since: 2017/03/31
reference:
1. Install guide: Raspberry Pi 3 + Raspbian Jessie + OpenCV 3 - PyImageSearch
2. Accessing the Raspberry Pi Camera with OpenCV and Python - PyImageSearch
A. 前置準備
1. Raspberry Pi 3
2. 16 GB microSD Card (編譯 OpenCV 大約會佔用 3G 的容量)
3. Camera Module
4. Enable Camera Interface
> $ sudo raspi-config
5. (optional) delete the Wolfram engine to free up some space
$ sudo apt-get purge wolfram-engine
6. check Python version
$ python -V
Python 2.7.9
$ python3 -V
Python 3.4.2
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B. 安裝相關套件
1. update and upgrade any existing packages
$ sudo apt-get update
$ sudo apt-get upgrade
2. install some developer tools
$ sudo apt-get install build-essential cmake pkg-config
3. install some image I/O packages
$ sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev
4. install some video I/O packages
$ sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
$ sudo apt-get install libxvidcore-dev libx264-dev
5. install the GTK development for highgui
$ sudo apt-get install libgtk2.0-dev
6. installing a few extra dependencies for optimized
$ sudo apt-get install libatlas-base-dev gfortran
7. install both the Python 2.7 and Python 3 header files
$ sudo apt-get install python2.7-dev python3-dev
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C. 下載 OpenCV source code
1. opencv
$ cd ~
$ wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.2.0.zip
$ chmod 755 opencv.zip
$ unzip opencv.zip
2. opencv_contrib (full install of OpenCV)
$ wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/3.2.0.zip
$ chmod 755 opencv_contrib.zip
$ unzip opencv_contrib.zip
Note: Make sure your opencv and opencv_contrib versions are the same
(in this case, 3.2.0)
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D. Install Python package manager
$ wget https://bootstrap.pypa.io/get-pip.py
$ chmod 755 get-pip.py
$ sudo python get-pip.py
$ sudo python3 get-pip.py
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E. Installing NumPy
$ pip install numpy (it may take a bit of time)
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F. Compile and Install OpenCV
$ cd ~/opencv-3.2.0
$ 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-3.2.0/modules -D BUILD_EXAMPLES=ON ..
備註: 選擇性參數: -D INSTALL_C_EXMAPLES=ON
=> Ensuring that Python 2.7 and Python 3 will be used
// The Raspberry Pi 3 has four cores, thus we supply a value of 4 to allow OpenCV
// to compile faster. However, due to race conditions, there are times when
// make errors out when using multiple cores.
// $ make -j4
$ make // about 7 hours
$ sudo make install
$ sudo ldconfig
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G. 檢查與測試安裝結果
1. 檢查
$ ls -al /usr/local/lib/python2.7/dist-packages/cv2.so
$ ls -al /usr/local/lib/python3.4/dist-packages/cv2.cpython-34m.so
//$ cd /usr/local/lib/python3.4/dist-packages/
//$ sudo cp cv2.cpython-34m.so cv2.so
2. 測試
// python2
$ cd
$ python
>>> import cv2
>>> cv2.__version__
'3.2.0'
>>> exit()
// python3
$ cd
$ python3
>>> import cv2
>>> cv2.__version__
'3.2.0'
>>> exit()
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H. 程式測試
1. Test out the camera module
$ raspistill -o output.jpg
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2. Installing picamera
// when using Python bindings, OpenCV represents images as NumPy arrays
$ pip install "picamera[array]"
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3. Grabbing a single image (需要先啟動 X Window: $ startx)
// test_image.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()
rawCapture = PiRGBArray(camera)
# allow the camera to warmup
time.sleep(0.1)
# 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)
$ python test_image.py
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// 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
$ python test_video.py
