This is the code repository for OpenCV with Python By Example , published by Packt.
Build real-world computer vision applications and develop cool demos using OpenCV for Python
Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel.
This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications.
This book covers the following exciting features:
- Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image
- Detect and track various body parts such as the face, nose, eyes, ears, and mouth
- Stitch multiple images of a scene together to create a panoramic image
- Make an object disappear from an image
- Identify different shapes, segment an image, and track an object in a live video
- Recognize objects in an image and understand the content
- Reconstruct a 3D map from images
- Build an augmented reality application
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
cv2.imshow('Input', img)
cv2.imshow('Output', img_output)
cv2.waitKey()
Prateek Joshi is a computer vision researcher with a primary focus on content-based analysis. He is particularly interested in intelligent algorithms that can understand images to produce scene descriptions in terms of constituent objects. He has a master's degree from the University of Southern California, specializing in computer vision. He was elected to become a member of the Honor Society for academic excellence and an ambassador for the School of Engineering. Over the course of his career, he has worked for companies such as Nvidia, Microsoft Research, Qualcomm, and a couple of early stage start-ups in Silicon Valley. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences. He has won many hackathons using a wide variety of technologies related to image recognition. He enjoys blogging about topics such as artificial intelligence, abstract mathematics, and cryptography. His blog has been visited by users in more than 200 countries, and he has been featured as a guest author in prominent tech magazines.
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