robokudo.annotators.cluster_color_histogram

Color histogram analysis for object hypotheses.

This module provides an annotator for:

  • Calculating 2D color histograms for object hypotheses

  • Analyzing hue and saturation distributions

  • Supporting masked ROI analysis

  • Optional visualization of histogram plots

The module uses:

  • HSV color space for analysis

  • 2D histograms for hue-saturation distributions

  • Matplotlib for optional visualization

Note

Histogram plotting is optional and can be disabled for performance.

Classes

ClusterColorHistogramAnnotator

Hue and saturation histogram analysis for object hypotheses with RGB ROI and Mask.

Module Contents

class robokudo.annotators.cluster_color_histogram.ClusterColorHistogramAnnotator(name='ClusterColorHistogramAnnotator', descriptor=Descriptor())

Bases: robokudo.annotators.core.BaseAnnotator

Hue and saturation histogram analysis for object hypotheses with RGB ROI and Mask.

This annotator:

  • Calculates 2D histograms of hue and saturation

  • Processes masked ROIs from object hypotheses

  • Normalizes histogram distributions

  • Optionally generates visualization plots

Warning

Histogram plotting can significantly impact performance (200-500ms).

class Descriptor

Bases: robokudo.annotators.core.BaseAnnotator.Descriptor

Configuration descriptor for color histogram analysis.

class Parameters

Parameters for configuring histogram calculation.

Histogram parameters:

Variables:

  • histogram_cols – Number of histogram columns (hue bins), defaults to 16

  • histogram_rows – Number of histogram rows (saturation bins), defaults to 16

Visualization:

Variables:

generate_plot_output – Whether to generate histogram plots, defaults to False

histogram_cols = 16
histogram_rows = 16
generate_plot_output = False
parameters
update()

Process object hypotheses and calculate color histograms.

The method:

  • Loads point cloud and color image from CAS

  • Creates color histogram annotations

  • Updates visualization if enabled

Returns:

SUCCESS after processing

Return type:

py_trees.Status

create_color_histogram_annotations(color: numpy.typing.NDArray)

Calculate 2D color histograms for object hypotheses.

For each object hypothesis with a valid ROI mask:

  • Extracts color image region and mask

  • Converts to HSV color space

  • Calculates 2D histogram of hue and saturation

  • Normalizes histogram distribution

  • Creates histogram annotation

  • Optionally generates visualization plot

Parameters:

color (numpy.ndarray) – Input color image

Returns:

Combined histogram plot image if enabled, None otherwise

Return type:

numpy.ndarray or None