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Computer Vision Intro OpenCV4 in Python with Deep Learning

Video Introducing this tutorial


Course Introduction and Setup :
Introduction
Introduction to Computer Vision and OpenCV
About this course
READ THIS - Guide to installing and setting up your OpenCV4.0.1 Virtual Machine
Recomended - Setup your OpenCV4.0.1 Virtual Machine
Installation of OpenCV & Python on Windows
Installation of OpenCV & Python on Mac
Installation of OpenCV & Python on Linux
Set up course materials (DOWNLOAD LINK BELOW) - Not needed if using the new VM

Basics of Computer Vision and OpenCV :
What are Images?
How are Images Formed?
Storing Images on Computers
Getting Started with OpenCV - A Brief OpenCV Intro
Grayscaling - Converting Color Images To Shades of Gray
Understanding Color Spaces - The Many Ways Color Images Are Stored Digitally
Histogram representation of Images - Visualizing the Components of Images
Creating Images & Drawing on Images - Make Squares, Circles, Polygons & Add Text

Image Manipulations & Processing :
Transformations, Affine And Non-Affine - The Many Ways We Can Change Images
Image Translations - Moving Images Up, Down. Left And Right
Rotations - How To Spin Your Image Around And Do Horizontal Flipping
Scaling, Re-sizing and Interpolations - Understand How Re-Sizing Affects Quality
Image Pyramids - Another Way of Re-Sizing
Cropping - Cut Out The Image The Regions You Want or Don't Want
Arithmetic Operations - Brightening and Darkening Images
Bitwise Operations - How Image Masking Works
Blurring - The Many Ways We Can Blur Images & Why It's Important
Sharpening - Reverse Your Images Blurs
Thresholding (Binarization) - Making Certain Images Areas Black or White
Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines
Edge Detection using Image Gradients & Canny Edge Detection
Perspective & Affine Transforms - Take An Off Angle Shot & Make It Look Top Down
Mini Project 1 - Live Sketch App - Turn your Webcam Feed Into A Pencil Drawing

Image Segmentation & Contours :
Segmentation and Contours - Extract Defined Shapes In Your Image
Sorting Contours - Sort Those Shapes By Size
Approximating Contours & Finding Their Convex Hull - Clean Up Messy Contours
Matching Contour Shapes - Match Shapes In Images Even When Distorted
Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
Line Detection - Detect Straight Lines E.g. The Lines On A Sudoku Game
Circle Detection
Blob Detection - Detect The Center of Flowers
Mini Project 3 - Counting Circles and Ellipses

Object Detection in OpenCV :
Object Detection Overview
Mini Project # 4 - Finding Waldo (Quickly Find A Specific Pattern In An Image)
Feature Description Theory - How We Digitally Represent Objects
Finding Corners - Why Corners In Images Are Important to Object Detection
SIFT, SURF, FAST, BRIEF & ORB - Learn The Different Ways To Get Image Features
Mini Project 5 - Object Detection - Detect A Specific Object Using Your Webcam
Histogram of Oriented Gradients - Another Novel Way Of Representing Images

Object Detection - Build a Face, People and Car/Vehicle Detectors :
HAAR Cascade Classifiers - Learn How Classifiers Work And Why They're Amazing
Face and Eye Detection - Detect Human Faces and Eyes In Any Image
Mini Project 6 - Car and Pedestrian Detection in Videos

Augmented Reality (AR) - Facial Landmark Identification (Face Swaps) :
Face Analysis and Filtering - Identify Face Outline, Lips, Eyes Even Eyebrows
Merging Faces (Face Swaps) - Combine Two Faces For Fun & Sometimes Scary Results
Mini Project 7 - Live Face Swapper (like MSQRD & Snapchat filters!!!)
Mini Project 8 - Yawn Detector and Counter

Simple Machine Learning using OpenCV :
Machine Learning Overview - What Is It & Why It's Important to Computer Vision
Mini Project 9 - Handwritten Digit Classification
Mini Project # 10 - Facial Recognition - Make Your Computer Recognize You

Object Tracking & Motion Analysis :
Filtering by Color
Background Subtraction and Foreground Subtraction
Using Meanshift for Object Tracking
Using CAMshift for Object Tracking
Optical Flow - Track Moving Objects In Videos
Mini Project # 11 - Ball Tracking

Computational Photography & Make a License Plate Reader :
Mini Project # 12 - Photo-Restoration
Mini Project # 13 - Automatic Number-Plate Recognition (ALPR)

Conclusion :
Course Summary and how to become an Expert
Latest Advances, 12 Startup Ideas & Implementing Computer VIsion in Mobile Apps

Deep Learning Computer Vision 1 - Setup a Deep Learning Virtual Machine :
Setup your Deep Learning Virtual Machine
Intro to Handwritten Digit Classification (MNIST)
Intro to Multiple Image Classification (CIFAR10)

Deep Learning Computer Vision 2 - Introduction to Neural Networks :
Neural Networks Chapter Overview
Machine Learning Overview
Neural Networks Explained
Forward Propagation
Activation Functions
Training Part 1 - Loss Functions
Training Part 2 - Backpropagation and Gradient Descent
Backpropagation & Learning Rates - A Worked Example
Regularization, Overfitting, Generalization and Test Datasets
Epochs, Iterations and Batch Sizes
Measuring Performance and the Confusion Matrix
Review and Best Practices

Deep Learning Computer Vision 3 - Intro to Convolutional Neural Networks (CNNs) :
Convolutional Neural Networks Chapter Overview
Introduction to Convolutional Neural Networks (CNNs)
Convolutions & Image Features
Depth, Stride and Padding
ReLU
Pooling
The Fully Connected Layer
Training CNNs
Designing Your Own CNN

Deep Learning Computer Vision 4 - Build CNNs in Python using Keras :
Introduction to Keras & Tensorflow
Building a CNN in Keras
Building a Handwriting Recognition CNN
Loading Our Data
Getting our data in ‘Shape’
Hot One Encoding
Building & Compiling Our Model
Training Our Classifier
Plotting Loss and Accuracy Charts
Saving and Loading Your Model
Displaying Your Model Visually
Building a Simple Image Classifier using CIFAR10

Deep Learning Computer Vision 5 - Build a Cats vs Dogs Classifier :
Data Augmentation Chapter Overview
Splitting Data into Test and Training Datasets
Train a Cats vs. Dogs Classifier
Boosting Accuracy with Data Augmentation
Types of Data Augmentation