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The lerobot-imgtransform-viz command generates visual examples of image transformations applied during dataset loading, helping you configure and debug image augmentation settings.

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Command

lerobot-imgtransform-viz [OPTIONS]
Location: src/lerobot/scripts/lerobot_imgtransform_viz.py

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Overview

This utility:
  • Visualizes effects of image transformations
  • Shows individual transforms separately
  • Generates examples of combined transforms
  • Saves output images for inspection
  • Helps tune augmentation parameters
  • Supports all LeRobot transform types

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Key Options

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Dataset Options

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--repo_id
str
required
Dataset repository ID (e.g., lerobot/pusht).
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--episodes
list[int]
Episode indices to use. Defaults to [0].
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--revision
str
Git revision of the dataset.
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--video_backend
str
Video backend: torchcodec, pyav, or video_reader.

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Transform Options

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--image_transforms.enable
bool
default:"false"
Enable image transformations. Must be True to generate transform examples.
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--image_transforms.tfs
dict
Dictionary of transform configurations. See examples below.

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Output Options

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--output_dir
str
default:"outputs/image_transforms"
Directory to save visualization images.
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--n_examples
int
default:"5"
Number of random examples to generate per transform.

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Usage Examples

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Basic Visualization

lerobot-imgtransform-viz \
  --repo_id=lerobot/pusht \
  --episodes='[0]' \
  --image_transforms.enable=true
Generates:
  • Original frame from first episode
  • Examples of combined transforms
  • Individual transform examples (if configured)

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Visualize Specific Transforms

lerobot-imgtransform-viz \
  --repo_id=lerobot/pusht \
  --episodes='[0]' \
  --image_transforms.enable=true \
  --image_transforms.tfs='{
    RandomCrop: {
      type: RandomCrop,
      kwargs: {size: [0.9, 1.0]}
    },
    ColorJitter: {
      type: ColorJitter,
      kwargs: {brightness: [0.8, 1.2], contrast: [0.8, 1.2]}
    }
  }'

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Multiple Augmentation Examples

lerobot-imgtransform-viz \
  --repo_id=lerobot/aloha_mobile_cabinet \
  --episodes='[0]' \
  --image_transforms.enable=true \
  --n_examples=10 \
  --output_dir=./transform_examples
Generates 10 random augmentation examples.

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Custom Transform Parameters

lerobot-imgtransform-viz \
  --repo_id=lerobot/pusht \
  --image_transforms.enable=true \
  --image_transforms.tfs='{
    SharpnessJitter: {
      type: SharpnessJitter,
      kwargs: {sharpness: [0.5, 1.5]}
    },
    RandomRotation: {
      type: RandomRotation,
      kwargs: {degrees: [-15, 15]}
    },
    GaussianBlur: {
      type: GaussianBlur,
      kwargs: {kernel_size: [3, 7], sigma: [0.1, 2.0]}
    }
  }'

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Output Structure

Generated files are organized as: 
outputs/image_transforms/pusht/
├── original_frame.png           # Original image from dataset
├── all/                        # Combined transforms
│   ├── 1.png
│   ├── 2.png
│   ├── 3.png
│   ├── 4.png
│   └── 5.png
├── RandomCrop/                 # Individual transform examples
│   ├── 1.png                    # Random examples
│   ├── 2.png
│   ├── 3.png
│   ├── min.png                  # Min parameter value
│   ├── mean.png                 # Average parameter value
│   └── max.png                  # Max parameter value
├── ColorJitter/
│   ├── 1.png
│   ├── ...
│   ├── min.png
│   ├── mean.png
│   └── max.png
└── SharpnessJitter/
    ├── 1.png
    ├── ...
    ├── min.png
    ├── mean.png
    └── max.png

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Transform Types

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RandomCrop

Randomly crops images to a fraction of original size: 
--image_transforms.tfs='{
  RandomCrop: {
    type: RandomCrop,
    kwargs: {size: [0.9, 1.0]}  # 90-100% of original size
  }
}'

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ColorJitter

Randomly adjusts brightness, contrast, saturation: 
--image_transforms.tfs='{
  ColorJitter: {
    type: ColorJitter,
    kwargs: {
      brightness: [0.8, 1.2],
      contrast: [0.8, 1.2],
      saturation: [0.8, 1.2],
      hue: [-0.05, 0.05]
    }
  }
}'

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SharpnessJitter

Randomly adjusts image sharpness: 
--image_transforms.tfs='{
  SharpnessJitter: {
    type: SharpnessJitter,
    kwargs: {sharpness: [0.5, 1.5]}  # 0.5x to 1.5x sharpness
  }
}'

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RandomRotation

Randomly rotates images: 
--image_transforms.tfs='{
  RandomRotation: {
    type: RandomRotation,
    kwargs: {degrees: [-10, 10]}  # -10 to +10 degrees
  }
}'

