colour_checker_detection.colour_checkers_coordinates_segmentation¶

colour_checker_detection.colour_checkers_coordinates_segmentation(image, additional_data=False)[source]¶

Detects the colour checkers coordinates in given image \(image\) using segmentation.

This is the core detection definition. The process is a follows:

Input image \(image\) is converted to a grayscale image \(image_g\).
Image \(image_g\) is denoised.
Image \(image_g\) is thresholded/segmented to image \(image_s\).
Image \(image_s\) is eroded and dilated to cleanup remaining noise.
Contours are detected on image \(image_s\).
Contours are filtered to only keep squares/swatches above and below defined surface area.
Squares/swatches are clustered to isolate region-of-interest that are potentially colour checkers: Contours are scaled by a third so that colour checkers swatches are expected to be joined, creating a large rectangular cluster. Rectangles are fitted to the clusters.
Clusters with an aspect ratio different to the expected one are rejected, a side-effect is that the complementary pane of the X-Rite ColorChecker Passport is omitted.
Clusters with a number of swatches close to SWATCHES are kept.

Parameters

image (array_like) – Image to detect the colour checkers in.
additional_data (bool, optional) – Whether to output additional data.

Returns

List of colour checkers coordinates or ColourCheckersDetectionData class instance with additional data.

Return type

list or ColourCheckersDetectionData

Notes

Multiple colour checkers can be detected if presented in image.

Examples

>>> import os
>>> from colour import read_image
>>> from colour_checker_detection import TESTS_RESOURCES_DIRECTORY
>>> path = os.path.join(TESTS_RESOURCES_DIRECTORY,
...                     'colour_checker_detection', 'detection',
...                     'IMG_1967.png')
>>> image = read_image(path)
>>> colour_checkers_coordinates_segmentation(image)  
[array([[1065,  707],
       [ 369,  688],
       [ 382,  226],
       [1078,  246]]...)]