Source code for brainlit.feature_extraction.neighborhood

import numpy as np
import brainlit
from brainlit.preprocessing import preprocess, image_process
from brainlit.utils.util import check_type, check_size
from scipy import ndimage as ndi
from pathlib import Path
import pandas as pd
from itertools import product
from typing import List, Optional, Dict, Tuple

from .base import BaseFeatures

[docs]class NeighborhoodFeatures(BaseFeatures): """Computes features based off neighborhood properties. Arguments: url: Precompued path either to a file URI or url URI of image data. radius: The radius around each point considered a neighborhood, in each dimension. If radius is [x,y,z], the neighborhood will be a [2x+1, 2y+1, 2z+1] volume centered at the point of interest. Defaults to [1, 1, 1]. offset: Added to the coordinates of a positive sample to generate a negative sample. Defaults to [15, 15, 15]. segment_url: Precompued path either to a file URI or url URI of segmentation data. Attributes: url: CloudVolumePrecomputedPath to image data. size: A size hyperparameter. In Neighborhoods, this is the radius. offset: Added to the coordinates of a positive sample to generate a negative sample. download_time: Tracks time taken to download the data. conversion_time: Tracks time taken to convert data to features. write_time: Tracks time taken to write features to files. segment_url: CloudVolumePrecomputedPath to segmentation data. """ def __init__( self, url: str, radius: List[int] = [1, 1, 1], offset: List[int] = [15, 15, 15], segment_url: Optional[str] = None, ): super().__init__(url=url, size=radius, offset=offset, segment_url=segment_url) def _convert_to_features(self, img: np.ndarray) -> Dict: """Computes features from image data by flattening the image.""" return dict(enumerate(img.flatten()))
def subsample( arr: np.ndarray, orig_shape: List[int], dest_shape: List[int] ) -> np.ndarray: """Subsamples a flattened neighborhood to a smaller flattened neighborhood. Arguments: arr: The flattened array orig_shape: The original shape of the array before flattening dest_shape: The desired shape of the array before flattening """ check_type(arr, np.ndarray) if len(orig_shape) != len(dest_shape): raise ValueError("Mismatched in and out dimensions.") if != len(arr): raise ValueError("Original shape is incorrect.") if len(orig_shape) == 3: check_size(orig_shape, dim=3) elif len(orig_shape) == 2: check_size(dest_shape, dim=2) else: raise NotImplementedError("Only 2 and 3 dimensions supported.") start = np.subtract(orig_shape, dest_shape) // 2 end = start + dest_shape if len(orig_shape) == 2: idx = np.ravel_multi_index( (np.mgrid[start[0] : end[0], start[1] : end[1]].reshape(2, -1)), orig_shape ) elif len(orig_shape) == 3: idx = np.ravel_multi_index( ( np.mgrid[ start[0] : end[0], start[1] : end[1], start[2] : end[2] ].reshape(3, -1) ), orig_shape, ) return arr[idx]