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DLIP
  • Introduction
  • Prerequisite
  • Image Processing Basics
    • Notes
      • Thresholding
      • Spatial Filtering
      • Masking with Bitwise Operation
      • Model n Calibration
    • Tutorial
      • Tutorial: Install OpenCV C++
      • Tutorial: Create OpenCV Project
      • Tutorial: C++ basics
      • Tutorial: OpenCV Basics
      • Tutorial: Image Watch for Debugging
      • Tutorial: Spatial Filter
      • Tutorial: Thresholding and Morphology
      • Tutorial: Camera Calibration
      • Tutorial: Color Image Processing
      • Tutorial: Edge Line Circle Detection
      • Tutorial: Corner Detection and Optical Flow
      • Tutorial: OpenCV C++ Cheatsheet
      • Tutorial: Installation for Py OpenCV
      • Tutorial: OpenCv (Python) Basics
    • LAB
      • Lab Report Template
      • Lab Report Grading Criteria
      • LAB Report Instruction
      • LAB: Grayscale Image Segmentation
        • LAB: Grayscale Image Segmentation -Gear
        • LAB: Grayscale Image Segmentation - Bolt and Nut
      • LAB: Color Image Segmentation
        • LAB: Facial Temperature Measurement with IR images
        • LAB: Magic Cloak
      • LAB: Straight Lane Detection and Departure Warning
      • LAB: Dimension Measurement with 2D camera
      • LAB: Tension Detection of Rolling Metal Sheet
  • Deep Learning for Perception
    • Notes
      • Lane Detection with Deep Learning
      • Overview of Deep Learning
        • Object Detection
        • Deep Learning Basics: Introduction
        • Deep Learning State of the Art
        • CNN, Object Detection
      • Perceptron
      • Activation Function
      • Optimization
      • Convolution
      • CNN Overview
      • Evaluation Metric
      • LossFunction Regularization
      • Bias vs Variance
      • BottleNeck Unit
      • Object Detection
      • DL Techniques
        • Technical Strategy by A.Ng
    • Tutorial - PyTorch
      • Tutorial: Install PyTorch
      • Tutorial: Python Numpy
      • Tutorial: PyTorch Tutorial List
      • Tutorial: PyTorch Example Code
      • Tutorial: Tensorboard in Pytorch
      • Tutorial: YOLO in PyTorch
        • Tutorial: Yolov8 in PyTorch
        • Tutorial: Train Yolo v8 with custom dataset
          • Tutorial: Train Yolo v5 with custom dataset
        • Tutorial: Yolov5 in Pytorch (VS code)
        • Tutorial: Yolov3 in Keras
    • LAB
      • Assignment: CNN Classification
      • Assignment: Object Detection
      • LAB: CNN Object Detection 1
      • LAB: CNN Object Detection 2
      • LAB Grading Criteria
    • Tutorial- Keras
      • Train Dataset
      • Train custom dataset
      • Test model
      • LeNet-5 Tutorial
      • AlexNet Tutorial
      • VGG Tutorial
      • ResNet Tutorial
    • Resource
      • Online Lecture
      • Programming tutorial
      • Books
      • Hardware
      • Dataset
      • Useful sites
  • Must Read Papers
    • AlexNet
    • VGG
    • ResNet
    • R-CNN, Fast-RCNN, Faster-RCNN
    • YOLOv1-3
    • Inception
    • MobileNet
    • SSD
    • ShuffleNet
    • Recent Methods
  • DLIP Project
    • Report Template
    • DLIP 2021 Projects
      • Digital Door Lock Control with Face Recognition
      • People Counting with YOLOv4 and DeepSORT
      • Eye Blinking Detection Alarm
      • Helmet-Detection Using YOLO-V5
      • Mask Detection using YOLOv5
      • Parking Space Management
      • Vehicle, Pedestrian Detection with IR Image
      • Drum Playing Detection
      • Turtle neck measurement program using OpenPose
    • DLIP 2022 Projects
      • BakeryCashier
      • Virtual Mouse
      • Sudoku Program with Hand gesture
      • Exercise Posture Assistance System
      • People Counting Embedded System
      • Turtle neck measurement program using OpenPose
    • DLIP Past Projects
  • Installation Guide
    • Installation Guide for Pytorch
      • Installation Guide 2021
    • Anaconda
    • CUDA cuDNN
      • CUDA 10.2
    • OpenCV
      • OpenCV Install and Setup
        • OpenCV 3.4.13 with VS2019
        • OpenCV3.4.7 VS2017
        • MacOS OpenCV C++ in XCode
      • Python OpenCV
      • MATLAB-OpenCV
    • Framework
      • Keras
      • TensorFlow
        • Cheat Sheet
        • Tutorial
      • PyTorch
    • IDE
      • Visual Studio Community
      • Google Codelab
      • Visual Studio Code
        • Python with VS Code
        • Notebook with VS Code
        • C++ with VS Code
      • Jupyter Notebook
        • Install
        • How to use
    • Ubuntu
      • Ubuntu 18.04 Installation
      • Ubuntu Installation using Docker in Win10
      • Ubuntu Troubleshooting
    • ROS
  • Programming
    • Python_Numpy
      • Python Tutorial - Tips
      • Python Tutorial - For Loop
      • Python Tutorial - List Tuple, Dic, Set
    • Markdown
      • Example: API documentation
    • Github
      • Create account
      • Tutorial: Github basic
      • Tutorial: Github Desktop
    • Keras
      • Tutorial Keras
      • Cheat Sheet
    • PyTorch
      • Cheat Sheet
      • Autograd in PyTorch
      • Simple ConvNet
      • MNIST using LeNet
      • Train ConvNet using CIFAR10
  • Resources
    • Useful Resources
    • Github
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  1. Installation Guide
  2. OpenCV

