Best Opencv Courses

Find the best online Opencv Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Opencv Courses.

Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs

Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps.

Created by Hadelin de Ponteves - AI Entrepreneur


Students: 42215, Price: $89.99

Students: 42215, Price:  Paid


You've definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer.

But what if you could also become a creator?

What if there was a way for you to easily break into the World of Artificial Intelligence and build amazing applications which leverage the latest technology to make the World a better place?

Sounds too good to be true, doesn't it?

But there actually is a way..

Computer Vision is by far the easiest way of becoming a creator.

And it's not only the easiest way, it's also the branch of AI where there is the most to create.

Why? You'll ask.

That's because Computer Vision is applied everywhere. From health to retail to entertainment - the list goes on. Computer Vision is already a $18 Billion market and is growing exponentially.

Just think of tumor detection in patient MRI brain scans. How many more lives are saved every day simply because a computer can analyze 10,000x more images than a human?

And what if you find an industry where Computer Vision is not yet applied? Then all the better! That means there's a business opportunity which you can take advantage of.

So now that raises the question: how do you break into the World of Computer Vision?

Up until now, computer vision has for the most part been a maze. A growing maze.

As the number of codes, libraries and tools in CV grows, it becomes harder and harder to not get lost.

On top of that, not only do you need to know how to use it - you also need to know how it works to maximise the advantage of using Computer Vision.

To this problem we want to bring... 

Computer Vision A-Z.

With this brand new course you will not only learn how the most popular computer vision methods work, but you will also learn to apply them in practice!

Can't wait to see you inside the class,

Kirill & Hadelin

Python for Computer Vision with OpenCV and Deep Learning

Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!

Created by Jose Portilla - Head of Data Science, Pierian Data Inc.


Students: 35961, Price: $99.99

Students: 35961, Price:  Paid

Welcome to the ultimate online course on Python for Computer Vision!

This course is your best resource for learning how to use the Python programming language for Computer Vision.

We'll be exploring how to use Python and the OpenCV (Open Computer Vision) library to analyze images and video data.

The most popular platforms in the world are generating never before seen amounts of image and video data. Every 60 seconds users upload more than 300 hours of video to Youtube, Netflix subscribers stream over 80,000 hours of video, and Instagram users like over 2 million photos! Now more than ever its necessary for developers to gain the necessary skills to work with image and video data using computer vision.

Computer vision allows us to analyze and leverage image and video data, with applications in a variety of industries, including self-driving cars, social network apps, medical diagnostics, and many more.

As the fastest growing language in popularity, Python is well suited to leverage the power of existing computer vision libraries to learn from all this image and video data.

In this course we'll teach you everything you need to know to become an expert in computer vision! This $20 billion dollar industry will be one of the most important job markets in the years to come.

We'll start the course by learning about numerical processing with the NumPy library and how to open and manipulate images with NumPy. Then will move on to using the OpenCV library to open and work with image basics. Then we'll start to understand how to process images and apply a variety of effects, including color mappings, blending, thresholds, gradients, and more.

Then we'll move on to understanding video basics with OpenCV, including working with streaming video from a webcam.  Afterwards we'll learn about direct video topics, such as optical flow and object detection. Including face detection and object tracking.

Then we'll move on to an entire section of the course devoted to the latest deep learning topics, including image recognition and custom image classifications. We'll even cover the latest deep learning networks, including the YOLO (you only look once) deep learning network.

This course covers all this and more, including the following topics:

  • NumPy

  • Images with NumPy

  • Image and Video Basics with NumPy

  • Color Mappings

  • Blending and Pasting Images

  • Image Thresholding

  • Blurring and Smoothing

  • Morphological Operators

  • Gradients

  • Histograms

  • Streaming video with OpenCV

  • Object Detection

  • Template Matching

  • Corner, Edge, and Grid Detection

  • Contour Detection

  • Feature Matching

  • WaterShed Algorithm

  • Face Detection

  • Object Tracking

  • Optical Flow

  • Deep Learning with Keras

  • Keras and Convolutional Networks

  • Customized Deep Learning Networks

  • State of the Art YOLO Networks

  • and much more!

Feel free to message me on Udemy if you have any questions about the course!

Thanks for checking out the course page, and I hope to see you inside!


Object Detection Web App with TensorFlow, OpenCV and Flask

Build an Object Detection Model from Scratch using Deep Learning and Transfer Learning

Created by Yaswanth Sai Palaghat - Founder of Techie Empire


Students: 26212, Price: $89.99

Students: 26212, Price:  Paid

Detecting Objects and finding out their names from images is a very challenging and interesting field of Computer Vision.

The core science behind Self Driving Cars, Image Captioning and Robotics lies in Object Detection.

In this course, you are going to build a Object Detection Model from Scratch using Python's OpenCV library using Pre-Trained Coco Dataset.

The model will be deployed as an Web App using Flask Framework of Python.


  • Python

  • Machine Learning

  • Deep Learning

  • Transfer Learning

  • Tensorflow

  • OpenCV

  • Flask

ROS for Beginners: Basics, Motion, and OpenCV

Become an expert and learn robotics with Robot Operating System (ROS) in little time and don't be lost in broad docs

Created by Anis Koubaa - Professor of Computer Science


Students: 12787, Price: $119.99

Students: 12787, Price:  Paid

News and Updates.

I course have been upgraded to the latest version of ROS, ROS Noetic, with several new videos explaining the fundamental concepts of ROS with hands-on illustrations. It will also give you the required skills to later learn ROS2 and navigation stack, as presented in my two other courses.

  • Oct 8, 2020: Adding a section on Turtlebot3 simulator and how to install it easily

  • Oct 8, 2020: I added slides for laser scanners and also an assignment using a Turtlebot 3 robot with laser scanners on ROS Noetics

  • Oct 2, 2020: update of ROS motion section with brand new tutorials and illustrations

  • Sep 17, 2020: update several videos of ROS custom message and ROS services.

  • Sep 11, 2020: Adding brand new lecture with high-quality videos and replace old videos on a detailed explanation of the ROS Computation Graph with a demo using the latest version of ROS Noetic.   

  • Aug 22, 2020: Adding instruction for installation of ROS Noetic along with the code of the course for this distribution.

Why am I  teaching this course?

Typically, new ROS users encounter a lot of difficulties when they start programming with ROS. Although there are so many tutorials, there are a  lot of tips and practical issues that could not be easily found in tutorials and not discussed and left to the developer's luckiness. In general, although there is much documentation for ROS, several are very broad and it takes too long to grasp well the concepts. This is where this course plays a role and provides an added value by providing a focused introduction to the BASICS of ROS. The course does not only presents the basic concepts of ROS but also addresses two important fields in robotics: (1) motion, (2) perception. We will apply the general concepts of ROS in the context of robotic motion and perception. The course will provide you an opportunity to learn about OpenCV, the most powerful computer vision library, that promotes robotic perception. 

My approach is to take you STEP BY STEP through the roadmap of learning ROS so that you learn the concepts in the right order and help you build an experience from one lecture to the other.

This is a course that provides the fundamental concepts and basics of the Robot Operating System (ROS). This course intends to give beginner ROS users with a quick and focused introduction on the basics of ROS, in addition to practical tips that help them manage better their first projects with ROS in C++ and Python. In particular, developing with C++ in ROS requires special care as compared to Python to configure well the compilation and runtime environment. This is presented in a clear manner in this course. 

There are mainly three majors steps in the course:

  1. ROS Basics and Foundation:  which deals with the general ROS concepts that everyone has to know, like ROS topics, Services, Messages, Nodes, ...

  2. Motion in ROS: We apply the concepts learned in Step 1 to make a robot move. We will develop a different trajectory in the context of a nice example simulating a cleaning robot. In particular, we illustrate how to represent the pose (position and orientation) of a robot in ROS, and how to send a motion control message to make the robot move. We clearly demonstrate how to implement a linear motion, a rotational motion, and spiral motion and how all of these be integrated to simulate a cleaning application. This part will you the background you need to understand robot kinematics and how motion is represented in ROS.

