README.md

Abstracta

Abstract Image Generator

Generate abstract images using triangles inspired by a target image.

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Installation

1. Clone the repository:

   git clone https://github.com/datavorous/abstracta.git  
   cd abstracta  

2. Run the program:

   uv run src/main.py  

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Demo

Took around 15 minutes with 1200 primitives on my i3-530.

Tree Approximation

Sunset Approximation

Rabbit Progression

50 Shapes	100 Shapes	150 Shapes

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Algorithm

1. Image Preprocessing

   • Load the target image and resize it to 256 x 256 pixels.
   • Convert the image to an RGB numpy array.
   • Initialize the current image with the average color of the target image.

2. Error Metric

   • Compute the Root Mean Square Error (RMSE) between the target image and the current image:
     RMSE = sqrt((1/n) * Σ (I_target - I_current)^2),
     where (n) is the total number of pixels.

3. Triangle Representation

   • Each triangle is defined by three vertices ((x1, y1), (x2, y2), (x3, y3)) and RGB color values ((r, g, b)).

4. Hill Climbing Optimization

   • Generate a random triangle.
   • Calculate its RMSE with the target image when drawn on the current image.
   • Mutate the triangle's vertices and colors to minimize RMSE.
   • Select the triangle with the lowest RMSE after multiple iterations.

5. Shape Addition

   • Draw the best triangle on the current image.
   • Add the triangle to the list of generated shapes.

6. Iteration

   • Repeat the optimization and shape addition steps until the specified number of shapes is added.

7. Final Image

   • Resize the current image to the desired resolution (e.g., 512 x 512).
   • Save the final abstract image.

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Pseudocode

# Input: target_image_path, max_shapes, target_resolution
# Output: abstract_image

# Step 1: Load and preprocess the target image
target_image = load_image(target_image_path)
target_image = resize_to(target_image, (256, 256))
current_image = fill_with_average_color(target_image)

# Step 2: Initialize variables
generated_shapes = []

# Step 3: Generate shapes iteratively
for _ in range(max_shapes):
    best_shape = None
    best_score = infinity

    # Step 3.1: Hill climbing to find the best shape
    for _ in range(20):
        shape = generate_random_triangle()
        score = calculate_rmse(current_image, target_image, shape)

        # Local search for better shapes
        for _ in range(50):
            mutated_shape = mutate_triangle(shape)
            mutated_score = calculate_rmse(current_image, target_image, mutated_shape)
            if mutated_score < score:
                shape = mutated_shape
                score = mutated_score

        if score < best_score:
            best_shape = shape
            best_score = score

    # Step 3.2: Add the best shape to the current image
    current_image = draw_triangle(current_image, best_shape)
    generated_shapes.append(best_shape)

    # Save progress periodically
    if iteration % 20 == 0:
        save_image(current_image, f"progress_{iteration}.png")

# Step 4: Resize and save the final image
final_image = resize_to(current_image, (target_resolution, target_resolution))
save_image(final_image, "final.png")

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