OpenCV: Computer Vision Projects with Python

Joseph Howse, Prateek Joshi, Michael Beyeler

• Editore: Packt Publishing
• Anno: 2016
• ISBN:
  9781787125490
• Edizione: 1
• Argomento: Computer Science
• Categoria: Programming in Python

Get savvy with OpenCV and actualize cool computer vision applications

About This Book

• Use OpenCV's Python bindings to capture video, manipulate images, and track objects
• Learn about the different functions of OpenCV and their actual implementations.
• Develop a series of intermediate to advanced projects using OpenCV and Python

Who This Book Is For

This learning path is for someone who has a working knowledge of Python and wants to try out OpenCV. This Learning Path will take you from a beginner to an expert in computer vision applications using OpenCV. OpenCV's application are humongous and this Learning Path is the best resource to get yourself acquainted thoroughly with OpenCV.

What You Will Learn

• Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect - all on Windows, Mac or Ubuntu
• Apply "curves" and other color transformations to simulate the look of old photos, movies, or video games
• Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image
• Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
• Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
• Detect and recognize street signs using a cascade classifier and support vector machines (SVMs)
• Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMs
• Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features

In Detail

OpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations. OpenCV for Python enables us to run computer vision algorithms in real time. This learning path proposes to teach the following topics. First, we will learn how to get started with OpenCV and OpenCV3's Python API, and develop a computer vision application that tracks body parts. Then, we will build amazing intermediate-level computer vision applications such as making an object disappear from an image, identifying different shapes, reconstructing a 3D map from images, and building an augmented reality application, Finally, we'll move to more advanced projects such as hand gesture recognition, tracking visually salient objects, as well as recognizing traffic signs and emotions on faces using support vector machines and multi-layer perceptrons respectively.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

• OpenCV Computer Vision with Python by Joseph Howse
• OpenCV with Python By Example by Prateek Joshi
• OpenCV with Python Blueprints by Michael Beyeler

Style and approach

This course aims to create a smooth learning path that will teach you how to get started with will learn how to get started with OpenCV and OpenCV 3's Python API, and develop superb computer vision applications. Through this comprehensive course, you'll learn to create computer vision applications from scratch to finish and more!.

OpenCV: Computer Vision Projects with Python

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Table of Contents

OpenCV: Computer Vision Projects with Python

OpenCV: Computer Vision Projects with Python

Credits

Preface

What this learning path covers

What you need for this learning path

Who this learning path is for

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Module 1

1. Setting up OpenCV

Choosing and using the right setup tools

Making the choice on Windows XP, Windows Vista, Windows 7, or Windows 8

Using binary installers (no support for depth cameras)

Using CMake and compilers

Making the choice on Mac OS X Snow Leopard, Mac OS X Lion, or Mac OS X Mountain Lion

Using MacPorts with ready-made packages

Using MacPorts with your own custom packages

Using Homebrew with ready-made packages (no support for depth cameras)

Using Homebrew with your own custom packages

Making the choice on Ubuntu 12.04 LTS or Ubuntu 12.10

Using the Ubuntu repository (no support for depth cameras)

Using CMake via a ready-made script that you may customize

Making the choice on other Unix-like systems

Running samples

Finding documentation, help, and updates

Summary

2. Handling Files, Cameras, and GUIs

Basic I/O scripts

Reading/Writing an image file

Converting between an image and raw bytes

Reading/Writing a video file

Capturing camera frames

Displaying camera frames in a window

Project concept

An object-oriented design

Abstracting a video stream – managers.CaptureManager

Abstracting a window and keyboard – managers.WindowManager

Applying everything – cameo.Cameo

Summary

3. Filtering Images

Creating modules

Channel mixing – seeing in Technicolor

Simulating RC color space

Simulating RGV color space

Simulating CMV color space

Curves – bending color space

Formulating a curve

Caching and applying a curve

Designing object-oriented curve filters

Emulating photo films

Emulating Kodak Portra

Emulating Fuji Provia

Emulating Fuji Velvia

Emulating cross-processing

Highlighting edges

Custom kernels – getting convoluted

Modifying the application

Summary

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

Swapping faces in one camera feed

Copying faces between camera feeds

Summary

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

A. Integrating with Pygame

Installing Pygame

Documentation and tutorials

Subclassing managers.WindowManager

Modifying the application

Further uses of Pygame

Summary

B. Generating Haar Cascades for Custom Targets

Gathering positive and negative training images

Finding the training executables

On Windows

On Mac, Ubuntu, and other Unix-like systems

Creating the training sets and cascade

Creating <negative_description>

Creating <positive_description>

Creating <binary_description> by running <opencv_createsamples>

Creating <cascade> by running <opencv_traincascade>

Testing and improving <cascade>

Summary

2. Module 2

1. Detecting Edges and Applying Image Filters

2D convolution

Blurring

The size of the kernel versus the blurriness

Edge detection

Motion blur

Under the hood

Sharpening

Understanding the pattern

Embossing

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

2. Cartoonizing an Image

Accessing the webcam

Under the hood

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

3. 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

Detecting eyes

Afterthought

Fun with eyes

Positioning the sunglasses

Detecting ears

Detecting a mouth

It's time for a moustache

Detecting a nose

Detecting pupils

Deconstructing the code

Summary

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

Matching keypoint descriptors

How did we match the keypoints?

Understanding the matcher object

Drawing the matching keypoints

Creating the panoramic image

Finding the overlapping regions

Stitching the images

What if the images are at an angle to each other?

Why does it look stretched?

Summary

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

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?

How does it work?

Watershed algorithm

Summary

8. Object Tracking

Frame differencing

Colorspace based tracking

Building an interactive object tracker

Feature based tracking

Background subtraction

Summary

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

10. Stereo Vision and 3D Reconstruction

What is stereo correspondence?

What is epipolar geometry?

Why are the lines different as compared to SIFT?

Building the 3D map

Summary

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?

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

3. Module 3

1. Fun with Filters

Planning the app

Creating a black-and-white pencil sketch

Implementing dodging and burning in OpenCV

Pencil sketch transformation

Generating a warmin...