Source code for histomicstk.segmentation.positive_pixel_count

from collections import namedtuple

from dask import delayed
import large_image
import numpy as np

from ..preprocessing.color_conversion import rgb_to_hsi


# This can be an enum in Python >= 3.4
class Labels:
    """Labels for the output image of the positive pixel count routines."""
    NEGATIVE = 0
    WEAK = 1
    PLAIN = 2
    STRONG = 3


[docs]class Parameters( namedtuple('Parameters', [ 'hue_value', 'hue_width', 'saturation_minimum', 'intensity_upper_limit', 'intensity_weak_threshold', 'intensity_strong_threshold', 'intensity_lower_limit', ]), ): """Parameters(hue_value, hue_width, saturation_minimum, intensity_upper_limit, intensity_weak_threshold, intensity_strong_threshold, intensity_lower_limit) Attributes ---------- hue_value: Center of the hue range in HSI space for the positive color, in the range [0, 1] hue_width: Width of the hue range in HSI space saturation_minimum: Minimum saturation of positive pixels in HSI space, in the range [0, 1] intensity_upper_limit: Intensity threshold in HSI space above which a pixel is considered negative, in the range [0, 1] intensity_weak_threshold: Intensity threshold in HSI space that separates weak-positive pixels (above) from plain positive pixels (below) intensity_strong_threshold: Intensity threshold in HSI space that separates plain positive pixels (above) from strong positive pixels (below) intensity_lower_limit: Intensity threshold in HSI space below which a pixel is considered negative """
OutputTotals = namedtuple('OutputTotals', [ 'NumberWeakPositive', 'NumberPositive', 'NumberStrongPositive', 'IntensitySumWeakPositive', 'IntensitySumPositive', 'IntensitySumStrongPositive', ]) Output = namedtuple('Output', OutputTotals._fields + ( 'IntensityAverage', 'RatioStrongToTotal', 'IntensityAverageWeakAndPositive', ))
[docs]def count_slide(slide_path, params, region=None, tile_grouping=256, make_label_image=False): """Compute a count of positive pixels in the slide at slide_path. This routine can also create a label image. Parameters --------- slide_path : string (path) Path to the slide to analyze. params : Parameters An instance of Parameters, which see for further documentation region : dict, optional A valid region dict (per a large_image TileSource.tileIterator's region argument) tile_grouping : int The number of tiles to process as part of a single task make_label_image : bool, default=False Whether to make a label image. See also "Notes" Returns ------- stats : Output Various statistics on the input image. See Output. label_image : array-like, only if make_label_image is set Notes ----- The return value is either a single or a pair -- it is in either case a tuple. Dask is used as configured to compute the statistics, but only if make_label_image is reset. If make_label_image is set, everything is computed in a single-threaded manner. """ ts = large_image.getTileSource(slide_path) kwargs = dict(format=large_image.tilesource.TILE_FORMAT_NUMPY) if region is not None: kwargs['region'] = region if make_label_image: tile = ts.getRegion(**kwargs)[0] return count_image(tile, params) else: results = [] total_tiles = ts.getSingleTile(**kwargs)['iterator_range']['position'] for position in range(0, total_tiles, tile_grouping): results.append(delayed(_count_tiles)( slide_path, params, kwargs, position, min(tile_grouping, total_tiles - position))) results = delayed(_combine)(results).compute() return _totals_to_stats(results),
def _combine(results): return OutputTotals._make(sum(r[i] for r in results) for i in range(len(OutputTotals._fields))) def _count_tiles(slide_path, params, kwargs, position, count): ts = large_image.getTileSource(slide_path) lpotf = len(OutputTotals._fields) total = [0] * lpotf for pos in range(position, position + count): tile = ts.getSingleTile(tile_position=pos, **kwargs)['tile'] subtotal = _count_image(tile, params)[0] for k in range(lpotf): total[k] += subtotal[k] return OutputTotals._make(total)
[docs]def count_image(image, params): """Count positive pixels, computing a label mask and summary statistics. Parameters ---------- image : array-like NxMx3 array of RGB data params : Parameters An instance of Parameters, which see for further documentation Returns ------- stats : Output Various statistics on the input image. See Output. label_image : array-like NxM array of pixel types. See Labels for the different values. """ total, masks = _count_image(image, params) mask_all_positive, mask_weak, mask_pos, mask_strong = masks label_image = np.full(image.shape[:-1], Labels.NEGATIVE, dtype=np.uint8) label_image[mask_all_positive] = ( mask_weak * Labels.WEAK + mask_pos * Labels.PLAIN + mask_strong * Labels.STRONG ) return _totals_to_stats(total), label_image
def _count_image(image, params): """A version of count_image that doesn't compute the label image and only computes the sums. """ p = params image_hsi = rgb_to_hsi(image / 255) mask_all_positive = ( (np.abs((image_hsi[..., 0] - p.hue_value + 0.5 % 1) - 0.5) <= p.hue_width / 2) & (image_hsi[..., 1] >= p.saturation_minimum) & (image_hsi[..., 2] < p.intensity_upper_limit) & (image_hsi[..., 2] >= p.intensity_lower_limit) ) all_positive_i = image_hsi[mask_all_positive, 2] mask_weak = all_positive_i >= p.intensity_weak_threshold nw, iw = np.count_nonzero(mask_weak), np.sum(all_positive_i[mask_weak]) mask_strong = all_positive_i < p.intensity_strong_threshold ns, is_ = np.count_nonzero(mask_strong), np.sum(all_positive_i[mask_strong]) mask_pos = ~(mask_weak | mask_strong) np_, ip = np.count_nonzero(mask_pos), np.sum(all_positive_i[mask_pos]) total = OutputTotals( NumberWeakPositive=nw, NumberPositive=np_, NumberStrongPositive=ns, IntensitySumWeakPositive=iw, IntensitySumPositive=ip, IntensitySumStrongPositive=is_, ) return total, (mask_all_positive, mask_weak, mask_pos, mask_strong) def _totals_to_stats(total): """Do the extra computations to convert an OutputTotals to an Output""" t = total all_positive = t.NumberWeakPositive + t.NumberPositive + t.NumberStrongPositive return Output( IntensityAverage=((t.IntensitySumWeakPositive + t.IntensitySumPositive + t.IntensitySumStrongPositive) / all_positive), RatioStrongToTotal=t.NumberStrongPositive / all_positive, IntensityAverageWeakAndPositive=( (t.IntensitySumWeakPositive + t.IntensitySumPositive) / (t.NumberWeakPositive + t.NumberPositive) ), **t._asdict() ) __all__ = ( 'Parameters', 'Output', 'count_slide', 'count_image', )