"""
This script demonstrates how to use torchvision's image transformation with LeRobotDataset for data
augmentation purposes. The transformations are passed to the dataset as an argument upon creation, and
transforms are applied to the observation images before they are returned in the dataset's __getitem__.
"""

from pathlib import Path

from torchvision.transforms import ToPILImage, v2

from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

dataset_repo_id = "lerobot/aloha_static_screw_driver"

# Create a LeRobotDataset with no transformations
dataset = LeRobotDataset(dataset_repo_id, episodes=[0])
# This is equivalent to `dataset = LeRobotDataset(dataset_repo_id, image_transforms=None)`

# Get the index of the first observation in the first episode
first_idx = dataset.episode_data_index["from"][0].item()

# Get the frame corresponding to the first camera
frame = dataset[first_idx][dataset.meta.camera_keys[0]]


# Define the transformations
transforms = v2.Compose(
    [
        v2.ColorJitter(brightness=(0.5, 1.5)),
        v2.ColorJitter(contrast=(0.5, 1.5)),
        v2.ColorJitter(hue=(-0.1, 0.1)),
        v2.RandomAdjustSharpness(sharpness_factor=2, p=1),
    ]
)

# Create another LeRobotDataset with the defined transformations
transformed_dataset = LeRobotDataset(dataset_repo_id, episodes=[0], image_transforms=transforms)

# Get a frame from the transformed dataset
transformed_frame = transformed_dataset[first_idx][transformed_dataset.meta.camera_keys[0]]

# Create a directory to store output images
output_dir = Path("outputs/image_transforms")
output_dir.mkdir(parents=True, exist_ok=True)

# Save the original frame
to_pil = ToPILImage()
to_pil(frame).save(output_dir / "original_frame.png", quality=100)
print(f"Original frame saved to {output_dir / 'original_frame.png'}.")

# Save the transformed frame
to_pil(transformed_frame).save(output_dir / "transformed_frame.png", quality=100)
print(f"Transformed frame saved to {output_dir / 'transformed_frame.png'}.")
