Introduction: Object Detection W/ Dragonboard 410c or 820c Using OpenCV and Tensorflow.
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This instructables describes how to install OpenCV, Tensorflow, and machine learning frameworks for Python 3.5 to run the Object Detection application.
Step 1: Requirements
You will need the following itens:
- A DragonBoard™ 410c or 820c;
- A clean installation of Linaro-alip:
- DB410c: tested in version v431.Link: https://snapshots.linaro.org/96boards/dragonboard...
- DB820c: tested in version v228.Link: https://snapshots.linaro.org/96boards/dragonboard8...
- At least an 16GB capacity MicroSD Card (If using the 410c);
Download the file (In the end of this step), unzip and copy to the MicroSD card;
Obs: If using an DB820c, download the file, unzip and move to /home/*USER*/ to ease the use of the commands.
- A USB Hub;
- A USB camera (Linux compatible);
- A USB mouse and keyboard;
- An internet connection.
Obs: Follow this instructables in the DragonBoard browser if possible, facilitating the copying of the commands.
Step 2: Mounting the MicroSD Card (only W/ DB410c)
- Open the terminal in the Dragonboard;
- In the terminal run fdisk:
$ sudo fdisk -l
- Insert the MicroSD card into the DragonBoard MicroSD card slot;
- Run fdisk again, looking for the name (and partition) of the new device in the list (e.g. mmcblk1p1)
$ sudo fdisk -l
- Go to the root directory:
$ cd ~
- Create a folder:
$ mkdir sdfolder
- Mount the MicroSD card:
$ mount /dev/<sd_card_partition_name> sdfolder
Step 3: Installing Required Frameworks
Open the terminal in the Dragonboard;
In the terminal, go to a chosen directory (using "~" for the 820c and the mounted SDCard for the 410c):
(820c) $ cd ~ (410c) $ cd ~/sdfolder
- Go to the Object Detector scripts folder:
$ cd object_detector_tensorflow_opencv/scripts/
- Run the environment setup script:
$ sudo bash set_Env.sh
- Update the system:
$ sudo apt update
- Install these packages:
$ sudo apt install -y protobuf-compiler gcc-aarch64-linux-gnu g++-aarch64-linux-gnu debootstrap schroot git curl pkg-config zip unzip python python-pip g++ zlib1g-dev default-jre libhdf5-dev libatlas-base-dev gfortran v4l-utils hdf5* libhdf5* libpng-dev build-essential cmake libreadline-gplv2-dev libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbm-dev libc6-dev libbz2-dev libjpeg-dev libtiff5-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev libgtk2.0-dev libgtk-3-dev ffmpeg python-opengl
- Go to this directory:
$ cd /usr/src
- Download Python 3.5:
$ sudo wget <a href="https://www.python.org/ftp/python/3.5.6/Python-3.5.6.tgz" rel="nofollow"> https://www.python.org/ftp/python/3.5.6/Python-3....</a>
- Extract the package:
$ sudo tar xzf Python-3.5.6.tgz
- Delete the compressed package:
$ sudo rm Python-3.5.6.tgz
- Go to the Python 3.5 directory:
$ cd Python-3.5.6
- Enable optimizations for the Python 3.5 compilation:
$ sudo ./configure --enable-optimizations
- Compile Python 3.5:
$ sudo make altinstall
- Upgrade pip and setup tools:
$ sudo python3.5 -m pip install --upgrade pip && python3.5 -m pip install --upgrade setuptools
- Install numpy:
$ python3.5 -m pip install numpy
- Go to the chosen directory:
(820c) $ cd ~ (410c) $ cd ~/sdfolder
Download Tensorflow 1.11 whl:
$ wget <a href="https://github.com/lhelontra/tensorflow-on-arm/releases/download/v1.11.0/tensorflow-1.11.0-cp35-none-linux_aarch64.whl" rel="nofollow"> https://github.com/lhelontra/tensorflow-on-arm/re...</a>
