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#!/usr/bin/env python
# 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 example demonstrates how to use image transforms with LeRobot datasets for data augmentation during training.
Image transforms are applied to camera frames to improve model robustness and generalization. They are applied
at training time only, not during dataset recording, allowing you to experiment with different augmentations
without re-recording data.
"""
import torch
from torchvision.transforms import v2
from torchvision.transforms.functional import to_pil_image
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.datasets.transforms import ImageTransformConfig, ImageTransforms, ImageTransformsConfig
def save_image(tensor, filename):
"""Helper function to save a tensor as an image file."""
if tensor.dim() == 3: # [C, H, W]
if tensor.max() > 1.0:
tensor = tensor / 255.0
tensor = torch.clamp(tensor, 0.0, 1.0)
pil_image = to_pil_image(tensor)
pil_image.save(filename)
print(f"Saved: {filename}")
else:
print(f"Skipped {filename}: unexpected tensor shape {tensor.shape}")
def example_1_default_transforms():
"""Example 1: Use default transform configuration and save original vs transformed images"""
print("\n Example 1: Default Transform Configuration with Image Saving")
repo_id = "pepijn223/record_main_0" # Example dataset
try:
# Load dataset without transforms (original)
dataset_original = LeRobotDataset(repo_id=repo_id)
# Load dataset with transforms enabled
transforms_config = ImageTransformsConfig(
enable=True, # Enable transforms (disabled by default)
max_num_transforms=2, # Apply up to 2 transforms per frame
random_order=False, # Apply in standard order
)
dataset_with_transforms = LeRobotDataset(
repo_id=repo_id, image_transforms=ImageTransforms(transforms_config)
)
# Save original and transformed images for comparison
if len(dataset_original) > 0:
frame_idx = 0 # Use first frame
original_sample = dataset_original[frame_idx]
transformed_sample = dataset_with_transforms[frame_idx]
print(f"Saving comparison images (frame {frame_idx}):")
for cam_key in dataset_original.meta.camera_keys:
if cam_key in original_sample and cam_key in transformed_sample:
cam_name = cam_key.replace(".", "_").replace("/", "_")
# Save original and transformed images
save_image(original_sample[cam_key], f"{cam_name}_original.png")
save_image(transformed_sample[cam_key], f"{cam_name}_transformed.png")
except Exception as e:
print(f"Could not load dataset '{repo_id}': {e}")
def example_2_custom_transforms():
"""Example 2: Create custom transform configuration and save examples"""
print("\n Example 2: Custom Transform Configuration")
repo_id = "pepijn223/record_main_0" # Example dataset
try:
# Create custom transform configuration with strong effects
custom_transforms_config = ImageTransformsConfig(
enable=True,
max_num_transforms=2, # Apply up to 2 transforms per frame
random_order=True, # Apply transforms in random order
tfs={
"brightness": ImageTransformConfig(
weight=1.0,
type="ColorJitter",
kwargs={"brightness": (0.5, 1.5)}, # Strong brightness range
),
"contrast": ImageTransformConfig(
weight=1.0, # Higher weight = more likely to be selected
type="ColorJitter",
kwargs={"contrast": (0.6, 1.4)}, # Strong contrast
),
"sharpness": ImageTransformConfig(
weight=0.5, # Lower weight = less likely to be selected
type="SharpnessJitter",
kwargs={"sharpness": (0.2, 2.0)}, # Strong sharpness variation
),
},
)
dataset_with_custom_transforms = LeRobotDataset(
repo_id=repo_id, image_transforms=ImageTransforms(custom_transforms_config)
)
# Save examples with strong transforms
if len(dataset_with_custom_transforms) > 0:
sample = dataset_with_custom_transforms[0]
print("Saving custom transform examples:")
for cam_key in dataset_with_custom_transforms.meta.camera_keys:
if cam_key in sample:
cam_name = cam_key.replace(".", "_").replace("/", "_")
save_image(sample[cam_key], f"{cam_name}_custom_transforms.png")
except Exception as e:
print(f"Could not load dataset '{repo_id}': {e}")
def example_3_torchvision_transforms():
"""Example 3: Use pure torchvision transforms and save examples"""
print("\n Example 3: Pure Torchvision Transforms")
repo_id = "pepijn223/record_main_0" # Example dataset
try:
# Create torchvision transform pipeline
torchvision_transforms = v2.Compose(
[
v2.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.1),
v2.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)),
v2.RandomRotation(degrees=10), # Small rotation
]
)
dataset_with_torchvision = LeRobotDataset(repo_id=repo_id, image_transforms=torchvision_transforms)
# Save examples with torchvision transforms
if len(dataset_with_torchvision) > 0:
sample = dataset_with_torchvision[0]
print("Saving torchvision transform examples:")
for cam_key in dataset_with_torchvision.meta.camera_keys:
if cam_key in sample:
cam_name = cam_key.replace(".", "_").replace("/", "_")
save_image(sample[cam_key], f"{cam_name}_torchvision.png")
except Exception as e:
print(f"Could not load dataset '{repo_id}': {e}")
def main():
"""Run all examples"""
print("LeRobot Dataset Image Transforms Examples")
example_1_default_transforms()
example_2_custom_transforms()
example_3_torchvision_transforms()
if __name__ == "__main__":
main()
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