  3. Perception in ROS: I will introduce how a robot sees the environment using a camera, how the images are collected in ROS, and how they are processed in OpenCV. 

  4. Arduino: you will also learn how to use Arduino boards and sensors with ROS using the ROSSERIAL Arduino interface. This will allow you to integrate any Arduino sensor and board into your robot and robotics applications.

Based on my experience, these are the most important things any new ROS user has to know to be able to go further with his own robotics project. 

I also provide some hands-on activities that allow the learner to assess his understanding and push him to practice the concepts he learned. 

My experience with ROS

I have been programming with ROS for many years both in academic and industrial projects. I am very passionate to develop a program with ROS. I have also been teaching ROS at the University and providing training programs. I am the leader of the Robotics and Internet-of-Things Lab at Prince Sultan University and also a consultant for Gaitech Robotics. I have developed many ROS packages for robots and drones. I have been leading international scientific activities around ROS, and in particular, I am the editor of three volumes of books with Springer entitled Robot Operating System, The Complete Reference. I gained a lot of experience in what difficulties new users encounter to learn ROS and this contributed to pin right to the point addressing these problems through the different lectures of the course. 

Welcome to the World of ROS. 

Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs

2020 Update with TensorFlow 2.0 Support. Become a Pro at Deep Learning Computer Vision! Includes 20+ Real World Projects

Created by Rajeev D. Ratan - Data Scientist, Computer Vision Expert & Electrical Engineer


Students: 11651, Price: $109.99

Students: 11651, Price:  Paid

Update: June-2020

  • TensorFlow 2.0 Compatible Code

  • Windows install guide for TensorFlow2.0 (with Keras), OpenCV4 and Dlib

Deep Learning Computer Vision™ Use Python & Keras to implement CNNs, YOLO, TFOD, R-CNNs, SSDs & GANs + A Free Introduction to OpenCV.

If you want to learn all the latest 2019 concepts in applying Deep Learning to Computer Vision, look no further - this is the course for you! You'll get hands  the following Deep Learning frameworks in Python:

  • Keras

  • Tensorflow 2.0

  • TensorFlow Object Detection API

  • YOLO (DarkNet and DarkFlow)

  • OpenCV4

All in an easy to use virtual machine, with all libraries pre-installed!


Apr 2019 Updates:

  • How to set up a Cloud GPU on PaperSpace and Train a CIFAR10 AlexNet CNN almost 100 times faster!

  • Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!

Mar 2019 Updates:

Newly added Facial Recognition & Credit Card Number Reader Projects

  • Recognize multiple persons using your webcam

  • Facial Recognition on the Friends TV Show Characters

  • Take a picture of a Credit Card, extract and identify the numbers on that card!


Computer vision applications involving Deep Learning are booming!

Having Machines that can 'see' will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to:

  • Perform surgery and accurately analyze and diagnose you from medical scans.

  • Enable self-driving cars

  • Radically change robots allowing us to build robots that can cook, clean and assist us with almost any task

  • Understand what's being seen in CCTV surveillance videos thus performing security, traffic management and a host of other services

  • Create Art with amazing Neural Style Transfers and other innovative types of image generation

  • Simulate many tasks such as Aging faces, modifying live video feeds and realistically replace actors in films

Huge technology companies such as Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily devoting billions to computer vision research.

As a result, the demand for computer vision expertise is growing exponentially!

However, learning computer vision with Deep Learning is hard!

  • Tutorials are too technical and theoretical

  • Code is outdated

  • Beginners just don't know where to start

That's why I made this course!

  • I  spent months developing a proper and complete learning path.

  • I teach all key concepts logically and without overloading you with mathematical theory while using the most up to date methods. 

  • I created a FREE Virtual Machine with all Deep Learning Libraries (Keras, TensorFlow, OpenCV, TFODI, YOLO, Darkflow etc) installed! This will save you hours of painfully complicated installs

  • I teach using practical examples and you'll learn by doing 18 projects!

Projects such as:

  1. Handwritten Digit Classification using MNIST

  2. Image Classification using CIFAR10

  3. Dogs vs Cats classifier

  4. Flower Classifier using Flowers-17

  5. Fashion Classifier using FNIST

  6. Monkey Breed Classifier

  7. Fruit Classifier

  8. Simpsons Character Classifier

  9. Using Pre-trained ImageNet Models to classify a 1000 object classes

  10. Age, Gender and Emotion Classification

  11. Finding the Nuclei in Medical Scans using U-Net

  12. Object Detection using a ResNet50 SSD Model built using TensorFlow Object Detection

  13. Object Detection with YOLO V3

  14. A Custom YOLO Object Detector that Detects London Underground Tube Signs

  15. DeepDream

  16. Neural Style Transfers

  17. GANs - Generate Fake Digits

  18. GANs - Age Faces up to 60+ using Age-cGAN

  19. Face Recognition

  20. Credit Card Digit Reader

  21. Using Cloud GPUs on PaperSpace

  22. Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!

And OpenCV Projects such as:

  1. Live Sketch

  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

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 Computer Vision in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.


As for Updates and support:

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 using Deep Learning today!


What previous students have said my other Udemy Course: 

"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."


Data Science:Hands-on Covid19 Face Mask Detection-CNN&OpenCV

A Practical Hands-on Data Science Guided Project on Covid-19 Face Mask Detection using Deep Learning & OpenCV

Created by School of Disruptive Innovation - Creative Learning Solutions for the Digital Age


Students: 10634, Price: $19.99

Students: 10634, Price:  Paid

Would you like to learn how to detect if someone is wearing a Face Mask or not using Artificial Intelligence that can be deployed in bus stations, airports, or other public places?

Would you like to build a Convolutional Neural Network model using Deep learning to detect Covid-19 Face Mask?

If the answer to any of the above questions is "YES", then this course is for you.

Enroll Now in this course and learn how to detect Face Mask on the static images as well as in the video streams using Tensorflow and OpenCV.

As we know, COVID-19 has affected the whole world very badly. It has a huge impact on our everyday life, and this crisis is increasing day by day. In the near future, it seems difficult to eradicate this virus completely.

To counter this virus, Face Masks have become an integral part of our lives. These Masks are capable of stopping the spread of this deadly virus, which will help to control the spread. As we have started moving forward in this ‘new normal’ world, the necessity of the face mask has increased. So here, we are going to build a model that will be able to classify whether the person is wearing a mask or not. This model can be used in crowded areas like Malls, Bus stands, and other public places.

This is a hands-on Data Science guided project on Covid-19 Face Mask Detection using Deep Learning and Computer Vision concepts. We will build a Convolutional Neural Network classifier to classify people based on whether they are wearing masks or not and we will make use of OpenCV to detect human faces on the video streams. No unnecessary lectures. As our students like to say :

"Short, sweet, to the point course"

The same techniques can be used in :

Skin cancer detection

Normal pneumonia detection

Brain defect analysis

Retinal Image Analysis

Enroll now and You will receive a CERTIFICATE OF COMPLETION and we encourage you to add this project to your resume. At a time when the entire world is troubled by Coronavirus, this project can catapult your career to another level.

So bring your laptop and start building, training and testing the Data Science Covid 19 Convolutional Neural Network model right now.

You will learn:

  • How to detect Face masks on the static images as well as in the video streams.

  • Classify people who are wearing masks or not using deep learning

  • Learn to Build and train a Convolutional neural network

  • Make a prediction on new data using the trained CNN Model

We will be completing the following tasks:

  • Task 1: Getting Introduced to Google Colab Environment & importing necessary libraries

  • Task 2: Downloading the dataset directly from the Kagge to the Colab environment.

  • Task :3 Data visualization (Image Visualization)

  • Task 4: Data augmentation & Normalization

  • Task 5: Building Convolutional neural network model

  • Task 6: Compiling & Training CNN Model

  • Task 7: Performance evaluation & Testing the model & saving the model for future use

  • Task 8: Make use of the trained model to detect face masks on the static image uploaded from the local system

  • Task 9: Make use of the trained model to detect face masks on the video streams

So, grab a coffee, turn on your laptop, click on the ENROLL NOW button, and start learning right now.

