def ApplyDepthImageDistortions(depth_images,
random_noise_level=0.05,
random_noise_apply_probability=0.5,
scaling_noise=True,
gamma_shape=1000.0,
gamma_scale_inverse=1000.0,
min_depth_allowed=0.25,
max_depth_allowed=2.5):
"""Apply photometric distortions to the input depth images.
Args:
depth_images: Tensor of shape [batch_size, h, w, 1] containing a batch of
depth images to apply the random photometric distortions to.
random_noise_level: The standard deviation of the Gaussian distribution for
the noise that is applied to the depth image. When 0.0, then no noise is
applied.
random_noise_apply_probability: Probability of applying additive random
noise to the images.
scaling_noise: If True; sample a random variable from a Gamma distribution
to scale the depth image.
gamma_shape: Float; shape parameter of a Gamma distribution.
gamma_scale_inverse: Float; inverse of scale parameter of a Gamma
distribution.
min_depth_allowed: Float; minimum clip value for depth.
max_depth_allowed: Float; max clip value for depth.
Returns:
depth_images: Tensor of shape [batch_size, h, w, 1] containing a
batch of images resulting from applying random photometric distortions to
the inputs.
"""
assert depth_images[0].get_shape().as_list()[-1] == 1
with tf.variable_scope('distortions_depth_images'):
# Add random Gaussian noise.
if random_noise_level:
for i, image in enumerate(depth_images):
img_shape = tf.shape(image)
rnd_noise = tf.random_normal(img_shape,
stddev=random_noise_level)
def ReturnImageTensor(value):
return lambda: value
if scaling_noise:
alpha = tf.random_gamma([], gamma_shape,
gamma_scale_inverse)
image = tf.cond(
tf.reduce_all(
tf.greater(tf.random.uniform([1]),
random_noise_apply_probability)),
ReturnImageTensor(image),
ReturnImageTensor(alpha * image + rnd_noise))
depth_images[i] = tf.reshape(image, img_shape)
# Clip to valid range.
for i, image in enumerate(depth_images):
depth_images[i] = tf.clip_by_value(image, min_depth_allowed,
max_depth_allowed)
return depth_images
def random_exponential(shape, rate=1.0, dtype=tf.float32, seed=None):
"""
Helper function to sample from the exponential distribution, which is not
included in core TensorFlow.
"""
return tf.random_gamma(shape,
alpha=1,
beta=1. / rate,
dtype=dtype,
seed=seed)