Python for Computer Vision with OpenCV and Deep Learning

Course Curriculum

Chapter 1: Course Overview and Introduction

Lecture 1: Course Overview

Lecture 2: FAQ – Frequently Asked Questions

Lecture 3: Course Curriculum Overview

Lecture 4: Getting Set-Up for the Course Content

Chapter 2: NumPy and Image Basics

Lecture 1: Introduction to Numpy and Image Section

Lecture 2: NumPy Arrays

Lecture 3: What is an image?

Lecture 4: Images and NumPy

Lecture 5: NumPy and Image Assessment Test

Lecture 6: NumPy and Image Assessment Test – Solutions

Chapter 3: Image Basics with OpenCV

Lecture 1: Introduction to Images and OpenCV Basics

Lecture 2: Opening Image files in a notebook

Lecture 3: Opening Image files with OpenCV

Lecture 4: Drawing on Images – Part One – Basic Shapes

Lecture 5: Drawing on Images Part Two – Text and Polygons

Lecture 6: Direct Drawing on Images with a mouse – Part One

Lecture 7: Direct Drawing on Images with a mouse – Part Two

Lecture 8: Direct Drawing on Images with a mouse – Part Three

Lecture 9: Image Basics Assessment

Lecture 10: Image Basics Assessment Solutions

Chapter 4: Image Processing

Lecture 1: Introduction to Image Processing

Lecture 2: Color Mappings

Lecture 3: Blending and Pasting Images

Lecture 4: Blending and Pasting Images Part Two – Masks

Lecture 5: Image Thresholding

Lecture 6: Blurring and Smoothing

Lecture 7: Blurring and Smoothing – Part Two

Lecture 8: Morphological Operators

Lecture 9: Gradients

Lecture 10: Histograms – Part One

Lecture 11: Histograms – Part Two – Histogram Eqaulization

Lecture 12: Histograms Part Three – Histogram Equalization

Lecture 13: Image Processing Assessment

Lecture 14: Image Processing Assessment Solutions

Chapter 5: Video Basics with Python and OpenCV

Lecture 1: Introduction to Video Basics

Lecture 2: Connecting to Camera

Lecture 3: Using Video Files

Lecture 4: Drawing on Live Camera

Lecture 5: Video Basics Assessment

Lecture 6: Video Basics Assessment Solutions

Chapter 6: Object Detection with OpenCV and Python

Lecture 1: Introduction to Object Detection

Lecture 2: Template Matching

Lecture 3: Corner Detection – Part One – Harris Corner Detection

Lecture 4: Corner Detection – Part Two – Shi-Tomasi Detection

Lecture 5: Edge Detection

Lecture 6: Grid Detection

Lecture 7: Contour Detection

Lecture 8: Feature Matching – Part One

Lecture 9: Feature Matching – Part Two

Lecture 10: Watershed Algorithm – Part One

Lecture 11: Watershed Algorithm – Part Two

Lecture 12: Custom Seeds with Watershed Algorithm

Lecture 13: Introduction to Face Detection

Lecture 14: Face Detection with OpenCV

Lecture 15: Detection Assessment

Lecture 16: Detection Assessment Solutions

Chapter 7: Object Tracking

Lecture 1: Introduction to Object Tracking

Lecture 2: Optical Flow

Lecture 3: Optical Flow Coding with OpenCV – Part One

Lecture 4: Optical Flow Coding with OpenCV – Part Two

Lecture 5: MeanShift and CamShift Tracking Theory

Lecture 6: MeanShift and CamShift Tracking with OpenCV

Lecture 7: Overview of various Tracking API Methods

Lecture 8: Tracking APIs with OpenCV

Chapter 8: Deep Learning for Computer Vision

Lecture 1: Introduction to Deep Learning for Computer Vision

Lecture 2: Machine Learning Basics

Lecture 3: Understanding Classification Metrics

Lecture 4: Introduction to Deep Learning Topics

Lecture 5: Understanding a Neuron

Lecture 6: Understanding a Neural Network

Lecture 7: Cost Functions

Lecture 8: Gradient Descent and Back Propagation

Lecture 9: Keras Basics

Lecture 10: MNIST Data Overview

Lecture 11: Convolutional Neural Networks Overview – Part One

Lecture 12: Convolutional Neural Networks Overview – Part Two

Lecture 13: Keras Convolutional Neural Networks with MNIST

Lecture 14: Keras Convolutional Neural Networks with CIFAR-10

Lecture 15: LINK FOR CATS AND DOGS ZIP

Lecture 16: Deep Learning on Custom Images – Part One

Lecture 17: Deep Learning on Custom Images – Part Two

Lecture 18: Deep Learning and Convolutional Neural Networks Assessment

Lecture 19: Deep Learning and Convolutional Neural Networks Assessment Solutions

Lecture 20: Introduction to YOLO v3

Lecture 21: YOLO Weights Download

Lecture 22: YOLO v3 with Python

Chapter 9: Capstone Project

Lecture 1: Introduction to CapStone Project

Lecture 2: Capstone Part One – Variables and Background function

Lecture 3: Capstone Part Two – Segmentation

Lecture 4: Capstone Part Three – Counting and ConvexHull

Lecture 5: Capstone Part Four – Bringing it all together