colour_checker_detection.segmenter_templated#

colour_checker_detection.segmenter_templated(image: ArrayLike, cctf_encoding: Callable = eotf_inverse_sRGB, apply_cctf_encoding: bool = True, additional_data: Literal[True] = True, **kwargs: Any) → DataSegmentationColourCheckers[source]#
colour_checker_detection.segmenter_templated(image: ArrayLike, cctf_encoding: Callable = eotf_inverse_sRGB, apply_cctf_encoding: bool = True, *, additional_data: Literal[False], **kwargs: Any) → NDArrayInt
colour_checker_detection.segmenter_templated(image: ArrayLike, cctf_encoding: Callable, apply_cctf_encoding: bool, additional_data: Literal[False], **kwargs: Any) → NDArrayInt

Detect the colour checker rectangles, clusters and swatches in specified image using segmentation with advanced filtering.

The process is as follows:

  1. Input image is converted to a grayscale image and normalised to range [0, 1].

  2. Image is denoised using multiple bilateral filtering passes.

  3. Image is thresholded.

  4. Image is eroded and dilated to cleanup remaining noise.

  5. Contours are detected.

  6. Contours are filtered to only keep squares/swatches above and below defined surface area, moreover they have to resemble a convex quadrilateral. Additionally, squareness, area, aspect ratio and orientation are used as features to remove any remaining outlier contours.

  7. Stacked contours are removed.

  8. 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 joined, creating a large rectangular cluster. Rectangles are fitted to the clusters.

  9. Clusters with a number of swatches close to the expected one are kept.

Parameters:

  • image (ArrayLike) – Image to detect the colour checker rectangles from.

  • cctf_encoding (Callable) – Encoding colour component transfer function / opto-electronic transfer function used when converting the image from float to 8-bit.

  • apply_cctf_encoding (bool) – Apply the encoding colour component transfer function / opto-electronic transfer function.

  • 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.

  • contour_approximation_factor – Approximation factor for the Douglas-Peucker polygon approximation algorithm. It controls how aggressively contours are simplified, expressed as a fraction of the contour’s perimeter. Lower values (e.g., 0.01) preserve more detail, higher values (e.g., 0.1) simplify more aggressively.

  • convolution_iterations – Number of iterations to use for the erosion / dilation process.

  • convolution_kernel – Convolution kernel to use for the erosion / dilation process.

  • dbscan_eps – DBSCAN epsilon parameter defining the maximum distance between two samples for them to be considered in the same neighborhood. Lower values create tighter clusters. Default is 0.5.

  • dbscan_min_samples – DBSCAN minimum samples parameter defining the number of samples in a neighborhood for a point to be considered a core point. Default is 5.

  • transformation_cost_threshold – Cost threshold for early termination of transformation search. If a transformation achieves an average distance below this threshold, the search stops immediately. Lower values require better matches. Default is 10.0.

  • 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:

  • colour_checker_detection.DataSegmentationColourCheckers

  • or np.ndarray

  • Colour checker rectangles and additional data or colour checker rectangles only.

Return type:

DataSegmentationColourCheckers | NDArrayInt

Examples

>>> import os
>>> from colour import read_image
>>> from colour_checker_detection import ROOT_RESOURCES_TESTS, segmenter_templated
>>> path = os.path.join(
...     ROOT_RESOURCES_TESTS,
...     "colour_checker_detection",
...     "detection",
...     "IMG_1967.png",
... )
>>> image = read_image(path)
>>> segmenter_templated(image)
array([[[ 357,  690],
        [ 373,  219],
        [1086,  244],
        [1069,  715]]], dtype=int32)
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