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Shannon Axelrod
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,295 @@ | ||
| from functools import partial | ||
| from typing import List, Optional, Tuple, Union | ||
|
|
||
| import numpy as np | ||
| import pandas as pd | ||
| import xarray as xr | ||
| from scipy.ndimage import label | ||
| from skimage.feature import peak_local_max | ||
| from skimage.measure import regionprops | ||
| from sympy import Line, Point | ||
| from tqdm import tqdm | ||
|
|
||
| from starfish.core.config import StarfishConfig | ||
| from starfish.core.image.Filter.util import determine_axes_to_group_by | ||
| from starfish.core.imagestack.imagestack import ImageStack | ||
| from starfish.core.spots.FindSpots import spot_finding_utils | ||
| from starfish.core.types import Axes, Features, Number, SpotAttributes, SpotFindingResults | ||
| from ._base import FindSpotsAlgorithm | ||
|
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||
|
|
||
| class LocalMaxPeakFinder(FindSpotsAlgorithm): | ||
| """ | ||
| 2-dimensional local-max peak finder that wraps skimage.feature.peak_local_max | ||
| Parameters | ||
| ---------- | ||
| min_distance : int | ||
| Minimum number of pixels separating peaks in a region of 2 * min_distance + 1 | ||
| (i.e. peaks are separated by at least min_distance). To find the maximum number of | ||
| peaks, use min_distance=1. | ||
| stringency : int | ||
| min_obj_area : int | ||
| max_obj_area : int | ||
| threshold : Optional[Number] | ||
| measurement_type : str, {'max', 'mean'} | ||
| default 'max' calculates the maximum intensity inside the object | ||
| min_num_spots_detected : int | ||
| When fewer than this number of spots are detected, spot searching for higher threshold | ||
| values. (default = 3) | ||
| is_volume : bool | ||
| Not supported. For 3d peak detection please use TrackpyLocalMaxPeakFinder. | ||
| (default=False) | ||
| verbose : bool | ||
| If True, report the percentage completed (default = False) during processing | ||
| Notes | ||
| ----- | ||
| http://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.peak_local_max | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, min_distance: int, stringency: int, min_obj_area: int, max_obj_area: int, | ||
| threshold: Optional[Number]=None, measurement_type: str='max', | ||
| min_num_spots_detected: int=3, is_volume: bool=False, verbose: bool=True | ||
| ) -> None: | ||
|
|
||
| self.min_distance = min_distance | ||
| self.stringency = stringency | ||
| self.min_obj_area = min_obj_area | ||
| self.max_obj_area = max_obj_area | ||
| self.threshold = threshold | ||
| self.min_num_spots_detected = min_num_spots_detected | ||
|
|
||
| self.measurement_function = self._get_measurement_function(measurement_type) | ||
|
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| self.is_volume = is_volume | ||
| self.verbose = verbose | ||
|
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||
| def _compute_num_spots_per_threshold(self, img: np.ndarray) -> Tuple[np.ndarray, List[int]]: | ||
| """Computes the number of detected spots for each threshold | ||
| Parameters | ||
| ---------- | ||
| img : np.ndarray | ||
| The image in which to count spots | ||
| Returns | ||
| ------- | ||
| np.ndarray : | ||
| thresholds | ||
| List[int] : | ||
| spot counts | ||
| """ | ||
|
|
||
| # thresholds to search over | ||
| thresholds = np.linspace(img.min(), img.max(), num=100) | ||
|
|
||
| # number of spots detected at each threshold | ||
| spot_counts = [] | ||
|
|
||
| # where we stop our threshold search | ||
| stop_threshold = None | ||
|
|
||
| if self.verbose and StarfishConfig().verbose: | ||
| threshold_iter = tqdm(thresholds) | ||
