https://github.com/selcuksert/edge-detection
A high-performance Java application that implements edge detection using the Sobel operator and TornadoVM
https://github.com/selcuksert/edge-detection
edge-detection hardware-acceleration image-processing java sobel-operator tornadovm
Last synced: 5 months ago
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A high-performance Java application that implements edge detection using the Sobel operator and TornadoVM
- Host: GitHub
- URL: https://github.com/selcuksert/edge-detection
- Owner: selcuksert
- License: mit
- Created: 2025-04-28T19:56:46.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-04T06:13:47.000Z (5 months ago)
- Last Synced: 2025-05-04T07:18:31.684Z (5 months ago)
- Topics: edge-detection, hardware-acceleration, image-processing, java, sobel-operator, tornadovm
- Language: Java
- Homepage: https://medium.com/@selcuk.sert/accelerating-java-with-tornadovm-14e3c8b2695f
- Size: 5.61 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Edge Detection with Sobel Method
A high-performance Java application that implements edge detection using the Sobel operator. This project showcases the power of modern Java features by implementing both standard CPU processing and hardware-accelerated processing using [TornadoVM](https://www.tornadovm.org/).
The project is implemented as a PoC for Medium article [Accelerating Java with TornadoVM](https://medium.com/@selcuk.sert/accelerating-java-with-tornadovm-14e3c8b2695f).
## Overview
This application provides edge detection functionality using the Sobel operator with two different implementation approaches:
- Standard CPU-based processing (`SobelStandard`)
- Hardware-accelerated processing using TornadoVM (`SobelTornado`)
The application features a JavaFX-based GUI for easy interaction and real-time image processing visualization.
## Prerequisites
- Java Development Kit (JDK) 21 or later
- Maven 3.x
- TornadoVM 1.1.0 runtime
- Graphics card supporting OpenCL (for TornadoVM acceleration)
## Installing TornadoVM
[Instructions](https://tornadovm.readthedocs.io/en/latest/installation.html#installation-configuration) from the official site can be used.
As an example for existing Eclipse Temurin JDK 21 on a macOS:
1. Clone TornadoVM repo and run installation script:
```shell
cd /usr/local/share/
git clone https://github.com/beehive-lab/TornadoVM.git
cd TornadoVM
./bin/tornadovm-installer --jdk temurin-jdk-21 --javaHome $JAVA_HOME --backend opencl
```
2. The following prompt should be displayed on console:
```text
###########################################################################
Tornado build success
Updating PATH and TORNADO_SDK to tornado-sdk-....
Commit : ....
###########################################################################
------------------------------------------
TornadoVM installation done
------------------------------------------
Creating source file ......................
........................................[ok]
```
3. Append the following line to `~/.zshrc` for environment settings:
```text
# Set TornadoVM variables
source /usr/local/share/TornadoVM/setvars.sh
```
4. On a new terminal shell list accelerator devices available to TornadoVM:
```shell
tornado --devices
```
An output like this should be displayed:
```text
Number of Tornado drivers: 1
Driver: OpenCL
Total number of OpenCL devices : 3
Tornado device=0:0 (DEFAULT)
OPENCL -- [Apple] -- Intel(R) UHD Graphics 630
Global Memory Size: 1,5 GB
Local Memory Size: 64,0 KB
Workgroup Dimensions: 3
Total Number of Block Threads: [256]
Max WorkGroup Configuration: [256, 256, 256]
Device OpenCL C version: OpenCL C 1.2
Tornado device=0:1
OPENCL -- [Apple] -- AMD Radeon Pro 5500M Compute Engine
Global Memory Size: 4,0 GB
Local Memory Size: 64,0 KB
Workgroup Dimensions: 3
Total Number of Block Threads: [256]
Max WorkGroup Configuration: [256, 256, 256]
Device OpenCL C version: OpenCL C 1.2
Tornado device=0:2
OPENCL -- [Apple] -- Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz
Global Memory Size: 32,0 GB
Local Memory Size: 32,0 KB
Workgroup Dimensions: 3
Total Number of Block Threads: [1024]
Max WorkGroup Configuration: [1024, 1, 1]
Device OpenCL C version: OpenCL C 1.2
```
5. Run TornadoVM unit tests with the following JVM parameters as a workaround if you are using Turkish locale ([issue](https://github.com/beehive-lab/TornadoVM/issues/605) filed to project):
```shell
tornado-test -V --jvm="-Duser.country=US -Duser.language=en"
```
Run a specific example to test installation:
```shell
tornado --jvm="-Duser.country=US -Duser.language=en" -m tornado.examples/uk.ac.manchester.tornado.examples.compute.MonteCarlo
```
## Features
- **Dual Implementation**: Choose between standard CPU processing or TornadoVM acceleration
- **Modern Java Features**:
- Module system integration
- Latest JavaFX controls and features
- **Real-time Processing**: Live preview of edge detection results
- **Hardware Acceleration**: TornadoVM support for GPU/FPGA acceleration
- **Configurable Parameters**: Adjust Sobel operator parameters in real-time
- **Comprehensive Logging**: Detailed logging using SLF4J and Logback
## Running the Application
### Using IDE
To run a TornadoVM app, one should use output of `tornado --printJavaFlags` CLI command. The following lines should be modified as this project is a JavaFx app.
