11. Processing Video Sequences | OpenCV Computer Vision Application Programming Cookbook Second Edition

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Table Of Contents

OpenCV Computer Vision Application Programming Cookbook Second Edition

By : Robert Laganiere

3.7 (3)

OpenCV Computer Vision Application Programming Cookbook Second Edition

3.7 (3)

By: Robert Laganiere

Overview of this book

OpenCV 3 Computer Vision Application Programming Cookbook is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers wishing to be introduced to the concepts of computer vision programming. It can also be used as a companion book in a university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Playing with Images

1. Playing with Images

Introduction

Installing the OpenCV library

Loading, displaying, and saving images

Exploring the cv::Mat data structure

Defining regions of interest

2. Manipulating Pixels

2. Manipulating Pixels

Introduction

Accessing pixel values

Scanning an image with pointers

Scanning an image with iterators

Writing efficient image-scanning loops

Scanning an image with neighbor access

Performing simple image arithmetic

Remapping an image

3. Processing Color Images with Classes

3. Processing Color Images with Classes

Introduction

Using the Strategy pattern in an algorithm design

Using a Controller design pattern to communicate with processing modules

Converting color representations

Representing colors with hue, saturation, and brightness

4. Counting the Pixels with Histograms

4. Counting the Pixels with Histograms

Introduction

Computing the image histogram

Applying look-up tables to modify the image appearance

Equalizing the image histogram

Backprojecting a histogram to detect specific image content

Using the mean shift algorithm to find an object

Retrieving similar images using the histogram comparison

Counting pixels with integral images

5. Transforming Images with Morphological Operations

5. Transforming Images with Morphological Operations

Introduction

Eroding and dilating images using morphological filters

Opening and closing images using morphological filters

Detecting edges and corners using morphological filters

Segmenting images using watersheds

Extracting distinctive regions using MSER

Extracting foreground objects with the GrabCut algorithm

6. Filtering the Images

6. Filtering the Images

Introduction

Filtering images using low-pass filters

Filtering images using a median filter

Applying directional filters to detect edges

Computing the Laplacian of an image

7. Extracting Lines, Contours, and Components

7. Extracting Lines, Contours, and Components

Introduction

Detecting image contours with the Canny operator

Detecting lines in images with the Hough transform

Fitting a line to a set of points

Extracting the components' contours

Computing components' shape descriptors

8. Detecting Interest Points

8. Detecting Interest Points

Introduction

Detecting corners in an image

Detecting features quickly

Detecting scale-invariant features

Detecting FAST features at multiple scales

9. Describing and Matching Interest Points

9. Describing and Matching Interest Points

Introduction

Matching local templates

Describing local intensity patterns

Describing keypoints with binary features

10. Estimating Projective Relations in Images

10. Estimating Projective Relations in Images

Introduction

Calibrating a camera

Computing the fundamental matrix of an image pair

Matching images using a random sample consensus

Computing a homography between two images

11. Processing Video Sequences

11. Processing Video Sequences

Introduction

Reading video sequences

Processing the video frames

Writing video sequences

Tracking feature points in a video

Extracting the foreground objects in a video

Index

Index

Writing video sequences

In the previous recipes, we learned how to read a video file and extract its frames. This recipe will show you how to write frames and, therefore, create a video file. This will allow us to complete the typical video-processing chain: reading an input video stream, processing its frames, and then storing the results in a new video file.

How to do it...

Writing video files in OpenCV is done using the cv::VideoWriter class. An instance is constructed by specifying the filename, the frame rate at which the generated video should play, the size of each frame, and whether or not the video will be created in color:

writer.open(outputFile, // filename
    codec,          // codec to be used 
    framerate,      // frame rate of the video
    frameSize,      // frame size
    isColor);       // color video?

In addition, you must specify the way you want the video data to be saved. This is the codec argument; this will be discussed at the end of this recipe.

Once the video file...

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Tech Concepts

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Programming languages

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