Summary
by Michael Beyeler, Prateek Joshi, Joseph Howse
OpenCV: Computer Vision Projects with Python
- OpenCV: Computer Vision Projects with Python
- Table of Contents
- OpenCV: Computer Vision Projects with Python
- OpenCV: Computer Vision Projects with Python
- Credits
- Preface
- 1. Module 1
- 1. Setting up OpenCV
- Choosing and using the right setup tools
- Running samples
- Finding documentation, help, and updates
- Summary
- 2. Handling Files, Cameras, and GUIs
- 3. Filtering Images
- 4. Tracking Faces with Haar Cascades
- 5. Detecting Foreground/Background Regions and Depth
- A. Integrating with Pygame
- B. Generating Haar Cascades for Custom Targets
- 1. Setting up OpenCV
- 2. Module 2
- 1. Detecting Edges and Applying Image Filters
- 2. Cartoonizing an Image
- 3. Detecting and Tracking Different Body Parts
- 4. Extracting Features from an Image
- Why do we care about keypoints?
- What are keypoints?
- Detecting the corners
- Good Features To Track
- Scale Invariant Feature Transform (SIFT)
- Speeded Up Robust Features (SURF)
- Features from Accelerated Segment Test (FAST)
- Binary Robust Independent Elementary Features (BRIEF)
- Oriented FAST and Rotated BRIEF (ORB)
- Summary
- 5. Creating a Panoramic Image
- 6. Seam Carving
- 7. Detecting Shapes and Segmenting an Image
- 8. Object Tracking
- 9. Object Recognition
- 10. Stereo Vision and 3D Reconstruction
- 11. Augmented Reality
- 3. Module 3
- 1. Fun with Filters
- 2. Hand Gesture Recognition Using a Kinect Depth Sensor
- 3. Finding Objects via Feature Matching and Perspective Transforms
- 4. 3D Scene Reconstruction Using Structure from Motion
- 5. Tracking Visually Salient Objects
- 6. Learning to Recognize Traffic Signs
- 7. Learning to Recognize Emotions on Faces
- A. Bibliography
- Index
We now have an application that uses a depth camera, facial tracking, copy operations, masks, and image filters. By developing this application, we have gained practice in leveraging the functionality of OpenCV, NumPy, and other libraries. We have also practiced wrapping this functionality in a high-level, reusable, and object-oriented design.
Congratulations! You now have the skill to develop computer vision applications in Python using OpenCV. Still, there is always more to learn and do! If you liked working with NumPy and OpenCV, please check out these other titles from Packt Publishing:
- NumPy Cookbook, Ivan Idris
- OpenCV 2 Computer Vision Application Programming Cookbook, Robert Laganière, which uses OpenCV's C++ API for desktops
- Mastering OpenCV with Practical Computer Vision Projects, (by multiple authors), which uses OpenCV's C++ API for multiple platforms
- The upcoming book, OpenCV for iOS How-to, which uses OpenCV's C++ API for iPhone and iPad
- OpenCV Android Application Programming, my upcoming book, which uses OpenCV's Java API for Android
Here ends of our tour of OpenCV's Python bindings. I hope you are able to use this book and its codebase as a starting point for rewarding work in computer vision. Let me know what you are studying or developing next!
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