The world's #1 online computer vision course.
You will learn image classification, object detection, and deep learning. Learn all the hot topics faster than any other course. Guaranteed.
Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? Or requires a degree in computer science?
That’s not the case.
All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. And that’s exactly what I do. My mission is to change education and how complex Artificial Intelligence topics are taught.
Welcome to PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today. Here you’ll learn how to successfully and confidently apply computer vision to your work, research, and projects. Join me in computer vision mastery.
Created by: Adrian Rosebrock, PhD • Last updated: 10/2024 • Languages: English
In order to be successful in PyImageSearch University, you need the following:
Learn to track objects, the foundations for hundreds of applications! OpenCV is a popular open-source computer vision library that can be used to track objects in images and videos. Inside this course you will learn how to track a ball in a video using OpenCV which is a foundational computer vision and deep learning task.
What you will learn?
Why You Should Learn This?
Get Started Today
This course is a great resource for anyone who wants to learn how to track a ball in a video using OpenCV. It is beginner friendly but still has something to teach everyone no matter how experienced you are. Deploy your first project today!
PyImageSearch University is a comprehensive set of self-paced courses for developers, students, and researchers who are ready to master computer vision, deep learning, and OpenCV. Inside this course you’ll learn how to successfully and confidently apply computer vision to your work, research, and projects.
Unlike other online courses, which are created once and never updated, leaving you with stale, out-of-date information, I keep PyImageSearch University up-to-date by releasing a brand new class every month!
Releasing a new class every month ensures you can keep up with the state-of-the-art in computer vision and deep learning, learn new algorithms and techniques, and:
To help you accomplish these goals, in each lesson I provide:
PyImageSearch University is without a doubt the most complete, comprehensive computer vision education online inside. I’ll see you inside.
Adrian Rosebrock
CEO, PyImageSearch.com
If any of these descriptions fit you, rest assured, PyImageSearch University is designed for you.
We don’t offer just one Certificate of Completion like most online courses. Instead, we offer a certificate for each of the 86 courses inside PyImageSearch University.
And since a brand new course is released every month, that means each month you receive…
PyImageSearch graduates have gone on to:
PyImageSearch University is your chance to join them in computer vision and deep learning mastery.
86 Courses • 348 Classes • 115 h 44m 57s Lectures
12 lessons, 2h 06m 18s
Loading and Displaying Images with OpenCV (12:15)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Image Fundamentals (15:07)
Drawing with OpenCV (16:38)
Translation (8:20)
Rotation (11:01)
Resizing (13:13)
Flipping (3:04)
Cropping (10:16)
Image Arithmetic (12:14)
Bitwise Operations (7:54)
Masking (5:52)
Splitting and Merging Channels (10:24)
Final exam
9 lessons, 2h 32m 07s
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Morphological Operations (19:53)
Smoothing and Blurring (19:57)
Color Spaces
Basic Thresholding (14:19)
Adaptive Thresholding (16:01)
Image Gradients (19:53)
Edge Detection (14:31)
Automatic Edge Detection (10:46)
Final exam
5 lessons, 1h 29m 44s
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Histogram and Adaptive Histogram Equalization (16:10)
Histogram Matching
Gamma Correction (11:26)
Automatic Color Correction (24:13)
Final exam
4 lessons, 1h 06m 51s
Face Detection with Haar Cascades (19:32)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Deep Learning Face Detection with OpenCV (15:42)
Deep Learning Face Detection with Dlib (18:40)
Choosing a Face Detection Method (12:57)
Final exam
4 lessons, 0h 51m 56s
Facial Landmarks with Dlib and and OpenCV (17:36)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Detecting Eyes, Nose, Lips, and Jaw with OpenCV (13:52)
Real-time Facial Landmark Detection (10:41)
5-point Facial Landmark Detection (9:47)
Final exam
3 lessons, 0h 59m 38s
What Is Face Recognition? (11:21)
Lesson Lesson assessment
Face Recognition with Local Binary Patterns (23:29)
OpenCV Eigenfaces for Face Recognition (24:48)
Final exam
6 lessons, 2h 12m 22s
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Generating ArUco Markers with OpenCV (19:36)