OpenCV Python For Beginners | Hands on Computer Vision

Hands on Practical Examples with OpenCV & Python

Created by Yogesh Patel - Software Developer and Programming Enthusiast


Students: 6367, Price: $89.99

Students: 6367, Price:  Paid

Welcome to this on OpenCV Python Tutorial For Beginners. OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python. it is Open Source and free. opencv is easy to use and install.

The goal of this course is to provide you with a working knowledge of OpenCV with Python. We'll start with the basics, starting from installing OpenCV with Python on Windows, Mac, and Ubuntu. Then we will see how to create your first OpenCV python script. Then we will dive deep into the amazing world of computer vision Using OpenCV and learn the most important concepts about computer vision using OpenCV .

So Let's get started !!!

Build Your own Self Driving Car | Deep Learning, OpenCV, C++

Learn Raspberry Pi, Arduino UNO, Image Processing and Neural Networks (Machine Learning) for any Embedded IOT Project

Created by Rajandeep Singh - Embedded System Engineer


Students: 3321, Price: $99.99

Students: 3321, Price:  Paid

"Machine Learning will change the lives of all of us. What is Machine Learning? It’s behind what makes self-driving cars a reality"

This unique course is a complete walk-through process to Design, Build and Program a Embedded IOT Project (Self driving Car). Everything is discussed with details and clear explanation. Whole Project is divided into 2 parts.

(Course - 1)

1. Learn to design complete hardware for self driving car

   a. Learn to setup Master device ( Raspberry Pi ) for any project

   b. Learn to setup Slave device ( Arduino UNO ) for any project

  c. Learn to Establish Communication link between Master and Slave device

2. Learn Image Processing using OpenCV4

3. Learn to driver robot on road lanes

(Course - 2)

1. Learn Essentials of Machine Learning

2. Learn to train your own cascade classifier to detect Stop Sign, Traffic Lights and any Object

3. Learn to design LED Dynamic Turn Indicators

4. Create your GitHub Repository

For More Information, don't hesitate to email:

OpenCV on Google Colab using Python

Image Processing on OpenCV using Google Colab and implementing practical algorithms on the Images

Created by ThinkIoT Solution - Expert Trainer in Embedded Devices and IoT


Students: 3063, Price: $19.99

Students: 3063, Price:  Paid

This course is a practical explanation on using the Google Colab for executing the Image Processing algorithms using OpenCV module available in Python. The course starts with explanation about the Google Colab and executing few basic codes in Python and then the basics of Image Processing are explained. Working with gray Images and Colour Images is taken up next and conversion from colour to gray is also explained. Then the Image Threshold and colour detection is explained by taking random images as inputs. The drawing tools are explained using which the images can be marked, lines, polygons and shapes can be drawn using the functions available in python.

This course will explain the concepts of Image Processing and learn how to access the Image data for a 2D and a 3D Image and this course can be used a foundation to build more complex algorithms in Image Processing. The image data for 2D and 3D image is explained and the red, blue and green channel in the image are extracted to understand exactly what a colour image consists of. This helps the students to learn the algorithms better and apply it in any further image processing. The Image Threshold and colour detection concepts are also explained by taking the image data as example which ensures that you understand the concepts very clearly.

OpenCV Starter Project – Pencil Sketch and Cartoon Paint

Apply non photo realistic effect to the image like, pencil and cartoon in Python using, OpenCV, matplotlib, ipywidgets.

Created by Data Science Anywhere - Team of Engineers


Students: 1492, Price: $19.99

Students: 1492, Price:  Paid

Welcome to Image to Cartoon Sketch using OpenCV in Python !!!

In this Course we will see, one of the most widely used image processing applications which is, Non- Photo Realistic Effects. You will generally see such an effect in most commercial software like Photoshop etc. Here, in this course using an open-source library in OpenCV, we will create such an effect by coding in Python.

What will you learn?

· Non-Photo Realistic Effect in OpenCV

· Pencil Sketch

· K Means Clustering in OpenCV

· Cartoon Paint using Bilateral Filter.

· Ipython Widgets for Hyperparameter Tuning.

In this class, we will develop two projects.

Project -1: Pencil Sketch & Tuning using Widgets

For any given image we will convert that into a perfect pencil sketch in Python. To develop this, we will use the following filter.

1. Gaussian Blur

2. Division Image

3. Gamma Correction

Here you will learn the use of these filters and how to apply them to any image. We will also create widgets. Using widgets, we will tune the hyperparameters for the best-desired pencil sketch.

Project - 2: Photo to Cartoon Photo

For any given image we will convert that into a perfect pencil sketch in Python. Just like a pencil sketch, we will create using multiple filters. The cartoon image is an extension of the pencil sketch. Here for a pencil sketch, we will segment the portion and paint it with appropriate colors using k means clustering. Finally, we will apply a Bilateral filter to get desired cartoon image. The following flow we will use to create a cartoon images.

1. Edge Mask image (pencil sketch)

2. Image Segmentation using K means Clustering.

3. Bilateral Filter.

We will also create the widgets to tune the hyperparameter to get the desired result. This is the perfect project to start your journey towards image processing.

We will see you in the class on how to develop this from scratch.

Hands on Computer Vision with OpenCV & Python

A comprehensive & easy to understand foundation to Computer Vision

Created by Shrobon Biswas - Researcher & OpenCV Lover


Students: 1089, Price: $89.99

Students: 1089, Price:  Paid

  • Wanted to learn Computer Vision but hurdled by the MATH HEAVY articles ? 
  • Enthusiastic about learning OpenCv and don’t know where to start ?
  • Want to learn about Object Tracking but bogged down by too much theory ? 
  • Wanting to build strong portfolio with Computer Vision & Image Processing Projects ? 
  • Looking to add Computer Vision algorithms in your current software project ?

Whatever be your motivation to learn OpenCV, i can assure you that you’ve come to the right course.

Hands on Computer Vision with OpenCV & Python is THE most comprehensive and cost-effective video course you will find on the web right now. 

This course is tailor made for an individual who wishes to transition quickly from an absolute beginner to an OpenCV expert in just three weeks. I ensure this by breaking down and articulating the most difficult concepts in plain and simple manner, replacing tough equations by examples and concepts by using small code snippets. This course covers topics using a methodical step-by-step approach with increasing difficulty, starting outright with the very basics and fundamentals.

My approach is simple - Don’t parrot rote code , rather Understand. 

I personally guarantee this is the number one course for you. This may not be your first OpenCV course, but trust me - It will definitely be your last. 

I assure you, that you will receive fast, friendly, responsive support by email, and on the Udemy.

Don't believe me? I offer a full money back guarantee, so long as you request it within 30 days of your purchase of the course.

If you're looking for a genuinely effective course that equips you all the tools, and more importantly the knowhow and behind the scenes magic of OpenCV, then look no further. 

Also the course is updated on a regular basis to add more new and exciting content.

Join the course right now. 

So what are you waiting for ?

Let’s meet at the other side of the course. 

Learn Computer Vision with OpenCV and Python

Image processing basics, Object detection and tracking, Deep Learning, Facial landmarks and many special applications

Created by Ibrahim Delibasoglu - Research Assistant in Sakarya University


Students: 835, Price: $89.99

Students: 835, Price:  Paid

Note: You will find real world examples (not only using implemented functions in OpenCV) and i'll add more by the time. It means that course content will expand with new special examples!.

***New Chapter***: "How to Prepare dataset and Train Your Deep Learning Model" was added to the course. You will learn how to prepare a simple dataset, label the objects and train your own deep learning model.

***New Special App***: "Search team logos" was added to the course. You will learn how you can compare images and find similar image/object in your dataset.

***New Chapter***: "Special Apps - Missing and Abandoned Object Detection" was added to the course. You will learn how to do an application for missing object detection and abandoned object detection

***New Chapter***: Facial Landmarks and Special Applications (real time sleep and smile detection) videos was added to the course!

