Implement a motion-based multiple object tracking system This webinar assumes some experience with MATLAB and Image Processing Toolbox. We will focus on the Computer Vision System Toolbox. About the Presenter: Bruce Tannenbaum works on image processing and computer vision applications in technical marketing at MathWorks. Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of.
Install and Use Computer Vision Toolbox OpenCV Interface
Use the OpenCV Interface files to integrate your OpenCV C++code into MATLAB® and build MEX-files that call OpenCV functions.The support package also contains graphics processing unit (GPU) support.
Installation
After you install third-party support files, you can use the data with the Computer Vision Toolbox™ product. To install the Add-on support files, use one of the following methods:
- Select Get Add-ons from the Add-ons drop-down menu from the MATLAB desktop. The Add-on files are in the “MathWorks Features” section.
- Type
visionSupportPackages
in a MATLAB Command Window and follow the prompts.
Note
You must have write privileges for the installation folder.
When a new version of MATLAB software is released, repeatthis process to check for updates. You can also check for updatesbetween releases. Juvenile juve the great zip.
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Support Package Contents
The OpenCV Interface support files are installed in the
The visionopencv
folder. To find the path to this folder, type the following command:visionopencv
folder contain these files and folder.Files | Contents |
---|---|
example folder | Template Matching, Foreground Detector, and Oriented FAST and Rotated BRIEF (ORB) examples, including a GPU version. Each subfolder in the example folder contains a README.txt file with step-by-step instructions. |
registry folder | Registration files. |
mexOpenCV.m file | Function to build MEX-files. |
README.txt file | Help file. |
The
mex
function uses prebuilt OpenCV libraries, which ship with the Computer Vision Toolbox product. Your compiler must be compatible with the one used to build the libraries. The following compilers are used to build the OpenCV libraries for MATLAB host: Operating System | Compatible Compiler |
---|---|
Windows® 64 bit | Microsoft® Visual Studio® 2015 Professional or Visual Studio 2017 |
Linux® 64 bit | gcc-4.9.3 (g++) |
Mac 64 bit | Xcode 6.2.0 (Clang++) |
Create MEX-File from OpenCV C++ file
This example creates a MEX-file from a wrapper C++ file andthen tests the newly created file. The example uses the OpenCV templatematching algorithm wrapped in a C++ file, which is located in the
example/TemplateMatching
folder.- Change your current working folder to the
example/TemplateMatching
folder: - Create the MEX-file from the source file:
- Run the test script, which uses the generated MEX-file:
Use the OpenCV Interface C++ API
The
mexOpenCV
interface utility functions convert data between OpenCV and MATLAB. These functions support CPP-linkage only. GPU support is available on glnxa64, win64, and Mac platforms. The GPU-specific utility functions support CUDA enabled NVIDIA GPU with compute capability 2.0 or higher. See the Parallel Computing Toolbox™ System Requirements, The GPU utility functions require the Parallel Computing Toolbox software.Function | Description |
---|---|
ocvCheckFeaturePointsStruct | Check that MATLAB struct represents feature points |
ocvStructToKeyPoints | Convert MATLAB feature points struct to OpenCV KeyPoint vector |
ocvKeyPointsToStruct | Convert OpenCV KeyPoint vector to MATLAB struct |
ocvMxArrayToCvRect | Convert a MATLAB struct representing a rectangle to an OpenCV CvRect |
ocvCvRectToMxArray | Convert OpenCV CvRect to a MATLAB struct |
ocvCvBox2DToMxArray | Convert OpenCV CvBox2D to a MATLAB struct |
ocvCvRectToBoundingBox_{DataType} | Convert vector<cv::Rect> to M-by-4 mxArray of bounding boxes |
ocvMxArrayToSize_{DataType} | Convert 2-element mxArray to cv::Size |
ocvMxArrayToImage_{DataType} | Convert column major mxArray to row major cv::Mat for image |
ocvMxArrayToMat_{DataType} | Convert column major mxArray to row major cv::Mat for generic matrix |
ocvMxArrayFromImage_{DataType} | Convert row major cv::Mat to column major mxArray for image |
ocvMxArrayFromMat_{DataType} | Convert row major cv::Mat to column major mxArray for generic matrix. |
ocvMxArrayFromVector | Convert numeric vectorT to mxArray |
ocvMxArrayFromPoints2f | Converts vector<cv::Point2f> to mxArray |
GPU Function | Description |
---|---|
ocvMxGpuArrayToGpuMat_{DataType} | Create cv::gpu::GpuMat from gpuArray |
ocvMxGpuArrayFromGpuMat_{DataType} | Create gpuArray from cv::gpu::GpuMat |
Create Your Own OpenCV MEX-files
Call the
mxArray
function with your source file.For help creating MEX files, at the MATLAB command prompt,type:
Run OpenCV Examples
Each example subfolder in the OpenCV Interface support packagecontains all the files you need to run the example. To run an example,you must call the
mexOpenCV
function with one ofthe supplied source files.See Also
C Matrix API |
mxArray
Related Topics
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Design and test computer vision, 3D vision, and video processing systems
Computer Vision Toolbox™ provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. For 3D vision, the toolbox supports single, stereo, and fisheye camera calibration; stereo vision; 3D reconstruction; and lidar and 3D point cloud processing. Computer vision apps automate ground truth labeling and camera calibration workflows.
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Tutorials
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