Detecting ArUco Markers with OpenCV (24:08)
Automatically Determining ArUco Marker Type (18:26)
Augmented Reality with ArUco Markers (24:18)
Real-time Augmented Reality with OpenCV (23:11)
Final exam
5 lessons, 0h 56m 35s
What is Deep Learning? (13:34)
Lesson Lesson assessment
Image Classification Basics (6:31)
The Deep Learning Classification Pipeline (5:11)
Your First Image Classifier: Using k-NN to Classify Images
Parameterized Learning and Neural Networks (11:19)
Final exam
4 lessons, 1h 13m 10s
Understanding and Implementing Gradient Descent (27:29)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Stochastic Gradient Descent (SGD) with Python (18:50)
Gradient Descent Algorithms and Variations (16:08)
Regularization Techniques (10:43)
Final exam
6 lessons, 2h 03m 30s
Introduction to Neural Networks (11:02)
Lesson Lesson assessment
Implementing the Perceptron Neural Network with Python (21:21)
Backpropagation from Scratch with Python (39:46)
Implementing Feedforward Neural Networks with Keras and TensorFlow (27:40)
The 4 Key Ingredients When Training Any Neural Network (14:25)
Understanding Weight Initialization for Neural Networks (9:16)
Final exam
3 lessons, 0h 49m 28s
Convolution and Cross-correlation in Neural Networks (15:33)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Convolutional Neural Networks (CNNs) and Layer Types (26:44)
Are CNNs Invariant to Translation, Rotation, and Scaling? (7:11)
Final exam
7 lessons, 1h 42m 49s
A Gentle Guide to Training your First CNN with Keras and TensorFlow (24:26)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Save Your Keras and TensorFlow Model to Disk (9:55)
Load a Trained Keras/TensorFlow Model from Disk (9:16)
LeNet: Recognizing Handwritten Digits
MiniVGGNet: Going Deeper with CNNs (20:54)
Visualizing Network Architectures Using Keras and TensorFlow (7:20)
Pre-trained CNNs for Image Classification (14:58)
Final exam
3 lessons, 1h 13m 07s
Regression with Neural Networks (23:41)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Regression with CNNs (25:15)
Combining Categorical, Numerical, and Image Data Into a Single Neural Network (24:11)
Final exam
3 lessons, 1h 19m 52s
A Gentle Introduction to tf.data with TensorFlow (28:49)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Data Pipelines with tf.data and TensorFlow (22:03)
Data Augmentation with tf.data and TensorFlow
Final exam
4 lessons, 1h 19m 24s
Introduction to Hyperparameter Tuning (24:30)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Hyperparameter Tuning for Computer Vision Projects (16:34)
Using scikit-learn to Tune Deep Learning Model Hyperparameters (18:28)
Easy Hyperparameter Tuning with Keras Tuner (19:52)
Final exam
5 lessons, 1h 28m 45s
Lesson Lesson assessment
Your First Neural Network with PyTorch (16:21)
Training Your First CNN with PyTorch (25:45)
Image Classification with Pre-Trained Networks and PyTorch (10:15)
Object Detection with Pre-Trained Networks and PyTorch (11:27)
Final exam
3 lessons, 1h 17m 24s
DataLoader for Image Data (23:07)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
PyTorch: Transfer Learning and Image Classification (47:49)
Introduction to Distributed Training in PyTorch (6:28)
Final exam
2 lessons, 0h 12m 00s
Training a DCGAN in PyTorch (5:54)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Training an object detector from scratch in PyTorch (6:06)
Final exam
4 lessons, 1h 36m 13s
Autoencoders with Keras and TensorFlow (27:23)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Denoising Autoencoders with Keras and TensorFlow (14:16)
Anomaly Detection with Autoencoders (29:04)
Autoencoders for Content-based Image Retrieval (CBIR) (25:30)
Final exam
4 lessons, 1h 52m 23s
Building Image Pairs for Siamese Networks (26:42)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Implementing Your First Siamese Network with Keras and TensorFlow (32:24)
Comparing Images for Similarity with Siamese Networks (23:12)
Improving Accuracy with Contrastlive Loss (30:05)
Final exam
5 lessons, 2h 26m 52s
Adversarial Images and Attacks with Keras and TensorFlow (26:38)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Targeted Adversarial Attacks with Keras and TensorFlow (40:02)
Adversarial Attacks with FGSM (Fast Gradient Signed Method) (21:03)
Defending Against Adverserial Attacks (27:50)
Mixing Normal Images and Adversarial Images when Training CNNs (31:19)
Final exam
7 lessons, 1h 51m 16s
Shape Detection with OpenCV (14:07)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Template Matching with OpenCV (14:52)
Multi-template Matching (15:17)
Multi-scale Template Matching (21:34)