- Install tensorflow:
$ sudo python3.5 -m pip install tensorflow-1.11.0-cp35-none-linux_aarch64.whl
- Clone OpenCV and OpenCV Contrib repositories:
$ sudo git clone -b 3.4 https://github.com/opencv/opencv.git && sudo git clone -b 3.4 https://github.com/opencv/opencv_contrib.git
- Go to directory:
$ cd opencv
- Create build directory and go to it:
$ sudo mkdir build && cd build
- Run CMake:
$ sudo cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D BUILD_opencv_java=OFF -D BUILD_opencv_python=OFF -D BUILD_opencv_python3=ON -D PYTHON3_DEFAULT_EXECUTABLE=$(which python3.5) -D PYTHON3_EXECUTABLE:FILEPATH=$(which python3.5) -D PYTHON_INCLUDE_DIR=/usr/local/include/python3.5m/ -D INSTALL_C_EXAMPLES=OFF -D INSTALL_PYTHON3_EXAMPLES=OFF -D BUILD_EXAMPLES=OFF -D WITH_CUDA=OFF -D BUILD_TESTS=OFF -D WITH_TBB=ON -DBUILD_TBB=ON -D OPENCV_ENABLE_NONFREE=ON -DBUILD_opencv_xfeatures2d=OFF -D OPENGL=ON -D OPENMP=ON -D ENABLE_NEON=ON -D BUILD_PERF_TESTS= OFF -D BUILD_OPENCV_DNN=ON -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules ..
- Compile OpenCV with 4 cores:
$ sudo make -j 4
- Install OpenCV :
$ sudo make install
- Go to the chosen directory:
(820c) $ cd ~ (410c) $ cd ~/sdfolder
- Go to scripts directory:
$ cd object_detector_tensorflow_opencv/scripts/
- Install Python3.5 requirements:
$ sudo python3.5 -m pip install -r requirements.txt --no-cache-dir
- Test imports:
$ python3.5 >> import cv2 >> import tensorflow
Obs: If cv2 returns import error, run make install in OpenCV build folder and try again.
- Go to the chosen directory:
(820c) $ cd ~ (410c) $ cd ~/sdfolder
- Download cocoapi repository:
$ git clone <a href="https://github.com/cocodataset/cocoapi.git" rel="nofollow"> https://github.com/cocodataset/cocoapi.git</a>
- Download Tensorflow models repository:
$ git clone <a href="https://github.com/tensorflow/models.git" rel="nofollow"> https://github.com/tensorflow/models.git</a>
- Go to this directory:
$ cd cocoapi/PythonAPI
- Edit the file Makefile, changing python to python3.5 in line 3 and 8 then save the file (using nano as an example):
$ nano Makefile
- Compile the cocoapi:
$ sudo make
Obs: If ‘make’ command doesn’t compile, try reinstalling cython with:
$ sudo python3.5 -m pip install cython
- Copy pycocotools to tensorflow /models/research directory:
(820c) $ cp -r pycocotools ~/models/research/ (410c) $ cp -r pycocotools ~/sdfolder/models/research/
- Go to the chosen directory:
(820c) $ cd ~ (410c) $ cd ~/sdfolder
- Go to models/research directory:
$ cd models/research
- Compile with protoc:
$ protoc object_detection/protos/*.proto --python_out=.
- Export environment variable:
$ export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
- Test the environment:
$ python3.5 object_detection/builders/model_builder_test.py
Obs: It must return OK, otherwise the aplication won't work. If don't, carefully search for any mistake in the process of installing the required frameworks.
Step 4: Running the Object Detection API
With all frameworks configured, it’s now possible to run the object detection API that uses OpenCV along with Tensorflow.
- Go to the chosen directory:
(820c) $ cd ~ (410c) $ cd ~/sdfolder
- Go to object detection directory:
$ cd object_detector_tensorflow_opencv/
- Now run the application:
$ python3.5 app.py
Now the Dragonboard will stream the video through the network. To see the output video open the browser in the DB and go to "0.0.0.0:5000".