(1) 在树莓派中安装opencv库

参考教程:关于opencv的编译安装,可以参考Adrian Rosebrock的Raspbian Stretch: Install OpenCV 3 + Python on your Raspberry Pi。

步骤一:拓展SD卡内存

sudo raspi-config #打开树莓派配置
#选择Advanced Options——>Expand fileSystem

sudo reboot #重启
df -h #查看内存使用情况,如果内存占用过多建议卸载LibreOffice和 Wolfram engine来释放空间
#卸载
sudo apt-get purge wolfram-engine
sudo apt-get purge libreoffice*
sudo apt-get clean
sudo apt-get autoremove

步骤二:安装依赖库(建议先换国内源,否则你就痛苦吧)

更新和升级软件包

 sudo apt-get update && sudo apt-get upgrade

安装一些开发者工具

 sudo apt-get install build-essential cmake pkg-config

安装image I/O所需的包

sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev

安装video I/O所需的包

sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev

安装编译highgui所需包

sudo apt-get install libgtk2.0-dev libgtk-3-dev

安装一些扩展包

sudo apt-get install libatlas-base-dev gfortran

安装python

sudo apt-get install python2.7-dev python3-dev

安装pip

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

python2安装opencv

python2 -m pip install opencv-python  -i https://pypi.tuna.tsinghua.edu.cn/simple

python3安装opencv

sudo pip3 install opencv_python-3.4.3.18-cp37-cp37m-linux_armv7l.whl  #这里是把所需的包事先下载到本地,进行本地安装。
sudo apt install libqt4-test      #安装缺少的依赖包
sudo apt install libqtgui4

测试是否编译成功

python2
import cv2
cv2.__version__

python3
import cv2
cv2.__version__

(2) 使用opencv和python控制树莓派的摄像头

参考教程:还是可以参考Adrian Rosebrock的Accessing the Raspberry Pi Camera with OpenCV and Python
跑通教程的示例代码(有可能要调整里面的参数)

安装picamare

pip install "picamera[array]" 

很好,我们已经有了picamera了,不需要安装了。

拍照这部分上次作业有做过,这里就不再重复了。

拍视频示例代码(test_video.py)

# 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()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))
# allow the camera to warmup
time.sleep(0.1)
# capture frames from the camera
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
	# grab the raw NumPy array representing the image, then initialize the timestamp
	# and occupied/unoccupied text
	image = frame.array
	# show the frame
	cv2.imshow("Frame", image)
	key = cv2.waitKey(1) & 0xFF
	# clear the stream in preparation for the next frame
	rawCapture.truncate(0)
	# if the `q` key was pressed, break from the loop
	if key == ord("q"):
		break

视频截图

(3) 利用树莓派的摄像头实现人脸识别

人脸识别有开源的python库face_recognition,这当中有很多示例代码
要求:跑通face_recognition的示例代码facerec_on_raspberry_pi.py以及facerec_from_webcam_faster.py
参考教程:树莓派上使用python实现人脸识别

安装dlib、facerecognition

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple some-package --default-timeout=100 dlib
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple some-package  --default-timeout=100 face_recognition

示例一代码

# coding = utf-8
import face_recognition
import picamera
import numpy as np
 
# 创建视频对象
camera = picamera.PiCamera()
# 设置分辨率
camera.resolution = (320, 240)
# 初始化一个空的ndarray类型的数据
rgb_frame = np.empty((240, 320, 3), dtype=np.uint8)
 
# 加载当前目录下名为'test.jpg'的照片,照片里需要有且仅有一张脸,这张脸将作为认识的脸
print('loading...')
image = face_recognition.load_image_file('test.jpg')
face_encoding = face_recognition.face_encodings(image)[0]
 
while True:
 
    print('Capturing image.')
    # 用Picamera读取一帧照片
    camera.capture(rgb_frame, format='rgb')
 
    # 获取这一帧图片里所有人脸的位置和特征值
    face_locations = face_recognition.face_locations(rgb_frame)
    print('Found {} faces in image.'.format(len(face_locations)))
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
 
    # 对获取的每张脸进行循环,判断是否是认识的脸
    for face_encoding in face_encodings:
        # 判断当前的脸是否与认识的脸匹配
        match = face_recognition.compare_faces([face_encoding], face_encoding)
        name = '<Unknown Person>'
 
        if match[0]:
            name = 'test'  # test为'test.jpg'里面人脸的名字
        print('I see someone named {}!'.format(name))

在代码目录下添加一张test.jpg

结果如下

示例二代码

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()

在工作目录下存放Obama和biden的图片

结果如下

(4) 结合微服务的进阶任务

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

首先,先在树莓派上安装docker

sudo curl -sSL https://get.docker.com | sh

然后,拉取opencv镜像

sudo docker pull  sixsq/opencv-python 

建一个工作目录,编写dockerfile,将所需文件放进来

dockerfile

FROM sixsq/opencv-python
MAINTAINER gg
WORKDIR /usr/local/opencv
COPY ./face_recognition-1.3.0-py2.py3-none-any.whl /usr/local/opencv
COPY ./face_recognition_models-0.3.0-py2.py3-none-any.whl  /usr/local/opencv
RUN sudo pip install -i  https://pypi.tuna.tsinghua.edu.cn/simple --upgrade pip && \
    pip install --upgrade -i  https://pypi.tuna.tsinghua.edu.cn/simple --default-timeout=500 "picamera[array]" dlib && \
    python3 -m pip install wheel face_recognition_models-0.3.0-py2.py3-none-any.whl && \
    python3 -m pip install wheel face_recognition-1.3.0-py2.py3-none-any.whl 
EXPOSE 80

构建镜像

sudo docker build -t myopencv .

用该镜像创建容器

#创建并运行容器
docker run -it --rm --name myopencv -v /home/pi/docker/opencv:/usr/local/opencv --device=/dev/vchiq --device=/dev/video0 myopencv /bin/bash
#在容器内部运行人脸识别代码
python3 ./face_test1.py

结果:

选做:在opencv的docker容器中跑通步骤(3)的示例代码facerec_from_webcam_faster.py

(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

小组成员

031702518 吴长星
031702526 周华
031702103 朱雅珊

遇到的问题

一、安装OpenCV时提示缺少boostdesc_bgm.i文件

解决方案:参考安装OpenCV时提示缺少boostdesc_bgm.i文件的问题解决方案(附带资源)

二、安装opencv时提示没有找到cuda.hpp文件

解决方案:找到cuda.hpp,记下它的绝对路径,找到报错的文件(这里是matchers.hpp),用文本编辑器打开,找到出错的语句(这里是" # include "opencv/..." "),将
引号中的路径改为绝对路径。
后来也遇到多次相同的错误,也是同样的处理:找到缺失的文件的绝对路径,在找到并修改出错的文件。

三、编译时遇到内存不足的情况

解决方案:参考linux问题记录

四、编译时


啧,这个问题没解决,我们决定换成用pip安装opencv。(明明用pip安装那么方便,为什么百度上一堆都是用make编译的勒?)

五、在pip install 时经常出现read timeout

解决方案:1、使用-i 参数换成国内源;2、使用--default-timeout=100参数避免下载速度过慢导致的timeout

协作截图

(6) 参考资料

子豪兄教你在树莓派上安装OpenCV