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OpenCV Android Programming By Example
OpenCV Android Programming By Example

OpenCV Android Programming By Example: Leverage OpenCV to develop vision-aware and intelligent Android applications.

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Profile Icon Amgad Muhammad
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1 Ratings)
Paperback Dec 2015 202 pages 1st Edition
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Arrow left icon
Profile Icon Amgad Muhammad
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1 Ratings)
Paperback Dec 2015 202 pages 1st Edition
eBook
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Paperback
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Renews at €18.99p/m
eBook
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OpenCV Android Programming By Example

Chapter 2. App 1 - Building Your Own Darkroom

In this chapter, you will learn about how images are stored and represented in OpenCV and how to utilize this representation to implement interesting algorithms that will enhance how your images look.

We will first explain the digital image representation and different color spaces to explore the important Mat class in OpenCV.

Then, we will go through the steps to load an image from your phone gallery and display it on your device screen regardless of the image resolution.

Finally, you will learn about the image histograms and how to calculate and use them to enhance your images, whether they are black and white or colored.

We will cover the following topics in this chapter:

  • Digital images
  • Processing the images stored on your phone
  • Calculating an image histogram
  • Enhancing the image contrast

Digital images

Images can be found around us wherever we look; so it is very important to understand how images are represented and how the images' colors are mapped if we want to understand, process, and analyze these images automatically.

Color spaces

We live in a continuous world, so to capture a scene in a discreet digital sensor, a discrete spatial (layout) and intensity (color information) mapping has to happen in order to store the real-world data in a digital image.

The two-dimensional digital image, D(i,j), represents a sensor response value at the pixel indicated by the row number i and column number j, starting from the left upper corner as i=j=0.

To represent colors, a digital image usually contains one or more channels to store the intensity value (color) of each pixel. The most widely used color representation is a one-channel image, also known as a grayscale image, where every pixel is assigned a shade of gray depending on its intensity value: zero is black and the maximum...

Processing the images stored on your phone

In this section, you will learn how to load an image from your phone and apply some interesting image processing algorithms to it, such as contrast enhancing, smoothing (removing noise from your image), and applying some filters.

Loading an image to a Mat object

Let's get started by first creating a new Android project. As you've seen in the previous chapter, in order to start using the OpenCV algorithms, you will need to add the OpenCV library to your project:

  1. Launch Eclipse.
  2. Create a new Android project application; let's name it DarkRoom.
  3. Choose the package name. In this example, I chose it to be com.example.chapter2.darkroom.
  4. Set the minimum required SDK to be anything above API 11 (Android 3.0). In my case, and it is highly recommended, I chose it to be API 16 (Android 4.1). For the target SDK, you should choose API 19 because there is an issue when loading the OpenCV library if you are using a target SDK higher than 19.
  5. Click Next...

Calculating an image histogram

We are one step closer to understanding the image content, and one of the fundamental image analysis techniques is calculating the image histogram.

What are histograms?

Histograms are plots used to give you an overall idea about the distribution of the intensity values of a given image. In the x axis, the plot will have values ranging from 0 to 255 depending on the image depth as explained earlier and the y axis will represent the number of occurrences of the corresponding intensity value.

Once you calculate and display the histogram of an image, you can easily gain some insights about the image contrast, intensity distribution, and so on. Actually, if you normalize the histogram, making it sum to one, you can treat the histogram as a probability density function and answer questions such as what is the probability of a given intensity value to occur on an image and the answer is simply the y axis reading at that intensity value. In the following figure, you...

Enhancing the image contrast

Now that you understand what a histogram is and how to calculate it, it is time to look at one of the most widely used image enhancing techniques: the histogram equalization. The histogram equalization technique is used to enhance the image contrast, that is, the difference between the minimum and maximum intensity values in order to strengthen image details that could be washed out.

Understanding histogram equalization

From an abstract point of view, what the histogram equalization does is it finds a function that takes the image's original histogram and transforms it to a stretched histogram with a uniform distribution of the image intensity values, thus enhancing the image contrast.

In practice, histogram equalization doesn't produce a perfectly equalized output histogram; however, it forms a good approximation of the needed transformation that spreads the intensity values more evenly over the defined equalization range of the image:

Understanding histogram equalization

Enhancing grayscale...

