import numpy as np
[docs]def average_gradient(data, *kwargs):
""" Compute average gradient norm of an image
"""
return np.average(np.array(np.gradient(data))**2)
[docs]def contrast_rms(data, *kwargs):
""" Compute RMS contrast norm of an image
"""
av = np.average(data, *kwargs)
mal = 1 / (data.shape[0] * data.shape[1])
return np.sqrt(mal * np.sum(np.square(data - av)))
[docs]def spectral(data, lambd, *kwargs):
""" Compute spectral contrast of image
Performs bandpass filtering in Fourier space according to optical
limit of detection system, approximated by twice the wavelength.
Parameters
----------
data : 2d ndarray
the image to compute the norm from
lambd : float
wavelength of the light in pixels
"""
# Set up fast fourier transform
# if not data.dtype == np.dtype(np.complex):
# data = np.array(data, dtype=np.complex)
# fftplan = fftw3.Plan(data.copy(), None, nthreads = _ncores,
# direction="forward", flags=_fftwflags)
# fftdata = np.zeros(data.shape, dtype=np.complex)
# fftplan.guru_execute_dft(data, fftdata)
# fftw.destroy_plan(fftplan)
fftdata = np.fft.fftn(data)
# Filter Fourier transform
fftdata[0, 0] = 0
kx = 2 * np.pi * np.fft.fftfreq(data.shape[0]).reshape(1, -1)
ky = 2 * np.pi * np.fft.fftfreq(data.shape[1]).reshape(-1, 1)
kmax = (2 * np.pi) / (2 * lambd)
fftdata[np.where(kx**2 + ky**2 > kmax**2)] = 0
spec = np.sum(np.log(1 + np.abs(fftdata))) / np.sqrt(np.prod(data.shape))
return spec