import os
import random
from PIL import Image, ImageEnhance, ImageOps, ImageFilter

# Create output directory if it doesn't exist
output_dir = "generated_images"
os.makedirs(output_dir, exist_ok=True)

# Load base images
base_images = [Image.open(f"pic{i}.jpg") for i in range(1, 5)]

# Define the number of images to generate per base image
num_variations = 250  # 250 variations for each of the 4 images = 1000 images total

# Function to apply random transformations
def apply_random_transformations(image):
    # Apply random brightness adjustment
    if random.choice([True, False]):
        enhancer = ImageEnhance.Brightness(image)
        image = enhancer.enhance(random.uniform(0.7, 1.3))

    # Apply random color adjustment
    if random.choice([True, False]):
        enhancer = ImageEnhance.Color(image)
        image = enhancer.enhance(random.uniform(0.8, 1.2))

    # Apply random contrast adjustment
    if random.choice([True, False]):
        enhancer = ImageEnhance.Contrast(image)
        image = enhancer.enhance(random.uniform(0.8, 1.2))

    # Apply random rotation
    if random.choice([True, False]):
        image = image.rotate(random.randint(-15, 15))  # Rotate between -15 and 15 degrees

    # Apply random flip
    if random.choice([True, False]):
        image = ImageOps.mirror(image)

    # Apply random Gaussian blur
    if random.choice([True, False]):
        image = image.filter(ImageFilter.GaussianBlur(random.uniform(0, 1.5)))

    # Apply random resize (zoom in/out effect)
    if random.choice([True, False]):
        scale_factor = random.uniform(0.9, 1.1)
        new_size = (int(image.width * scale_factor), int(image.height * scale_factor))
        image = image.resize(new_size, Image.ANTIALIAS)
        image = image.crop((0, 0, image.width, image.height))  # Ensure it fits original size

    return image

# Generate images
count = 0

for idx, base_image in enumerate(base_images):
    for i in range(num_variations):
        # Apply transformations
        transformed_image = apply_random_transformations(base_image)

        # Save the transformed image
        transformed_image.save(os.path.join(output_dir, f"image_{idx+1}_{i+1}.jpg"))
        
        count += 1
        if count >= 10:
            break  # Stop once we've reached 1000 images

print(f"Generated {count} images with subtle variations.")