https://github.com/aetheryx/image-resizing

https://github.com/aetheryx/image-resizing

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/aetheryx/image-resizing
• Owner: aetheryx
• Created: 2024-11-09T13:46:12.000Z (about 2 months ago)
• Default Branch: master
• Last Pushed: 2024-11-09T14:15:48.000Z (about 2 months ago)
• Last Synced: 2024-11-09T14:33:59.658Z (about 2 months ago)
• Language: C++
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# image-resizing

This repository includes the source code, tooling and statistical analysis for the SE R&D research paper.

## Application

### Setup (macOS)

1. Install the C++ compiler through Xcode: `xcode-select --install`

2. Install the `ImageMagick` programs through Brew: `brew install imagemagick`

3. Install the `just` recipe runner through Brew: `brew install just`

### Setup (GPUs)

1. Deploy a server with an NVIDIA GPU, e.g. from [TensorDock](https://www.tensordock.com/)

2. Install the CUDA toolkit from [developer.nvidia.com](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_network)

3. Install the `ImageMagick` programs through apt: `sudo apt install imagemagick`

4. Install the `just` recipe runner, see [casey/just](https://github.com/casey/just?tab=readme-ov-file#linux).

### Usage

1. `mkdir build && mkdir input_images`

2. Generate the input images from a URL using `just gen_inputs`, e.g. `just gen_inputs https://4kwallpapers.com/images/wallpapers/mclaren-w1-supercar-7680x4320-19246.jpg`

3. Now you can run the application: run `just run  `, e.g. `just run cpu bilinear-interpolation`. This will write the downsized image to `build/output.rgba`

4. To convert the image from RGBA to PNG and open it, run `just render `, where `` is the output resolution configured by the scaling factor in `src/constants.hpp`. Example: `just render 1920x1080`

## Benchmarks

- Raw data was generated using [`benchmarking/runner/run.js`](benchmarking/runner/run.js), which runs each configuration (across factors, platforms and algorithms) 10 times.

- Data is aggregated to a CSV file using [`benchmarking/runner/aggregate.js`](benchmarking/runner/aggregate.js).

- CSV file is read by the Jupyter notebooks, which use pandas and matplotlib to generate graphs. See [bar](benchmarking/notebooks/bar.ipynb), [line](benchmarking/notebooks/line.ipynb) and [energy](benchmarking/notebooks/energy.ipynb).