def get_lowpass_mask(shape, radius=0.2, softness=0):
radius = round(min(shape) * radius)
window = np.kaiser(radius, softness)
mask = get_mask(shape, window)
return mask
@methods
def get_var(masked_spectrum, distance, wavelength, (dx, dy)):
propagation_array = get_propagation_array(masked_spectrum.shape, distance,
wavelength, (dx, dy))
propagated = propagation_array * masked_spectrum
reconstructed = get_shifted_idft(propagated)
module = get_module(reconstructed)
module_zone = get_best_contrast_zone(module)
# showimage(normalize(module_zone))
fitness = module_zone.var()
return fitness
@methods
def get_diff_var(masked_spectrum, distance, wavelength, (dx, dy)):
fitness = get_var(masked_spectrum, distance, wavelength, (dx, dy))
fitness -= get_var(masked_spectrum, -distance, wavelength, (dx, dy))
return fitness
@methods
@cache.hybrid
def module(self):
print("Module")
return get_module(self.reconstructed)
def module(self):
print("Module")
return get_module(self.reconstructed)
def get_lowpass_mask(shape, radius=0.2, softness=0):
radius = round(min(shape) * radius)
window = np.kaiser(radius, softness)
mask = get_mask(shape, window)
return mask
@methods
def get_var(masked_spectrum, distance, wavelength, (dx, dy)):
propagation_array = get_propagation_array(masked_spectrum.shape,
distance, wavelength, (dx, dy))
propagated = propagation_array * masked_spectrum
reconstructed = get_shifted_idft(propagated)
module = get_module(reconstructed)
module_zone = get_best_contrast_zone(module)
# showimage(normalize(module_zone))
fitness = module_zone.var()
return fitness
@methods
def get_diff_var(masked_spectrum, distance, wavelength, (dx, dy)):
fitness = get_var(masked_spectrum, distance, wavelength, (dx, dy))
fitness -= get_var(masked_spectrum, -distance, wavelength, (dx, dy))
return fitness
@methods
@cache.hybrid
