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ambiguity_measure module

The ambiguity_measure module contains the AmbiguityMeasure class.

Class AmbiguityMeasure

Bases: object

The AmbiguityMeasure class is a tool that allows to obtain an ambiguity measure of vision-based models that output pixel-wise value estimates. This tool can be used in combination with vision-based manipulation models such as Transporter Nets [1].

The AmbiguityMeasure class has the following public methods:

AmbiguityMeasure constructor

AmbiguityMeasure(self, threshold, temperature)

Constructor parameters:

  • threshold: float, default=0.5
    Ambiguity threshold, should be in [0, 1).
  • temperature: float, default=1.0
    Temperature of the sigmoid function. Should be > 0. Higher temperatures will result in higher ambiguity measures.

AmbiguityMeasure.get_ambiguity_measure

AmbiguityMeasure.get_ambiguity_measure(self, heatmap)

This method allows to obtain an ambiguity measure of the output of a model.

Parameters:

  • heatmap: np.ndarray
    Pixel-wise value estimates. These can be obtained using from for example a Transporter Nets model [1].

Demos and tutorial

A demo showcasing the usage and functionality of the AmbiguityMeasure is available here.

Examples

  • Ambiguity measure example

    This example shows how to obtain the ambiguity measure from pixel-wise value estimates.

    import numpy as np
from opendr.utils.ambiguity_measure.ambiguity_measure import AmbiguityMeasure

# Simulate image and value pixel-wise value estimates (normally you would get this from a model such as Transporter)
img = 255 * np.random.random((128, 128, 3))
img = np.asarray(img, dtype="uint8")
heatmap = 10 * np.random.random((128, 128))

# Initialize ambiguity measure
am = AmbiguityMeasure(threshold=0.1, temperature=3)

# Get ambiguity measure of the heatmap
ambiguous, locs, maxima, probs = am.get_ambiguity_measure(heatmap)

# Plot ambiguity measure
am.plot_ambiguity_measure(heatmap, locs, probs, img)

References

[1] Zeng, A., Florence, P., Tompson, J., Welker, S., Chien, J., Attarian, M., ... & Lee, J. (2021, October). Transporter networks: Rearranging the visual world for robotic manipulation. In Conference on Robot Learning (pp. 726-747). PMLR.