Mô tả

Welcome to one of the most thorough and well-taught courses on OpenCV, where you'll learn how to Master Computer Vision using the newest version of OpenCV4 in Python!

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NOTE: Many of the earlier poor reviews was during a period of time when the course material was outdated and many of the example code was broken, however, this has been fixed as of early 2019 :)

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Computer Vision is an area of Artificial Intelligence that deals with how computer algorithms can decipher what they see in images! Master this incredible skill and be able to complete your University/College Projects, automate something at work, start developing your startup idea or gain the skills to become a high paying ($400-$1000 USD/Day) Computer Vision Engineer.

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Last Updated Aug 2019, you will be learning:

  1. Key concepts of Computer Vision & OpenCV (using the newest version OpenCV4)

  2. Image manipulations (dozens of techniques!) such as transformations, cropping, blurring, thresholding, edge detection and cropping.

  3. Segmentation of 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.

  4. Feature detection (SIFT, SURF, FAST, BRIEF & ORB) to do object detection.

  5. Object Detection for faces, people & cars.

  6. Extract facial landmarks for face analysis, applying filters, and face swaps.

  7. Machine Learning in Computer Vision for handwritten digit recognition.

  8. Facial Recognition.

  9. Motion Analysis & Object Tracking.

  10. Computational photography techniques for Photo Restoration (eliminate marks, lines, creases, and smudges from old damaged photos).

  11. Deep Learning ( 3+ hours of Deep Learning with Keras in Python)

  12. Computer Vision Product and Startup Ideas

  13. Multi-Object Detection (90 Object Types)

  14. Colorize Black & White Photos and Video (using Caffe)

  15. Neural Style Transfers - Apply the artistic style of Van Gogh, Picasso, and others to any image even your webcam input

  16. Automatic Number-Plate Recognition (ALPR

  17. Credit Card Number Identification (Build your own OCR Classifier with PyTesseract)

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You'll also be implementing 21 awesome projects! 

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OpenCV Projects Include:

  1. Live Drawing Sketch using your webcam

  2. Identifying Shapes

  3. Counting Circles and Ellipses

  4. Finding Waldo

  5. Single Object Detectors using OpenCV

  6. Car and Pedestrian Detector using Cascade Classifiers

  7. Live Face Swapper (like MSQRD & Snapchat filters!!!)

  8. Yawn Detector and Counter

  9. Handwritten Digit Classification

  10. Facial Recognition

  11. Ball Tracking

  12. Photo-Restoration

  13. Automatic Number-Plate Recognition (ALPR)

  14. Neural Style Transfer Mini Project

  15. Multi-Object Detection in OpenCV (up to 90 Objects!) using SSD (Single Shot Detector)

  16. Colorize Black & White Photos and Video

Deep Learning Projects Include:

  1. Build a Handwritten Digit Classifier

  2. Build a Multi-Image Classifier

  3. Build a Cats vs Dogs Classifier

  4. Understand how to boost CNN performance using Data Augmentation

  5. Extract and Classify Credit Card Numbers

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What previous students have said: 

"I'm amazed at the possibilities. Very educational, learning more than what I ever thought was possible. Now, being able to actually use it in a practical purpose is intriguing... much more to learn & apply"

"Extremely well taught and informative Computer Vision course! I've trawled the web looking for Opencv python tutorials resources but this course was by far the best amalgamation of relevant lessons and projects. Loved some of the projects and had lots of fun tinkering them."

"Awesome instructor and course. The explanations are really easy to understand and the materials are very easy to follow. Definitely a really good introduction to image processing."

"I am extremely impressed by this course!! I think this is by far the best Computer Vision course on Udemy. I'm a college student who had previously taken a Computer Vision course in undergrad. This 6.5 hour course blows away my college class by miles!!"

"Rajeev did a great job on this course. I had no idea how computer vision worked and now have a good foundation of concepts and knowledge of practical applications. Rajeev is clear and concise which helps make a complicated subject easy to comprehend for anyone wanting to start building applications."

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Why Learn Computer Vision in Python using OpenCV?

Computer vision applications and technology are exploding right now! With several apps and industries making amazing use of the technology, from billion-dollar apps such as Pokémon GO, Snapchat and up and coming apps like MSQRD and PRISMA.

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 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.

This is the course for you!

In this course, you will discover the power of OpenCV in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.

You get 3+ Hours of Deep Learning in Computer Vision using Keras, which includes:

  • A free Virtual Machine with all Deep Learning Python Libraries such as Keras and TensorFlow pre-installed

  • Detailed Explanations on Neural Networks and Convolutional Neural Networks

  • Understand how Keras works and how to use and create image datasets

  • 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

As for Updates and support:

I will be continuously adding updates, fixes, and new amazing projects every month! 

I will be active daily in the 'questions and answers' area of the course, so you are never on your own.    

So, are you ready to get started? Enroll now and start the process of becoming a master in Computer Vision today!

