Source code for livecellx.trajectory.feature_extractors

from typing import List, Union, Tuple
from typing import Dict
import skimage
import skimage.measure
from pandas import Series
import numpy as np
import pandas as pd

from livecellx.core.single_cell import SingleCellStatic
from livecellx.preprocess.utils import normalize_img_to_uint8
from livecellx.core.parallel import parallelize


def _compute_feature_wrapper(sc, func, params=dict()):
    features = func(sc=sc, **params)
    return features, sc


def parallelize_compute_features(
    scs: List[SingleCellStatic], func: callable, params: dict, cores=None
) -> Tuple[List, List]:
    """
    Compute features in parallel for a list of SingleCellStatic objects.

    Args:
        scs (List[SingleCellStatic]): List of SingleCellStatic objects to compute features for.
        func (callable): Function to use for feature computation.
        params (dict): Dictionary of parameters to pass to the feature computation function.
        cores (int, optional): Number of CPU cores to use for parallelization. Defaults to None.

    Returns:
        Tuple[List, List]: Tuple containing a list of computed features and a list of corresponding SingleCellStatic objects.
    """
    inputs = []
    for sc in scs:
        inputs.append({"sc": sc, "func": func, "params": params})

    outputs = parallelize(_compute_feature_wrapper, inputs, cores=cores)
    features = [output[0] for output in outputs]
    unordered_res_scs = [output[1] for output in outputs]
    sc_id_to_sc = {sc.id: sc for sc in unordered_res_scs}
    # reorder res_scs according sc_id in original scs
    res_scs = []
    for sc in scs:
        sc_id = sc.id
        res_scs.append(sc_id_to_sc[sc_id])
    return features, res_scs


[docs]def compute_haralick_features( sc: SingleCellStatic, feature_key="haralick", ignore_zeros=True, return_mean=True, ret_arr=True, add_feature_to_sc=True, **kwargs ) -> Union[np.array, Series]: """Returns a list of texture features for the given image. Parameters ---------- image : ndarray The image to extract features from. Returns ------- list A list of texture features. """ import mahotas.features.texture image = sc.get_contour_img(crop=True) features = mahotas.features.texture.haralick(image, ignore_zeros=ignore_zeros, return_mean=return_mean, **kwargs) if ret_arr: return features feature_names = ["haralick_" + str(i) for i in range(len(features.flatten()))] features_series = Series(features.ravel(), index=feature_names) if add_feature_to_sc: sc.add_feature(feature_key, features_series) return features_series
# https://scikit-image.org/docs/stable/api/skimage.measure.html SELECTED_SKIMAGE_REGIONPROPOS_COL_DTYPES = { "area": float, "area_bbox": float, "area_convex": float, "area_filled": float, "axis_major_length": float, "axis_minor_length": float, # "bbox": int, "centroid": float, "centroid_local": float, "centroid_weighted": float, "centroid_weighted_local": float, "eccentricity": float, "equivalent_diameter_area": float, "euler_number": int, "extent": float, "feret_diameter_max": float, "inertia_tensor": float, "inertia_tensor_eigvals": float, "intensity_max": float, "intensity_mean": float, "intensity_min": float, "label": int, "moments": float, "moments_central": float, "moments_hu": float, "moments_normalized": float, "moments_weighted": float, "moments_weighted_central": float, "moments_weighted_hu": float, "moments_weighted_normalized": float, "orientation": float, "perimeter": float, "perimeter_crofton": float, "solidity": float, }
[docs]def compute_skimage_regionprops( sc: SingleCellStatic, feature_key="skimage", props=SELECTED_SKIMAGE_REGIONPROPOS_COL_DTYPES.keys(), add_feature_to_sc=True, preprocess_img_func=None, sc_level_normalize=True, ) -> pd.Series: label_mask = sc.get_contour_mask().astype(int) intensity_mask = sc.get_contour_img(crop=True, preprocess_img_func=preprocess_img_func) if sc_level_normalize and preprocess_img_func: intensity_mask = preprocess_img_func(intensity_mask) regionprops_results = skimage.measure.regionprops_table(label_mask, intensity_mask, properties=props) feature_keys = list(regionprops_results.keys()) # Convert to pandas series for key in feature_keys: if not hasattr(regionprops_results[key], "__len__"): pass if len(regionprops_results[key]) == 1: regionprops_results[key] = regionprops_results[key][0] elif len(regionprops_results[key]) != 1: # TODO: Handle this case, probably by appending index suffix to key raise ValueError( "Regionprops should only return one value per property, %s contains %d values" % (key, len(regionprops_results[key])) ) res_table = pd.Series(regionprops_results) if add_feature_to_sc: sc.add_feature(feature_key, res_table) return res_table