English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 6h 14m | 1.42 GB
Learn Computer Vision concepts by building 12 projects, including handwriting recognition, face filters, and car and people detection!
Computer vision applications and technology are exploding right now, with several apps and industries making amazing use of the technology—ranging from up-and-coming apps such as MSQRD, and PRISMA to billion-dollar apps such as Pokémon GO and Snapchat! Even Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily utilizing computer vision for face and 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 Existing online tutorials, textbooks, and free MOOCs are often outdated, using older and incompatible libraries, or are too theoretical, making the subject difficult to understand.
This was the author’s problem when learning Computer Vision and it became incredibly frustrating. Even simply running example code found online proved difficult as libraries and functions were often outdated. The author created this course to teach you all the key concepts without the heavy mathematical theory—all the while using the most up-to-date methods. At the end of the course, you will be able to build 12 awesome Computer Vision apps using OpenCV (the best supported open-source computer vision library that exists today!) in Python. Using it in Python is just fantastic as Python allows us to focus on the problem at hand without getting bogged down in complex code. If you’re an academic or college student but want to learn more, the author still points you in the right direction by linking the research papers for techniques used. So if you want to get an excellent foundation in Computer Vision, look no further. This is the course for you!
Learn Computer Vision using OpenCV in Python, using the latest 2018 concepts, and implement 12 awesome projects! In this course, you will discover the power of OpenCV in Python, and obtain the skills to dramatically increase your career prospects as a Computer Vision developer.
What You Will Learn
- How to build complex Computer Vision applications using the latest techniques in OpenCV
- Use Machine Learning and Augmented Reality in Computer Vision
- Face detection and recognition (face swapping and filters!)
- Object detection, tracking, and motion analysis
- Handwriting recognition
- Programming skills such as basic Python and NumPy
- Understand how to use computer vision when executing cool startup ideas
Table of Contents
Course Introduction and Setup
1 Introduction
2 Introduction to Computer Vision and OpenCV
3 About this course
4 Setting up our course materials
Basics of Computer Vision and OpenCV
5 What are Images
6 How are Images Formed
7 Storing Images on Computers
8 Getting Started with OpenCV – A Brief OpenCV Intro
9 Grayscaling – Converting Color Images to Shades of Gray
10 Understanding Color Spaces – The Many Ways Color Images Are Stored Digitally
11 Histogram representation of Images – Visualizing the Components of Images
12 Creating Images & Drawing on Images – Make Squares, Circles, Polygons & Add Text
Image Manipulations – The Many Ways You Can Change Images
13 Transformations, Affine and Non-Affine – The Many Ways We Can Change Images
14 Image Translations – Moving Images Up, Down. Left and Right
15 Rotations – How to Spin Your Image Around and Do Horizontal Flipping
16 Scaling, Re-sizing and Interpolations – Understand How Re-Sizing Affects Quality
17 Image Pyramids – Another Way of Re-Sizing
18 Cropping – Cut Out the Image the Regions You Want or Don’t Want
19 Arithmetic Operations – Brightening and Darkening Images
20 Bitwise Operations – How Image Masking Works
21 Blurring – The Many Ways We Can Blur Images & Why It’s Important
22 Sharpening – Reverse Your Images Blurs
23 Thresholding (Binarization) – Making Certain Images Areas Black or White
24 Dilation, Erosion, OpeningClosing – Importance of ThickeningThinning Lines
25 Edge Detection using Image Gradients & Canny Edge Detection
26 Perspective & Affine Transforms – Take an Off Angle Shot & Make It Look Top Down
27 Mini Project 1 – Live Sketch App – Turn your Webcam Feed into a Pencil Drawing
Image Segmentation – Extract Areas Specific Areas of an Image Automatically
28 Segmentation and Contours – Extract Defined Shapes in Your Image
29 Sorting Contours – Sort Those Shapes by Size
30 Approximating Contours & Finding Their Convex Hull – Clean Up Messy Contours
31 Matching Contour Shapes – Match Shapes in Images Even When Distorted
32 Mini Project 2 – Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
33 Line Detection – Detect Straight Lines E.g. the Lines on a Sudoku Game
34 Blob Detection – Detect the Centre of Flowers
35 Mini Project 3 – Counting Circles and Ellipses
Object Detection – Use Computer Vision to Detect Objects in an Image
36 Object Detection Overview
37 Mini Project # 4 – Finding Waldo (Quickly Find A Specific Pattern in an Image)
38 Feature Description Theory – How We Digitally Represent Objects
39 Finding Corners – Why Corners in Images Are Important to Object Detection
40 SIFT, SURF, FAST, BRIEF & ORB – Learn the Different Ways to Get Image Features
41 Mini Project 5 – Object Detection – Detect a Specific Object Using Your Webcam
42 Histogram of Oriented Gradients – Another Novel Way of Representing Images
Face, People and Car Detection
43 HAAR Cascade Classifiers – Learn How Classifiers Work and Why They’re Amazing
44 Face and Eye Detection – Detect Human Faces and Eyes in Any Image
45 Mini Project 6 – Car and Pedestrian Detection in Videos
Augmented Reality – Facial Landmark Identification (Filters, Swaps & Analysis)
46 Face Analysis and Filtering – Identify Face Outline, Lips, Eyes Even Eyebrows
47 Merging Faces (Face Swaps) – Combine Two Faces for Fun & Sometimes Scary Results
48 Mini Project 7 – Live Face Swapper (like MSQRD & Snapchat filters!!!)
49 Mini Project 8 – Yawn Detector and Counter
Machine Learning in Computer Vision
50 Machine Learning Overview – What Is It & Why It’s Important to Computer Vision
51 Mini Project 9 – Handwritten Digit Classification
52 Mini Project # 10 – Facial Recognition – Make Your Computer Recognize You
Motion Analysis and Object Tracking
53 Filtering by Color
54 Background Subtraction and Foreground Subtraction
55 Using Meanshift for Object Tracking
56 Using CAMshift for Object Tracking
57 Optical Flow – Track Moving Objects in Videos
58 Mini Project # 11 – Ball Tracking
BONUS – Computation Photography
59 Mini Project # 12 – Photo-Restoration
Conclusion
60 Course Summary and how to become an Expert
61 Latest Advances, 12 Startup Ideas & Implementing Computer Vision in Mobile Apps
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