Bạn sẽ học được gì

Understand and use OpenCV4 in Python

How to use Deep Learning using Keras & TensorFlow in Python

Create Face Detectors & Recognizers and create your own advanced face swaps using DLIB

Object Detection, Tracking and Motion Analysis

Create Augmented Reality Apps

Programming skills such as basic Python and Numpy

How to use Computer Vision in executing cool startup ideas

Understand Neural and Convolutional Neural Networks

Learn to build simple Image Classifiers in Python

Learn to build an OCR Reader for Credit Cards

Learn to Perform Neural Style Transfer Using OpenCV

Learn how to do Multi Object Detection in OpenCV (up to 90 Objects!) using SSDs (Single Shot Detector)

Learn how to convert black and white Images to color using Caffe

Learn to build an Automatic Number (License) Plate Recognition (ALPR)

Learn the Basics of Computer Vision and Image Processing

Yêu cầu

  • Little to no programming knowledge is needed, but basic programing knowledge will help
  • Windows 10 or Ubuntu or a MacOS system
  • A webcam to implement some of the mini projects

Nội dung khoá học

20 sections

Course Introduction and Setup

9 lectures
Introduction
02:05
Introduction to Computer Vision and OpenCV
03:08
About this course
05:14
READ THIS - Guide to installing and setting up your OpenCV4.0.1 Virtual Machine
00:51
Recomended - Setup your OpenCV4.0.1 Virtual Machine
05:41
Installation of OpenCV & Python on Windows
08:54
Installation of OpenCV & Python on Mac
01:32
Installation of OpenCV & Python on Linux
01:13
Set up course materials (DOWNLOAD LINK BELOW) - Not needed if using the new VM
01:42

Basics of Computer Vision and OpenCV

8 lectures
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
09:19
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

Image Manipulations & Processing

15 lectures
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, Opening/Closing - Importance of Thickening/Thinning 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

Image Segmentation & Contours

9 lectures
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
05:28
Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
05:29
Line Detection - Detect Straight Lines E.g. The Lines On A Sudoku Game
06:24
Circle Detection
00:32
Blob Detection - Detect The Center of Flowers
03:20
Mini Project 3 - Counting Circles and Ellipses
06:06

Object Detection in OpenCV

7 lectures
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
06:46
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

Object Detection - Build a Face, People and Car/Vehicle Detectors

3 lectures
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

Augmented Reality (AR) - Facial Landmark Identification (Face Swaps)

4 lectures
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

Simple Machine Learning using OpenCV

3 lectures
Machine Learning Overview - What Is It & Why It's Important to Computer Vision
08:54
Mini Project 9 - Handwritten Digit Classification
20:00
Mini Project # 10 - Facial Recognition - Make Your Computer Recognize You
12:07

Object Tracking & Motion Analysis

6 lectures
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

Computational Photography & Make a License Plate Reader

2 lectures
Mini Project # 12 - Photo-Restoration
06:34
Mini Project # 13 - Automatic Number-Plate Recognition (ALPR)
00:23

Conclusion

2 lectures
Course Summary and how to become an Expert
02:50
Latest Advances, 12 Startup Ideas & Implementing Computer VIsion in Mobile Apps
07:06

BONUS - Deep Learning Computer Vision 1 - Setup a Deep Learning Virtual Machine

3 lectures
Setup your Deep Learning Virtual Machine
10:28
Intro to Handwritten Digit Classification (MNIST)
05:46
Intro to Multiple Image Classification (CIFAR10)
02:52

BONUS - Deep Learning Computer Vision 2 - Introduction to Neural Networks

12 lectures
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

BONUS - Deep Learning Computer Vision 3 - Convolutional Neural Networks (CNNs)

9 lectures
Convolutional Neural Networks Chapter Overview
00:59
Introduction to Convolutional Neural Networks (CNNs)
05:24
Convolutions & Image Features
13:19
Depth, Stride and Padding
06:51
ReLU
01:47
Pooling
04:37
The Fully Connected Layer
02:08
Training CNNs
03:08
Designing Your Own CNN
03:48

BONUS - Deep Learning Computer Vision 4 - Build CNNs in Python using Keras

12 lectures
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
04:58
Plotting Loss and Accuracy Charts
02:52
Saving and Loading Your Model
02:50
Displaying Your Model Visually
02:43
Building a Simple Image Classifier using CIFAR10
07:19

BONUS - Deep Learning Computer Vision 5 - Build a Cats vs Dogs Classifier

5 lectures
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

BONUS - Build a Credit Card Number Reader

4 lectures
Step 1 - Creating a Credit Card Number Dataset
04:59
Step 2 - Training Our Model
01:42
Step 3 - Extracting A Credit Card from the Background
03:33
Step 4 - Use our Model to Identify the Digits & Display it onto our Credit Card
01:48

BONUS - Neural Style Transfer with OpenCV

1 lectures
Perform Neural Style Transfer Using OpenCV4
01:55

BONUS - Object Detection - Use SSDs (Single Shot Detector) for Detecting Objects

1 lectures
Using an SSD In OpenCV
03:11

BONUS - Colorize Black and White Images

1 lectures
Colorizing Black and White Images Using Caffe
01:44

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