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You can master Computer Vision, Deep Learning, and OpenCV. I’ve taken some of my best material from the past 5 years running PyImageSearch and designed a fully personalized, 17-lesson crash course on how to learn Computer Vision, Deep Learning, and OpenCV. Get instant access now. Start Your First Lesson Adrian Rosebrock, PhD Featured In Need help learning Computer Vision, Deep Learning, and OpenCV? Let me guide you. Whether you’re brand new to the world of computer vision and deep learning or you’re already a seasoned practitioner, you’ll find tutorials for both beginners and experts alike. Here are some of the most popular categories and tutorials on the PyImageSearch blog. How Do I Get Started? You’re interested in Computer Vision, Deep Learning, and OpenCV… but you don’t know how to get started . Follow these tutorials to get OpenCV installed on your system, learn the fundamentals of Computer Vision, and graduate to more advanced topics, including Deep Learning, Face Recognition, Object Detection, and more! Featured articles pip install OpenCV September 19, 2018 In this tutorial, you will learn how to pip install OpenCV on Ubuntu, macOS, and the Raspberry Pi. In previous OpenCV install tutorials I have recommended compiling from source; however, in the past year it has become possible to install… OpenCV Tutorial: A Guide to Learn OpenCV July 19, 2018 Whether you’re interested in learning how to apply facial recognition to video streams, building a complete deep learning pipeline for image classification, or simply want to tinker with your Raspberry Pi and add image recognition to a hobby project, you’ll… Bubble sheet multiple choice scanner and test grader using OMR, Python, and OpenCV October 3, 2016 Over the past few months I’ve gotten quite the number of requests landing in my inbox to build a bubble sheet/Scantron-like test reader using computer vision and image processing techniques. And while I’ve been having a lot of fun doing… See more How Do I Get Started Articles Deep Learning Deep Learning algorithms are revolutionizing the Computer Vision field, capable of obtaining unprecedented accuracy in Computer Vision tasks, including Image Classification, Object Detection, Segmentation, and more. Follow these tutorials and you’ll have enough knowledge to start applying Deep Learning to your own projects. Featured articles U-Net Image Segmentation in Keras February 21, 2022 In this tutorial, you will learn how to create U-Net, an image segmentation model in TensorFlow 2 / Keras. We will first present a brief introduction on image segmentation, U-Net architecture, and then walk through the code implementation with a… Image Super Resolution February 14, 2022 In this tutorial, you will learn to use image super resolution. This lesson is part of a 3-part series on Super Resolution: OpenCV Super Resolution with Deep Learning Image Super Resolution (this tutorial) Pixel Shuffle Super Resolution with TensorFlow, Keras,… Torch Hub Series #6: Image Segmentation January 24, 2022 In this tutorial, you will learn the concept behind Fully Convolutional Networks (FCNs) for segmentation. In addition, we will see how we can use Torch Hub to import a pre-trained FCN model and use it in our projects to get… See more deep learning articles Face Applications Computer Vision algorithms can be used to perform face recognition, enhance security, aid law enforcement, detect tired, drowsy drivers behind the wheel, or build a virtual makeover system. Follow these tutorials learn the basics of facial applications using Computer Vision. Featured articles OpenCV Eigenfaces for Face Recognition May 10, 2021 In this tutorial, you will learn how to implement face recognition using the Eigenfaces algorithm, OpenCV, and scikit-learn. Our previous tutorial introduced the concept of face recognition — detecting the presence of a face in an image/video and then subsequently… Face Recognition with Local Binary Patterns (LBPs) and OpenCV May 3, 2021 In this tutorial, you will learn how to perform face recognition using Local Binary Patterns (LBPs), OpenCV, and the cv2.face.LBPHFaceRecognizer_create function. In our previous tutorial, we discussed the fundamentals of face recognition, including: The difference between face detection and face… What is face recognition? May 1, 2021 In this tutorial, you will learn about face recognition, including: How face recognition works How face recognition is different from face detection A history of face recognition algorithms State-of-the-art algorithms used for face recognition today Next week we will start… See more face applications articles All Topics Image Processing Machine Learning Deep Learning Raspberry Pi OpenCV Tutorials Object Detection Interviews dlib Optical Character Recognition (OCR) Keras and TensorFlow Embedded/IoT and Computer Vision Face Applications Object Tracking Medical Computer Vision Student Success Stories What People Are Saying Adrian has helped me with my Computer Vision journey more than anyone ever has. If I need to learn anything his courses or the blog are the first thing I refer to. And if still in doubt just comment on the blog and he is very likely to respond to each and every question. Thanks Adrian. Harsh Balot , Android Developer and Computer Vision Practitioner Adrian’s deep learning book book is a great, in-depth dive into practical deep learning for computer vision. I found it to be an approachable and enjoyable read: explanations are clear and highly detailed. You’ll find many practical tips and recommendations that are rarely included in other books or in university courses. I highly recommend it, both to practitioners and beginners. Francois Chollet , Creator of Keras and AI researcher at Google I consider PyImageSearch the best collection of tutorials for beginners in computer vision. Adrian’s explanations are easy to get started with and at the same time cover enough depth to quickly feel at home in the official documentation. This combination is a rare treasure in today’s overload of carelessly written tutorials. I’ve recommended PyImageSearch already numerous times. Sandro Kalbermatter , Student at ETH Zürich Adrian’s Practical Python and OpenCV is the perfect first step if you are interested in computer vision but don’t know where to start…You’ll be glued to your workstation as you try out just one more example. Jason Brownlee , Creator of MachineLearningMastery.com I highly recommend grabbing a copy of Deep Learning for Computer Vision with Python. It goes into a lot of detail and has tons of detailed examples. It’s the only book I’ve seen so far that covers both how things work and how to actually use them in the real world to solve difficult problems. Check it out! Adam Geitgey , Author of Machine Learning is Fun! blog series Practical Python and OpenCV is a non-intimidating introduction to basic image processing tasks in Python. While reading the book, it feels as if Adrian is right next to you, helping you understand the many code examples without getting lost in mathematical details. Dr. Tomasz Malisiewicz , Principal Engineer and Deep Learning Practitioner at MagicLeap Phenomenal. The concepts on deep learning are so well explained that I will be recommending this book [Deep Learning for Computer Vision with Python] to anybody not just involved in computer vision...