| print('Determining optimal threshold ...') | ||
| else: | ||
| threshold_iter = thresholds | ||
|
|
||
| for stop_index, threshold in enumerate(threshold_iter): | ||
| spots = peak_local_max( | ||
| img, | ||
| min_distance=self.min_distance, | ||
| threshold_abs=threshold, | ||
| exclude_border=False, | ||
| indices=True, | ||
| num_peaks=np.inf, | ||
| footprint=None, | ||
| labels=None | ||
| ) | ||
|
|
||
| # stop spot finding when the number of detected spots falls below min_num_spots_detected | ||
| if len(spots) <= self.min_num_spots_detected: | ||
| stop_threshold = threshold | ||
| if self.verbose: | ||
| print(f'Stopping early at threshold={threshold}. Number of spots fell below: ' | ||
| f'{self.min_num_spots_detected}') | ||
| break | ||
| else: | ||
| spot_counts.append(len(spots)) | ||
|
|
||
| if stop_threshold is None: | ||
| stop_threshold = thresholds.max() | ||
|
|
||
| if len(thresholds > 1): | ||
| thresholds = thresholds[:stop_index] | ||
| spot_counts = spot_counts[:stop_index] | ||
|
|
||
| return thresholds, spot_counts | ||
|
|
||
| def _select_optimal_threshold(self, thresholds: np.ndarray, spot_counts: List[int]) -> float: | ||
| # calculate the gradient of the number of spots | ||
| grad = np.gradient(spot_counts) | ||
| optimal_threshold_index = np.argmin(grad) | ||
|
|
||
| # only consider thresholds > than optimal threshold | ||
| thresholds = thresholds[optimal_threshold_index:] | ||
| grad = grad[optimal_threshold_index:] | ||
|
|
||
| # if all else fails, return 0. | ||
| selected_thr = 0 | ||
|
|
||
| if len(thresholds) > 1: | ||
|
|
||
| distances = [] | ||
|
|
||
| # create a line whose end points are the threshold and and corresponding gradient value | ||
| # for spot_counts corresponding to the threshold | ||
| start_point = Point(thresholds[0], grad[0]) | ||
| end_point = Point(thresholds[-1], grad[-1]) | ||
| line = Line(start_point, end_point) | ||
|
|
||
| # calculate the distance between all points and the line | ||
| for k in range(len(thresholds)): | ||
| p = Point(thresholds[k], grad[k]) | ||
| dst = line.distance(p) | ||
| distances.append(dst.evalf()) | ||
|
|
||
| # remove the end points | ||
| thresholds = thresholds[1:-1] | ||
| distances = distances[1:-1] | ||
|
|
||
| # select the threshold that has the maximum distance from the line | ||
| # if stringency is passed, select a threshold that is n steps higher, where n is the | ||
| # value of stringency | ||
| if distances: | ||
| thr_idx = np.argmax(np.array(distances)) | ||
|
|
||
| if thr_idx + self.stringency < len(thresholds): | ||
| selected_thr = thresholds[thr_idx + self.stringency] | ||
| else: | ||
| selected_thr = thresholds[thr_idx] | ||
|
|
||
| return selected_thr | ||
|
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||
| def _compute_threshold(self, img: Union[np.ndarray, xr.DataArray]) -> float: | ||
| """Finds spots on a number of thresholds then selects and returns the optimal threshold | ||
| Parameters | ||
| ---------- | ||
| img: Union[np.ndarray, xr.DataArray] | ||
| data array in which spots should be detected and over which to compute different | ||
| intensity thresholds | ||
| Returns | ||
| ------- | ||
| Number : #TODO ambrosejcarr this should probably be a float | ||
| The intensity threshold | ||
| """ | ||
| img = np.asarray(img) | ||
| thresholds, spot_counts = self._compute_num_spots_per_threshold(img) | ||
| if len(spot_counts) == 0: | ||
| # this only happens when we never find more spots than `self.min_num_spots_detected` | ||
| return img.min() | ||
| return self._select_optimal_threshold(thresholds, spot_counts) | ||
|
|
||
| def image_to_spots(self, data_image: Union[np.ndarray, xr.DataArray]) -> SpotAttributes: | ||
