OpenCV Computer Vision Training

What You Will Learn

  • Create powerful GUIs for computer vision applications with panels, scroll panes, radio buttons, sliders, windows, and mouse interaction using the popular Swing GUI widget toolkit
  • Stretch, shrink, warp, and rotate images, as well as apply image transforms to find edges, lines, and circles, and even use Discrete Fourier Transforms (DFT)
  • Detect foreground or background regions and work with depth images with a Kinect device
  • Learn how to add computer vision capabilities to rock solid Java web applications allowing you to upload photos and create astonishing effects
  • Track faces and apply mixed reality effects such as adding virtual hats to uploaded photos
  • Filter noisy images, work with morphological operators, use flood fill, and threshold the important regions of an image
  • Open and process video streams from webcams or video files


Course Contents

1. Setting Up OpenCV for Java

  • Getting OpenCV for Java development
  • Building OpenCV from the source code
  • The Java OpenCV project in Eclipse
  • The NetBeans configuration
  • A Java OpenCV simple application
  • Building your project with Ant
  • The Java OpenCV Maven configuration
  • Creating a Windows Java OpenCV Maven project pointing to the Packt repository
  • Creating a Java OpenCV Maven project pointing to a local repository

2. Handling Matrices, Files, Cameras, and GUIs

  • Basic matrix manipulation
  • Pixel manipulation
  • Loading and displaying images from files
  • Displaying an image with Swing
  • Capturing a video from a camera
  • Video playback
  • Swing GUI’s integration with OpenCV

3. Image Filters and Morphological Operators

  • Smoothing
  • Averaging
  • Gaussian
  • Median filtering
  • Bilateral filtering
  • Morphological operators
  • Flood filling
  • Image pyramids
  • Thresholding

4. Image Transforms

  • The Gradient and Sobel derivatives
  • The Laplace and Canny transforms
  • The line and circle Hough transforms
  • Geometric transforms – stretch, shrink, warp, and rotate
  • Discrete Fourier Transform and Discrete Cosine Transform
  • Integral images
  • Distance transforms
  • Histogram equalization

5. Object Detection Using Ada Boost and Haar Cascades

  • The boosting theory
  • AdaBoost
  • Cascade classifier detection and training
  • Detection
  • Training

6. Detecting Foreground and Background Regions and Depth with a Kinect Device

  • Background subtraction
  • Frame differencing
  • Averaging a background method
  • The mixture of Gaussians method
  • Contour finding
  • Kinect depth maps
  • The Kinect setup
  • The driver setup
  • The OpenCV Kinect support
  • The Kinect depth application

7. OpenCV on the Server Side

  • Setting up an OpenCV web application
  • Creating a Maven-based web application
  • Adding OpenCV dependencies
  • Running the web application
  • Importing the project to Eclipse
  • Mixed reality web applications
  • Image upload
  • Image processing
  • The response image

Call : +91 9176HADOOP / 044 – 42645495


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