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Course Content
1. Course Introduction and Setup
0/6
Introduction
02:05
Introduction to Computer Vision and OpenCV
03:08
About this course
05:14
Recomended – Setup your OpenCV4.0.1 Virtual Machine
05:41
Installation of OpenCV & Python on Windows
08:53
Set up course materials (DOWNLOAD LINK BELOW) – Not needed if using the new VM
01:42
2. Basics of Computer Vision and OpenCV
0/8
What are Images
02:27
How are Images Formed
03:20
Storing Images on Computers
05:24
Getting Started with OpenCV – A Brief OpenCV Intro
05:24
Grayscaling – Converting Color Images To Shades of Gray
01:59
Understanding Color Spaces – The Many Ways Color Images Are Stored Digitally
12:12
Histogram representation of Images – Visualizing the Components of Images
04:37
Creating Images & Drawing on Images – Make Squares, Circles, Polygons & Add Text
03:47
3. Image Manipulations & Processing
0/15
Transformations, Affine And Non-Affine – The Many Ways We Can Change Images
02:22
Image Translations – Moving Images Up, Down. Left And Right
02:47
Rotations – How To Spin Your Image Around And Do Horizontal Flipping
03:11
Scaling, Re-sizing and Interpolations – Understand How Re-Sizing Affects Quality
04:27
Image Pyramids – Another Way of Re-Sizing
01:53
Cropping – Cut Out The Image The Regions You Want or Don’t Want
02:42
Arithmetic Operations – Brightening and Darkening Images
03:36
Bitwise Operations – How Image Masking Works
03:36
Blurring – The Many Ways We Can Blur Images & Why It’s Important
07:28
Sharpening – Reverse Your Images Blurs
01:51
Thresholding (Binarization) – Making Certain Images Areas Black or White
08:39
Dilation, Erosion, OpeningClosing – Importance of ThickeningThinning Lines
04:57
Edge Detection using Image Gradients & Canny Edge Detection
04:52
Perspective & Affine Transforms – Take An Off Angle Shot & Make It Look Top Down
03:55
Mini Project 1 – Live Sketch App – Turn your Webcam Feed Into A Pencil Drawing
05:02
4. Image Segmentation & Contours
0/8
Segmentation and Contours – Extract Defined Shapes In Your Image
11:11
Sorting Contours – Sort Those Shapes By Size
13:00
Approximating Contours & Finding Their Convex Hull – Clean Up Messy Contours
05:41
Matching Contour Shapes – Match Shapes In Images Even When Distorted
05:27
Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
05:29
Detect Straight Lines E.g. The Lines On A Sudoku Game
06:24
Blob Detection – Detect The Center of Flowers
03:20
Mini Project 3 – Counting Circles and Ellipses
06:06
5. Object Detection in OpenCV
0/7
Object Detection Overview
03:20
Mini Project # 4 – Finding Waldo (Quickly Find A Specific Pattern In An Image)
02:45
Feature Description Theory – How We Digitally Represent Objects
04:37
Finding Corners – Why Corners In Images Are Important to Object Detection
06:45
SIFT, SURF, FAST, BRIEF & ORB – Learn The Different Ways To Get Image Features
10:16
Mini Project 5 – Object Detection – Detect A Specific Object Using Your Webcam
14:57
Histogram of Oriented Gradients – Another Novel Way Of Representing Images
08:09
6. Object Detection – Build a Face, People and CarVehicle Detectors
0/3
HAAR Cascade Classifiers – Learn How Classifiers Work And Why They’re Amazing
05:12
Face and Eye Detection – Detect Human Faces and Eyes In Any Image
10:40
Mini Project 6 – Car and Pedestrian Detection in Videos
06:46
7. Augmented Reality (AR) – Facial Landmark Identification (Face Swaps)
0/4
Face Analysis and Filtering – Identify Face Outline, Lips, Eyes Even Eyebrows
10:56
Merging Faces (Face Swaps) – Combine Two Faces For Fun & Sometimes Scary Results
09:27
Mini Project 7 – Live Face Swapper (like MSQRD & Snapchat filters!!!)
