Using OpenCV with MATLAB
MATLAB® and OpenCV are complementary tools for algorithm development, image and video analysis, and vision system design. MATLAB provides integration with OpenCV through the OpenCV C++ API.
Last Updated: 2014/08/26 This tutorial documents step by step how to install Xcode, OpenCV (with homebrew) and mexopencv (for use of OpenCV in MATLAB). There are also two coding examples that instruct how to setup and run a C example in Xcode and an m-file example in MATLAB. Hi, I have to manual contour an image.The manual contours are first centered and transformed to the same scale and orientation. Then they are automatically discretized into 50 ordered landmark points using geometry features such as maximum curvature. Could any1 help me with this in matlab.
By integrating OpenCV with MATLAB, you can:
- Use and explore current research algorithms, whether they are implemented in MATLAB or OpenCV
- Use OpenCV algorithms with the convenience of the data access, image acquisition, and visualization capabilities in MATLAB
- Use MATLAB to explore, analyze, and debug designs that incorporate OpenCV algorithms
The OpenCV interface makes it easy to bring single functions and entire OpenCV-based C++ projects into MATLAB using MEX. The OpenCV interface provides:
- Prebuilt OpenCV binaries that eliminate the need to compile and build OpenCV
- Build script to create OpenCV based MEX-files
- Data type conversions between MATLAB and OpenCV
- Examples to help you get started with common workflows such as feature detection and extraction, image processing, and motion estimation
You can get started using this quick command line example:
Full details about installing the OpenCV support package and this example are explained in detail in the Using OpenCV with MATLAB video. This video includes instructions for downloading the support package, understanding and using the syntax, and exploration of examples included in the download.
Computer Vision with MATLAB
MATLAB and Computer Vision Toolbox™ offer functionality not available in OpenCV. The toolbox provides algorithms for object detection, image recognition, and 3D lidar processing. Interactive apps such as the Camera Calibration App and Image Labeling App can save significant time when developing image algorithms.
MATLAB has new capabilities in deep learning for computer vision including access to the latest deep learning models, and training acceleration using multiple GPUs, the cloud, or clusters. You can convert your models to CUDA code with GPU Coder™. Generated CUDA code runs models up to 7x faster than TensorFlow.
Calling MATLAB from C++ and Python Applications
Interact with MATLAB functions and data types from other programming languages through MATLAB Engine:
- For documentation on calling MATLAB within Python using MATLAB Engine, see the MATLAB API for Python documentation.
- For documentation on calling MATLAB from C++ applications, see Calling MATLAB from C and C++ applications.
Examples and How To
- MATLAB and OpenCV - File Exchange
- MATLAB and Python - Examples
- Integrating MATLAB into your C/C++ Product Development Workflow (40:52) - Video
Software Reference
- Using the OpenCV C++ Interface - Documentation
- Getting Started with Python in MATLAB - Documentation
See also: object detection, image recognition, object recognition, stereo vision, feature extraction, point cloud
This page provides a guide on how to install mexopencv with MATLAB on Ubuntu.It covers OpenCV 3 and latest mexopencv.
We compile OpenCV with 'contrib' modules, which provide non-free featuressuch as SIFT and SURF, as well as other experimental algorithms not includedin main distribution.
The instructions below are meant for Ubuntu. Other Debian-like distroswill probably also have these packages or similarly named ones available.Adjust accordingly for other Linux distributions.
1) OpenCV
Here we will build
opencv
+ opencv_contrib
from source(this requires about 2 to 3GB of free disk space).The instructions below are similar to those in the official tutorial.
NOTE: If you had previously installed OpenCV 2.x package from Ubuntu,it would be better to remove it before continuing with OpenCV 3.x:
if previously installed from source, do:
sudo make uninstall
.This step is not mandatory, it is only suggested to avoid any conflictsin the libraries. In fact, you can have both OpenCV 2.x and 3.x installedside-by-side, as long as they are not both installed system-wide but locally.In this case, you will have to manually manage locations by using environmentvariables like
PKG_CONFIG_PATH
and LD_LIBRARY_PATH
to switch between thetwo installations. In the rest of this guide, we assume that only OpenCV 3 isinstalled.We start by installing some build dependencies(some are required, others are optional):
Then we download OpenCV 3.4.1 sources:
Next we build and install it:
Finally we check the output of
pkg-config
to verify the installation:Mac Install Opencv
2) mexopencv
Download the latest version of mexopencv:
Compile the MEX-files for MATLAB:
Mac Install Opencv Python
Once it's done, you can start using OpenCV functions in MATLAB:
Open Cv
You might wanna use
savepath()
if you don't want to have to repeat theaddpath()
calls every time MATLAB is started.Manual Install Opencv Mac Matlab Tutorial
To verify the installation, you can optionally run the full test suite: