Random Art Generator

This project generates random art images based on a grammar that defines a set of operations and how they can be combined. The grammar uses a seed to produce a tree-like structure of operations, which are then evaluated to create a color value for each pixel in the output image. It also allows you to render the generated art in a window using a simple shader.

Usage

Building

To build the project, you need to have Rust and Cargo installed. Then, navigate to the project directory in your terminal and run:

cargo build --release

This will create an optimized executable in the target/release/ directory.

Running

You can run the project using Cargo:

cargo run --release -- [options]

Options

• -s, --seed <SEED_STRING>: Sets a custom seed for the random number generator. The seed can be a string or an integer. If no seed is provided, the current time is used as the seed.
• -d, --depth <DEPTH>: Sets the depth of the expression tree to generate. The default value is 5.
• -o, --output <OUTPUT_FILE>: Sets the output file name for the generated image. The default value is generated/random_art.png.
• -r, --render_mode <RENDER_MODE>: Select render mode. Possible values are file (render to a file) and window ( render to a window).

Examples

Generate an image with a specific seed:

cargo run --release -- -s "my_custom_seed" -o "output/my_image.png"

Generate an image with a random seed (based on the current time) and save it to the default location:

cargo run --release

Generate an image with a numeric seed and a depth of 7:

cargo run --release -- -s 12345 -d 7 -o "seed_12345_depth_7.png"

Render to a window with a specific seed and a depth of 10:

cargo run --release -- -s "my_window_seed" -d 10 -r window

Example Images

The examples/ directory contains some example images generated by the program. Here are a few examples presented in a table:

Image	Seed	Depth
	7d3e25258f36b0c4de712969b13fb031	5
	8e2d537406dcb677	5
	89c5c8b2f6ecbb74	5
	141b38c32ba9a283dcd2db6b482b8259	5
	32822bf7da3b063e	5
	bec07cf2eb7fd31affa6081ee1b18845	5
	d561f7618bdf18f7f0622c99408e609a	10

Future Improvements

• Offscreen Rendering for Shaders: Add a way to save the output of the WindowRenderer (which uses a shader) to a PNG file. This will involve offscreen rendering techniques.
• GPU-based Renderer: Implement a full-fledged GPU-based renderer (GPURenderer) using a compute shader to significantly improve performance, especially for higher resolutions and complex expressions. Consider using libraries like wgpu.
• Animation Generation:
  • Add an option to generate animations by varying the expression tree over time (e.g., by modifying the t variable or changing the seed).
  • Implement a way to save animations as a sequence of PNG files or directly as a video file (using a library like ffmpeg).
• More Operations: Add more operations to the grammar to create more complex and varied images:
  • Trigonometric functions (cos, tan, atan2).
  • Noise functions (Perlin noise, Simplex noise).
  • Fractal functions (Mandelbrot set, Julia set).
  • Conditional operations (if-else).
  • Image loading (load an image and use it as input).
• Configurable Probabilities: Allow configuring the probabilities of each operation in the grammar, potentially through a configuration file.
• Graphical User Interface (GUI): Develop a GUI to make it easier to interact with the program, allowing users to:
  • Adjust parameters (seed, depth, operation probabilities).
  • Select the renderer (file, window, GPU).
  • Preview the generated art.
  • Save images and animations.
  • Consider using GUI frameworks like egui, iced, or druid.
• Multithreading: Parallelize the pixel evaluation in FileRenderer using threads (e.g., with the rayon crate) for performance gains.
• Caching: Implement caching to store the results of sub-tree evaluations to avoid redundant computations.
• SIMD: Explore using SIMD instructions for pixel operations to improve performance.
• Configuration File: Allow users to define custom grammars and settings in a configuration file (e.g., TOML or YAML).
• Testing: Add comprehensive unit tests to ensure code correctness and prevent regressions.

License

This project is licensed under the MIT License.