OpenCV released OpenCV-3.4.4 and OpenCV-4.0.0 on 20th November. There have been a lot of bug fixes and other changes in these versions. The release highlights are as follows:
- OpenCV is now C++11 library and requires C++11-compliant compiler. Minimum required CMake version has been raised to 3.5.1.
- A lot of C API from OpenCV 1.x has been removed.
- Persistence (storing and loading structured data to/from XML, YAML or JSON) in the core module has been completely reimplemented in C++ and lost the C API as well.
- New module G-API has been added, it acts as an engine for very efficient graph-based image procesing pipelines.
- dnn module now includes experimental Vulkan backend and supports networks in ONNX format.
- The popular Kinect Fusion algorithm has been implemented and optimized for CPU and GPU (OpenCL)
QR code detector and decoder have been added to the objdetect module.
- Very efficient and yet high-quality DIS dense optical flow algorithm has been moved from opencv_contrib to the video module.
In this post, we will provide a bash script for installing OpenCV-4.0 (C++ and Python 3.6) on Ubuntu 18.04. We will also briefly study the script to understand what’s going in it. Note that this script will install OpenCV in a local directory and not on the entire system.
1. Install OpenCV 4.0
Step 0: Select OpenCV version to install
echo "OpenCV installation by learnOpenCV.com" # Define OpenCV Version to install cvVersion="master"
We are also going to clean
build directories and create
# Clean build directories rm -rf opencv/build rm -rf opencv_contrib/build
# Create directory for installation mkdir installation mkdir installation/OpenCV-"$cvVersion"
Finally, we will be storing the current working directory in
cwd variable. We are also going to refer to this directory as OpenCV_Home_Dir throughout this blog.
# Save current working directory cwd=$(pwd)
Step 1: Update Packages
sudo apt -y update sudo apt -y upgrade
Step 2: Install OS Libraries
sudo apt -y remove x264 libx264-dev ## Install dependencies sudo apt -y install build-essential checkinstall cmake pkg-config yasm sudo apt -y install git gfortran sudo apt -y install libjpeg8-dev libpng-dev sudo apt -y install software-properties-common sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main" sudo apt -y update sudo apt -y install libjasper1 sudo apt -y install libtiff-dev sudo apt -y install libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev sudo apt -y install libxine2-dev libv4l-dev cd /usr/include/linux sudo ln -s -f ../libv4l1-videodev.h videodev.h cd "$cwd" sudo apt -y install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev sudo apt -y install libgtk2.0-dev libtbb-dev qt5-default sudo apt -y install libatlas-base-dev sudo apt -y install libfaac-dev libmp3lame-dev libtheora-dev sudo apt -y install libvorbis-dev libxvidcore-dev sudo apt -y install libopencore-amrnb-dev libopencore-amrwb-dev sudo apt -y install libavresample-dev sudo apt -y install x264 v4l-utils # Optional dependencies sudo apt -y install libprotobuf-dev protobuf-compiler sudo apt -y install libgoogle-glog-dev libgflags-dev sudo apt -y install libgphoto2-dev libeigen3-dev libhdf5-dev doxygen
Step 3: Install Python Libraries
sudo apt -y install python3-dev python3-pip sudo -H pip3 install -U pip numpy sudo apt -y install python3-testresources
We are also going to install
virtualenvwrapper modules to create Python virtual environments.
cd $cwd ############ For Python 3 ############ # create virtual environment python3 -m venv OpenCV-"$cvVersion"-py3 echo "# Virtual Environment Wrapper" >> ~/.bashrc echo "alias workoncv-$cvVersion=\"source $cwd/OpenCV-$cvVersion-py3/bin/activate\"" >> ~/.bashrc source "$cwd"/OpenCV-"$cvVersion"-py3/bin/activate # now install python libraries within this virtual environment pip install wheel numpy scipy matplotlib scikit-image scikit-learn ipython dlib # quit virtual environment deactivate
To easily follow along this tutorial, please download installation script by clicking on the button below. It’s FREE!
Step 4: Download opencv and opencv_contrib
git clone https://github.com/opencv/opencv.git cd opencv git checkout $cvVersion cd .. git clone https://github.com/opencv/opencv_contrib.git cd opencv_contrib git checkout $cvVersion cd ..
Step 5: Compile and install OpenCV with contrib modules
First we navigate to the build directory.
cd opencv mkdir build cd build
Next, we start the compilation and installation process.
cmake -D CMAKE_BUILD_TYPE=RELEASE \ -D CMAKE_INSTALL_PREFIX=$cwd/installation/OpenCV-"$cvVersion" \ -D INSTALL_C_EXAMPLES=ON \ -D INSTALL_PYTHON_EXAMPLES=ON \ -D WITH_TBB=ON \ -D WITH_V4L=ON \ -D OPENCV_PYTHON3_INSTALL_PATH=$cwd/OpenCV-$cvVersion-py3/lib/python3.5/site-packages \ -D WITH_QT=ON \ -D WITH_OPENGL=ON \ -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \ -D BUILD_EXAMPLES=ON ..
make -j4 make install
2. How to use OpenCV in C++
The basic structure of your CMakeLists.txt will be as follows:
cmake_minimum_required(VERSION 3.1) # Enable C++11 set(CMAKE_CXX_STANDARD 11) set(CMAKE_CXX_STANDARD_REQUIRED TRUE)
You will have to set OpenCV_DIR as shown below.
Make sure that you replace OpenCV_Home_Dir with correct path. For example, in my case:
Once you have made your CMakeLists.txt, follow the steps given below.
mkdir build && cd build cmake .. cmake --build . --config Release
This will generate your executable file in build directory.
3. How to use OpenCV in Python
To use the OpenCV version installed using Python script, first we activate the correct Python Virtual Environment.
For OpenCV-4 : Python 3
Once you have activated the virtual environment, you can enter Python shell and test OpenCV version.
ipython import cv2 print(cv2.__version__)
Hope this script proves to be useful for you :). Stay tuned for more interesting stuff. In case of any queries, feel free to comment below and we will get back to you as soon as possible.
Subscribe & Download Code
If you liked this article and would like to download code (C++ and Python) and example images used in this post, please subscribe to our newsletter. You will also receive a free Computer Vision Resource Guide. In our newsletter, we share OpenCV tutorials and examples written in C++/Python, and Computer Vision and Machine Learning algorithms and news.