06:07
Mini Project 8 – Yawn Detector and Counter
08:44
8. Simple Machine Learning using OpenCV
0/3
Machine Learning Overview – What Is It & Why It’s Important to Computer Vision
08:54
Mini Project 9 – Handwritten Digit Classification
19:59
Facial Recognition – Make Your Computer Recognize You
12:07
9. Object Tracking & Motion Analysis
0/6
Filtering by Color
06:15
Background Subtraction and Foreground Subtraction
06:54
Using Meanshift for Object Tracking
04:55
Using CAMshift for Object Tracking
04:04
Optical Flow – Track Moving Objects In Videos
07:17
Mini Project # 11 – Ball Tracking
05:01
10. Computational Photography & Make a License Plate Reader
0/1
Mini Project # 12 – Photo-Restoration
06:34
11. Conclusion
0/2
Course Summary and how to become an Expert
02:50
Latest Advances, 12 Startup Ideas & Implementing Computer VIsion in Mobile Apps
07:06
12. BONUS – Deep Learning Computer Vision 1 – Setup a Deep Learning Virtual Machine
0/3
Setup your Deep Learning Virtual Machine
10:28
Intro to Handwritten Digit Classification (MNIST)
05:46
Intro to Multiple Image Classification (CIFAR10)
02:52
13. BONUS – Deep Learning Computer Vision 2 – Introduction to Neural Networks
0/12
Neural Networks Chapter Overview
01:34
Machine Learning Overview
08:26
Neural Networks Explained
03:50
Forward Propagation
08:34
Activation Functions
08:31
Training Part 1 – Loss Functions
09:13
Training Part 2 – Backpropagation and Gradient Descent
09:57
Backpropagation & Learning Rates – A Worked Example
13:35
Regularization, Overfitting, Generalization and Test Datasets
15:24
Epochs, Iterations and Batch Sizes
03:37
Measuring Performance and the Confusion Matrix
07:06
Review and Best Practices
04:15
14. BONUS – Deep Learning Computer Vision 3 – Convolutional Neural Networks (CNNs)
0/9
Convolutional Neural Networks Chapter Overview
00:59
Introduction to Convolutional Neural Networks (CNNs)
05:24
Convolutions & Image Features
13:19
Depth, Stride and Padding
06:50
ReLU
01:47
Pooling
04:37
The Fully Connected Layer
02:08
Training CNNs
03:07
Designing Your Own CNN
03:48
15. BONUS – Deep Learning Computer Vision 4 – Build CNNs in Python using Keras
0/12
Introduction to Keras & Tensorflow
01:03
Building a CNN in Keras
12:15
Building a Handwriting Recognition CNN
01:48
Loading Our Data
05:42
Getting our data in ‘Shape’
04:04
Hot One Encoding
02:54
Building & Compiling Our Model
03:45
Training Our Classifier
04:57
Plotting Loss and Accuracy ChartsDraft Lesson
02:52
Saving and Loading Your Model
02:50
Displaying Your Model Visually
02:43
Building a Simple Image Classifier using CIFAR10
07:19
16. BONUS – Deep Learning Computer Vision 5 – Build a Cats vs Dogs Classifier
0/5
Data Augmentation Chapter Overview
01:00
Splitting Data into Test and Training Datasets
10:13
Train a Cats vs. Dogs Classifier
04:03
Boosting Accuracy with Data Augmentation
05:13
Types of Data Augmentation
05:13
17. BONUS – Build a Credit Card Number Reader
18. BONUS – Neural Style Transfer with OpenCV
19. BONUS – Object Detection – Use SSDs (Single Shot Detector) for Detecting Objects
20. BONUS – Colorize Black and White Images
Master Computer Vision™ OpenCV4 in Python with Deep Learning
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