An Android library to bring your ordinary photos to life with an awesome crystallized effect.

Table of Contents

• Introduction
• Lowpoly Samples
• Installation
• Library Details
• Usage Examples
• Critical Analysis
• Sample App
• How to Contribute
• About the Author
• License

Introduction

RxLowpoly serves as an improvement over XLowPoly by

• fixing out of memory crashes by scaling down the image in a loss-less manner before processing.
• providing better quality results by using 4000 as the point count by default which provides a good trade-off between speed and time.
• the higher point count leads to a longer execution period, but it is significantly reduced by scaling down the image before processing.
• provides wider choice of input sources like Bitmap, File, Uri or Drawable resource.
• natively using RxJava for background processing thereby reducing boilerplate code on the developer's end.

Lowpoly Samples

Original Image	Lowpoly Image

Installation

Add the dependency in your app module's build.gradle file

dependencies {
    ...
        implementation "com.zebrostudio.rxlowpoly:rxlowpoly:{latest_version}"
}

That's it!

Library Details

• RxLowpoly uses JNI with 64 bit support to meet google specified requirement for all apps to be 64 bit enabled by August 2019.
• Use of JNI enables much faster execution than other similar libraries.

• Use of Sobel Operator for edge detection.

  • Use of Delaunay Triangulation on the result from the sobel operator to construct the final crystallized lowpoly effect on the image.

  JNI

  RxLowpoly uses the Java Native Interface to run native code written in C which provides much faster processing for edge detection using the Sobel Operator and then implementing the Delaunay Triangulation algorithm.

  Sobel Operator

  The Sobel Edge Detector is a gradient based edge detection algorithm which provides us with separate planes on which the Delaunay Triangulation can be applied.

  Delaunay Triangulation

  We take a set P of discrete points on an image plane and apply a href="https://en.wikipedia.org/wiki/Delaunay_triangulation"Delaunay Triangulation DT(P) to produce triangles connecting 3 points at a time such that no point in P is inside the circum-circle of any triangle in DT(P). These separate triangles taken together in-turn provide us with the image having a crystallized effect.

  

  Which leads to the resultant crystallized image as :-
  

  

Usage Examples

Asynchronous call

• The most simple use case is :-

    RxLowpoly.with(context)
        .input(bitmap) 
        .generateAsync()
  

  Along with Bitmaps we can also use Drawables or Files or Uri as input.

  • When we need to downscale the image :-

    RxLowpoly.with(context)
        .input(bitmap)
        .overrideScaling(downScalingFactor)
        .generateAsync()
    

  • When we need to set a maximum width for the image :-

    RxLowpoly.with(context)
        .input(bitmap)
        .overrideScaling(maxWidth)
        .generateAsync()
    

  • We can also set a quality for the lowpoly image :-

    RxLowpoly.with(context)
        .input(inputUri)
        .overrideScaling(downScalingFactor)
        .quality(Quality.HIGH)
        .generateAsync()
    

    VERY_HIGH, MEDIUM, LOW and VERY_LOW are also available as Quality configurations

• To save the lowpoly image to a file :-

   RxLowpoly.with(context)
       .input(inputUri)
       .overrideScaling(downScalingFactor)
       .quality(Quality.HIGH)
       .output(outputFile) // An Uri of a File is also supported as an output destination
       .generateAsync()
  

All asynchronous operation is done on the IO scheduler.

Synchronous call

Replacing generateAsync() with generate() in each of the Asynchronous call examples leads to a synchronous call with a lowpoly Bitmap as a result.

A Bitmap of the generated lowpoly image is always returned irrespective of synchronous or asynchronous calls and whether an output File or Uri is supplied using the output method.

Note : A full implementation can be found in the app module of this repository or in the open sourced WallR app.

Critical Analysis

The following tests have been performed on a Xiaomi Redmi Note 5 Pro with 6 gb Ram.

Original Image	Lowpoly Image	Input Source	Quality	Execution Time (ms)
		Bitmap	Very High	15813
		Drawable	Very High	16275
		File	Very High	15987
		Uri	Very High	15931
		Bitmap	High	4547
		Drawable	High	5088
		File	High	4734
		Uri	High	4612
		Bitmap	Medium	1113
		Drawable	Medium	1672
		File	Medium	1297
		Uri	Medium	1152
		Bitmap	Low	918
		Drawable	Low	1496
		File	Low	1091
		Uri	Low	996
		Bitmap	Very Low	850
		Drawable	Very Low	1024
		File	Very Low	923
		Uri	Very Low	876

Thus it is evident that when quality is set to High, a good trade-off between speed and texture is obtained, hence the default value of Quality is set to HIGH.
Also, we can see that Bitmap is the input format of choice as it is processed the fastest, followed by Uri, File and Drawable respectively.

Sample App

The sample app provides an implementation of various configurations of RxLowpoly which one can experience and assess first hand before using the library.

Screenshots

       

How to Contribute

Please feel free to raise an issue in-case you come across a bug or even if you have any minor suggestion.

About the Author

Abhriya Roy

Android Developer with 2 years of experience in building apps that look and feel great. Enthusiastic towards writing clean and maintainable code. Open source contributor.

       

License

Copyright 2019 Abhriya Roy

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.