MATLAB-OpenCV

PreviousPython OpenCVNextFramework

Last updated 3 years ago

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Using OpenCV in Matlab

Computer Vision Toolbox OpenCV Interface

Requirement: Computer Vision Toolbox

The Computer Vision System Toolbox OpenCV Interface enables you to bring existing OpenCV files and projects into MATLAB using MEX. The support package includes: • Data type conversions between MATLAB and OpenCV • Examples showing how to interface OpenCV and MATLAB Opening the .mlpkginstall file from your operating system or from within MATLAB will initiate the installation process available for the release you have. This .mlpkginstall file is functional for R2014b and beyond. Watch this video to learn more about the package:

How to Install

  • Type in the following in Matlab Command Line

>> visionSupportPackages
  • Select Computer Vision Toolbox OpenCV Interface and install

  • After installation setup the compiler by mex -setup c++

  • Choose the visual studio C++ compiler version (e.g. V.S 2017)

  • Check where the toolbox package is located in your computer which mexOpenCV

How to Use

  • You can get started using this quick command-line example: (MATLAB 2020a)

%To run the Oriented FAST and Rotated BRIEF (ORB) example, follow these steps:

%1. Change your current working folder to example/ORB where source files
%detectORBFeaturesOCV.cpp and extractORBFeaturesOCV.cpp are located

%2. Create MEX-file for the detector from the source file:
mexOpenCV detectORBFeaturesOCV.cpp

%3. Create MEX-file for the extractor from source file:
mexOpenCV extractORBFeaturesOCV.cpp

%4. Run the test script:
testORBFeaturesOCV.m 

%The test script uses the generated MEX-files.
  • Refer to MATLAB_OpenCV examples

  • Read OpenCV with MATLAB for more info

http://youtu.be/BasC2jkgyaM
LogoInstall and Use Computer Vision Toolbox Interface for OpenCV in MATLAB - MATLAB & Simulink
LogoUsing OpenCV with MATLAB and Simulink