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refactoring allen smFish with new spot finding
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Shannon Axelrod
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Oct 1, 2019
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179
starfish/core/spots/FindSpots/trackpy_local_max_peak_finder.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,179 @@ | ||
| import warnings | ||
| from functools import partial | ||
| from typing import Optional, Tuple, Union | ||
|
|
||
| import numpy as np | ||
| import xarray as xr | ||
| from trackpy import locate | ||
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| 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, SpotAttributes, SpotFindingResults | ||
| from ._base import FindSpotsAlgorithm | ||
|
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| class TrackpyLocalMaxPeakFinder(FindSpotsAlgorithm): | ||
| """ | ||
| Find spots using a local max peak finding algorithm | ||
| This is a wrapper for :code:`trackpy.locate`, which implements a version of the | ||
| `Crocker-Grier <crocker_grier>`_ algorithm. | ||
| .. _crocker_grier: https://physics.nyu.edu/grierlab/methods3c/ | ||
| Parameters | ||
| ---------- | ||
| spot_diameter : odd integer or tuple of odd integers. | ||
| This may be a single number or a tuple giving the feature’s extent in each dimension, | ||
| useful when the dimensions do not have equal resolution (e.g. confocal microscopy). | ||
| The tuple order is the same as the image shape, conventionally (z, y, x) or (y, x). | ||
| The number(s) must be odd integers. When in doubt, round up. | ||
| min_mass : float, optional | ||
| The minimum integrated brightness. This is a crucial parameter for eliminating spurious | ||
| features. Recommended minimum values are 100 for integer images and 1 for float images. | ||
| Defaults to 0 (no filtering). | ||
| max_size : float | ||
| maximum radius-of-gyration of brightness, default None | ||
| separation : float or tuple | ||
| Minimum separtion between features. Default is diameter + 1. May be a tuple, see | ||
| diameter for details. | ||
| percentile : float | ||
| Features must have a peak brighter than pixels in this percentile. This helps eliminate | ||
| spurious peaks. | ||
| noise_size : float or tuple | ||
| Width of Gaussian blurring kernel, in pixels Default is 1. May be a tuple, see diameter | ||
| for details. | ||
| smoothing_size : float or tuple | ||
| The size of the sides of the square kernel used in boxcar (rolling average) smoothing, | ||
| in pixels Default is diameter. May be a tuple, making the kernel rectangular. | ||
| threshold : float | ||
| Clip bandpass result below this value. Thresholding is done on the already | ||
| background-subtracted image. By default, 1 for integer images and 1/255 for float | ||
| images. | ||
| measurement_type : str ['max', 'mean'] | ||
| name of the function used to calculate the intensity for each identified spot area | ||
| preprocess : boolean | ||
| Set to False to turn off bandpass preprocessing. | ||
| max_iterations : integer | ||
| Max number of loops to refine the center of mass, default 10 | ||
| is_volume : bool | ||
| if True, run the algorithm on 3d volumes of the provided stack | ||
| verbose : bool | ||
| If True, report the percentage completed (default = False) during processing | ||
| Notes | ||
| ----- | ||
| See also trackpy locate: http://soft-matter.github.io/trackpy/dev/generated/trackpy.locate.html | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, spot_diameter, min_mass, max_size, separation, percentile=0, | ||
| noise_size: Tuple[int, int, int] = (1, 1, 1), smoothing_size=None, threshold=None, | ||
| preprocess: bool = False, max_iterations: int = 10, measurement_type: str = 'max', | ||
| is_volume: bool = False, verbose=False) -> None: | ||
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| self.diameter = spot_diameter | ||
| self.minmass = min_mass | ||
| self.maxsize = max_size | ||
| self.separation = separation | ||
| self.noise_size = noise_size | ||
| self.smoothing_size = smoothing_size | ||
| self.percentile = percentile | ||
| self.threshold = threshold | ||
| self.measurement_function = self._get_measurement_function(measurement_type) | ||
| self.preprocess = preprocess | ||
| self.max_iterations = max_iterations | ||
| self.is_volume = is_volume | ||
| self.verbose = verbose | ||
|
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| def image_to_spots(self, data_image: Union[np.ndarray, xr.DataArray]) -> SpotAttributes: | ||
| """ | ||
| Parameters | ||
| ---------- | ||
| data_image : np.ndarray | ||
| three-dimensional image containing spots to be detected | ||
| Returns | ||
| ------- | ||
| SpotAttributes : | ||
| spot attributes table for all detected spots | ||
| """ | ||
| data_image = np.asarray(data_image) | ||
| with warnings.catch_warnings(): | ||
| warnings.simplefilter('ignore', FutureWarning) # trackpy numpy indexing warning | ||
| warnings.simplefilter('ignore', UserWarning) # yielded if black images | ||
| attributes = locate( | ||
| data_image, | ||
| diameter=self.diameter, | ||
| minmass=self.minmass, | ||
| maxsize=self.maxsize, | ||
| separation=self.separation, | ||
| noise_size=self.noise_size, | ||
| smoothing_size=self.smoothing_size, | ||
| threshold=self.threshold, | ||
| percentile=self.percentile, | ||
| preprocess=self.preprocess, | ||
| max_iterations=self.max_iterations, | ||
| ) | ||
|
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| # when zero spots are detected, 'ep' is missing from the trackpy locate results. | ||
| if attributes.shape[0] == 0: | ||
| attributes['ep'] = [] | ||
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| # TODO ambrosejcarr: data should always be at least pseudo-3d, this may not be necessary | ||
| # TODO ambrosejcarr: this is where max vs. sum vs. mean would be parametrized. | ||
| # here, total_intensity = sum, intensity = max | ||
| new_colnames = [ | ||
| 'y', 'x', 'total_intensity', 'radius', 'eccentricity', 'intensity', 'raw_mass', 'ep' | ||
| ] | ||
| if len(data_image.shape) == 3: | ||
| attributes.columns = ['z'] + new_colnames | ||
| else: | ||
| attributes.columns = new_colnames | ||
|
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| attributes['spot_id'] = np.arange(attributes.shape[0]) | ||
| return SpotAttributes(attributes) | ||
|
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| def run( | ||
| self, | ||
| primary_image: ImageStack, | ||
| reference_image: Optional[ImageStack] = None, | ||
| n_processes: Optional[int] = None, | ||
| *args, | ||
| ) -> SpotFindingResults: | ||
| """ | ||
| Find spots. | ||
| Parameters | ||
| ---------- | ||
| primary_image : 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) | ||
| 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( | ||
| primary_image, | ||
| reference_spots, | ||
| measurement_function=self.measurement_function) | ||
| else: | ||
| spot_attributes_list = primary_image.transform( | ||
| func=spot_finding_method, | ||
| group_by=determine_axes_to_group_by(self.is_volume), | ||
| n_processes=n_processes | ||
| ) | ||
| results = SpotFindingResults(imagestack_coords=primary_image.xarray.coords, | ||
| log=primary_image.log, | ||
| spot_attributes_list=spot_attributes_list) | ||
| return results |
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