第7次实践作业 25组
（1）在树莓派中安装opencv库
安装依赖
# 更新软件源，更新软件
sudo apt-get update && sudo apt-get upgrade
# Cmake等开发者工具
sudo apt-get install build-essential cmake pkg-config
# 图片I/O包
sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev
# 视频I/O包
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev
# OpenCV用于显示图片的子模块需要GTK
sudo apt-get install libgtk2.0-dev libgtk-3-dev
# 性能优化包
sudo apt-get install libatlas-base-dev gfortran
# 安装 Python2.7 & Python3
sudo apt-get install python2.7-dev python3-dev
下载OpenCV源代码
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
获取opencv_contrib存储库
wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/4.1.2.zip
unzip opencv_contrib.zip
注意：确保 opencv和 opencv_contrib版本相同。
准备编译环境
# 安装pip
wget https://bootstrap.pypa.io/get-pip.py
sudo python get-pip.py
sudo python3 get-pip.py
# 安装虚拟环境，防止依赖冲突
sudo pip install virtualenv virtualenvwrapper
sudo rm -rf ~/.cache/pip
# > 重定向输出流 >> 表示追加
echo -e "\n# virtualenv and virtualenvwrapper" >> ~/.profile
echo "export WORKON_HOME=$HOME/.virtualenvs" >> ~/.profile
echo "export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3" >> ~/.profile
echo "source /usr/local/bin/virtualenvwrapper.sh" >> ~/.profile
# 每次新开终端，需要虚拟环境时都要运行
source ~/.profile
# 创建虚拟环境cv
mkvirtualenv cv -p python3
# 进入虚拟环境
workon cv
# 安装numpy，较耗时
pip install numpy
编译opencv
要确保已经进入了cv虚拟环境，命令提示符开头有(cv)。
# 这里我们用的是3.3.0版本
cd ~/opencv-3.3.0/
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-3.3.0/modules \
-D BUILD_EXAMPLES=ON ..
为了避免编译时内存不足导致的CPU挂起，调整swap交换文件大小：
# CONF_SWAPSIZE由100改为1024，编译完成后改回来
sudo nano /etc/dphys-swapfile
# 重启swap服务
sudo /etc/init.d/dphys-swapfile stop
sudo /etc/init.d/dphys-swapfile start
到这里就完成了大部分准备工作，开始编译：
# 开始编译，很耗时
make -j4
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
测试opencv
source ~/.profile
workon cv
python
import cv2
cv2.__version__
（2）使用opencv和python控制树莓派的摄像头
安装picreame
source ~/.profile
workon cv
pip install "picamera[array]"
拍照测试
# 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(5)
# 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
安装依赖库dlib，face_recognition
source ~/.profile
workon cv
pip install dlib
pip install face_recognition
切换到放有要加载图片和python代码的目录下
1.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)")
image = face_recognition.load_image_file("Einstein_origin.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 = "Einstein"
print("I see someone named {}!".format(name))
2.facerec_from_webcam_faster.py
示例代码如下：
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.
Einstein_image = face_recognition.load_image_file("Einstein.jpg")
Einstein_face_encoding = face_recognition.face_encodings(Einstein_image)[0]
# Load a second sample picture and learn how to recognize it.
Planck_image = face_recognition.load_image_file("Planck.jpg")
Planck_face_encoding = face_recognition.face_encodings(Planck_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
Einstein_face_encoding,
Planck_face_encoding
]
known_face_names = [
"Einstein",
"Planck"
]
# 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
安装Docker
下载安装脚本
curl -fsSL https://get.docker.com -o get-docker.sh
image-20200603113918613
运行安装脚本(阿里云镜像)
sh get-docker.sh --mirror Aliyun
查看docker版本，验证是否安装成功
sudo docker --version
添加用户到docker组
sudo usermod -aG docker pi
重新登陆让用户组生效
exit
ssh pi@raspiberry
重启之后,docker指令之前就不需要加sudo了
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 /myapp
WORKDIR /myapp
COPY myapp .
构建镜像
docker build -t opencv2 .
查看镜像
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：在opencv的docker容器中跑通步骤（3）的示例代码facerec_from_webcam_faster.py
在Windows系统中安装Xming
开启树莓派的ssh配置中的X11
查看DISPLAY环境变量值
printenv
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 \
opencv2 \
python3 facerec_from_webcam_faster.py
打开终端，运行run.sh
sh run.sh
可以看到在windows的Xvideo可以正确识别人脸。
