English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 10.5 Hours | 3.63 GB
Learn OpenCV4, Dlib, Keras, TensorFlow & Caffe while completing over 21 projects such as classifiers, detectors & more!
Welcome to one of the most thorough and well taught courses on OpenCV, where you’ll learn how to
You will be learning:
- The key concepts of Computer Vision & OpenCV (using the newest version OpenCV 4)
- To perform image manipulations such as transformations, cropping, blurring, thresholding, edge detection and cropping.
- To segment images by understanding contours, circle, and line detection. You’ll even learn how to approximate contours, do contour filtering and ordering as well as approximations.
- Use feature detection (SIFT, SURF, FAST, BRIEF & ORB) to do object detection.
- Implement Object Detection for faces, people & cars.
- Extract facial landmarks for face analysis, applying filters and face swaps.
- Implement Machine Learning in Computer Vision for handwritten digit recognition.
- Implement Facial Recognition.
- Implement and understand Motion Analysis & Object Tracking.
- Use basic computational photography techniques for Photo Restoration (eliminate marks, lines, creases, and smudges from old damaged photos).
- How to become a true computer vision expert by getting started in Deep Learning ( 3+ hours of Deep Learning with Keras in Python)
- How to develop Computer Vision Product Ideas
- How to perform Multi Object Detection (90 Object Types)
- How to colorize Black & White Photos and Video
- Neural Style Transfers – Apply the artistic style of Van Gogh, Picasso and others to any image even your webcam input
- How to make your own Automatic Number-Plate Recognition (ALPR
- Credit Card Number Identification (Build your own OCR Classifier with PyTesseract)
You’ll also be implementing 21 awesome projects!
OpenCV Projects Include:
- Live Drawing Sketch using your webcam
- Identifying Shapes
- Counting Circles and Ellipses
- Finding Waldo
- Single Object Detectors using OpenCV
- Car and Pedestrian Detector using Cascade Classifiers
- Live Face Swapper (like MSQRD & Snapchat filters!!!)
- Yawn Detector and Counter
- Handwritten Digit Classification
- Facial Recognition
- Ball Tracking
- Automatic Number-Plate Recognition (ALPR)
- Neural Style Transfer Mini Project
- Multi Object Detection in OpenCV (up to 90 Objects!) using SSD (Single Shot Detector)
- Colorize Black & White Photos and Video
Deep Learning Projects Include:
- Build a Handwritten Digit Classifier
- Build a Multi Image Classifier
- Build a Cats vs Dogs Classifier
- Understand how to boost CNN performance using Data Augmentation
- Extract and Classify Credit Card Numbers
Why Learn Computer Vision in Python using OpenCV?
Computer vision applications and technology are exploding right now!
Even Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily utilizing computer vision for face & object recognition, image searching and especially in Self-Driving Cars!
As a result, the demand for computer vision expertise is growing exponentially!
However, learning computer vision is hard! Existing online tutorials, textbooks, and free MOOCs are often outdated, using older an incompatible libraries or are too theoretical, making it difficult to understand.
This was my problem when learning Computer Vision and it became incredibly frustrating. Even simply running example code I found online proved difficult as libraries and functions were often outdated.
I created this course to teach you all the key concepts without the heavy mathematical theory while using the most up to date methods.
I take a very practical approach, using more than 50 Code Examples.
At the end of the course, you will be able to build 12 Awesome Computer Vision Apps using OpenCV in Python.
I use OpenCV which is the most well supported open source computer vision library that exists today! Using it in Python is just fantastic as Python allows us to focus on the problem at hand without being bogged down by complex code.
If you’re an academic or college student I still point you in the right direction if you wish to learn more by linking the research papers of techniques we use.
So if you want to get an excellent foundation in Computer Vision, look no further.
Course Introduction and Setup
2 Introduction to Computer Vision and OpenCV
3 About this course
4 READ THIS – Guide to installing and setting up your OpenCV4.0.1 Virtual Machine
5 Recomended – Setup your OpenCV4.0.1 Virtual Machine
6 Installation of OpenCV Python on Windows
7 Installation of OpenCV Python on Mac
8 Installation of OpenCV Python on Linux
9 Set up course materials (DOWNLOAD LINK BELOW) – Not needed if using the new VM
Basics of Computer Vision and OpenCV
10 What are Images
11 How are Images Formed
12 Storing Images on Computers
13 Getting Started with OpenCV – A Brief OpenCV Intro
14 Grayscaling – Converting Color Images To Shades of Gray
15 Understanding Color Spaces – The Many Ways Color Images Are Stored Digitally
16 Histogram representation of Images – Visualizing the Components of Images
17 Creating Images Drawing on Images – Make Squares, Circles, Polygons Add Text
Image Manipulations Processing
18 Transformations, Affine And Non-Affine – The Many Ways We Can Change Images
19 Sharpening – Reverse Your Images Blurs
20 Thresholding (Binarization) – Making Certain Images Areas Black or White
21 Dilation, Erosion, OpeningClosing – Importance of ThickeningThinning Lines
22 Edge Detection using Image Gradients Canny Edge Detection
