Opencv: Computer Vision Projects with Python

Cover
Copyright
Credits
Preface
Table of Contents
Module 1: OpenCV Computer Vision with Python
Chapter 1: Setting up OpenCV
Choosing and using the right setup tools
Running samples
Finding documentation, help, and updates
Summary
Chapter 2: Handling Files, Cameras, and GUIs
Basic I/O scripts
Project concept
An object-oriented design
Summary
Chapter 3: Filtering Images
Creating modules
Channel mixing – seeing in Technicolor
Curves – bending color space
Highlighting edges
Custom kernels – getting convoluted
Modifying the application
Summary
Chapter 4: Tracking Faces with Haar Cascades
Conceptualizing Haar cascades
Getting Haar cascade data
Creating modules
Defining a face as a hierarchy of rectangles
Tracing, cutting, and pasting rectangles
Adding more utility functions
Tracking faces
Modifying the application
Summary
Chapter 5: Detecting Foreground/Background Regions and Depth
Creating modules
Capturing frames from a depth camera
Creating a mask from a disparity map
Masking a copy operation
Modifying the application
Summary
Appendix A: Integrating with Pygame
Installing Pygame
Documentation and tutorials
Subclassing managers.WindowManager
Modifying the application
Further uses of Pygame
Summary
Appendix B: Generating Haar Cascades for Custom Targets
Gathering positive and negative training images
Finding the training executables
Creating the training sets and cascade
Testing and improving
Summary
Module 2: OpenCV with Python By Example
Chapter 1: Detecting Edges and Applying Image Filters
2D convolution
Blurring
Edge detection
Motion blur
Sharpening
Embossing
Erosion and dilation
Creating a vignette filter
Enhancing the contrast in an image
Summary
Chapter 2: Cartoonizing an Image
Accessing the webcam
Keyboard inputs
Mouse inputs
Interacting with a live video stream
Cartoonizing an image
Summary
Chapter 3: Detecting and Tracking Different Body Parts
Using Haar cascades to detect things
What are integral images?
Detecting and tracking faces
Fun with faces
Detecting eyes
Fun with eyes
Detecting ears
Detecting a mouth
It's time for a moustache
Detecting a nose
Detecting pupils
Summary
Chapter 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
Chapter 5: Creating a Panoramic Image
Matching keypoint descriptors
Creating the panoramic image
What if the images are at an angle to each other?
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?
Summary
Chapter 7: Detecting Shapes and Segmenting an Image
Contour analysis and shape matching
Approximating a contour
Identifying the pizza with the slice taken out
How to censor a shape?
What is image segmentation?
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?
How do we actually implement this?
Summary
Chapter 10: Stereo Vision and 3D Reconstruction
What is stereo correspondence?
What is epipolar geometry?
Building the 3D map
Summary
Chapter 11: 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?
How to augment our reality?
Let's add some movements
Summary
Module 3: OpenCV with Python Blueprints
Chapter 1: Fun with Filters
Planning the app
Creating a black-and-white pencil sketch
Generating a warming/cooling filter
Cartoonizing an image
Putting it all together
Summary
Chapter 2: Hand Gesture Recognition Using a Kinect Depth Sensor
Planning the app
Setting up the app
Tracking hand gestures in real time
Hand region segmentation
Hand shape analysis
Hand gesture recognition
Summary
Chapter 3: Finding Objects via Feature Matching and Perspective Transforms
Tasks performed by the app
Planning the app
Setting up the app
The process flow
Feature extraction
Feature matching
Feature tracking
Seeing the algorithm in action
Summary
Chapter 4: 3D Scene Reconstruction Using Structure from Motion
Planning the app
Camera calibration
Setting up the app
Estimating the camera motion from a pair of images
Reconstructing the scene
3D point cloud visualization
Summary
Chapter 5: Tracking Visually Salient Objects
Planning the app
Setting up the app
Visual saliency
Mean-shift tracking
Putting it all together
Summary
Chapter 6: Learning to Recognize Traffic Signs
Planning the app
Supervised learning
The GTSRB dataset
Feature extraction
Support Vector Machine
Putting it all together
Summary
Chapter 7: Learning to Recognize Emotions on Faces
Planning the app
Face detection
Facial expression recognition
Putting it all together
Bibliography
Preface.pdf
_GoBack