Added example of torchvision image augmentation on LeRobotDataset

examples/6_show_image_transforms.py

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+from pathlib import Path
+
+from torchvision.transforms import ToPILImage, v2
+
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+
+to_pil = ToPILImage()
+
+# Create a directory to store the training checkpoint.
+output_dir = Path("outputs/image_transforms")
+output_dir.mkdir(parents=True, exist_ok=True)
+
+repo_id = "lerobot/aloha_static_tape"
+
+# Create a LeRobotDataset with no transformations
+dataset = LeRobotDataset(repo_id, transform=None)
+
+# Get the index of the first frame in the first episode
+first_idx = dataset.episode_data_index["from"][0].item()
+
+# Get the frame from the first camera
+frame = dataset[first_idx][dataset.camera_keys[0]]
+
+# Save the original frame
+to_pil(frame).save(output_dir / "original_frame.png", quality=100)
+print(f"Original frame saved to {output_dir / 'original_frame.png'}.")
+
+
+# Define the transformations
+transforms = v2.Compose(
+    [
+        v2.ColorJitter(brightness=(0.5, 1.5)),
+        v2.ColorJitter(contrast=(0.5, 1.5)),
+        v2.RandomAdjustSharpness(sharpness_factor=2, p=1),
+    ]
+)
+
+# Create a LeRobotDataset with the defined transformations
+transformed_dataset = LeRobotDataset(repo_id, transform=transforms)
+
+# Get a frame from the transformed dataset
+transformed_frame = transformed_dataset[first_idx][transformed_dataset.camera_keys[0]]
+
+# 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'}.")