***Different Special Applications Chapter***:  new videos in different topics will be shared under this chapter. You can look at "Soccer players detection" and "deep learning based API for object detection" examples. 

In this course, you are going to learn computer vision & image processing from scratch. You will reach all resources, have many examples and explanations of these examples.

The explanations are easy to understand and also you can ask the points you need.

I have shared key concepts with you without the heavily mathematical theory, so we can focus the implementation.

Maybe you can find some other resources, videos or blogs to learn about some of these topics explained in my course, but the advantage of this course is that, you will learn computer vision from scratch by following an order, so that you will not loss yourself between many different sources.

You will also find many special examples beside the fundamental topics.

I preferred to use OpenCV which is an open source computer vision library used and supported by many people!. I have used OpenCV with Python, because Python allows us to focus on the problem easily without spending time for programming syntax/complex codes.

I wish this course to be useful for you to learn computer vision, and Actively we can use 'questions and answers' area to share information...

You will learn the topics:

  • The key concepts of computer Vision & OpenCV

  • Basic operations: histogram equalization,thresholding, convolution, edge detection, sharpening ,morphological operations, image pyramids.

  • Keypoints and keypoint matching

  • Special App : mini game by using key points

  • Image segmentation: segmentation and contours, contour properties, line detection, circle detection, blob detection, watershed segmentation.

  • Special App: People counter 

  • Object tracking:Tracking APIs, Filtering by Color.

  • Special App: Tracking of moving object

  • Object detection: haarcascade face and eye detection, HOG pedestrian detection

  • Object detection with Deep Learning

  • Extra Chapter: How to Prepare dataset and Train Your Deep Learning Model

  • Extra Chapter: Special Apps - Missing and Abandoned Object Detection

  • Extra Chapter: Facial Landmarks and Special Applications (real time sleep and smile detection)

  • Extra Chapter: Different Special Applications ( will be updated with special examples in different topics )

Computer Vision Bootcamp™ with Python (OpenCV) – YOLO, SSD

Viola-Jones method, HOG features, R-CNNs, YOLO and SSD (Single Shot) Object Detection Approaches with Python and OpenCV

Created by Holczer Balazs - Software Engineer


Students: 787, Price: $19.99

Students: 787, Price:  Paid

This course is about the fundamental concept of image processing, focusing on face detection and object detection.  These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to crime investigation.  Self-driving cars (for example lane detection approaches) relies heavily on computer vision.

With the advent of deep learning and graphical processing units (GPUs) in the past decade it's become possible to run these algorithms even in real-time videos. So what are you going to learn in this course?

Section 1 - Image Processing Fundamentals:

  • computer vision theory

  • what are pixel intensity values

  • convolution and kernels (filters)

  • blur kernel

  • sharpen kernel

  • edge detection in computer vision (edge detection kernel)

Section 2 - Serf-Driving Cars and Lane Detection

  • how to use computer vision approaches in lane detection

  • Canny's algorithm

  • how to use Hough transform to find lines based on pixel intensities

Section 3 - Face Detection with Viola-Jones Algorithm:

  • Viola-Jones approach in computer vision

  • what is sliding-windows approach

  • detecting faces in images and in videos

Section 4 - Histogram of Oriented Gradients (HOG) Algorithm

  • how to outperform Viola-Jones algorithm with better approaches

  • how to detects gradients and edges in an image

  • constructing histograms of oriented gradients

  • using suppor vector machines (SVMs) as underlying machine learning algorithms

Section 5 - Convolution Neural Networks (CNNs) Based Approaches

  • what is the problem with sliding-windows approach

  • region proposals and selective search algorithms

  • region based convolutional neural networks (C-RNNs)

  • fast C-RNNs

  • faster C-RNNs

Section 6 - You Only Look Once (YOLO) Object Detection Algorithm

  • what is the YOLO approach?

  • constructing bounding boxes

  • how to detect objects in an image with a single look?

  • intersection of union (IOU) algorithm

  • how to keep the most relevant bounding box with non-max suppression?

Section 7 - Single Shot MultiBox Detector (SSD) Object Detection Algorithm SDD

  • what is the main idea behind SSD algorithm

  • constructing anchor boxes

  • VGG16 and MobileNet architectures

  • implementing SSD with real-time videos

We will talk about the theoretical background of face recognition algorithms and object detection in the main then we are going to implement these problems on a step-by-step basis.

Thanks for joining the course, let's get started!

Learning Path: OpenCV: Master Image Processing with OpenCV 3

Develop interactive computer vision applications with the popular C++ libraries of OpenCV 3

Created by Packt Publishing - Tech Knowledge in Motion


Students: 596, Price: $89.99

Students: 596, Price:  Paid

OpenCV 3 is a native cross-platform C++ Library for computer vision, machine learning, and image processing. Computer vision applications are the latest buzz of recent time! Big brands such as Microsoft, Apple, Google, Facebook, and Apple are increasingly making use of computer vision for object, pattern, image, and face recognition. This has led to a very high demand for computer vision expertise. So, if you're interested to know how to use the OpenCV library to build computer vision applications, then go for this Learning Path.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

The highlights of this Learning Path are:

  • Dive into the essentials of OpenCV and build your own projects
  • Learn how to apply complex visual effects to images
  • Reconstruct a 3D scene from images
  • Master the fundamental concepts in computer vision and image processing

Let’s take a quick look at your learning journey. This Learning Path helps you to get started with the OpenCV library and shows you how to install and deploy it to write effective computer vision applications following good programming practices. You will learn how to read and display images. You will then be introduced to the basic OpenCV data structures.

Further, you will start a new project and see how to load an image file and show it. Next, you'll find out how to handle keyboard events in our display window. In the next project, you will jump into interactively adjusting image brightness. You will then learn to add a miniaturizing tilt-shift effect and how to blur images. In the final project, you will learn to apply Instagram-like color ambiance filters to images.

By the end of this Learning Path, you will be able to build computer vision applications that make the most of OpenCV 3.

Meet Your Experts:

We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:

  • Robert Laganiere is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content-based video analysis, visual surveillance, driver-assistance, object detection, and tracking. Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development, published by McGraw Hill in 2001. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of startups companies such as Cognivue Corp, iWatchlife, and Tempo Analytics.
  • AdiShavit is an experienced software architect and has been an OpenCV user since it was in early beta back in 2000. Since then he has been using it pretty much continuously to build systems and products ranging from embedded, vehicle, and mobile apps to desktops and large, distributed cloud-based servers and services. His specialty is in computer vision, image processing, and machine learning with an emphasis on real-time applications. He specializes in cross-platform, high performance software combined with a high production-quality maintainable code base. He builds many products, apps, and services that leverage OpenCV.

Learning Path: OpenCV: Real-Time Computer Vision with OpenCV

Harness the power of OpenCV 3 to build practical computer vision projects

Created by Packt Publishing - Tech Knowledge in Motion


Students: 566, Price: $89.99

Students: 566, Price:  Paid

Are you looking forward to developing interesting computer vision applications? If yes, then this Learning Path is for you.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Computer vision and machine learning concepts are frequently used together in practical projects based on computer vision. Whether you are completely new to the concept of computer vision or have a basic understanding of it, this Learning Path will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects.

OpenCV is a cross-platform, open source library that is used for face recognition, object tracking, and image and video processing. By learning the basic concepts of computer vision algorithms, models, and OpenCV’s API, you will be able to develop different types of real-world applications.

Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis and text recognition in complex scenes. You’ll explore the commonly used computer vision techniques to build your own OpenCV projects from scratch. Next, we’ll teach you how to work with the various OpenCV modules for statistical modeling and machine learning. You’ll start by preparing your data for analysis, learn about supervised and unsupervised learning, and see how to use them. Finally, you’ll learn to implement efficient models using the popular machine learning techniques such as classification, regression, decision trees, K-nearest neighbors, boosting, and neural networks with the aid of C++ and OpenCV.

By the end of this Learning Path, you will be familiar with the basics of OpenCV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.

