目录
（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
偶尔有几个卡壳的就加上--fix-missing再执行几次
（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
#最好挂tz加快速度
（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 ..
要确保路径齐全
-
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
参考1
-
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test.jpg
（1）基于picamera的人脸识别
isLeiJun.py
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("test.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 = "雷军"
print("I see someone named {}!".format(name))
（2）基于opencv的人脸识别
recognition_opcv.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.
leijun_image = face_recognition.load_image_file("leijun.jpg")
leijun_face_encoding = face_recognition.face_encodings(leijun_image)[0]
# Load a second sample picture and learn how to recognize it.
liuzuohu_image = face_recognition.load_image_file("liuzuohu.jpg")
liuzuohu_face_encoding = face_recognition.face_encodings(liuzuohu_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
leijun_face_encoding,
liuzuohu_face_encoding
]
known_face_names = [
"LeiJun",
"LiuZuoHu"
]
# 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
-
创建对应目录
-
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 isLeiJun.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
采用的是在Windows上通过ssh连接树莓派并传送X11数据 参考1 参考2
-
环境准备
-
windows端安装XMing
-
检测ssh配置文件中X11是否开启
cat /etc/ssh/sshd_config
-
支持X11转发的ssh客户端
由于安装XMing的时候创建了链接到putty，所以此处打个勾就行
-
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
su run.sh效果同上，不再录制视频了
（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）踩过的坑
- 原本使用windows terminal ssh连接树莓派时找不到DISPLAY环境变量，即使使用了 ssh -X pi@raspi
- 后面换了putty，打勾一下X11转发即可
- 期间各种下载和安装依赖实在慢
- 直接在路由器端转发国外的流量到代理，也不用一直换源了
- 一开始插入视频标签无法显示
（2）小组成员名单
- 031702626杨世杰
- 031702625杨蓝宇
- 171709012沈鸿骁
（3）在线协作图片
- 采用的是群内分享屏幕，三位同学一起查找资料和解决困难，由杨世杰进行主要操作
