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第7次實踐作業

本文轉載自   查看原文   2020-05-31 17:34   518 

目錄

（1） 在樹莓派中安裝opencv庫

參考1 參考2

（1）安裝依賴

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

偶爾有幾個卡殼的就加上--fix-missing再執行幾次

（2）下載OpenCV源碼

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
#最好掛tz加快速度

（3）安裝pip

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

（4）安裝Python虛擬機

sudo pip install virtualenv virtualenvwrapper
sudo rm -rf ~/.cache/pip
  • 配置~/.profile ，添加如下，並使用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安裝虛擬機
 mkvirtualenv cv -p python3
  • 進入虛擬機
source ~/.profile && workon cv

​ 可以看到前面有cv虛擬機的標識

  • 安裝numpy
pip install numpy

（5）編譯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 ..

要確保路徑齊全

  • 增大交換區內存到1024

  • 重啟swap服務並開始編譯

    sudo /etc/init.d/dphys-swapfile stop && sudo /etc/init.d/dphys-swapfile start
make -j4

  • 1小時+后看到編譯完成

（6）安裝opencv

sudo make install
sudo ldconfig

  • 檢查安裝位置

（7）驗證安裝

  • 退出python虛擬機命令deactivate

（2） 使用opencv和python控制樹莓派的攝像頭

參考1 參考2

  • 安裝picamera（虛擬機環境下）

    source ~/.profile
workon cv
pip install "picamera[array]"

  • 使用python和opencv控制攝像頭

    # 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） 利用樹莓派的攝像頭實現人臉識別

​ 參考1

  • 安裝所需庫

    pip install dlib &&
pip install face_recognition &&
pip install numpy	#前面安裝過就不用了

  • 同目錄下放置一張用於識別test.jpg

（1）基於picamera的人臉識別

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 = "雷軍"

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

（2）基於opencv的人臉識別

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） 結合微服務的進階任務

（1）使用微服務，部署opencv的docker容器（要能夠支持arm），並在opencv的docker容器中跑通（3）的示例代碼facerec_on_raspberry_pi.py

  • 准備操作

    #退出python虛擬機
deactivate
#腳本安裝docker
sudo curl -sSL https://get.docker.com | sh
#填加用戶到docker組
sudo usermod -aG docker pi
#重新登陸以用戶組生效
exit && ssh pi@raspiberry
#驗證docker版本
docker --version

  • 創建對應目錄

  • 拉取arm可用的docker鏡像

    docker pull sixsq/opencv-python

  • 進入容器並安裝所需庫

    docker run -it [imageid] /bin/bash
pip install "picamera[array]" dlib face_recognition

  • comiit鏡像

    docker commit [containerid] my-opencv

  • 自定義鏡像

    • Dockerfile

      FROM my-opencv

MAINTAINER GROUP13

RUN mkdir /myapp

WORKDIR /myapp

ENTRYPOINT ["python3"]

    • 生成鏡像

      docker build -t my-opencv-test .

    • 運行腳本

      docker run -it --rm --name my-running-py -v ${PWD}/workdir:/myapp --device=/dev/vchiq --device=/dev/video0 my-opencv-test isLeiJun.py

（2）選做：在opencv的docker容器中跑通步驟（3）的示例代碼facerec_from_webcam_faster.py

采用的是在Windows上通過ssh連接樹莓派並傳送X11數據 參考1 參考2

  • 環境准備

    • windows端安裝XMing

    • 檢測ssh配置文件中X11是否開啟 cat /etc/ssh/sshd_config

    • 支持X11轉發的ssh客戶端

      由於安裝XMing的時候創建了鏈接到putty，所以此處打個勾就行

    • 查看DISPLAY環境變量值printenv

  • 編輯啟動腳本 run.sh

    xhost +	#允許來自任何主機的連接
docker run -it \
        --rm \
        -v ${PWD}/workdir:/myapp \
        --net=host \
        -v $HOME/.Xauthority:/root/.Xauthority \
        -e DISPLAY=:10.0  \	#此處填寫上面查看到的變量值
        -e QT_X11_NO_MITSHM=1 \
        --device=/dev/vchiq \
        --device=/dev/video0 \
        --name my-running-py \
        my-opencv-test \
        recognition.py

  • 執行腳本sh run.sh

    效果同上，不再錄制視頻了

（5） 以小組為單位，發表一篇博客，記錄遇到的問題和解決方法，提供小組成員名單以及在線協作的圖片

（1）踩過的坑

  • 原本使用windows terminal ssh連接樹莓派時找不到DISPLAY環境變量，即使使用了 ssh -X pi@raspi
    • 后面換了putty，打勾一下X11轉發即可
  • 期間各種下載和安裝依賴實在慢
    • 直接在路由器端轉發國外的流量到代理，也不用一直換源了
  • 一開始插入視頻標簽無法顯示

（2）小組成員名單

  • 031702626楊世傑
  • 031702625楊藍宇
  • 171709012沈鴻驍

（3）在線協作圖片

  • 采用的是群內分享屏幕，三位同學一起查找資料和解決困難，由楊世傑進行主要操作


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