def get_fmt(array, high_pass=0.15):
"""
Follows this algoritm:
* FFT with centered frecuencies
* convolucionar la magnitud con un filtro high pass
* Logpolar
* FFT with centered frecuencies
"""
fourier = get_shifted_dft(array)
magnitude = np.abs(fourier)
high_passed = get_high_pass_filter(magnitude, high_pass, 0)
logpolar = get_logpolar(high_passed, 3)
fmt = get_shifted_dft(logpolar)
return fmt
def get_initial_phase(hologram, wavelength=1):
fringes = hologram / float(hologram.ptp())
fringes = (fringes + fringes.min()) * 2 - 1
image_dft = get_shifted_dft(fringes)
cos_alpha, cos_beta = calculate_director_cosines(image_dft, wavelength, (1, 1))
maxrow = fringes.shape[0]
maxcol = fringes.shape[1]
row, col = np.ogrid[:maxrow:1., :maxcol:1.]
plane = cos_alpha * col + cos_beta * row
def fitness((alpha, beta, offset)):
alpha = (sigmoid(alpha) - 0.5) * 2 ** 2
beta = (sigmoid(beta) - 0.5) * 2 ** 2
offset = sigmoid(offset) * tau / 2
correction = cos_alpha * col + cos_beta * row + offset
sintetic_fringes = get_fringes(plane + correction, wavelength)
score = np.sum(np.fabs(fringes - sintetic_fringes))
print(alpha, beta, offset, score)
return score
alpha, beta, offset = generic_minimizer(fitness, [0, 0, 0])
alpha = (sigmoid(alpha) - 0.5) * 2 ** 2
beta = (sigmoid(beta) - 0.5) * 2 ** 2
offset = sigmoid(offset) * tau / 2
correction = alpha * col + beta * row + offset
plane = plane + correction
return plane
def get_initial_phase(hologram, wavelength=1):
fringes = hologram / float(hologram.ptp())
fringes = (fringes + fringes.min()) * 2 - 1
image_dft = get_shifted_dft(fringes)
cos_alpha, cos_beta = calculate_director_cosines(image_dft, wavelength,
(1, 1))
maxrow = fringes.shape[0]
maxcol = fringes.shape[1]
row, col = np.ogrid[:maxrow:1., :maxcol:1.]
plane = cos_alpha * col + cos_beta * row
def fitness((alpha, beta, offset)):
alpha = (sigmoid(alpha) - 0.5) * 2**2
beta = (sigmoid(beta) - 0.5) * 2**2
offset = sigmoid(offset) * tau / 2
correction = cos_alpha * col + cos_beta * row + offset
sintetic_fringes = get_fringes(plane + correction, wavelength)
score = np.sum(np.fabs(fringes - sintetic_fringes))
print(alpha, beta, offset, score)
return score
alpha, beta, offset = generic_minimizer(fitness, [0, 0, 0])
alpha = (sigmoid(alpha) - 0.5) * 2**2
beta = (sigmoid(beta) - 0.5) * 2**2
offset = sigmoid(offset) * tau / 2
correction = alpha * col + beta * row + offset
plane = plane + correction
return plane
def phase_detection(masked_spectrum, distance):
shape = masked_spectrum.shape
shape_center = [dim / 2 for dim in shape]
filtered_hologram = get_shifted_idft(masked_spectrum)
focus_mask = get_mask(shape, np.ones(20), shape_center)
focus_feature = filtered_hologram * focus_mask
feature_spectrum = get_shifted_dft(focus_feature)
propagation_array = get_propagation_array(shape, distance)
propagated_spectrum = propagation_array * feature_spectrum
propagated_hologram = get_shifted_idft(propagated_spectrum)
radious = 50
separation = 400
window = np.ones(radious)
spectrum2sensor = np.angle
spectrum2sensor = np.abs
spectrum2sensor = get_intensity
spectrum2sensor = normalize
left_center = shape_center[0], shape_center[1] - separation / 2
left_mask = get_mask(shape, window, left_center)
# left_bundle = left_mask * propagated_spectrum
left_bundle = left_mask * propagated_hologram
left_sensor = spectrum2sensor(get_shifted_dft(left_bundle))
left_peak = get_circles(left_sensor, 1, 50)[0][1]
right_center = shape_center[0], shape_center[1] + separation / 2
right_mask = get_mask(shape, window, right_center)
right_bundle = right_mask * propagated_hologram
right_sensor = spectrum2sensor(get_shifted_dft(right_bundle))
right_peak = get_circles(right_sensor, 1, 50)[0][1]
# showimage(normalize(np.maximum(left_bundle, right_bundle)))
# showimage(normalize(np.maximum(left_sensor, right_sensor)))
distance = get_distance(left_peak, right_peak)
fitness = distance
return fitness
def phase_detection(masked_spectrum, distance):
shape = masked_spectrum.shape
shape_center = [dim / 2 for dim in shape]
filtered_hologram = get_shifted_idft(masked_spectrum)
focus_mask = get_mask(shape, np.ones(20), shape_center)
focus_feature = filtered_hologram * focus_mask
feature_spectrum = get_shifted_dft(focus_feature)
propagation_array = get_propagation_array(shape, distance)
propagated_spectrum = propagation_array * feature_spectrum
propagated_hologram = get_shifted_idft(propagated_spectrum)
radious = 50
separation = 400
window = np.ones(radious)
spectrum2sensor = np.angle
spectrum2sensor = np.abs
spectrum2sensor = get_intensity
spectrum2sensor = normalize
left_center = shape_center[0], shape_center[1] - separation / 2
left_mask = get_mask(shape, window, left_center)
# left_bundle = left_mask * propagated_spectrum
left_bundle = left_mask * propagated_hologram
left_sensor = spectrum2sensor(get_shifted_dft(left_bundle))
left_peak = get_circles(left_sensor, 1, 50)[0][1]
right_center = shape_center[0], shape_center[1] + separation / 2
right_mask = get_mask(shape, window, right_center)
right_bundle = right_mask * propagated_hologram
right_sensor = spectrum2sensor(get_shifted_dft(right_bundle))
right_peak = get_circles(right_sensor, 1, 50)[0][1]
# showimage(normalize(np.maximum(left_bundle, right_bundle)))
# showimage(normalize(np.maximum(left_sensor, right_sensor)))
distance = get_distance(left_peak, right_peak)
fitness = distance
return fitness
def get_spectrum(rhologram, **kw):
spectrum = get_shifted_dft(rhologram)
return spectrum
def spectrum(self):
print("Spectrum (dft(hologram))")
if self.use_refbeam:
return get_shifted_dft(self.hologram)
else:
return self.ispectrum
def ispectrum(self):
print("DFT(image)")
return get_shifted_dft(self.image)
def get_spectrum(rhologram, **kw):
spectrum = get_shifted_dft(rhologram)
return spectrum
def spectrum(self):
print("Spectrum (dft(hologram))")
if self.use_refbeam:
return get_shifted_dft(self.hologram)
else:
return self.ispectrum
def ispectrum(self):
print("DFT(image)")
return get_shifted_dft(self.image)
