ai-baby-generator

This crate provides a simple and efficient way to generate hypothetical baby images based on provided parent images. It is designed to be a convenient tool for exploring potential future family resemblances.

Installation

To use ai-baby-generator in your Rust project, add the following to your Cargo.toml file: toml [dependencies] ai-baby-generator = "0.1.0" # Replace with the latest version

Usage Examples

Here are a few examples demonstrating how to use the ai-baby-generator crate:

Example 1: Basic Image Generation rust use ai_baby_generator::generate_baby_image; use image::{ImageBuffer, Rgb};

fn main() -> Result<(), String> { // Load parent images (replace with actual image loading logic) let parent1_image_path = "path/to/parent1.jpg"; let parent2_image_path = "path/to/parent2.jpg";

let parent1_image: ImageBuffer<Rgb<u8>, Vec<u8>> = image::open(parent1_image_path)
    .map_err(|e| format!("Failed to open parent1 image: {}", e))?
    .to_rgb8();

let parent2_image: ImageBuffer<Rgb<u8>, Vec<u8>> = image::open(parent2_image_path)
    .map_err(|e| format!("Failed to open parent2 image: {}", e))?
    .to_rgb8();

// Generate the baby image
let baby_image = generate_baby_image(&parent1_image, &parent2_image)?;

// Save the generated image (replace with actual image saving logic)
baby_image.save("baby.png").map_err(|e| format!("Failed to save baby image: {}", e))?;

println!("Baby image generated successfully!");
Ok(())

}

Example 2: Handling Errors

This example demonstrates how to gracefully handle potential errors during image generation. rust use ai_baby_generator::generate_baby_image; use image::{ImageBuffer, Rgb};

fn main() { // Load parent images (replace with actual image loading logic) let parent1_image_path = "path/to/parent1.jpg"; let parent2_image_path = "path/to/parent2.jpg";

let parent1_image: Result<ImageBuffer<Rgb<u8>, Vec<u8>>, String> = image::open(parent1_image_path)
    .map_err(|e| format!("Failed to open parent1 image: {}", e))
    .map(|img| img.to_rgb8());

let parent2_image: Result<ImageBuffer<Rgb<u8>, Vec<u8>>, String> = image::open(parent2_image_path)
    .map_err(|e| format!("Failed to open parent2 image: {}", e))
    .map(|img| img.to_rgb8());

match (parent1_image, parent2_image) {
    (Ok(img1), Ok(img2)) => {
        match generate_baby_image(&img1, &img2) {
            Ok(baby_image) => {
                if let Err(e) = baby_image.save("baby.png") {
                    eprintln!("Failed to save baby image: {}", e);
                } else {
                    println!("Baby image generated successfully!");
                }
            }
            Err(e) => eprintln!("Error generating baby image: {}", e),
        }
    }
    (Err(e), _) => eprintln!("Error loading parent1 image: {}", e),
    (_, Err(e)) => eprintln!("Error loading parent2 image: {}", e),
}

}

Example 3: Integrating with a Web Server (Hypothetical)

This is a conceptual example, as the crate provided is a stub. It illustrates how the crate could be integrated into a web application using a framework like rocket or actix-web. rust // Note: This example is conceptual and requires a web framework. // It assumes the ai-baby-generator crate performs actual image generation.

// use rocket::form::{Form, FromForm}; // use rocket::response::content::Html; // use rocket::fs::TempFile;

// #[derive(FromForm)] // struct ImageUpload<'r> { // parent1: TempFile<'r>, // parent2: TempFile<'r>, // }

// #[post("/generate", data = "")] // async fn generate(data: Form<ImageUpload<'_>>) -> Html { // // Load images from TempFiles (implementation details omitted) // // let parent1_image = load_image_from_tempfile(data.parent1).await; // // let parent2_image = load_image_from_tempfile(data.parent2).await;

// // Generate the baby image using ai-baby-generator // // let baby_image = generate_baby_image(&parent1_image, &parent2_image).unwrap();

// // Return the generated image as HTML (implementation details omitted) // // Html(format!("", base64_encode(baby_image))) // Html("

Baby Image Generated!

Feature Summary

• Generates hypothetical baby images from two parent images.
• Provides a simple and easy-to-use API.
• Handles image loading and saving (requires integration with image libraries like image).
• Offers error handling for robust applications.

License

MIT

This crate is part of the ai-baby-generator ecosystem. For advanced features and enterprise-grade tools, visit: https://supermaker.ai/image/ai-baby-generator/