Haar Cascades with OpenCV (13:03)
Deep Learning Object Detectors with OpenCV (17:21)
Real-time Deep Learning Object Detection with OpenCV (15:02)
Final exam
4 lessons, 2h 29m 02s
Turning Any Deep Learning Image Classifier into an Object Detector (44:28)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Selective Search for Object Detection (19:51)
Region Proposal Object Detection (25:34)
Training Your Own R-CNN Object Detector (59:09)
Final exam
2 lessons, 1h 01m 06s
Bounding Box Regression (31:45)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Multi-class Bounding Box Regression (29:21)
Final exam
3 lessons, 0h 32m 07s
What is Optical Character Recognition (OCR)? (17:20)
Lesson Lesson assessment
Installing Tesseract, PyTesseract, and Python OCR Packages On Your System (5:51)
Your First OCR Project with Tesseract and Python (8:56)
Final exam
5 lessons, 0h 42m 11s
Detecting and OCR’ing Digits with Tesseract and Python (4:15)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Whitelisting and Blacklisting Characters with Tesseract and Python (7:46)
Correcting Text Orientation with Tesseract and Python (7:27)
Language Translation and OCR with Tesseract and Python (7:37)
Using Tesseract with Non-English Languages (15:06)
Final exam
3 lessons, 0h 56m 31s
Making OCR "Easy" with EasyOCR (11:56)
Lesson Code download Pre-configured Jupyter Notebook Lesson assessment
Image/Document Alignment and Registration (19:22)
OCR’ing a Document, Form, or Invoice (25:13)
Final exam
11 lessons, 1h 22m 16s
Welcome to Visual Sensor Fusion (2:13)
Lesson assessment
What to Expect from This Course
Understanding Cameras (11:47)
Understanding LiDARs (8:23)
Review of Sensors in Self-Driving Cars
Sensor Fusion (8:51)
Point Pixel Project (4:08)
Projecting a LiDAR Point (3D) to an Image (2D) (4:30)
Applying the Magic Formula (5:53)
3D-2D Visualizations (11:58)
Coding the Magic Forumula (24:33)
Final exam
PyImageSearch University is really the best Computer Visions "Masters" Degree that I wish I had when starting out. Being able to access all of Adrian's tutorials in a single indexed page and being able to start playing around with the code without going through the nightmare of setting up everything is just amazing. 10/10 would recommend.
Not going to kid you: PyImageSearch University is worth every cent. I get asked ALL the time at my talks how I got started. PyImageSearch was the foundation.
This is a fantastic, unique resource. Where else can you get such brilliant tuition in such a wide variety of computer vision topics for such a low monthly cost? Nowhere is the answer. Highly recommended.
At the age of 58, learning ML, Computer Vision and Python all in parallel with no prior programming background was a steep learning curve and without PyImageSearch this could not have been possible. PyImageSearch brought it all nicely together.
When I first undertook my current ongoing robotics project my goals were very modest. Then I discovered PyImageSearch and found that I could go light-years beyond what I thought myself capable of back then. Through Adrian's detailed and easy-to-follow tutorials, I have achieved functionality goals I wouldn't have dared dream of before. My understanding and implementation of Python, along with a number of computer vision and machine learning concepts puts me on a par with some of the best programmers I've worked with. I couldn't have achieved this level of satisfaction without Adrian and his organization, and I am very grateful.
As a CS professor, I scaffold experiences so that my students build confidence, comfort, and enjoyment across all of the "pixel-processing's realm." Adrian's Jupyter/Colab materials are both invaluable -- and far more valuable than their price!
The PyImageSearch tutorials have been the most to the point content I have seen. I have always been able to get straightforward solutions for most of my Computer Vision and Deep Learning problems that I face in my day-to-day work life. Courses like this is what helps people and industries around the world to make quick and efficient solutions to their problems in real time.
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Full access to PyImageSearch University
Brand new courses released every month, ensuring you can keep up with state-of-the-art techniques
115 hours on-demand video
86 courses on essential computer vision, deep learning, and OpenCV topics
94 Certificates of Completion
540 tutorials and downloadable resources
Pre-configured Jupyter Notebooks in Google Colab for 338 PyImageSearch tutorials
Run all code examples in your web browser — works on Windows, macOS, and Linux (no dev environment configuration required!)
