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iOS Application Development with OpenCV 3

By : Joseph Howse

4 (5)

iOS Application Development with OpenCV 3

4 (5)

By: Joseph Howse

Overview of this book

iOS Application Development with OpenCV 3 enables you to turn your smartphone camera into an advanced tool for photography and computer vision. Using the highly optimized OpenCV library, you will process high-resolution images in real time. You will locate and classify objects, and create models of their geometry. As you develop photo and augmented reality apps, you will gain a general understanding of iOS frameworks and developer tools, plus a deeper understanding of the camera and image APIs. After completing the book's four projects, you will be a well-rounded iOS developer with valuable experience in OpenCV.

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. Setting Up Software and Hardware

1. Setting Up Software and Hardware

Setting up Apple's developer tools

Setting up the OpenCV framework

Developing a minimal application

Distributing to testers and customers

Finding documentation and support

Understanding the camera and setting up photographic accessories

Summary

2. Capturing, Storing, and Sharing Photos

2. Capturing, Storing, and Sharing Photos

Planning a photo sharing application

Configuring the project

Defining and laying out the view controller

Controlling the camera

Working with various color formats

Starting and stopping the busy mode

Saving an image to the Photos library

Displaying an alert

Sharing an image via social media

Running the application

Summary

3. Blending Images

3. Blending Images

Thinking about hybrid images

Planning the blending controls

Expanding the view controller's interface

Expanding the view controller's implementation

Using the application for practical purposes

Summary

4. Detecting and Merging Faces of Mammals

4. Detecting and Merging Faces of Mammals

Understanding detection with cascade classifiers

Understanding transformations

Planning a face merging application

Configuring the project

Defining faces and a face detector

Defining and laying out the view controllers

Detecting a hierarchy of face elements

Aligning and blending face elements

Using the application and acting like a cat

Learning more about face analysis

Summary

5. Classifying Coins and Commodities

5. Classifying Coins and Commodities

Understanding blob detection

Understanding histogram analysis

Understanding keypoint matching

Planning an object classification application

Configuring the project

Defining blobs and a blob detector

Defining blob descriptors and a blob classifier

Laying out the splash screen

Defining and laying out the view controllers

Detecting blobs against a plain background

Classifying blobs by color and keypoints

Using the application and testing the tough cases

Taking your study of OpenCV to the next level

Summary

Index

Index

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Defining blobs and a blob detector

For our purposes, a blob simply has an image and label. The image is cv::Mat and the label is an unsigned integer. The label's default value is 0, which shall signify that the blob has not yet been classified. Create a new header file, Blob.h, and fill it with the following declaration of a Blob class:

#ifndef BLOB_H
#define BLOB_H

#include <opencv2/core.hpp>

class Blob
{
public:
  Blob(const cv::Mat &mat, uint32_t label = 0ul);
  
  /**
   * Construct an empty blob.
   */
  Blob();
  
  /**
   * Construct a blob by copying another blob.
   */
  Blob(const Blob &other);
  
  bool isEmpty() const;
  
  uint32_t getLabel() const;
  void setLabel(uint32_t value);
  
  const cv::Mat &getMat() const;
  int getWidth() const;
  int getHeight() const;
  
private:
  uint32_t label;
  
  cv::Mat mat;
};

#endif // BLOB_H

The image of Blob does not change after construction, but the label may change as a result of our classification process. Note...

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