Computer Vision with Python OpenCV

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Course Overview

OpenCV, once released by INTEL to benchmark their processors, comes with a huge array of functionality, even deep learning-based, and allows for efficient image processing and computer vision. Tracking, Object Detection, Facial Recognition, and much more – all in one library. Understanding how to work with image and video data is becoming vital in modern applications. The most popular platforms in the world are generating never before seen amounts of image and video data. Every 60 seconds users upload more than 300 hours of video to Youtube, Netflix subscribers stream over 80,000 hours of video, and Instagram users like over 2 million photos!

Private classes on this topic are available. We can address your organization’s issues, time constraints, and save you money, too. Contact us to find out how.

This course includes 6-months access to the full course content in on-demand format to support post-class reference and review.

Who Should Attend

Intermediate to experienced Python developers or experienced developers coming from another programming language.

Course Objectives

    • Understand basics of NumPy
    • Manipulate and open Images with NumPy
    • Use OpenCV to work with image files
    • Use Python and OpenCV to draw shapes on images and videos
    • Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.
    • Create Color Histograms with OpenCV
    • Open and Stream video with Python and OpenCV
    • Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python
    • Create Face Detection Software
    • Segment Images with the Watershed Algorithm
    • Track Objects in Video
    • Use Python and Deep Learning to build image classifiers
    • Work with Tensorflow, Keras, and Python to train on your own custom images.

Course Outline

Numpy

Image Handling with Python & OpenCV

  • Image I/O
  • Thresholding
  • Filtering
  • Histograms
  • Template Matching
  • Gradient based Methods
  • Edge Detection
  • Object Detection
  • Facial Recognition (you cover face detection, but expansion to facial recognition is trivial with dlib – Lets discuss!)

Video Handling with Python & OpenCV

  • Video & Webcam I/O
  • Optical Flow
  • Tracking

Deep Learning

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Class Dates & Times

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This is a 3-day class

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The classes listed are available to all United Training customers and does not reflect, in any way,
the availability or support of technology within the University of Maine System.