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

Detect the colour checkers swatches in specified 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.

  • extractor (Callable) – Callable responsible to extract the colour checker data from the segmented rectangles.

  • extractor_kwargs (dict | None) – Keyword arguments to pass to the extractor.

  • 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, apply_cctf_decoding=True)
...
(array([[  1.06804565e-01,   4.08109687e-02,   1.31266015e-02],
       [  3.49683374e-01,   1.29072860e-01,   4.77699488e-02],
       [  9.05181840e-02,   8.14911500e-02,   6.78349435e-02],
       [  7.58600757e-02,   6.07827008e-02,   1.10303042e-02],
       [  1.45751402e-01,   8.31151009e-02,   7.72669688e-02],
       [  1.00159235e-01,   1.62195116e-01,   6.91367984e-02],
       [  4.20038819e-01,   1.01927504e-01,   6.38073916e-03],
       [  6.02441281e-02,   5.23594357e-02,   8.94734785e-02],
       [  3.43939215e-01,   5.94407171e-02,   2.92109884e-02],
       [  7.70443529e-02,   2.71991435e-02,   3.07092462e-02],
       [  2.01171950e-01,   1.77795976e-01,   2.66185054e-03],
       [  3.81304830e-01,   1.33062363e-01,   1.23752898e-03],
       [  3.14101577e-02,   2.89250631e-02,   6.13652393e-02],
       [  6.45340234e-02,   1.22705154e-01,   1.41930664e-02],
       [  2.68294245e-01,   3.78271416e-02,   1.45846475e-02],
       [  4.78512675e-01,   2.28180945e-01,   4.21307486e-04],
       [  2.93216556e-01,   5.40601388e-02,   5.87599911e-02],
       [  2.61003822e-02,   8.16240311e-02,   7.08173811e-02],
       [  5.06426811e-01,   3.29764754e-01,   1.61412135e-01],
       [  3.52356732e-01,   2.30743274e-01,   1.14147641e-01],
       [  2.27152765e-01,   1.47627085e-01,   7.27180764e-02],
       [  1.12553783e-01,   7.42645189e-02,   3.58113647e-02],
       [  5.65953329e-02,   3.79680507e-02,   1.81693807e-02],
       [  2.30163466e-02,   1.61432363e-02,   7.43864896e-03]]...),)