OpenCV 3.x with Python by example make the most of OpenCV and Python to build applications for object recognition and augmented reality
What You Will Learn Detect shapes and edges from images and videos How to apply filters on images and videos Use different techniques to manipulate and improve images Extract and manipulate particular parts of images and videos Track objects or colors from videos Recognize specific object or faces f...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing
2018
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| Edition: | Second edition |
| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Packt Upsell
- Table of Contents
- Preface
- Chapter 1: Applying Geometric Transformations to Images
- Installing OpenCV-Python
- Windows
- macOS X
- Linux (for Ubuntu)
- Virtual environments
- Troubleshooting
- OpenCV documentation
- Reading, displaying, and saving images
- What just happened?
- Loading and saving an image
- Changing image format
- Image color spaces
- Converting color spaces
- What just happened?
- Splitting image channels
- Merging image channels
- Image translation
- What just happened?
- Image rotation
- What just happened?
- Image scaling
- What just happened?
- Affine transformations
- What just happened?
- Projective transformations
- What just happened?
- Image warping
- Summary
- Chapter 2: Detecting Edges and Applying Image Filters
- 2D convolution
- Blurring
- Size of the kernel versus blurriness
- Motion blur
- Under the hood
- Sharpening
- Understanding the pattern
- Embossing
- Edge detection
- Erosion and dilation
- Afterthought
- Creating a vignette filter
- What's happening underneath?
- How do we move the focus around?
- Enhancing the contrast in an image
- How do we handle color images?
- Summary
- Chapter 3: Cartoonizing an Image
- Accessing the webcam
- Under the hood
- Extending capture options
- Keyboard inputs
- Interacting with the application
- Mouse inputs
- What's happening underneath?
- Interacting with a live video stream
- How did we do it?
- Cartoonizing an image
- Deconstructing the code
- Summary
- Chapter 4: Detecting and Tracking Different Body Parts
- Using Haar cascades to detect things
- What are integral images?
- Detecting and tracking faces
- Understanding it better
- Fun with faces
- Under the hood
- Removing the alpha channel from the overlay image
- Detecting eyes
- Afterthought
- Fun with eyes
- Positioning the sunglasses
- Detecting ears
- Detecting a mouth
- It's time for a moustache
- Detecting pupils
- Deconstructing the code
- Summary
- Chapter 5: 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
- Chapter 6: Seam Carving
- Why do we care about seam carving?
- How does it work?
- How do we define interesting?
- How do we compute the seams?
- Can we expand an image?
- Can we remove an object completely?
- How did we do it?
- Summary
- Chapter 7: Detecting Shapes and Segmenting an Image
- Contour analysis and shape matching
- Approximating a contour
- Identifying a pizza with a slice taken out
- How to censor a shape?
- What is image segmentation?
- How does it work?
- Watershed algorithm
- Summary
- Chapter 8: Object Tracking
- Frame differencing
- Colorspace based tracking
- Building an interactive object tracker
- Feature-based tracking
- Background subtraction
- Summary
- Chapter 9: Object Recognition
- Object detection versus object recognition
- What is a dense feature detector?
- What is a visual dictionary?
- What is supervised and unsupervised learning?
- What are support vector machines?
- What if we cannot separate the data with simple straight lines?
- How do we actually implement this?
- What happened inside the code?
- How did we build the trainer?
- Summary
- Chapter 10: Augmented Reality
- What is the premise of augmented reality?
- What does an augmented reality system look like?
- Geometric transformations for augmented reality
- What is pose estimation?
- How to track planar objects
- What happened inside the code?
- How to augment our reality
- Mapping coordinates from 3D to 2D
- How to overlay 3D objects on a video
- Let's look at the code
- Let's add some movements
- Summary
- Chapter 11: Machine Learning by an Artificial Neural Network
- Machine learning (ML) versus artificial neural network (ANN)
- How does ANN work?
- How to define multi-layer perceptrons (MLP)
- How to implement an ANN-MLP classifier?
- Evaluate a trained network
- Classifying images
- Summary
- Other Books You May Enjoy
- Index