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GaussianBlur

Applies Gaussian blur: 
--image_transforms.tfs='{
  GaussianBlur: {
    type: GaussianBlur,
    kwargs: {
      kernel_size: [3, 7],  # Odd numbers only
      sigma: [0.1, 2.0]
    }
  }
}'

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RandomGrayscale

Randomly converts to grayscale: 
--image_transforms.tfs='{
  RandomGrayscale: {
    type: RandomGrayscale,
    kwargs: {p: [0.0, 0.2]}  # 0-20% probability
  }
}'

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Combining Transforms

Apply multiple transforms in sequence: 
lerobot-imgtransform-viz \
  --repo_id=lerobot/pusht \
  --image_transforms.enable=true \
  --image_transforms.tfs='{
    RandomCrop: {
      type: RandomCrop,
      kwargs: {size: [0.95, 1.0]}
    },
    ColorJitter: {
      type: ColorJitter,
      kwargs: {brightness: [0.9, 1.1], contrast: [0.9, 1.1]}
    },
    SharpnessJitter: {
      type: SharpnessJitter,
      kwargs: {sharpness: [0.8, 1.2]}
    }
  }'
The all/ directory shows results of all transforms applied together.

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Interpreting Results

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Check Augmentation Strength

Compare transformed images to original:
  • Too subtle: Increase parameter ranges
  • Too aggressive: Reduce parameter ranges
  • Good balance: Natural variation without distortion

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Evaluate Individual Transforms

Check min.png, mean.png, max.png for each transform:
  • min.png: Minimum parameter value
  • mean.png: Average parameter value
  • max.png: Maximum parameter value
Ensure all three are reasonable for your task.

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Verify Combined Effects

Check all/ directory:
  • Multiple transforms should create diverse but realistic variations
  • No extreme distortions or artifacts
  • Key visual features still recognizable

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Tuning Guidelines

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Conservative Augmentation

For sim-to-real or precise tasks: 
--image_transforms.tfs='{
  RandomCrop: {type: RandomCrop, kwargs: {size: [0.98, 1.0]}},
  ColorJitter: {
    type: ColorJitter,
    kwargs: {brightness: [0.95, 1.05], contrast: [0.95, 1.05]}
  }
}'

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Aggressive Augmentation

For diverse real-world environments: 
--image_transforms.tfs='{
  RandomCrop: {type: RandomCrop, kwargs: {size: [0.8, 1.0]}},
  ColorJitter: {
    type: ColorJitter,
    kwargs: {brightness: [0.7, 1.3], contrast: [0.7, 1.3], saturation: [0.7, 1.3]}
  },
  RandomRotation: {type: RandomRotation, kwargs: {degrees: [-20, 20]}},
  GaussianBlur: {type: GaussianBlur, kwargs: {kernel_size: [3, 7], sigma: [0.1, 2.0]}}
}'

--image_transforms.tfs='{
  RandomCrop: {type: RandomCrop, kwargs: {size: [0.9, 1.0]}},
  ColorJitter: {
    type: ColorJitter,
    kwargs: {brightness: [0.8, 1.2], contrast: [0.8, 1.2]}
  },
  SharpnessJitter: {type: SharpnessJitter, kwargs: {sharpness: [0.8, 1.2]}}
}'

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Using in Training

Once you’ve tuned transforms, use in training: 
lerobot-train \
  --policy.type=act \
  --dataset.repo_id=lerobot/pusht \
  --dataset.image_transforms.enable=true \
  --dataset.image_transforms.tfs='{
    RandomCrop: {type: RandomCrop, kwargs: {size: [0.9, 1.0]}},
    ColorJitter: {
      type: ColorJitter,
      kwargs: {brightness: [0.8, 1.2], contrast: [0.8, 1.2]}
    }
  }'

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Programmatic Usage

from lerobot.scripts.lerobot_imgtransform_viz import visualize_image_transforms
from lerobot.configs.default import DatasetConfig
from lerobot.datasets.transforms import ImageTransformsConfig, TransformConfig
from pathlib import Path

config = DatasetConfig(
    repo_id="lerobot/pusht",
    episodes=[0],
    image_transforms=ImageTransformsConfig(
        enable=True,
        tfs={
            "RandomCrop": TransformConfig(
                type="RandomCrop",
                kwargs={"size": [0.9, 1.0]}
            ),
            "ColorJitter": TransformConfig(
                type="ColorJitter",
                kwargs={"brightness": [0.8, 1.2], "contrast": [0.8, 1.2]}
            ),
        }
    )
)

visualize_image_transforms(
    cfg=config,
    output_dir=Path("./transform_viz"),
    n_examples=10
)

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Tips

  1. Start Simple: Test one transform at a time
  2. Check Extremes: Look at min/max examples to avoid over-augmentation
  3. Use Real Data: Test on actual dataset images, not synthetic
  4. Iterate: Adjust parameters based on visual inspection
  5. Consider Task: Match augmentation to deployment environment

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See Also

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