Meet Your Experts:

We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:

David Millán Escrivá was eight years old when he wrote his first program on an 8086 PC with Basic language, which enabled the 2D plotting of basic equations. In 2005, he finished his studies in IT through the Universitat Politécnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96).

Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning.

Joe Minichino is a computer vision engineer for Hoolux Medical by day and a developer of the NoSQL database LokiJS by night. At Hoolux, he leads the development of an Android computer vision-based advertising platform for the medical industry.

Computer Vision in Python for Beginners (Theory & Projects)

Computer Vision-Become an ace of Computer Vision, Computer Vision for Apps using Python, OpenCV, TensorFlow, etc.

Created by AI Sciences - AI Experts & Data Scientists |4+ Rated | 160+ Countries


Students: 462, Price: $99.99

Students: 462, Price:  Paid

Comprehensive Course Description:

Computer vision (CV), a subfield of computer science, focuses on replicating the complex functionalities of the human visual system. In the CV process, real-world images and videos are captured, processed, and analyzed to allow machines to extract contextual, useful information from the physical world.

Until recently, computer vision functioned in a limited capacity. But due to the recent innovations in artificial intelligence and deep learning, this field has made great leaps. Today, CV surpasses humans in most routine tasks connected with detecting and labeling objects.

The high-quality content of the Mastering Computer Vision from the Absolute Beginning Using Python course presents you with a great opportunity to learn and become an expert. You will learn the core concepts of the CV field. This course will also help you to understand the digital imaging process and identify the key application areas of CV. The course is:

  • · Easy to understand.

  • · Descriptive.

  • · Comprehensive.

  • · Practical with live coding.

  • · Rich with state of the art and updated knowledge of this field.

Although this course is a compilation of all the basic concepts of CV, you are encouraged to step up and experience more than what you learn. Your understanding of every concept is tested at the end of each section. The Homework assignments/tasks/activities/quizzes along with solutions will assess your learning. Several of these activities are focused on coding so that you are ready to run with implementations.

The two hands-on projects in the last section—Change Detection in CCTV Cameras (Real-time) and Smart DVRs (Real-time)—make up the most important learning element of this course. They will help you sharpen your practical skills. Successful completion of these two projects will help you enrich your portfolio and kick-start your career in the CV field.

The course tutorials are divided into 320+ videos along with detailed code notebooks. The videos are available in HD, and the total runtime of the videos is 27 hours+.

Now is the perfect time to learn computer vision. Get started with this best-in-class course without any further delay!

Teaching is our passion:

In this course, we apply the proven learning by doing methodology. We build the interest of learners first. We start from the basics and focus on helping you understand each concept clearly. The explanation of each theoretical concept is followed by practical implementation. We then encourage you to create something new out of your learning.

Our aim is to help you master the basic concepts of CV before moving onward to advanced concepts. The course material includes online videos, course notes, hands-on exercises, project work, quizzes, and handouts. We also offer you learning support. You can approach our team in case of any queries, and we respond in quick time.

Course Content:

The comprehensive course consists of the following topics:

1. Introduction

a. Intro

i. What is computer vision?

2. Image Transformations

a. Introduction to images

i. Image data structure

ii. Color images

iii. Grayscale images

iv. Color spaces

v. Color space transformations in OpenCV

vi. Image segmentation using Color space transformations

b. 2D geometric transformations

i. Scaling

ii. Rotation

iii. Shear

iv. Reflection

v. Translation

vi. Affine transformation

vii. Projective geometry

viii. Affine transformation as a matrix

ix. Application of SVD (Optional)

x. Projective transformation (Homography)

c. Geometric transformation estimation

i. Estimating affine transformation

ii. Estimating Homography

iii. Direct linear transform (DLT)

iv. Building panoramas with manual key-point selection

3. Image Filtering and Morphology

a. Image Filtering

i. Low pass filter

ii. High pass filter

iii. Band pass filter

iv. Image smoothing

v. Image sharpening

vi. Image gradients

vii. Gaussian filter

viii. Derivative of Gaussians

b. Morphology

i. Image Binarization

ii. Image Dilation

iii. Image Erosion

iv. Image Thinning and skeletonization

v. Image Opening and closing

4. Shape Detection

a. Edge Detection

i. Definition of edge

ii. Naïve edge detector

iii. Canny edge detector

1. Efficient gradient computations

2. Non-maxima suppression using gradient directions

3. Multilevel thresholding- hysteresis thresholding

b. Geometric Shape detection


ii. Line detection through RANSAC

iii. Multiple lines detection through RANSAC

iv. Circle detection through RANSAC

v. Parametric shape detection through RANSAC

vi. Hough transformation (HT)

vii. Line detection through HT

viii. Multiple lines detection through HT

ix. Circle detection through HT

x. Parametric shape detection through HT

xi. Estimating affine transformation through RANSAC

xii. Non-parametric shapes and generalized Hough transformation

5. Key Point Detection and Matching

a. Corner detection (Key point detection)

i. Defining Corner

ii. Naïve corner detector

iii. Harris corner detector

1. Continuous directions

2. Tayler approximation

3. Structure tensor

4. Variance approximation

5. Multi-scale detection

b. Project: Building automatic panoramas

i. Automatic key point detection

ii. Scale assignment

iii. Rotation assignment

iv. Feature extraction (SIFT)

v. Feature matching

vi. Image stitching

6. Motion

a. Optical Flow, Global Flow

i. Brightness constancy assumption

ii. Linear approximation

iii. Lucas–Kanade method

iv. Global flow

v. Motion segmentation

b. Object Tracking

i. Histogram based tracking

ii. KLT tracker

iii. Multiple object tracking

iv. Trackers comparisons

7. Object detection

a. Classical approaches

i. Sliding window

ii. Scale space

iii. Rotation space

iv. Limitations

b. Deep learning approaches

i. YOLO a case study

8. 3D computer vision

a. 3D reconstruction

i. Two camera setups

ii. Key point matching

iii. Triangulation and structure computation

b. Applications

i. Mocap

ii. 3D Animations

9. Projects

a. Change detection in CCTV cameras (Real-time)

b. Smart DVRs (Real-time)

After completing this course successfully, you will be able to:

  • · Relate the concepts and theories in computer vision with real-world problems.

  • · Implement any project from scratch that requires computer vision knowledge.

  • · Know the theoretical and practical aspects of computer vision concepts.

Who this course is for:

  • · Learners who are absolute beginners and know nothing about Computer Vision.

  • · People who want to make smart solutions.

  • · People who want to learn computer vision with real data.

  • · People who love to learn theory and then implement it using Python.

  • · People who want to learn computer vision along with its implementation in realistic projects.

  • · Data Scientists.

  • · Machine learning experts.

Mastering OCR using Deep Learning and OpenCV-Python

A complete guide to optical character recognition pipeline using Deep Learning, python and OpenCV

Created by Pankaj Kang - Computer Vision Specialist | Blogger | Freelancer


Students: 358, Price: $29.99

Students: 358, Price:  Paid

Hi There!

Welcome to the course 'Mastering OCR using Deep Learning and OpenCV-Python'. This is the first course of my OCR series.

In this course we will start from the very basics. We will first discuss what is Optical Character Recognition and why you should invest your time in learning this.

Then we will move to the general pipeline used by most of the OCR systems available.

After this we will start learning each pipeline component in detail. We will start by learning some image pre-processing techniques commonly used in OCR systems.

Then we will learn some deep learning based text detection algorithms such as EAST and CTPN. We will also implement the EAST algorithm using OpenCV-Python.

Next we will learn the crux of the CTC which is widely used in developing text recognition systems. We will implement very famous text recognition algorithm that is CRNN.

Finally we will learn the last component of the OCR pipeline that is restructuring. In this we will discuss why is restructuring important for any OCR systems.

We will also discuss an open source end-to-end OCR engine which is pytesseract.

Finally we will run the complete OCR pipeline to extract the data from identification document using pytesseract.

So that's all for this course, see you soon in the class room. Happy learning and have a great time.

Stay safe, stay healthy.

