Updating a unit test.

odak/learn/tools/matrix.py

@@ -3,24 +3,27 @@
 import torch.nn
 
 
-def quantize(image_field, bits=4):
+def quantize(image_field, bits = 4, limits = [0., 1.]):
     """ 
     Definition to quantize a image field (0-255, 8 bit) to a certain bits level.
 
     Parameters
     ----------
     image_field : torch.tensor
-                  Input image field.
+                  Input image field between any range.
     bits        : int
                   A value in between 0 to 8. Can not be zero.
+    limits      : list
+                  The minimum and maximum of the image_field variable.
 
     Returns
     ----------
     new_field   : torch.tensor
                   Quantized image field.
     """
-    divider = 2**(8-bits)
-    new_field = image_field/divider
+    normalized_field = (image_field - limits[0]) / (limits[1] - limits[0])
+    divider = 2 ** bits
+    new_field = normalized_field * divider
     new_field = new_field.int()
     return new_field
 

odak/learn/wave/optimizers.py

@@ -3,7 +3,7 @@
 import numpy as np
 from tqdm import tqdm
 from .util import wavenumber, generate_complex_field, calculate_amplitude, calculate_phase
-from ..tools import torch_load, multi_scale_total_variation_loss
+from ..tools import torch_load, multi_scale_total_variation_loss, quantize
 from .propagators import propagator
 
 
@@ -386,7 +386,7 @@ def gradient_descent(self, number_of_iterations=100, weights=[1., 1., 0., 0.]):
         return hologram_phases.detach()
 
 
-    def optimize(self, number_of_iterations=100, weights=[1., 1., 1.]):
+    def optimize(self, number_of_iterations=100, weights=[1., 1., 1.], bits = 8):
         """
         Function to optimize multiplane phase-only holograms.
 
@@ -396,6 +396,8 @@ def optimize(self, number_of_iterations=100, weights=[1., 1., 1.]):
                                      Number of iterations.
         weights                    : list
                                      Loss weights.
+        bits                       : int
+                                     Quantizes the hologram using the given bits and reconstructs.
 
         Returns
         -------
@@ -409,6 +411,7 @@ def optimize(self, number_of_iterations=100, weights=[1., 1., 1.]):
                                                 number_of_iterations=number_of_iterations,
                                                 weights=weights
                                                )
+        hologram_phases = quantize(hologram_phases % (2 * np.pi), bits = bits, limits = [0., 2 * np.pi]) / 2 ** bits * 2 * np.pi
         torch.no_grad()
         reconstruction_intensities = self.propagator.reconstruct(hologram_phases)
         laser_powers = self.propagator.get_laser_powers()

test/test_learn_wave_propagator.py

@@ -11,13 +11,13 @@ def test():
     pixel_pitch = 3.74e-6
     number_of_frames = 3
     number_of_depth_layers = 3
-    volume_depth = 1e-2
-    image_location_offset = 5e-3
+    volume_depth = 5e-3
+    image_location_offset = 0.
     propagation_type = 'Bandlimited Angular Spectrum'
     propagator_type = 'forward'
     laser_channel_power = None
     aperture = None
-    aperture_size = None
+    aperture_size = 1800
     method = 'conventional'
     device = torch.device('cpu')
     hologram_phases_filename = './test/data/sample_hologram.png'
@@ -37,6 +37,8 @@ def test():
                                             propagation_type = propagation_type,
                                             propagator_type = propagator_type,
                                             laser_channel_power = laser_channel_power,
+                                            aperture_size = aperture_size,
+                                            aperture = aperture,
                                             method = method,
                                             device = device
                                            )