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RustyFilters

RustyFilters Logo

RustyFilters is an image processing tool written in Rust that applies various filters and effects to images.

Features

  • Add grain effect
  • Enhance colors
  • Apply subtle glow
  • Sharpen images

Prerequisites

Before running this project, ensure you have the following installed:

  1. Rust: You can install Rust from https://www.rust-lang.org/.
  2. ffmpeg: This project uses ffmpeg to optimize the size of the output images. Follow the instructions below to install ffmpeg on your system.

Installing ffmpeg

Windows

  1. Download the ffmpeg executable from the official website.
  2. Extract the downloaded archive.
  3. Add the bin directory containing ffmpeg.exe to your system's PATH environment variable.

Linux

Install ffmpeg using your package manager. For example, on Ubuntu, you can run:

sudo apt-get update
sudo apt-get install ffmpeg# RustyFilters

RustyFilters is an image processing tool written in Rust that applies various filters and effects to images.

Features

  • Add grain effect
  • Enhance colors
  • Apply subtle glow
  • Sharpen images

Installation

  1. Make sure you have Rust installed on your system. If not, you can install it from https://www.rust-lang.org/.

  2. Clone this repository:

    git clone https://github.com/Cliffy57/RustyFilters.git
cd rustyfilters

  3. Build the project:

    cargo build --release

Usage

  1. Place your input image in the src directory and name it input.png.

  2. Run the program:

    cargo run --release

    Debug mode:

    cargo build --release
set RUST_LOG=info  
.\target\release\rust_image_filter.exe

  3. The processed image will be saved as output.png in the src directory.

Documentation

This project uses Rust's built-in documentation system. To generate and view the documentation:

  1. Generate the documentation:

    cargo doc --open

  2. This will build the documentation and open it in your default web browser.

To update the documentation, simply run the cargo doc command again after making changes to your code.

Reference

This project provides the following functions:

  • add_grain: Adds a grain effect to the image.
  • enhance_colors: Enhances the colors of the image.
  • apply_glow: Applies a subtle glow effect to the image.
  • sharpen: Sharpens the image.
  • save_image: Saves the image to the specified file path.
  • load_image: Loads an image from the specified file path.
  • optimize_image: Optimizes the size of the image using ffmpeg.
  • process_image: Processes the image using the specified filters.
  • main: The main function that runs the program.
  • get_image_path: Gets the path to the input image.
  • get_output_path: Gets the path to the output image.
  • get_image: Gets the image from the specified file path.
  • get_image_dimensions: Gets the dimensions of the image.
  • get_image_data: Gets the image data.
  • set_image_data: Sets the image data.
  • get_pixel: Gets the pixel at the specified coordinates.
  • set_pixel: Sets the pixel at the specified coordinates.
  • clamp: Clamps the value to the specified range.
  • apply_filter: Applies the specified filter to the image.
  • apply_grain: Applies the grain effect to the image.
  • apply_color_enhancement: Enhances the colors of the image.
  • apply_glow_effect: Applies the glow effect to the image.
  • apply_sharpening: Sharpens the image.
  • apply_convolution: Applies a convolution filter to the image.
  • convolve: Convolves the image with the specified kernel.

For more detailed information about each function, including parameters and return types, please refer to the generated documentation.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

  • image - A basic image processing library.

  • imageproc - A library for image processing.

  • ffmpeg - A complete, cross-platform solution to record, convert, and stream audio and video.

  • Rust - A language empowering everyone to build reliable and efficient software.

  • Cliffy57 - The author of this project.