Hands-On Computer Vision with OpenCV 4, Keras & TensorFlow 2

Build your own computer vision deep learning classifiers

Created by Packt Publishing - Tech Knowledge in Motion


Students: 346, Price: $89.99

Students: 346, Price:  Paid

Do you want to understand how computers see images and videos? Using artificial intelligence, we can enable computers and smart devices to interpret what is in an image (computer vision).

This can provide massive benefits when it comes to automating tasks for which images are vital, such as examining medical images or enabling self-driving cars to see. Already, these applications are creating a massive industry around computer vision—one that is set to grow rapidly, with some sources predicting that it will be worth over $43 billion by 2023.

This course provides you with a perfect foundation from which to understand computer vision and supports your professional development in this fast-growing arena. We first learn the basic concepts and explore these using OpenCV4, the most popular open-source computer vision library. Next, we explore using Machine Learning in computer vision, including the use of deep learning (using TensorFlow 2.0 and Keras) to implement advanced image classifiers.

This course is designed to help data scientists, and those who already have some familiarity with ML and DL (and experience with Python, Keras, and TensorFlow), to gain a solid understanding of OpenCV and train their own computer vision deep learning models.

About the Author

Rajeev Ratan is a data scientist and computer vision engineer. He has a BSc in Computer & Electrical Engineering and an MSc in Artificial Intelligence from the University of Edinburgh, where he gained extensive knowledge of Machine Learning, computer vision, and intelligent robotics.

He has published research on using data-driven methods for Probabilistic Stochastic Modeling in the Public Transport arena and was part of a group that won a robotics competition at the University of Edinburgh.

Rajeev launched his own computer vision startup based on using Deep Learning in education. Since then, he has contributed to 2 more startups in computer vision domains and one multinational company in the data science field.

Previously, he worked for 8 years at two of the Caribbean's largest telecommunications operators, where he gained experience in managing technical staff and deploying complex telecommunications projects.

Computer Vision and Machine Learning with OpenCV 4

Grasp the concepts of OpenCV 4 to build powerful machine learning systems and computer vision applications with OpenCV 4

Created by Packt Publishing - Tech Knowledge in Motion


Students: 344, Price: $99.99

Students: 344, Price:  Paid

The application of Machine Learning and Deep Learning is rapidly gaining significance in Computer Vision. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art Computer Vision and Machine Learning algorithms. If you wish to build systems that are smarter, faster, sophisticated, and more practical by combining the power of Computer Vision, Machine Learning, and Deep Learning with OpenCV 4, then you should surely go for this Learning Path.

This hands-on course on OpenCV not only helps you learn computer vision and ML with OpenCV 4 but also enables you to apply these skills to your projects. You will firstly set up your development environment for building 5 interesting computer vision applications for Face and Eyes detection, Emotion recognition, and Fast QR code detection. You will then explore essential machine learning and deep learning concepts such as supervised learning, unsupervised learning, neural networks, and learn how to combine them with other OpenCV functionality for image processing and object detection. Along the way, you will also get some tips and tricks to work efficiently.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Hands-On OpenCV 4 with Python, is designed for you to develop some real-world computer vision applications. You will begin with setting up your environment. You will then build five exciting applications. You will also be introduced to all necessary concepts and then moving into the field of Artificial Intelligence (AI) and deep learning such as classification and object detection with OpenCV 4.

The second course, OpenCV 4 Computer Vision with Python Recipes, starts off with an introduction to OpenCV 4 and familiarizes you with the advancements in this version. You will learn how to handle images, enhance, and transform them. You will also develop some cool applications including Face and Eyes detection, Emotion recognition, and Fast QR code detection & decoding which can be deployed anywhere.

The third course, Hands-On Machine Learning with OpenCV 4, will immerse you in Machine Learning and Deep Learning, and you'll learn about key topics and concepts along the way.

By the end of this course, you will be able to tackle increasingly challenging computer vision problems faced in day-to-day life and leverage the power of machine learning algorithms to build machine learning systems and computer vision applications that are smarter, faster, more complex, and more practical.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

  • Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help their clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as Big Data, Data Science, Machine Learning, and Cloud Computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping each of them to better make sense of their data, and process it in more intelligent ways.
    The company lives by their motto: Data -> Intelligence -> Action.

  • Sourav Johar has over two years of experience with OpenCV and over three years of experience coding in Python. He has also developed an open source library built on top of OpenCV. Along with this, he has developed several Deep Learning solutions, using OpenCV for video analysis. As a computer vision enthusiast, he completely understands what problems students face. He is very passionate about programming and enjoys making programming tutorials on YouTube. He is currently working for Colibri Digital (@colibri_digital) as an instructor.

  • Muhammad Hamza Javed is a self-taught Machine Learning engineer, an entrepreneur and an author having over five years of industrial experience. He and his team has been working on several Computer Vision and Machine Learning international projects. He started working when he was 17 and kept learning new technologies and skills since then. His areas of expertise include Computer Vision, Machine Learning and Deep Learning. He learned skills own his own without a direct mentor - so he knows how troublesome it is for everyone to find to-the-point content that really improves one’s skill-set. He’s designed this course considering the challenges he faced when he learned and, in the projects, so you don’t have to spend too much time on finding what’s best for you.

Start OpenCV with Python: Real-time Processing with Webcam

Get started with Computer Vision and become a real-time processing Wizard with OpenCV & Python with fully working games

Created by Rune Thomsen - Computer Science, PhD/CS, MBA


Students: 336, Price: $89.99

Students: 336, Price:  Paid

This course will start your Computer Vision journey. You will learn how a computer extracts high-level understanding of what happens in a video. This will all be done by combining theory directly with hands on projects to speed up your learning curve.

Computer Vision is one of most interesting areas in computer science. For obvious reasons:

  • How can a computer understand what happens in an image or a video?

  • It is simple for you and I to understand what happens in an image or a video

  • ...but it is not trivial for computers to gain that understanding

At the end of this course you will create two interactive Computer Vision games that extract high level understanding from a real-time webcam flow. All this will be achieved with no prior Computer Vision knowledge. We learn and built along the way. Combining Computer Vision theory immediately by implementing it in useful scenarios.

This is a entertaining way to learn Computer Vision with practical projects at each stage in your learning journey.

Most Computer Vision courses focus on covering a broad basis, with the cost of given a overload of information, which the student will not fully master. This course focuses on learning what is needed to make full interactive games, and it will cover the theory when needed to keep the student engaged and applying the concepts immediately. This will ensure the best learning experience.

When you master something in depth, it will be easier to expand your basis to make more complex projects later. This is the best way to learn a new area. To make fully working projects based on a full understanding of the underlying theory. This is what this course gives you.

Why learn Computer Vision with OpenCV and Python?

  • If you want to use the strongest Computer Vision library supported by broad set of languages and most platforms

  • OpenCV is a Computer Vision library and is highly optimized with focus on real-time applications.

  • OpenCV integrates with C++, Python and Java interfaces on Linux, MacOS, Windows, iOS, and Android

  • Python combines the power of being easy to learn and leaves the heavy processing in libraries (like OpenCV)

The best learning practices applied in this course

  • New concepts need to be applied immediately after you learn them, otherwise you will forget them

  • You need to understand why you need new concepts in order to be engaged in the learning process

  • This course has short learning cycles with motivated concepts that are immediately applied in projects

  • ...finally, if you want to build something entertaining, then you are highly motivated

How will you benefit from this course?

  • You will master Computer Vision approaches for real-time video applications.

  • Have full projects with OpenCV in Python using your webcam

  • Master real-time processing of a video stream with OpenCV and Python

  • Practical programming experience on how Computer Vision extracts high level understanding of a live webcam stream

  • How to extract moving parts from a frame

If you want to become a comfortable with Computer Vision you need to have some basic understanding of the underlying concepts. This course will teach you the main principles in real-time Computer Vision and you will create two interactive games with your webcam stream.

In this course we will cover all concepts for real-time application, like noise tolerant motion detection, inserting objects, interact with objects from webcam to the frames, and combining that to interactive games.

This course covers the following.

  • Update or install the newest Python and PyCharm (one of the best environment to develop Python code in).

