<reponame>dstansby/cellfinder-core<filename>src/cellfinder_core/detect/filters/plane/plane_filter.py
from dataclasses import dataclass
from typing import Tuple

import numpy as np

from cellfinder_core.detect.filters.plane.classical_filter import enhance_peaks
from cellfinder_core.detect.filters.plane.tile_walker import TileWalker


@dataclass
class TileProcessor:
    clipping_value: float
    threshold_value: float
    soma_diameter: float
    log_sigma_size: float
    n_sds_above_mean_thresh: float

    def get_tile_mask(
        self, plane: np.ndarray
    ) -> Tuple[np.ndarray, np.ndarray]:
        """
        Warning: this modifies ``plane`` in place.
        """
        laplace_gaussian_sigma = self.log_sigma_size * self.soma_diameter
        plane = plane.T
        np.clip(plane, 0, self.clipping_value, out=plane)

        walker = TileWalker(plane, self.soma_diameter)

        walker.walk_out_of_brain_only()

        thresholded_img = enhance_peaks(
            walker.thresholded_img,
            self.clipping_value,
            gaussian_sigma=laplace_gaussian_sigma,
        )

        # threshold
        avg = thresholded_img.ravel().mean()
        sd = thresholded_img.ravel().std()

        plane[
            thresholded_img > avg + self.n_sds_above_mean_thresh * sd
        ] = self.threshold_value
        return plane, walker.good_tiles_mask.astype(np.uint8)

from dataclasses import dataclass
from typing import Tuple

import numpy as np

from cellfinder_core.detect.filters.plane.classical_filter import enhance_peaks
from cellfinder_core.detect.filters.plane.tile_walker import TileWalker


@dataclass
class TileProcessor:
    clipping_value: float
    threshold_value: float
    soma_diameter: float
    log_sigma_size: float
    n_sds_above_mean_thresh: float

    def get_tile_mask(
        self, plane: np.ndarray
    ) -> Tuple[np.ndarray, np.ndarray]:
        """
        Warning: this modifies ``plane`` in place.
        """
        laplace_gaussian_sigma = self.log_sigma_size * self.soma_diameter
        plane = plane.T
        np.clip(plane, 0, self.clipping_value, out=plane)

        walker = TileWalker(plane, self.soma_diameter)

        walker.walk_out_of_brain_only()

        thresholded_img = enhance_peaks(
            walker.thresholded_img,
            self.clipping_value,
            gaussian_sigma=laplace_gaussian_sigma,
        )

        # threshold
        avg = thresholded_img.ravel().mean()
        sd = thresholded_img.ravel().std()

        plane[
            thresholded_img > avg + self.n_sds_above_mean_thresh * sd
        ] = self.threshold_value
        return plane, walker.good_tiles_mask.astype(np.uint8)
Warning: this modifies ``plane`` in place. # threshold