Access to centralized code repos for all 348 tutorials on PyImageSearch
Easy one-click downloads for code, datasets, pre-trained models, etc.
Access on mobile, laptop, desktop, etc.
As a CS professor, I scaffold experiences so that my students build confidence, comfort, and enjoyment across all of the "pixel-processing's realm." Adrian's Jupyter/Colab materials are both invaluable -- and far more valuable than their price!
PyImageSearch University is really the best Computer Visions "Masters" Degree that I wish I had when starting out. Being able to access all of Adrian's tutorials in a single indexed page and being able to start playing around with the code without going through the nightmare of setting up everything is just amazing. 10/10 would recommend.
When I first undertook my current ongoing robotics project my goals were very modest. Then I discovered PyImageSearch and found that I could go light-years beyond what I thought myself capable of back then. Through Adrian's detailed and easy-to-follow tutorials, I have achieved functionality goals I wouldn't have dared dream of before. My understanding and implementation of Python, along with a number of computer vision and machine learning concepts puts me on a par with some of the best programmers I've worked with. I couldn't have achieved this level of satisfaction without Adrian and his organization, and I am very grateful.
At the age of 58, learning ML, Computer Vision and Python all in parallel with no prior programming background was a steep learning curve and without PyImageSearch this could not have been possible. PyImageSearch brought it all nicely together.
Not going to kid you: PyImageSearch University is worth every cent. I get asked ALL the time at my talks how I got started. PyImageSearch was the foundation.
This is a fantastic, unique resource. Where else can you get such brilliant tuition in such a wide variety of computer vision topics for such a low monthly cost? Nowhere is the answer. Highly recommended.
The PyImageSearch tutorials have been the most to the point content I have seen. I have always been able to get straightforward solutions for most of my Computer Vision and Deep Learning problems that I face in my day-to-day work life. Courses like this is what helps people and industries around the world to make quick and efficient solutions to their problems in real time.
Thank you for being a member of PyImageSearch University! You can login here.
We assume you have some prior programming experience (e.g. you know what a variable, function, loop, etc. are). You should have more skills than a novice, but certainly not an intermediate or advanced developer. As long as you understand basic programming logic flow you'll be successful inside PyImageSearch University.
No. The courses inside PyImageSearch University will teach you computer vision, deep learning, and OpenCV. As long as you have basic programming experience you will be successful inside PyImageSearch University.
After you purchase you will be able to login and immediately access any code downloads, Jupyter Notebooks, video tutorials, courses, certificates of completion, etc.
Our support team is happy to work with you to figure out which of our 50+ courses you should be taking and in the best order to address your personal learning goals. If you are a current customer, reply to your onboarding emails or email us directly at ask@pyimagesearch.com with Subj: Customize My Learning Path. A real human (and AI Engineer) will respond to help you.
No. All of our courses, coding exercises, etc. can be completed inside your browser using our pre-configured Jupyter Notebooks running in Google Colab. If you prefer to instead configure your local development environment, we provide install instructions as well.
For monthly and yearly memberships, you will be charged on a recurring monthly or yearly basis, depending on your subscription type, starting from the sign-up date. You can cancel at any time.
There are no recurring payments for the lifetime membership — you will have access to PyImageSearch University at no additional cost.
Yes! Simply select the membership you would like to upgrade to and join. Your old membership will be automatically cancelled so you don’t have to worry about cancelling it or being double-billed.
Yes. Once you login, click your profile icon, followed by “Settings” and “Billing Info”. From there you can edit your payment method or cancel/pause your membership.
After taking this curriculum, if you haven't learned any of the aforementioned courses, then we don't want your money. That's why we offer a 100% Money-Back Guarantee. Simply send us an email and ask for a refund- up to 30 days after your purchase. With all the copies we've sold, we can count the number of refunds on the one hand. Our readers are satisfied, and we're sure you will be too. For subscription products, please cancel before your renewal date. You can cancel at any time, so refunds will not be processed for renewals. Reach out to our team if you are considering canceling, as we'll be happy to generate a custom learning path or point you in the best direction for your current learning. For our complete Terms of Use, please visit: pyimagesearch.com/terms-of-use/
Yes! Just send me a message via my contact form and we can schedule a call to discuss getting your organization access to PyImageSearch University.