colour_checker_detection.colour_checkers_coordinates_segmentation(image: ArrayLike, additional_data: bool = False, **kwargs: Any) Union[colour_checker_detection.detection.segmentation.DataColourCheckersCoordinatesSegmentation, Tuple[numpy.ndarray, ...]][source]#

Detect 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 the expected one are kept.

  • image (ArrayLike) – Image to detect the colour checkers in.

  • additional_data (bool) – Whether to output additional data.

  • aspect_ratio – Colour checker aspect ratio, e.g. 1.5.

  • aspect_ratio_minimum – Minimum colour checker aspect ratio for detection: projective geometry might reduce the colour checker aspect ratio.

  • aspect_ratio_maximum – Maximum colour checker aspect ratio for detection: projective geometry might increase the colour checker aspect ratio.

  • swatches – Colour checker swatches total count.

  • swatches_horizontal – Colour checker swatches horizontal columns count.

  • swatches_vertical – Colour checker swatches vertical row count.

  • swatches_count_minimum – Minimum swatches count to be considered for the detection.

  • swatches_count_maximum – Maximum swatches count to be considered for the detection.

  • swatches_chromatic_slice – A slice instance defining chromatic swatches used to detect if the colour checker is upside down.

  • swatches_achromatic_slice – A slice instance defining achromatic swatches used to detect if the colour checker is upside down.

  • swatch_minimum_area_factor – Swatch minimum area factor \(f\) with the minimum area \(m_a\) expressed as follows: \(m_a = image_w * image_h / s_c / f\) where \(image_w\), \(image_h\) and \(s_c\) are respectively the image width, height and the swatches count.

  • swatch_contour_scale – As the image is filtered, the swatches area will tend to shrink, the generated contours can thus be scaled.

  • cluster_contour_scale – As the swatches are clustered, it might be necessary to adjust the cluster scale so that the masks are centred better on the swatches.

  • working_width – Size the input image is resized to for detection.

  • fast_non_local_means_denoising_kwargs – Keyword arguments for cv2.fastNlMeansDenoising() definition.

  • adaptive_threshold_kwargs – Keyword arguments for cv2.adaptiveThreshold() definition.

  • interpolation_method – Interpolation method used when resizing the images, cv2.INTER_CUBIC and cv2.INTER_LINEAR methods are recommended.

  • kwargs (Any) –


Tuple of colour checkers coordinates or DataColourCheckersCoordinatesSegmentation class instance with additional data.

Return type

colour_checker_detection.detection.segmentation.DataColourCheckersCoordinatesSegmentation or tuple


  • Multiple colour checkers can be detected if presented in image.


>>> 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([[ 369,  688],
       [ 382,  226],
       [1078,  246],
       [1065,  707]]...)