OpenCV with Python By Example

Build real-world computer vision applications and develop cool demos using OpenCV for Python

OpenCV with Python By Example

This ebook is included in a Mapt subscription
Prateek Joshi

3 customer reviews
Build real-world computer vision applications and develop cool demos using OpenCV for Python
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RRP $49.99
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Book Details

ISBN 139781785283932
Paperback296 pages

Book Description

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 will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner’s level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples.

The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation.

Table of Contents

Chapter 1: Applying Geometric Transformations to Images
Installing OpenCV-Python
Reading, displaying, and saving images
Image color spaces
Image translation
Image rotation
Image scaling
Affine transformations
Projective transformations
Image warping
Summary
Chapter 2: 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 3: Cartoonizing an Image
Accessing the webcam
Keyboard inputs
Mouse inputs
Interacting with a live video stream
Cartoonizing an image
Summary
Chapter 4: 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 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: Creating a Panoramic Image
Matching keypoint descriptors
Creating the panoramic image
What if the images are at an angle to each other?
Summary
Chapter 7: 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 8: 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 9: Object Tracking
Frame differencing
Colorspace based tracking
Building an interactive object tracker
Feature based tracking
Background subtraction
Summary
Chapter 10: 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 11: Stereo Vision and 3D Reconstruction
What is stereo correspondence?
What is epipolar geometry?
Building the 3D map
Summary
Chapter 12: 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

What You Will Learn

  • 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

Authors

Table of Contents

Chapter 1: Applying Geometric Transformations to Images
Installing OpenCV-Python
Reading, displaying, and saving images
Image color spaces
Image translation
Image rotation
Image scaling
Affine transformations
Projective transformations
Image warping
Summary
Chapter 2: 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 3: Cartoonizing an Image
Accessing the webcam
Keyboard inputs
Mouse inputs
Interacting with a live video stream
Cartoonizing an image
Summary
Chapter 4: 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 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: Creating a Panoramic Image
Matching keypoint descriptors
Creating the panoramic image
What if the images are at an angle to each other?
Summary
Chapter 7: 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 8: 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 9: Object Tracking
Frame differencing
Colorspace based tracking
Building an interactive object tracker
Feature based tracking
Background subtraction
Summary
Chapter 10: 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 11: Stereo Vision and 3D Reconstruction
What is stereo correspondence?
What is epipolar geometry?
Building the 3D map
Summary
Chapter 12: 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

Book Details

ISBN 139781785283932
Paperback296 pages
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