create_test_dataset.py

"""
Create Test Dataset
Generate sample images for testing the training pipeline
"""

from PIL import Image, ImageDraw, ImageFont
import numpy as np
from pathlib import Path
import random


def create_test_dataset(output_dir: str = "./dataset", num_images: int = 10):
    """
    Create a test dataset with synthetic images
    
    Args:
        output_dir: Output directory
        num_images: Number of test images to create
    """
    output_path = Path(output_dir)
    output_path.mkdir(parents=True, exist_ok=True)
    
    print(f"Creating {num_images} test images in {output_path}...")
    
    # Color palettes
    colors = [
        (255, 99, 71),    # Tomato
        (64, 224, 208),   # Turquoise
        (255, 215, 0),    # Gold
        (138, 43, 226),   # Blue Violet
        (50, 205, 50),    # Lime Green
        (255, 165, 0),    # Orange
        (219, 112, 147),  # Pale Violet Red
        (70, 130, 180),   # Steel Blue
        (255, 192, 203),  # Pink
        (144, 238, 144),  # Light Green
    ]
    
    shapes = ['circle', 'rectangle', 'triangle']
    
    captions = []
    
    for i in range(num_images):
        # Generate random image
        img_size = 512
        img = Image.new('RGB', (img_size, img_size), color=(240, 240, 240))
        draw = ImageDraw.Draw(img)
        
        # Random parameters
        num_shapes = random.randint(3, 8)
        bg_color = random.choice(colors)
        
        # Draw background gradient
        for y in range(img_size):
            r = int(bg_color[0] * (0.8 + 0.2 * y / img_size))
            g = int(bg_color[1] * (0.8 + 0.2 * y / img_size))
            b = int(bg_color[2] * (0.8 + 0.2 * y / img_size))
            draw.line([(0, y), (img_size, y)], fill=(r, g, b))
        
        # Draw random shapes
        for _ in range(num_shapes):
            shape = random.choice(shapes)
            color = tuple(random.randint(50, 255) for _ in range(3))
            
            x1 = random.randint(50, img_size - 50)
            y1 = random.randint(50, img_size - 50)
            size = random.randint(30, 100)
            
            if shape == 'circle':
                bbox = [x1, y1, x1 + size, y1 + size]
                draw.ellipse(bbox, fill=color, outline=(0, 0, 0))
            elif shape == 'rectangle':
                bbox = [x1, y1, x1 + size, y1 + size // 2]
                draw.rectangle(bbox, fill=color, outline=(0, 0, 0))
            elif shape == 'triangle':
                points = [
                    (x1, y1),
                    (x1 + size, y1),
                    (x1 + size // 2, y1 + size)
                ]
                draw.polygon(points, fill=color, outline=(0, 0, 0))
        
        # Save image
        img_path = output_path / f"test_image_{i+1:03d}.jpg"
        img.save(img_path, quality=95)
        
        # Create caption
        caption = f"A colorful abstract composition with {num_shapes} geometric shapes on a {bg_color[0]} background"
        captions.append(caption)
        
        # Save caption
        caption_path = output_path / f"test_image_{i+1:03d}.txt"
        with open(caption_path, 'w', encoding='utf-8') as f:
            f.write(caption)
        
        print(f"  Created: {img_path.name}")
    
    print(f"\n✓ Test dataset created successfully!")
    print(f"  Location: {output_path.absolute()}")
    print(f"  Images: {num_images}")
    print(f"\nTo train with this dataset:")
    print(f"  python train.py --config config.yaml --train_data {output_path}")


if __name__ == "__main__":
    create_test_dataset()