colour_checker_detection.detect_colour_checkers_segmentation#

colour_checker_detection.detect_colour_checkers_segmentation(image: str | ArrayLike, samples: int = 32, cctf_decoding: Callable = eotf_sRGB, apply_cctf_decoding: bool = False, segmenter: Callable = segmenter_default, segmenter_kwargs: dict | None = None, show: bool = False, additional_data: bool = False, **kwargs: Any) → Tuple[DataDetectionColourChecker | NDArrayFloat, ...][source]#

Detect the colour checkers swatches in given image using segmentation.

Parameters:

image (str | ArrayLike) – Image (or image path to read the image from) to detect the colour checkers swatches from.
samples (int) – Sample count to use to average (mean) the swatches colours. The effective sample count is \(samples^2\).
cctf_decoding (Callable) – Decoding colour component transfer function / opto-electronic transfer function used when converting the image from 8-bit to float.
apply_cctf_decoding (bool) – Apply the decoding colour component transfer function / opto-electronic transfer function.
segmenter (Callable) – Callable responsible to segment the image and extract the colour checker rectangles.
segmenter_kwargs (dict | None) – Keyword arguments to pass to the segmenter.
show (bool) – Whether to show various debug images.
additional_data (bool) – Whether to output additional data.
adaptive_threshold_kwargs – Keyword arguments for cv2.adaptiveThreshold() definition.
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.
bilateral_filter_iterations – Number of iterations to use for bilateral filtering.
bilateral_filter_kwargs – Keyword arguments for cv2.bilateralFilter() definition.
convolution_iterations – Number of iterations to use for the erosion / dilation process.
convolution_kernel – Convolution kernel to use for the erosion / dilation process.
interpolation_method – Interpolation method used when resizing the images, cv2.INTER_CUBIC and cv2.INTER_LINEAR methods are recommended.
reference_values – Reference values for the colour checker of interest.
swatch_contour_scale – As the image is filtered, the swatches area will tend to shrink, the generated contours can thus be scaled.
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.
swatches – Colour checker swatches total count.
swatches_achromatic_slice – A slice instance defining achromatic swatches used to detect if the colour checker is upside down.
swatches_chromatic_slice – A slice instance defining chromatic swatches used to detect if the colour checker is upside down.
swatches_count_maximum – Maximum swatches count to be considered for the detection.
swatches_count_minimum – Minimum swatches count to be considered for the detection.
swatches_horizontal – Colour checker swatches horizontal columns count.
swatches_vertical – Colour checker swatches vertical row count.
transform – Transform to apply to the colour checker image post-detection.
working_width – Width the input image is resized to for detection.
working_height – Height the input image is resized to for detection.
kwargs (Any) –

Returns:

Tuple of DataDetectionColourChecker class instances or colour checkers swatches.

Return type:

class`tuple`

Examples

>>> 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)
>>> detect_colour_checkers_segmentation(image)  
(array([[ 0.360005  ,  0.22310828,  0.11760835],
       [ 0.6258309 ,  0.39448667,  0.24166533],
       [ 0.33198   ,  0.31600377,  0.28866866],
       [ 0.3046006 ,  0.273321  ,  0.10486555],
       [ 0.41751358,  0.31914026,  0.30789137],
       [ 0.34866226,  0.43934596,  0.29126382],
       [ 0.67983997,  0.35236534,  0.06997226],
       [ 0.27118555,  0.25352538,  0.33078724],
       [ 0.62091863,  0.27034152,  0.18652563],
       [ 0.3071613 ,  0.17978874,  0.19181632],
       [ 0.48547146,  0.4585586 ,  0.03294956],
       [ 0.6507678 ,  0.40023172,  0.01607676],
       [ 0.19286253,  0.18585181,  0.27459183],
       [ 0.28054565,  0.38513032,  0.1224441 ],
       [ 0.5545431 ,  0.21436104,  0.12549178],
       [ 0.72068894,  0.51493925,  0.00548734],
       [ 0.5772921 ,  0.2577179 ,  0.2685553 ],
       [ 0.17289193,  0.3163792 ,  0.2950853 ],
       [ 0.7394083 ,  0.60953134,  0.4383072 ],
       [ 0.6281671 ,  0.51759964,  0.37215686],
       [ 0.51360977,  0.42048824,  0.2985709 ],
       [ 0.36953217,  0.30218402,  0.20827036],
       [ 0.26286703,  0.21493268,  0.14277342],
       [ 0.16102524,  0.13381621,  0.08047409]]...),)