| """measure attributes of spots detected by binarizing the image using the selected threshold | ||
| Parameters | ||
| ---------- | ||
| data_image : Union[np.ndarray, xr.DataArray] | ||
| image containing spots to be detected | ||
| Returns | ||
| ------- | ||
| SpotAttributes | ||
| Attributes for each detected spot | ||
| """ | ||
|
|
||
| threshold = self._compute_threshold(data_image) | ||
|
|
||
| data_image = np.asarray(data_image) | ||
|
|
||
| # identify each spot's size by binarizing and calculating regionprops | ||
| masked_image = data_image[:, :] > threshold | ||
| labels = label(masked_image)[0] | ||
| spot_props = regionprops(np.squeeze(labels)) | ||
|
|
||
| # mask spots whose areas are too small or too large | ||
| for spot_prop in spot_props: | ||
| if spot_prop.area < self.min_obj_area or spot_prop.area > self.max_obj_area: | ||
| masked_image[0, spot_prop.coords[:, 0], spot_prop.coords[:, 1]] = 0 | ||
|
|
||
| # store re-calculated regionprops and labels based on the area-masked image | ||
| labels = label(masked_image)[0] | ||
|
|
||
| if self.verbose: | ||
| print('computing final spots ...') | ||
|
|
||
| spot_coords = peak_local_max( | ||
| data_image, | ||
| min_distance=self.min_distance, | ||
| threshold_abs=threshold, | ||
| exclude_border=False, | ||
| indices=True, | ||
| num_peaks=np.inf, | ||
| footprint=None, | ||
| labels=labels | ||
| ) | ||
| res = {Axes.X.value: spot_coords[:, 2], | ||
| Axes.Y.value: spot_coords[:, 1], | ||
| Axes.ZPLANE.value: spot_coords[:, 0], | ||
| Features.SPOT_RADIUS: 1, | ||
| Features.SPOT_ID: np.arange(spot_coords.shape[0]), | ||
| Features.INTENSITY: data_image[spot_coords[:, 0], | ||
| spot_coords[:, 1], | ||
| spot_coords[:, 2]] | ||
| } | ||
|
|
||
| return SpotAttributes(pd.DataFrame(res)) | ||
|
|
||
| def run( | ||
| self, | ||
| image_stack: ImageStack, | ||
| reference_image: Optional[ImageStack] = None, | ||
| n_processes: Optional[int] = None, | ||
| *args, | ||
| **kwargs | ||
| ) -> SpotFindingResults: | ||
| """ | ||
| Find spots in the given ImageStack using a gaussian blob finding algorithm. | ||
| If a reference image is provided the spots will be detected there then measured | ||
| across all rounds and channels in the corresponding ImageStack. If a reference_image | ||
| is not provided spots will be detected _independently_ in each channel. This assumes | ||
| a non-multiplex imaging experiment, as only one (ch, round) will be measured for each spot. | ||
| Parameters | ||
| ---------- | ||
| image_stack : ImageStack | ||
| ImageStack where we find the spots in. | ||
| reference_image : xr.DataArray | ||
| (Optional) a reference image. If provided, spots will be found in this image, and then | ||
| the locations that correspond to these spots will be measured across each channel. | ||
| n_processes : Optional[int] = None, | ||
| Number of processes to devote to spot finding. | ||
| """ | ||
| spot_finding_method = partial(self.image_to_spots, *args, **kwargs) | ||
| if reference_image: | ||
| data_image = reference_image._squeezed_numpy(*{Axes.ROUND, Axes.CH}) | ||
| reference_spots = spot_finding_method(data_image) | ||
| results = spot_finding_utils.measure_spot_intensities( | ||
| data_image=image_stack, | ||
| reference_spots=reference_spots, | ||
| measurement_function=self.measurement_function) | ||
| else: | ||
| spot_attributes_list = image_stack.transform( | ||
| func=spot_finding_method, | ||
| group_by=determine_axes_to_group_by(self.is_volume), | ||
| n_processes=n_processes | ||
| ) | ||
| results = SpotFindingResults(imagestack_coords=image_stack.xarray.coords, | ||
| log=image_stack.log, | ||
| spot_attributes_list=spot_attributes_list) | ||
| return results |