxx.whl + 容器ID:+文件要储存的位置

传好以后还是按照刚才的方法安装，最后commit

Build镜像

文件夹结构如下：

Dockerfile

FROM opencv2
RUN mkdir /myapp
WORKDIR /myapp
COPY myapp .

build镜像

docker build -t opencv_test .

运行代码

运行容器执行face_recognition_test.py

docker run -it --device=/dev/vchiq --device=/dev/video0 --name facerec opencv_test
python3 facerec_on_raspberry_pi.py

结果如下

附加选做:opencv的docker容器中运行facerec_from_webcam_faster.py

先安装Xming和Putty，官网安装即可
检查树莓派的ssh配置中的X11是否开启

cat /etc/ssh/sshd_config

X11Forwarding yes即可
打开putty，选择ssh/x11,Enable X11 Forwarding勾上

回到session，填入树莓派的ip地址

点击open，登录使用user:pi pwd:raspberry

输入printenv

记录DISPLAY=localhost:10.0，回到树莓派，编写启动脚本，首先安装x11-xserver-utils
桌面新建run.sh，内容如下

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 \
        opencv_test \
	python3 faces.py

启动时间比较慢，而且画面会出现卡顿，最后结果如图

以小组为单位，发表一篇博客，记录遇到的问题和解决方法

遇到的问题

首先是编译中出现缺少文件的情况，如图所示（网图，忘记截图了

最后查阅资料得知需要重新下载"boostdesc_bgm_i"文件，再放入到/home/pi/opencv/opencv_contrib-3.3.0/modules/xfeatures2d/src目录即可继续编译

引入opencv出现undefined symbol: __atomic_fetch_add_8

提示找到不到cv2.cpython-37m-arm-linux-gnueabihf.so 一个未定义的__atomic_fetch_add_8, 这个是一个bug， 其实只需要加载一下一个库文件就好了。

sudo vi ~/.profile
添加：export LD_PRELOAD=/usr/lib/arm-linux-gnueabihf/libatomic.so.1 
source .profile即可

face_recognition安装太慢

方法一：pip3换源，但发现并没有什么用
方法二：离线安装，文件已经上传百度云，详细看第二部即可

小组协作

学号	姓名	分工
031702145	马连政	操作查阅资料修改代码
031702142	林德辉	查阅资料检查修改代码
031702108	叶心言	查阅资料检查修改代码