  • Install OpenCV and ensure you have correct version running.

  • Understand how webcam can be configured and the limitations.

  • Measure Frames-per-seconds and understand the process flow from webcam to screen.

  • Understand how Python interacts with OpenCV and keeps processing speed high.

  • Learn how frames are represented in Numpy and how they are processed.

  • Basic Numpy understandning for OpenCV needs.

  • Modifying frames: resize, gray scale, Gaussian blur.

  • Working with region of interest (ROI) and inserting objects in frames

  • How motion detection works.

  • Implementing a simple and noise tolerant motion detection.

  • Optimizing processing for noise tolerant motion detection.

  • Creating games where you interact through the webcam.

The course is structured in an easy understandable way

  • Starting with the simple webcam processing flow with OpenCV and Python

  • Adding concepts and processing as we go along with each example having visual explanation and coding examples

  • Structure the code to easily expand the concepts and make more advanced processing

  • Adding pieces together in a simple way - focus on keeping things understandable

You code along - you only learn by trying yourself - 40 coding lectures

  • At each step you make the implementation along with me.

  • You implement it in all stages to increase your understanding of Computer Vision with OpenCV and Python.

  • Basically, we learn along the way with 40 coding lectures that adds further knowledge at each step.

What is needed to fully understand this course?

  • You have basic understand of Python (see prerequisite for full requirements).

  • An idea of the Object Oriented Programming concept - only needed in the end and is not high level.

Who is this course for?

  • This course is for you, if you want to learn and get started with Computer Vision in a fun way.

  • If you like to learn concepts and theory while making projects.

  • Those who want to learn the depth of each lesson by programming examples to fully understand it.

All questions posted in the course will be answered within a day and in most cases within an hour. We strive to give you the best experience to kick-start your journey.

The course has a 30 day money back guarantee that ensures if you are not satisfied, you will get your money back. Also, feel free to contact me directly if you have any questions.

OpenCV Practical with Python – 3 Complete Projects + CODE

OpenCV to Computer Vision app Like Face Recognition, Motion Detector, Hand Detector

Created by Up Degree - New Skills Everyday!


Students: 260, Price: $19.99

Students: 260, Price:  Paid



Do you want to Make Practical Application Using Python and OpenCV?

If yes then this course is designed for you.

In this course, we are going to make 3 Interactive Projects using Python+ OpenCV

  1. Project # 1: Building a Motion Detector App

  2. Project # 2: Building a Hand Detector App

  3. Project #3 : Face Recognition App

Before Taking the Course:

You SHOULD HAVE the Basic Knowledge in OpenCV and Python. Here we just Develop the app and its features - 100% Practical.

What is OpenCV?

OpenCV (Open Source Computer Vision) is an open source library of computer vision, image analysis and machine learning. To do this, it has an infinity of algorithms that allow, just by writing a few lines of code, identifying faces, recognizing objects, classifying them, detecting hand movements ...

OpenCV is a multiplatform library available for Windows, Mac, Linux and Android distributed under BSD license. It can be programmed with C, C ++, Python, Java and Matlab.

Image Processing using OpenCV from Zero to Hero, 8 Projects

Complete practical and project based learning on image processing with OpenCV Python

Created by Data Science Anywhere - Team of Engineers


Students: 132, Price: $89.99

Students: 132, Price:  Paid

Welcome to "Image Processing using OpenCV from Zero to Hero" !!!

Image Processing is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course is completely project-based learning. Where you will do the project after completion of every module. Here I will cover the image processing from basics to advanced techniques including applied machine learning algorithms and models to images.


  • Image Basics

  • Drawings

  • Image Translation

  • Image Processing Techniques

  • Smoothing Filters

  • Filters

  • Graphical Use Interphase  (GUI) in OpenCV

Key Highlights in Section 1 to 7

We will start the course with very basic like load, display images. With that, we will understand the basic mathematics background behind the images. Also, I will teach you the concepts of Drawings and Videos.

Projects (Object Detection):

  1. Face Detection using Viola-Jones Algorithm

  2. Face Detection using Deep Neural Networks (SSD ResNet 10, Caffe Implementation)

  3. Real-Time Face Detection

  4. Facial Landmark Detection

Key Highlights in Section 8 to 11

We will slowly move into image processing concepts related to image transformations like image translation, flipping, rotating, and cropping. I will also teach arithmetic operations in OpenCV.

Project (Brightness Control):

  5. GUI based Brightness Control in Images

  6. Real-Time Brightness Control

Key Highlights in Section 12,13

In these sections, I will introduce new concepts on bitwise operations and masking, where you will learn the truth table and different bitwise operations like "AND", "OR", "NOT", "XOR".

Key Highlights in Section 14

Then we will extend our discussion on Smoothing Filter which is a very important image processing technique. In this section, I will teach smoothing techniques like Average Blur, Gaussian Blur, Median Blur & Bilateral Filter.

You will have complete access to Images, Data, Jupyter Notebook files that are used in this course. The code used in this course is written in such a way that you can directly plug the function into the real-time scenario and get the output. 

I will see you inside the course!!!


Srikanth Guskra

Computer Vision with OpenCV | Deep Learning CNN Projects

Learn Python OpenCV 4,Computer Vision and Deep Learning Projects from scratch to expert level| Deep Learning CNN Project

Created by Goeduhub Technologies - Technical Training Provider Company.


Students: 129, Price: $49.99

Students: 129, Price:  Paid

OpenCV-Python is an appropriate tool for fast prototyping of Computer Vision problems.

Hands-on Computer Vision with OpenCV from scratch to real-time project development.

Computer Vision is the hottest field in the era of Artificial Intelligence. It is making enormous advances in Self-driving cars, Robotics, Medical as well as in various image correction apps.

Computer Vision is showing us the future of technology and we can't even imagine what will be the end of its possibilities.

OpenCV library uses NumPy and all its array structures convert to and from NumPy arrays.

OpenCV is used to develop real-time computer vision applications. It is capable of processing images and videos to identify objects, faces, or even handwriting.

You will learn the topics:

  • The key concepts of computer Vision & OpenCV

  • Basic operations: Image read and display, Image Properties, Image resize and write, ROI and Color Mapping, Horizontal & Vertical flipping of images.

  • Drawing function in OpenCV

  • Working with Live Camera

  • Object detection: haarcascade face detection in images and Live camera

  • Convolutional Neural Network (CNN)

  • Deep Learning with Keras

  • Project: Handwritten Digit Classification using MNIST

  • Project: Fashion Classifier using FNIST

  • Project: Dogs vs Cats classifier

  • Project: Object Detection using YOLOv3

  • Project: Social Distancing Detector COVID-19

Feel free to message us on Udemy if you have any questions about the course!

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

OpenCV Fundamentals (Updated March 2021)

A Practical Guide to Computer Vision

Created by Sandeep Arneja - Engineer


Students: 77, Price: $89.99

Students: 77, Price:  Paid

This course covers the fundamental of OpenCV and it is designed to be project oriented. Instead of going over command after command available in OpenCV, we will be tackling various computer vision problems. And then while working through each vision problem, we will learn new openCV commands and apply them to solve the vision problem at hand.

Below are 7 vision problems we will work through. The vision problems in the beginning are rather simple where you will encounter many new concepts and commands. As we progress through, the problems will get harder and they will build on top of the knowledge you would have gained in the previous problems.

  1. Encode/Decode Secret Messages in the Mona Lisa Picture

  2. Adding time Elapsed to an existing video, like in those director's cut movies

  3. Determine how many pieces of candy are left

  4. Count Candy pieces off Camera Video

  5. Finding the queen in the game of carrom board

  6. Determine if an employee wearing company t-shirt is present on the floor via camera

  7. Count the number of eggs in the box

Here are the most common concepts your will learn and apply in this course:

  1. Image & Video Data Structure

  2. Image Pre-processing (Erode, Dilate etc.)

  3. Contour Detection

  4. Region of Interest Detection

  5. Color Space Manipulation

  6. Image Processing Pipeline

Note: This course has been updated and as of March 2021 it utilizes the newer constructs of OpenCV

Hands-On Machine Learning with OpenCV 4

Apply Machine Learning on Images and Videos using OpenCV 4

Created by Packt Publishing - Tech Knowledge in Motion


Students: 69, Price: $89.99

Students: 69, Price:  Paid

Computer Vision has been  booming in the past few years and it has become a highly sought-after  skill. There are tons of real-life problems for which Machine  Learning-based solutions provide significantly better results than  traditional ad-hoc approaches. The application of Machine Learning and  Deep Learning is rapidly gaining significance in Computer Vision.

