Master Computer Vision™ OpenCV4 in Python with Deep Learning

Master OpenCV4 like a pro while learning Dlib, Deep Learning Computer Vision (Keras, TensorFlow & Caffe) + 21 Projects!

4.10 (3623 reviews)
Udemy
platform
English
language
Programming Languages
category
instructor
21,395
students
11 hours
content
Mar 2020
last update
$74.99
regular price

What you will learn

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

Description

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NOTE: Many of the earlier poor reviews was during a period of time when the course material was outdated and many of the example code was broken, however, this has been fixed as of early 2019 :)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

Content

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

BONUS - 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)

BONUS - 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

BONUS - Deep Learning Computer Vision 3 - 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

BONUS - 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

BONUS - 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

BONUS - Build a Credit Card Number Reader

Step 1 - Creating a Credit Card Number Dataset
Step 2 - Training Our Model
Step 3 - Extracting A Credit Card from the Background
Step 4 - Use our Model to Identify the Digits & Display it onto our Credit Card

BONUS - Neural Style Transfer with OpenCV

Perform Neural Style Transfer Using OpenCV4

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

Using an SSD In OpenCV

BONUS - Colorize Black and White Images

Colorizing Black and White Images Using Caffe

Screenshots

Master Computer Vision™ OpenCV4 in Python with Deep Learning - Screenshot_01Master Computer Vision™ OpenCV4 in Python with Deep Learning - Screenshot_02Master Computer Vision™ OpenCV4 in Python with Deep Learning - Screenshot_03Master Computer Vision™ OpenCV4 in Python with Deep Learning - Screenshot_04

Reviews

Niels
March 8, 2023
It is good, but he is actually going a bit too fast sometimes. Often just shows how it works and not telling why it works the way it works> This makes it hard for me to actually store it in my memory. Other minor point is that the sound quality is subpar.. would recommend using an external mic next time you record something!
Michael
January 7, 2023
I don't like having to download a virtual machine with some unknown version of ubuntu to run open source code that will run on literally any machine.
I
December 30, 2022
A very good course, but i would like it more if you could go more into depth(as little as possible) into the function parameters. To some functions you just say these are the parameters, not specifying what are they
Gilberto
May 27, 2022
Hi, i am electrian at VW company, well i work here with roboter end plc industrial maintenance. But I want to learn more about python end opencv. Yes, i know every day the technogies are so much evolution, but ok. i believe that i can learn basic concepts and step by step growing. i have been measuring some little courses here Udemy about other programming leangueges, thas is been very good for me. thanks for all... and i will join our course now.!!! In moment i w'd like to see some thing like this ... distances between objects, e.g. gap measurement with python and openCV of course. I believe that this course will give me more knowledge and experience.
Benjamin
May 8, 2022
Fair. I like the information, but little more than I could discover in their docs. This may change as the course goes on, but a little more depth on each topic would be nice, and how it applies to real-world circumstances.
Robert
March 9, 2022
Thank you for the interesting course! The content of all chapters is segmented well. All necessary resources were accessible. I especially liked virtual Ubuntu machines with ready to use environments both for OpenCV and Deep Learning. It was great introduction to Computer Vision and Deep Learning.
Joseph
March 3, 2022
I would love for a little more in depth explanation of some of the topics. The lessons feel like a very brief overview that is just showing me what opencv is capable of.
Craig
January 14, 2022
Course was good in parts, but rather awkward in others. Some areas the instructor did a good job with the theory, but in other areas there was too much screen flipping back and forth, especiallyin the the neural net sections describing back propagation) Code was good overall with good examples, but sometimes the instructor would gloss over how values are determined, and often would say "go look it up", but a basic explanation would be helpful as to how certain values are determined.
Hadi
January 13, 2022
Very bad course, not recommended at all the lecturer only shows slide and the documentation of OpenCV he doesn't write code and explain concepts. And the lecturer voice very low and make you wanna sleep A lot of things are poorly explained. This is the worst OpenCV on the internet
Dan
September 30, 2021
Still seeing a disconnect between the presentation and the ISO image we were given - a lot of the things needed to get things working properly weren't always the best explained. Really enjoying the machine learning part we're doing now though.
Vineet
September 10, 2021
Fantastic course, very useful and informative. Love the the presenter explains things step by step. Some of the codes / libraries require updating though, but overall 5 stars
Prasoon
July 13, 2021
A well thought out course. Almost covers all the essential topics used in OpenCV. Excellent Resource material. Highly recommended!
Akshay
July 9, 2021
No coding Only theory and code is shown on screen and the instructor is just explaining the code line by line.
Anuthama
July 7, 2021
Very Well explained. Easy to understand for beginners. Detailed step by step tutorial with details on how and where to download.
Balaji
June 22, 2021
The instructor is not explaining many things in opencv. This is not opencv beginners course. Dont expect more deep learning content(only basics available). Instructor walk through the code thats all.

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950614
udemy ID
9/5/2016
course created date
6/1/2019
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