dcnum.feat.feat_contour

Feature computation: OpenCV moments-based features

Submodules

Functions

moments_based_features(mask, pixel_size[, ret_contour])

Compute moment-based features for a mask image

volume_from_contours(contour, pos_x, pos_y, pixel_size)

Calculate the volume of a polygon revolved around an axis

Package Contents

dcnum.feat.feat_contour.moments_based_features(mask: numpy.ndarray, pixel_size: float, ret_contour: bool = False)[source]

Compute moment-based features for a mask image

Parameters:

  • mask (np.ndarray) – 3D stack of 2D boolean mask images to analyze

  • pixel_size (float) – pixel size of the mask image in µm

  • ret_contour (bool) – whether to also return the raw contour

dcnum.feat.feat_contour.volume_from_contours(contour: list[numpy.ndarray], pos_x: numpy.ndarray, pos_y: numpy.ndarray, pixel_size: float)[source]

Calculate the volume of a polygon revolved around an axis

The volume estimation assumes rotational symmetry.

Parameters:

  • contour (list of ndarrays of shape (N,2)) – One entry is a 2D array that holds the contour of an event

  • pos_x (float ndarray of length N) – The x coordinate(s) of the centroid of the event(s) [µm]

  • pos_y (float ndarray of length N) – The y coordinate(s) of the centroid of the event(s) [µm]

  • pixel_size (float) – The detector pixel size in µm.

Returns:

volume – volume in um^3

Return type:

float ndarray

Notes

The computation of the volume is based on a full rotation of the upper and the lower halves of the contour from which the average is then used.

The volume is computed radially from the center position given by (pos_x, pos_y). For sufficiently smooth contours, such as densely sampled ellipses, the center position does not play an important role. For contours that are given on a coarse grid, as is the case for deformability cytometry, the center position must be given.

References