# Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ 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'}.")