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Programming to Offload Image Processing on Android* Applications

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Programming to Offload Image Processing on Android* Applications Programming to Offload Image Processing on Android* Applications 1 INTRODUCTION This article walks through an example Android application that offloads image processing using OpenCL™ and RenderScript programming languages. These programming languages are designed to take advantage of highly parallel graphics hardware (shaders) to compute large data sets and highly repetitive tasks. Although you can use other languages to offload image processing in Android applications, this article shows OpenCL and RenderScript sample code for developing application infrastructure and image processing algorithm code. The OpenCL API wrapper class is also shown and is used to facilitate programming and execution of OpenCL for image processing algorithms. The OpenCL API wrapper class source code is available license-free for anyone to use. You should be familiar with OpenCL, RenderScript, and Android programming concepts as this article only covers the instructions to offload image processing or media generation computes. You should also have an Android device that is equipped, enabled, and configured to run OpenCL (refer to Intel® SDK for OpenCL for Android device installation). Note: While other languages and techniques to offload image processing or media generation are available, here, the goal is to only highlight code differences. Future article is planned that will highlight performance differences between OpenCL and RenderScript executing on GPUs. 1.1 APPLICATION UI DESIGN In the sample application, the UI has three radio buttons so that users can quickly switch application execution between RenderScript, OpenCL, or native code. Another menu setting allows users to select whether to run OpenCL on the CPU or GPU. The menu also gives users the list of implemented effects to run, so they can select the effect they want to run. Selecting a device only applies to OpenCL (not RenderScript or native code). Intel® x86 platforms include OpenCL runtime support on both CPU and GPU. Below is a screenshot of the main UI which shows a version of the plasma effect being processed by OpenCL. The sample application UI shows performance results when running OpenCL, RenderScript, or native code. Settings Menu The performance metrics include frame per second (fps), frame render, and effect compute elapsed time. The performance metrics are highlighted in the screenshot below. Note that performance numbers shown on the screen capture are sample metrics; actual performance metrics will vary depending on the device. 1.2 APIS AND SDKS In addition to the ADT (Android Development Tool which also includes the Android SDK), the main Android based APIs utilized to program the sample application are RenderScript and Intel® SDK for OpenCL for Android applications. The Intel OpenCL™ SDK is based on and adheres to the OpenCL™ specification which is an open, royalty- free standard for cross-platform programming. For more details, refer to the OpenCL™ standard from the Khronos web site. RenderScript first became available in 2.2 ADT (API Level 8) and is a framework for running compute- intensive tasks on Android. RenderScript is primarily oriented for use with data-parallel computations, although serial computational workloads can benefit as well. Refer to the Android developer site for more information. The latest ADT available from Google’s open source repository includes the appropriate packages that need to be imported to use RenderScript, JNI (Java* Native Interface), and runtime APIs. For OpenCL setup, configuration, and runtime refer to this OpenCL Development for Android OS article. For additional programming details see RenderScript or OpenCL. 1.3 INFRASTRUCTURE CODE The infrastructure code consists of the “main” activity and helper functions. This section highlights helper functions and code for setting up the UI, selecting which effect and language technology to run, and, for OpenCL, which compute device to use. While several helper functions were implemented to integrate the user selection commands, only two are highlighted here: The backgroundThread() helper function starts a thread that periodically calls the step process function to process image effects. The code and functionality used in this function is reused from another sample application posted in the Getting Started with RenderScript article, and you can find further details here (PDF). The processStep() function is called by the backgroundThread() to process and run the image effects. The function relies on a radio button callback function to determine which language to use. The processStep() function invokes the appropriate method to process the image effect using OpenCL, RenderScript, or plain