Learn how to use the image-processing power of OpenCV 3 to add object, facial, and feature detection to your Python applications.
- [Patrick] From enabling real-time vision for autonomous cars to creating augmented reality, image processing is a driving force behind some of today's most cutting-edge technology. As our mobile devices become more powerful, and cloud computing becomes more prevalent, we can do more now today with image processing than ever before. The Open Computer Vision library, or OpenCV for short, is one of the most widely used image processing libraries. It is an open source, cross-platform library packed with image processing algorithms.
Hi, I'm Patrick W. Crawford, and I'm a developer who has created OpenCV applications for desktop, mobile devices and even small embedded microprocessors. In this course, we're going to take a look at using OpenCV for basic image operations, object detection, facial and feature detection, and many other areas. We've got a lot to cover, and away we go!
AuthorPatrick W. Crawford
- Installing and configuring OpenCV
- Data types and structures
- Image types
- Manipulating pixels
- Scaling and rotating images
- Using video inputs
- Creating custom interfaces
- Object detection
- Face and feature detection
- Template matching
Skill Level Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
Python: Programming Efficientlywith Michele Vallisneri2h 15m Intermediate
1. Install and Configure OpenCV
2. Basic Image Operations
3. Object Detection
4. Face and Feature Detection
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