* Install JavaFx runtime to your PC and prepend the lib path to module path setting:
```text
--module-path [JAVA_FX_PATH/lib]:[TORNADO CLI COMMAND MODULE PATH]
```
* Add modules for JavaFX:
``` text
--add-modules [TORNADO CLI COMMAND MODULES],javafx.controls,javafx.fxml
```
* Add the following as a workaround for host with Turkish locale
``` text
-Duser.country=EN
-Duser.language=us
```
An example VM option set:
```text
-server
-XX:-UseCompressedOops
-XX:+UnlockExperimentalVMOptions
-XX:+EnableJVMCI
-XX:-UseCompressedClassPointers
--enable-preview
-Djava.library.path=/usr/local/share/TornadoVM/bin/sdk/lib
--module-path
/usr/local/share/JavaFX/javafx-sdk-21.0.7/lib:/usr/local/share/TornadoVM/bin/sdk/share/java/tornado
-Dtornado.load.api.implementation=uk.ac.manchester.tornado.runtime.tasks.TornadoTaskGraph
-Dtornado.load.runtime.implementation=uk.ac.manchester.tornado.runtime.TornadoCoreRuntime
-Dtornado.load.tornado.implementation=uk.ac.manchester.tornado.runtime.common.Tornado
-Dtornado.load.annotation.implementation=uk.ac.manchester.tornado.annotation.ASMClassVisitor
-Dtornado.load.annotation.parallel=uk.ac.manchester.tornado.api.annotations.Parallel
--upgrade-module-path
/usr/local/share/TornadoVM/bin/sdk/share/java/graalJars
-XX:+UseParallelGC
@/usr/local/share/TornadoVM/bin/sdk/etc/exportLists/common-exports
@/usr/local/share/TornadoVM/bin/sdk/etc/exportLists/opencl-exports
--add-modules
ALL-SYSTEM,tornado.runtime,tornado.annotation,tornado.drivers.common,tornado.drivers.opencl,javafx.controls,javafx.fxml
-Dtornado.debug=False
-Dtornado.printKernel=False
-Dtornado.threadInfo=False
-Duser.country=EN
-Duser.language=us
```
## Implementation Details
### Standard Implementation (CPU)
- Implements classical Sobel operator algorithm
### TornadoVM Implementation
- Leverages GPU/FPGA acceleration
- Parallel processing of image data
- Dynamic hardware selection based on availability
## Dependencies
- **JavaFX** (21.0.5): UI framework
- **TornadoVM** (1.1.0): Hardware acceleration
- **ControlsFX** (11.2.1): Enhanced JavaFX controls
- **SLF4J/Logback** (2.0.17/1.5.17): Logging framework
- **JUnit** (5.10.2): Testing framework
## Configuration
### Logging
- Log files are stored in the `logs` directory
- Main log file: `data.log`
- Configure logging levels in `logback.xml`
### TornadoVM Settings
- Default device selection: First available GPU
- Fallback to CPU if no GPU is available
- Configure TornadoVM settings through environment variables
## Performance Considerations
- GPU acceleration provides significant speedup for large images
- Memory usage is optimized for both implementations
## Troubleshooting
### Common Issues
1. **TornadoVM not detected**
- Ensure TornadoVM is properly installed
- Check OpenCL installation
- Verify GPU drivers are up to date
2. **Performance Issues**
- Try switching between implementations
- Check system resource usage
- Verify image size constraints
### Error Logging
Check the `logs/data.log` file for detailed error information and stack traces.
## Future Enhancements
- [ ] Add support for additional edge detection algorithms
- [ ] Implement batch processing capability
- [ ] Add more hardware acceleration options
- [ ] Enhance UI with additional controls and features
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## Acknowledgments
- [TornadoVM](https://www.tornadovm.org/) team for the acceleration framework
- JavaFX community for UI components