colour_checker_detection.plot_detection_results#

colour_checker_detection.plot_detection_results(colour_checkers_data: Tuple[DataDetectionColourChecker, ...], swatches_horizontal: int, swatches_vertical: int, segmentation_data: DataSegmentationColourCheckers | None = None, image: NDArrayReal | None = None) → None[source]#

Visualize colour checker detection results.

This function provides consistent visualization across all detection methods (inference, segmentation, and templated).

Parameters:

colour_checkers_data (Tuple[DataDetectionColourChecker, ...]) – Tuple of detected colour checker data objects.
swatches_horizontal (int) – Number of horizontal swatch columns.
swatches_vertical (int) – Number of vertical swatch rows.
segmentation_data (DataSegmentationColourCheckers | None) – Optional segmentation data containing swatches, clusters, and segmented image. Required for visualizing intermediate segmentation results.
image (NDArrayReal | None) – Optional original image for drawing contours overlay. Required if segmentation_data is provided.

Return type:

None

Notes

Generates 2 plots per colour checker if segmentation_data is None (inference method).
Generates 4 additional plots if segmentation_data is provided (segmentation/templated methods).

Examples

>>> from colour_checker_detection import detect_colour_checkers_segmentation
>>> import os
>>> from colour import read_image
>>> from colour_checker_detection import ROOT_RESOURCES_TESTS
>>> path = os.path.join(
...     ROOT_RESOURCES_TESTS,
...     "colour_checker_detection",
...     "detection",
...     "IMG_1967.png",
... )
>>> image = read_image(path)
>>> results = detect_colour_checkers_segmentation(image, additional_data=True)
>>> plot_detection_results(results, 6, 4)
...

colour_checker_detection.plot_detection_results#

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