PYME.Analysis.thresholding module

PYME.Analysis.thresholding.isodata(img, tol=0.001)

correct isodata implementation - slower than above tolerance indicates fraction of data range when delta is deemed not to have changed

PYME.Analysis.thresholding.isodata_f(img, nbins=255, bin_spacing='linear', tol=1e-05)

calculate isodata threshold - does iterations on histogrammed data rather than raw data to improve speed

img - the image data nbins - the number of bins used for the histogram

PYME.Analysis.thresholding.otsu(image, nbins=256, bin_spacing='linear')

Return threshold value based on Otsu’s method.

Adapted from the skimage implmentation to allow non-linear bins

Parameters

image(N, M) ndarray

Grayscale input image.

nbinsint, optional

Number of bins used to calculate histogram. This value is ignored for integer arrays.

Returns

——-

thresholdfloat

Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground.

Raises

——

ValueError

If image only contains a single grayscale value.

References

———-

.. [1] Wikipedia, https://en.wikipedia.org/wiki/Otsu’s_Method

Examples

——–

>>> from skimage.data import camera

>>> image = camera()

>>> thresh = threshold_otsu(image)

>>> binary = image <= thresh

Notes

—–

The input image must be grayscale.

PYME.Analysis.thresholding.signalFraction(img, frac, nbins=5000)

threshold to include a certain fraction of the total signal