Book Image

iOS Application Development with OpenCV 3

By : Joseph Howse
4 (1)
Book Image

iOS Application Development with OpenCV 3

4 (1)
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.
Table of Contents (13 chapters)
iOS Application Development with OpenCV 3
Credits
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
Index

Configuring the project


Create an Xcode project named BeanCounter. As usual, choose the Single View Application template. Follow the configuration instructions in Chapter 1, Setting Up Software and Hardware and Chapter 2, Capturing, Storing, and Sharing Photos. (See the Configuring the project section of each chapter.) BeanCounter depends on the same frameworks and device capabilities as LightWork.

Our blob classifier will depend on a configuration file and set of training images that we provide. As a starting point, you may want to use the training set of beans and Canadian coins, as provided in the book's GitHub repository. Alternatively, under the Supporting Files folder, add your own training images and create a new file called BlobClassifierTraining.plist. Edit the PLIST file to define labels and training images according to the format in the following screenshot:

For example, Item 0 in blobs is a training image with the filename CanadianNickel_Heads_000.png and the label 2. We can look...