All the latest tech—from self-driving cars to autonomous drones—uses AI  running on images and videos. If you want to get your hands dirty with  this technology and use it to craft your own unique solutions, then look  no further because this course is perfect for you!

This hands-on course will immerse you in Machine Learning, and you'll  learn about key topics and concepts along the way. This course is  perfect for people who wish to explore the possibilities inherent in  Machine Learning. 

About the Author

Colibri Digital is a  technology consultancy company founded in 2015 by James and Ingrid  Cross. The company works to help its clients navigate the rapidly  changing and complex world of emerging technologies, with deep expertise  in areas such as big data, data science, Machine Learning, and cloud  computing.

Over the past few years, they have worked with some of the World's  largest and most prestigious companies, including tier 1 investment  banks, a leading management consultancy group, and one of the world's  most popular soft drinks companies, helping each of them to better make  sense of its data, and process it in more intelligent ways.

At the frontier of AI, big data and cloud computing, we are Colibri Digital.

Sourav Johar has over two years' experience with OpenCV and over 3  years' experience coding in Python. He has also developed an open-source  library, which is built on top of OpenCV. Along with this, he has  developed several Machine Learning and Deep Learning solutions, using  OpenCV for video analysis.

As a Computer Vision enthusiast, he completely understands the problems  students face. He is very passionate about programming and enjoys making  programming tutorials on YouTube. He is currently working for Colibri  Digital (@colibri_digital) as an instructor. 

Python AI Machine Learning, OpenCV

Start your career path in Python Artificial Intelligence Machine Learning now!!

Created by Ayur Ninawe - Software Engineer


Students: 34, Price: $89.99

Students: 34, Price:  Paid

Ready to explore machine learning and artificial intelligence in python? This python  Artificial Intelligence machine learning and OpenCV course (A-Z) contains 5 different series designed to teach you the ins and outs of Machine Learning and Artificial intelligence. It talks about fundamental Machine Learning algorithms, neural networks, Deep Learning, OpenCV and finally developing an Artificial Intelligence that can play the game of Flappy Bird.

Python – OpenCV and PyQt5 together

Create desktop App with image processing techniques and Machine Learning Algorithms.

Created by Nico @softcademy - IT | Web & Software developer | Teacher & Instructor


Students: 32, Price: $34.99

Students: 32, Price:  Paid

Learning from videos is one of the best way to learn! This course explains basics and advanced topics in OpenCV library that is used for machine vision, and also PyQt5 to create real Desktop App with Machine Learning Algorithms. A short overview on some Machine Learning Algorithms explained with pros and cons of each of them. After this knowledge, you should be able to create other applications with UI, processing images, and with Machine Learning algorithm either for classification, regression or clustering. With some basics in Python, you will understand every single coma in the videos. The course is made to be for 'All Levels', so everyone should understand everything without basic knowledge on libraries that are used. However, as it is said several times in the videos, it is a better way to go by learning a language before leaning a library. This tutorial is made to help, remember that it could help someone else even though it does not help a particular group.

Learn Object Detection with OpenCV and TensorFlow

Object Detection

Created by Nasr Ullah - Technical Consultant


Students: 27, Price: $89.99

Students: 27, Price:  Paid

  • Introduction of Object Detection

  • Installation of all prerequisites to write the code for object detection on Mac Machine

  • Why we are using the OpenCV for Object Detection?

  • Why we are using the TensorFlow library for Object Detection?

  • Write and Run the Code for

    • Object Detection in Images

    • Object Detection in Videos

    • Object Detection in Live Streaming Videos with WebCam

    • Object Size and Position in Images, Videos and Live Streaming

    • Object Uploading on Server and Showing on Web Page

Learn OpenCV in 2 Hours with 6 Hands-On Projects in 2020

Learn the best of Computer Vision by working on real world applications. No Boring Stuff, Projects will help you learn

Created by SRM Labs - Computer Vision & Machine Learning Research Scientist


Students: 20, Price: $89.99

Students: 20, Price:  Paid

Welcome to the ultimate online course on Computer Vision!

In this course we'll go over everything you need to know to become an expert in computer vision! This $20 billion dollar industry will be one of the most important job markets in the years to come.

We'll start the course by learning about the basics of the OpenCV library and how to open and manipulate images. Then will move on to using the library to understand how to process images and apply a variety of effects, including color mappings, blending, thresholds, gradients, and more.

Then we'll move on to understanding video basics with OpenCV,. Afterwards we'll learn about direct video topics, such as object detection, face detection, human pose detection etc.

We'll look into state of the art technologies using the latest face recognition applications and using OpenPose to detect Human Poses

All in all, this course is designed to take a beginner in Python and get them experienced the advanced concepts in computer vision.

Python and OpenCV for Computer Vision – Quick Starter

Learn Python with Numpy & Pandas and OpenCV algorithms to build your own Computer Vision and Deep Learning Solution

Created by Vineeta Vashistha - Senior Technical Architect - Machine Learning


Students: 4, Price: $89.99

Students: 4, Price:  Paid

This is the best course to quickly grasp the knowledge of Python and OpenCV and become proficient to design Computer Vision and Deep Learning solutions.

With the AI-fueled organization trend getting momentum, the industry is in dire need of Computer Vision experts who are proficient in Python and OpenCV. This course has been designed to start with the basics of Python coding language comprising of Data Types, Operators, Loops, Functions, Modules, File Handling, Exception Handling along with Popular Coding Practices and then slowly take you through the advanced Python concepts such as Lambda, Map, Filter, Object Oriented Programming, Decorator, Generator, DateTime, Math, Random, Statistics, Sys, OS, Numpy, Pandas, Matplotlib and OpenPyXL in detail.

Not only this, the course takes it one step further by providing comprehensive coverage of OpenCV topics including Image Thresholding, Image Noise Removal, Image Cropping & Rotation, Image Annotation, Image Detection and also OpenCV for Videos with 35+ supporting notebooks available for download that contain examples for practice. The quiz at the end of each key topic helps you to assess your knowledge and identify the improvement areas. In addition to this, the 5 LIVE projects towards the end of course are the most sought-after computer vision solutions in industry right now on which you get a detailed code walkthrough along with downloadable source code.

Here are just few of the topics we will be learning:

· Python and OpenCV Setup

· Python Data Types & Operators

· Python Loops - For, While, If-Else

· Python - Functions, Modules & File Handling

· Popular Coding Practices and Exception Handling

· Advanced Functions - Lambda, Map, Filter, Reuse

· Object Oriented Programming, Decorator and Generator

· Built-in Modules - DateTime, Math, Random, Statistics, Sys, OS

· External Libraries - Numpy, Pandas, Matplotlib, OpenPyXL

· Image Thresholding – Simple, Adaptive and Otsu’s Binarization

· Noise Removal Techniques - Morphological Operations, Small Dots and Noise, Image Blurring, Dilation, Erosion and Kernels for Image Processing

· Image Cropping & Rotation

· Image Annotation – Draw text, rectangle, circle and line on image

· Image Detection – Blob, Edge and Contour Detection

· OpenCV - Reading from a Recorded Video

· OpenCV - Reading and Writing from LIVE camera

· Python Web Scraping using BeautifulSoup and RegEx Solution

· Sending Email with Python (Flask) Solution

· Extract text from PDF using Python Solution

· Template matching using OpenCV Solution

· Track Object by Marking in Live Camera using OpenCV Solution

Enroll in this course and become a Computer Vision expert !!