Duration
2 days
What you'll learn
- 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 and Image Basics
- Introduction to Numpy and Image Section
- NumPy Arrays
- What is an image?
- Images and NumPy
Image Basics with OpenCV
- Introduction to Images and OpenCV Basics
- Opening Image files in a notebook
- Opening Image files with OpenCV
- Drawing on Images - Part One - Basic Shapes
- Drawing on Images Part Two - Text and Polygons
- Direct Drawing on Images with a mouse
Image Processing
- Introduction to Image Processing
- Color Mappings
- Blending and Pasting Images
- Image Thresholding
- Blurring and Smoothing
- Morphological Operators
- Gradients
- Histograms
Video Basics with Python and OpenCV
- Introduction to Video Basics
- Connecting to Camera
- Using Video Files
- Drawing on Live Camera
Object Detection with OpenCV and Python
- Introduction to Object Detection
- Template Matching
- Corner Detection
- Edge Detection
- Grid Detection
- Contour Detection
- Feature Matching
- Watershed Algorithm
- Custom Seeds with Watershed Algorithm
- Introduction to Face Detection
- Face Detection with OpenCV
Object Tracking
- Introduction to Object Tracking
- Optical Flow
- Optical Flow Coding with OpenCV
- MeanShift and CamShift Tracking Theory
- MeanShift and CamShift Tracking with OpenCV
- Overview of various Tracking API Methods
- Tracking APIs with OpenCV
Deep Learning for Computer Vision
- Introduction to Deep Learning for Computer Vision
- Machine Learning Basics
- Understanding Classification Metrics
- Introduction to Deep Learning Topics
- Understanding a Neuron
- Understanding a Neural Network
- Cost Functions
- Gradient Descent and Back Propagation
- Keras Basics
- MNIST Data Overview
- Convolutional Neural Networks Overview
- Keras Convolutional Neural Networks with MNIST
- Keras Convolutional Neural Networks with CIFAR-10
- LINK FOR CATS AND DOGS ZIP
- Deep Learning on Custom Images
Programme scope, delivery arrangements and suitability are confirmed during enquiry.