23 Perspective Affine Transforms – Take An Off Angle Shot Make It Look Top Down
24 Mini Project 1 – Live Sketch App – Turn your Webcam Feed Into A Pencil Drawing
25 Image Translations – Moving Images Up, Down. Left And Right
26 Rotations – How To Spin Your Image Around And Do Horizontal Flipping
27 Scaling, Re-sizing and Interpolations – Understand How Re-Sizing Affects Quality
28 Image Pyramids – Another Way of Re-Sizing
29 Cropping – Cut Out The Image The Regions You Want or Dont Want
30 Arithmetic Operations – Brightening and Darkening Images
31 Bitwise Operations – How Image Masking Works
32 Blurring – The Many Ways We Can Blur Images Why Its Important
Image Segmentation Contours
33 Segmentation and Contours – Extract Defined Shapes In Your Image
34 Sorting Contours – Sort Those Shapes By Size
35 Approximating Contours Finding Their Convex Hull – Clean Up Messy Contours
36 Matching Contour Shapes – Match Shapes In Images Even When Distorted
37 Mini Project 2 – Identify Shapes (Square, Rectangle, Circle, Triangle Stars)
38 Line Detection – Detect Straight Lines E.g. The Lines On A Sudoku Game
39 Circle Detection
40 Blob Detection – Detect The Center of Flowers
41 Mini Project 3 – Counting Circles and Ellipses
Object Detection in OpenCV
42 Object Detection Overview
43 Mini Project 4 – Finding Waldo (Quickly Find A Specific Pattern In An Image)
44 Feature Description Theory – How We Digitally Represent Objects
45 Finding Corners – Why Corners In Images Are Important to Object Detection
46 SIFT, SURF, FAST, BRIEF ORB – Learn The Different Ways To Get Image Features
47 Mini Project 5 – Object Detection – Detect A Specific Object Using Your Webcam
48 Histogram of Oriented Gradients – Another Novel Way Of Representing Images
Object Detection – Build a Face, People and CarVehicle Detectors
49 HAAR Cascade Classifiers – Learn How Classifiers Work And Why Theyre Amazing
50 Face and Eye Detection – Detect Human Faces and Eyes In Any Image
51 Mini Project 6 – Car and Pedestrian Detection in Videos
Augmented Reality (AR) – Facial Landmark Identification (Face Swaps)
52 Face Analysis and Filtering – Identify Face Outline, Lips, Eyes Even Eyebrows
53 Merging Faces (Face Swaps) – Combine Two Faces For Fun Sometimes Scary Results
54 Mini Project 7 – Live Face Swapper (like MSQRD Snapchat filters)
55 Mini Project 8 – Yawn Detector and Counter
Simple Machine Learning using OpenCV
56 Machine Learning Overview – What Is It Why Its Important to Computer Vision
57 Mini Project 9 – Handwritten Digit Classification
58 Mini Project 10 – Facial Recognition – Make Your Computer Recognize You
Object Tracking Motion Analysis
59 Filtering by Color
60 Background Subtraction and Foreground Subtraction
61 Using Meanshift for Object Tracking
62 Using CAMshift for Object Tracking
63 Optical Flow – Track Moving Objects In Videos
64 Mini Project 11 – Ball Tracking
Computational Photography Make a License Plate Reader
65 Mini Project 12 – Photo-Restoration
66 Mini Project 13 – Automatic Number-Plate Recognition (ALPR)
67 Course Summary and how to become an Expert
68 Latest Advances, 12 Startup Ideas Implementing Computer VIsion in Mobile Apps
BONUS – Deep Learning Computer Vision 1 – Setup a Deep Learning Virtual Machine
69 Setup your Deep Learning Virtual Machine
70 Intro to Handwritten Digit Classification (MNIST)
71 Intro to Multiple Image Classification (CIFAR10)
BONUS – Deep Learning Computer Vision 2 – Introduction to Neural Networks
72 Neural Networks Chapter Overview
73 Epochs, Iterations and Batch Sizes
74 Measuring Performance and the Confusion Matrix
75 Review and Best Practices
76 Machine Learning Overview
77 Neural Networks Explained
78 Forward Propagation
79 Activation Functions
80 Training Part 1 Loss Functions
81 Training Part 2 Backpropagation and Gradient Descent
82 Backpropagation Learning Rates A Worked Example
83 Regularization, Overfitting, Generalization and Test Datasets
BONUS – Deep Learning Computer Vision 3 – Convolutional Neural Networks (CNNs)
84 Convolutional Neural Networks Chapter Overview
85 Introduction to Convolutional Neural Networks (CNNs)
86 Convolutions Image Features
87 Depth, Stride and Padding
90 The Fully Connected Layer
91 Training CNNs
92 Designing Your Own CNN
BONUS – Deep Learning Computer Vision 4 – Build CNNs in Python using Keras
93 Introduction to Keras Tensorflow
94 Saving and Loading Your Model
95 Displaying Your Model Visually
96 Building a Simple Image Classifier using CIFAR10
97 Building a CNN in Keras
98 Building a Handwriting Recognition CNN
99 Loading Our Data
100 Getting our data in Shape
101 Hot One Encoding
102 Building Compiling Our Model
103 Training Our Classifier
104 Plotting Loss and Accuracy Charts
BONUS – Deep Learning Computer Vision 5 – Build a Cats vs Dogs Classifier
105 Data Augmentation Chapter Overview
106 Splitting Data into Test and Training Datasets
107 Train a Cats vs. Dogs Classifier
108 Boosting Accuracy with Data Augmentation
109 Types of Data Augmentation
BONUS – Build a Credit Card Number Reader
110 Step 1 – Creating a Credit Card Number Dataset
111 Step 2 – Training Our Model
112 Step 3 – Extracting A Credit Card from the Background
113 Step 4 – Use our Model to Identify the Digits Display it onto our Credit Card
BONUS – Neural Style Transfer with OpenCV
114 Perform Neural Style Transfer Using OpenCV4
BONUS – Object Detection – Use SSDs (Single Shot Detector) for Detecting Objects
115 Using an SSD In OpenCV
BONUS – Colorize Black and White Images
116 Colorizing Black and White Images Using Caffe