Optimize image processing on Android using Halide
Introduction
Install and configure Halide for Arm development
Build a simple camera image processing workflow
Apply operator fusion in Halide for real-time image processing
Cross-compile optimized Halide pipelines for Android
Integrate Halide into an Android project with Kotlin
Next Steps
Optimize image processing on Android using Halide
Who is this for?
This is an introductory topic for developers interested in learning how to use Halide for image processing.
What will you learn?
After completing this Learning Path, you'll be able to perform the following tasks:
- Learn the basics of Halide and set up your development environment
- Build a simple real-time image processing pipeline with Halide
- Make your image processing faster by combining operations in Halide
- Use Halide pipelines in Android apps written with Kotlin
Prerequisites
Before starting, you'll need the following prerequisites:
- Basic C++ knowledge
- Android Studio with Android Emulator
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
You’ll build and integrate a Halide-based image processing pipeline on Android. You’ll prototype Gaussian blur and thresholding, then use parallelization, tiling, and loop inspection to tune execution. After comparing fused stages with materialized intermediates, you’ll generate a cross-compiled library for an Android ABI. You’ll then integrate the library into a Kotlin app, and validate the processed frames on Android.
Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
Start with parallelization and tiling. Use
print_loop_nest() to see how your schedule arranges loops and to verify changes behave as expected.Fuse stages to reduce memory traffic when results are used once and fit well in cache. Materialize with
compute_root() or compute_at() for large filters or when an intermediate is reused by multiple consumers.Verify that the build uses the intended ABI, such as
arm64-v8a, and ensure your Android project uses the same ABI. If the ABIs don’t match, your app might not load the pipeline library.No. OpenCV is used earlier to capture webcam frames for prototyping the pipeline on the host. You then call the compiled Halide pipeline from the Android app.
Check that the library was built for the same ABI as the app and that it’s included in the Android project configuration. Also confirm the pipeline artifact from the AOT step is packaged with the app.