Python and OpenCV for Computer Vision Applications Bespoke

Day-1:

1. Introduction to Python

   1. Installing and running Python
      1. Local computer
      2. On cloud, and
      3. Using Docker container
2. 2. Using Jupyter Notebook
   3. Python Data Types (List, Tuples and Dictionary etc)
   4. Loops and functions
   5. Introduction to Matplotlib and image visualization

Hands on Session: Opening images with Matplotlib

2. Image Processing using Python
   1. Introduction to Numpy and Scikit-image
   2. Exploring colour and gray images
   3. Histogram
   4. Histogram equalization and thresholding
   5. Sensors interfacing
   6. Image filters
   7. Edge detection

Hands on Session:

1. 1. 1. Applying filters and edge detection using Scikit-image
      2. Image compression and decompression

2. OpenCV and Image Processing

   1. Introduction to OpenCV
   2. Difference between Python (Matplotlib) and OpenCV
   3. Reading images and videos
   4. Drawing different geometric shapes
   5. Basic image Arithmetic
   6. How to Flip, resize an image, how to draw shapes and write on an image

Hands on Session:

1. 1. 1. Applying various transformations on images
      2. Write on each frame of Video

Day-2:

Applications of Computer Vision

1. 1. Face detection
      1. Feature detections
      2. SIFT and SURF
      3. Image Matching
      4. Denoising

Hands on Session: Face Detection

2. Object Detection
   1. 1. In Object detection introduction and Deep learning
      2. Explore the Dataset
      3. Setup Training and Validation Data Generators
      4. Create a Convolutional Neural Network (CNN) Model
      5. Train and Evaluate Model
      6. Using model

Hands on Session: Object detection with YOLOv3

3. Digit Recognition with Raspberry PI
   1. 1. Introduction to Raspberry Pi
      2. Introduction to MNIST dataset
      3. Object detection models

Hands on Session: Digit Recognition with Raspberry Pi camera