trackpy.locate¶

trackpy.locate(raw_image, diameter, minmass=None, maxsize=None, separation=None, noise_size=1, smoothing_size=None, threshold=None, invert=False, percentile=64, topn=None, preprocess=True, max_iterations=10, filter_before=None, filter_after=None, characterize=True, engine='auto')¶

Locate Gaussian-like blobs of some approximate size in an image.

Preprocess the image by performing a band pass and a threshold. Locate all peaks of brightness, characterize the neighborhoods of the peaks and take only those with given total brightness (“mass”). Finally, refine the positions of each peak.

Parameters:

raw_imagearray (any dimensions): Image used for final characterization. Ideally, pixel values of this image are not rescaled, but it can also be identical to image.
imagearray (same size as raw_image): Processed image used for centroid-finding and most particle measurements.
diameterodd 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.
minmassfloat: 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). .. warning:: The mass value is changed since v0.3.0 .. warning:: The default behaviour of minmass has changed since v0.4.0
maxsizefloat: maximum radius-of-gyration of brightness, default None
separationfloat or tuple: Minimum separation between features. Default is diameter + 1. May be a tuple, see diameter for details.
noise_sizefloat or tuple: Width of Gaussian blurring kernel, in pixels Default is 1. May be a tuple, see diameter for details.
smoothing_sizefloat 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.
thresholdfloat: 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.
invertboolean: This will be deprecated. Use an appropriate PIMS pipeline to invert a Frame or FramesSequence. Set to True if features are darker than background. False by default.
percentilefloat: Features must have a peak brighter than pixels in this percentile. This helps eliminate spurious peaks.
topninteger: Return only the N brightest features above minmass. If None (default), return all features above minmass.
preprocessboolean: Set to False to turn off bandpass preprocessing.
max_iterationsinteger: max number of loops to refine the center of mass, default 10
filter_beforeboolean: filter_before is no longer supported as it does not improve performance.
filter_afterboolean: This parameter has been deprecated: use minmass and maxsize.
characterizeboolean: Compute “extras”: eccentricity, signal, ep. True by default.
engine{‘auto’, ‘python’, ‘numba’}

Returns:

DataFrame([x, y, mass, size, ecc, signal, raw_mass]): where “x, y” are appropriate to the dimensionality of the image, mass means total integrated brightness of the blob, size means the radius of gyration of its Gaussian-like profile, ecc is its eccentricity (0 is circular), and raw_mass is the total integrated brightness in raw_image.