Summary

By now you should have learned about how images are represented and stored in OpenCV. You also developed your own darkroom application, loaded images from your gallery, calculated and displayed their histograms, and executed histogram equalization on different color spaces in order to enhance how the image looks.

In the next chapter, we will develop a new application to utilize more of the OpenCV image processing and computer vision algorithms. We will use algorithms to smooth images and detect ages, lines, and circles.

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Key benefits

  • • This is the most up-to-date book on OpenCV Android programming on the market at the moment. There is no direct competition for our title.
  • • Based on a technology that is increasing in popularity, proven by activity in forums related to this topic.
  • • This book uniquely covers applications such as the Panoramic viewer and Automatic Selfie, among others.

Description

Starting from the basics of computer vision and OpenCV, we'll take you all the way to creating exciting applications. You will discover that, though computer vision is a challenging subject, the ideas and algorithms used are simple and intuitive, and you will appreciate the abstraction layer that OpenCV uses to do the heavy lifting for you. Packed with many examples, the book will help you understand the main data structures used within OpenCV, and how you can use them to gain performance boosts. Next we will discuss and use several image processing algorithms such as histogram equalization, filters, and color space conversion. You then will learn about image gradients and how they are used in many shape analysis techniques such as edge detection, Hough Line Transform, and Hough Circle Transform. In addition to using shape analysis to find things in images, you will learn how to describe objects in images in a more robust way using different feature detectors and descriptors. By the end of this book, you will be able to make intelligent decisions using the famous Adaboost learning algorithm.

Who is this book for?

If you are an Android developer and want to know how to implement vision-aware applications using OpenCV, then this book is definitely for you. It would be very helpful if you understand the basics of image processing and computer vision, but no prior experience is required

What you will learn

  • Identify and install all the elements needed to start building vision-aware Android applications
  • Explore image representation, colored and gray scale
  • Recognize and apply convolution operations and filtering to deal with noisy data
  • Use different shape analysis techniques
  • Extract and identify interest points in an image
  • Understand and perform object detection
  • Run native computer vision algorithms and gain performance boosts

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Length: 202 pages
Edition : 1st
Language : English
ISBN-13 : 9781783550593
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Publication date : Dec 15, 2015
Length: 202 pages
Edition : 1st
Language : English
ISBN-13 : 9781783550593
Category :
Languages :
Tools :

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Table of Contents

7 Chapters
1. Getting Yourself Ready Chevron down icon Chevron up icon
2. App 1 - Building Your Own Darkroom Chevron down icon Chevron up icon
3. App 2 - Software Scanner Chevron down icon Chevron up icon
4. App 2 - Applying Perspective Correction Chevron down icon Chevron up icon
5. App 3 - Panoramic Viewer Chevron down icon Chevron up icon
6. App 4 – Automatic Selfie Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Empty star icon 4
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1 star 0%
Walter Sep 13, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book covers a interesting topic, but there is certainly some bugfixing involved if you use a newer library version and android obviously too:Obviously first of all you can just use IntelliJ or android studio for android developmentIt was challenging to get into the first chapters, because the file / image chooser from the beginning works differently in 2023, as android doesnt like file paths at all by now, and the file choosing must happen as input stream directly from the loaded Uri. The getPath method is obsolete because android doesnt want you to use them anymore, instead use a mediaquery. Android will refuse to give the app read access like back when in 2015 the book released.--->To inread image from storage:InputStream inputStream = getContentResolver().openInputStream(selectedImageUri); Mat imageMat = Imgcodecs.imdecode(new MatOfByte(IOUtils.toByteArray(inputStream)), Imgcodecs.IMREAD_COLOR);-> obviously you have to fix the load-image method aswell and skip the path making thing and just set the loaded image as original image to work with, instead of reading the path.----I also had trouble with installing Open Cv itself, but that can be fixed by using opencv from quickbirds from GitHub and use their solution by loading it with the (app)-build.gradle file.-----The async method, to check if open cv is loaded, made my app crash aswell, just use a log msg: Log.d("OpenCv loaded: " + OpenCVLoader.initDebug());-----Other than that i am half way through the book and some method names have changed, still its a great book to get into OpenCv i guess.
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