native C/C++ code. Since this code runs on a background thread, users can select a language to run by simply clicking or touching a radio button, even while an effect is being processed. The application dynamically switches to execute the appropriate step render function for a given image effect. // The processStep() method runs in a separate (background) thread. private void processStep() { try { switch (this.type.getCheckedRadioButtonId()) { case R.id.type_renderN: oclFlag = 0; // OpenCL is OFF stepRenderNative(); break; case R.id.type_renderOCL: oclFlag = 1; // OpenCL is ON stepRenderOpenCL(); break; case R.id.type_renderRS: oclFlag = 0; // OpenCL is OFF stepRenderScript(); break; default: return; } } catch (RuntimeException ex) { // Handle exception as appropriate and log error Log.wtf("Android Image Processing", "render failed", ex); } } 1.4 JAVA DEFINITION OF NATIVE FUNCTIONS The sample application implements a NativeLib class, which primarily defines functions that call into the native functionality through JNI to process a given effect. For instance, the sample application implements three effects: plasma, sepia, and monochrome. As such, the class defines the renderPlasma(…), renderSepia(…), and renderMonoChrome(…) functions. These Java functions serve as entry points through JNI to either run native or OpenCL functionality. The JNI function executes either C/C++ code or sets up and executes the OpenCL program that implements the image effect. The class uses Android bitmap and AssetManager packages. The BitMap objects are utilized to pass and return data for the image or media being processed. The application relies on the AssetManager object to gain access to the OpenCL files (i.e., sepia.cl) where the OpenCL kernels are defined. Below is the actual NativeLib Java class definition. The //TODO comment is included to illustrate that the application can be easily extended to implement additional image effects. package com.example.imageprocessingoffload; import android.content.res.AssetManager; import android.graphics.Bitmap; public class NativeLib { // Implemented in libimageeffects.so public static native void renderPlasma(Bitmap bitmapIn, int renderocl, long time_ms, String eName, int devtype, AssetManager mgr); public static native void renderMonoChrome(Bitmap bitmapIn, Bitmap bitmapOut, int renderocl, long time_ms, String eName, int simXtouch, int simYtouch, int radHi, int radLo, int devtype, AssetManager mgr); public static native void renderSepia(Bitmap bitmapIn, Bitmap bitmapOut, int renderocl, long time_ms, String eName, int simXtouch, int simYtouch, int radHi, int radLo, int devtype, AssetManager mgr); //TODO public static native <return type> render<Effectname>(…); //load actual native library static { System.loadLibrary("imageeffectsoffloading"); } } Note that the Android AssetManager and BitMap objects are passed for image input and image results to the native code. The AssetManager object is used by native code to be able to access CL files where the OpenCL kernels are defined. The BitMap object is used to make pixel data available for native code to compute and produce image results. The UI parameter deviceType is used to indicate whether to execute OpenCL on the CPU or the GPU. The Android system must be configured and capable of running OpenCL on both devices. Modern Intel® Atom™ and Intel® Core™ processors can run OpenCL on the CPU and the integrated graphics processor or GPU. The eName parameter is passed to indicate which OpenCL kernel to compile and run. Although the sample application implements a JNI function per image effect, this might appear unnecessary. However, it is possible to define multiple related image effects in a single CL file and/or JNI function. In such cases the eName would be used to compile and load the appropriate CL program and/or kernel. The renderocl parameter is used as a flag that indicates whether to run OpenCL or native C/C++ code. This flag is only set when a user selects the OpenCL radio button; otherwise, it remains unset. The time_ms parameter is used to pass a time stamp (milliseconds), which is used to calculate the performance metrics. In the plasma effect the time stamp is used to calculate the plasma effect stepping. Other arguments are specific to the image effect algorithm to render the effect radially from the center of the image. For example, the simXtouch, simYtouch, radLo, and radHi parameters along with the width and height are used to calculate and show radial progress of the monochrome and sepia effects. 1.5 DEFINITIONS AND RESOURCES TO RUN NATIVE CODE (C OR OPENCL) This section includes the JNI native function definitions for each effect implemented in the sample application. As previously mentioned, one function per effect is utilized to simplify the explanation and illustrate the functional elements used to offload image effect
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