frames : list (or iterable) of images
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.
minmass : float
The minimum integrated brightness.
Default is 100 for integer images and 1 for float images, but a good
value is often much higher. This is a crucial parameter for eliminating
spurious features.
.. warning:: The mass value was changed since v0.3.0
maxsize : 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.
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
Size of boxcar smoothing, in pixels
Default is diameter. May be a tuple, see diameter for details.
threshold : float
Clip bandpass result below this value.
Default, None, defers to default settings of the bandpass function.
invert : boolean
Set to True if features are darker than background. False by default.
percentile : float
Features must have a peak brighter than pixels in this
percentile. This helps eliminate spurious peaks.
topn : integer
Return only the N brightest features above minmass.
If None (default), return all features above minmass.
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
filter_before : boolean
Use minmass (and maxsize, if set) to eliminate spurious features
based on their estimated mass and size before refining position.
Default (None) defers to trackpy, to optimize for performance.
filter_after : boolean
Use final characterizations of mass and size to eliminate spurious
features. True by default.
characterize : boolean
Compute “extras”: eccentricity, signal, ep. True by default.
engine : {‘auto’, ‘python’, ‘numba’}
output : {None, trackpy.PandasHDFStore, SomeCustomClass}
If None, return all results as one big DataFrame. Otherwise, pass
results from each frame, one at a time, to the put() method
of whatever class is specified here.
meta : filepath or file object, optional
If specified, information relevant to reproducing this batch is saved
as a YAML file, a plain-text machine- and human-readable format.
By default, this is None, and no file is saved.
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