def _build_graph(self, x_train, hm_reg_scale, init_gamma, height_map_noise):
input_img, depth_map = x_train
with tf.device('/device:GPU:0'):
height_map = optics.get_fourier_height_map(self.wave_resolution[0],
0.75,
height_map_regularizer=optics.laplace_l1_regularizer(
hm_reg_scale))
optical_system = optics.SingleLensSetup(height_map=height_map,
wave_resolution=self.wave_resolution,
wave_lengths=self.wave_lengths,
sensor_distance=self.sensor_distance,
sensor_resolution=(self.patch_size, self.patch_size),
input_sample_interval=self.sampling_interval,
refractive_idcs=self.refractive_idcs,
height_tolerance=height_map_noise,
use_planar_incidence=False,
depth_bins=self.depth_bins,
upsample=False,
psf_resolution=self.wave_resolution,
target_distance=None)
sensor_img = optical_system.get_sensor_img(input_img=input_img,
noise_sigma=None,
depth_dependent=True,
depth_map=depth_map)
U_net = net.U_Net()
output_image = U_net.build(sensor_img)
optics.attach_summaries('output_image', output_image, image=True, log_image=False)
return output_image
def _build_graph(self, x_train, global_step, hm_reg_scale, init_gamma, height_map_noise, learned_target_depth, hm_init_type='random_normal'):
input_img, depth_map = x_train
with tf.device('/device:GPU:0'):
with tf.variable_scope("optics"):
height_map = optics.get_fourier_height_map(self.wave_resolution[0],
0.625,
height_map_regularizer=optics.laplace_l1_regularizer(hm_reg_scale))
target_depth_initializer = tf.constant_initializer(1.)
target_depth = tf.get_variable(name="target_depth",
shape=(),
dtype=tf.float32,
trainable=True,
initializer=target_depth_initializer)
target_depth = tf.square(target_depth)
tf.summary.scalar('target_depth', target_depth)
optical_system = optics.SingleLensSetup(height_map=height_map,
wave_resolution=self.wave_resolution,
wave_lengths=self.wave_lengths,
sensor_distance=self.distance,
sensor_resolution=(self.patch_size, self.patch_size),
input_sample_interval=self.input_sample_interval,
refractive_idcs=self.refractive_idcs,
height_tolerance=height_map_noise,
use_planar_incidence=False,
depth_bins=self.depth_bins,
upsample=False,
psf_resolution=self.wave_resolution,
target_distance=target_depth)
noise_sigma = tf.random_uniform(minval=0.001, maxval=0.02, shape=[])
sensor_img = optical_system.get_sensor_img(input_img=input_img,
noise_sigma=noise_sigma,
depth_dependent=True,
depth_map=depth_map)
output_image = tf.cast(sensor_img, tf.float32)
# Now we deconvolve
pad_width = output_image.shape.as_list()[1]//2
output_image = tf.pad(output_image, [[0,0],[pad_width, pad_width],[pad_width,pad_width],[0,0]], mode='SYMMETRIC')
output_image = deconv.inverse_filter(output_image, output_image, optical_system.target_psf, init_gamma=init_gamma)
output_image = output_image[:,pad_width:-pad_width,pad_width:-pad_width,:]
optics.attach_summaries('output_image', output_image, image=True, log_image=False)
return output_image