def _fit_spline(train_points, train_values, order):
# shapes
train_points_shape = train_points.shape
batch_shape = list(train_points_shape[:-2])
num_batch_dims = len(batch_shape)
n = train_points_shape[-2]
pd = train_values.shape[-1]
# BS x N x 1
c = train_points
# BS x N x PD
f_ = train_values
# BS x N x N
matrix_a = _phi(_pairwise_distance(c, c), order)
# BS x N x 1
ones = _ivy.ones_like(c[..., :1])
# BS x N x 2
matrix_b = _ivy.concatenate([c, ones], -1)
# BS x 2 x N
matrix_b_trans = _ivy.transpose(
matrix_b,
list(range(num_batch_dims)) + [num_batch_dims + 1, num_batch_dims])
# BS x N+2 x N
left_block = _ivy.concatenate([matrix_a, matrix_b_trans], -2)
# BS x 2 x 2
lhs_zeros = _ivy.zeros(batch_shape + [2, 2])
# BS x N+2 x 2
right_block = _ivy.concatenate([matrix_b, lhs_zeros], -2)
# BS x N+2 x N+2
lhs = _ivy.concatenate([left_block, right_block], -1)
# BS x 2 x PD
rhs_zeros = _ivy.zeros(batch_shape + [2, pd])
# BS x N+2 x PD
rhs = _ivy.concatenate([f_, rhs_zeros], -2)
# BS x N+2 x PD
w_v = _ivy.matmul(_ivy.pinv(lhs), rhs)
# BS x N x PD
w = w_v[..., :n, :]
# BS x 2 x PD
v = w_v[..., n:, :]
# BS x N x PD, BS x 2 x PD
return w, v
def _update_seq_info_for_window(self, seq_info):
if not ivy.exists(seq_info):
return
seq_idx = int(seq_info.seq_idx[0])
seq_len = int(seq_info.length[0])
new_len = self._compute_seq_len(seq_idx, seq_len,
self._spec.containers_to_skip)
seq_info = seq_info.copy()
seq_info.length = ivy.ones_like(seq_info.length) * new_len
return seq_info
def __init__(self,
img_mean: ivy.Array,
cam_rel_mat: ivy.Array,
img_var: ivy.Array = None,
validity_mask: ivy.Array = None,
pose_mean: ivy.Array = None,
pose_cov: ivy.Array = None):
"""
Create esm image measurement container
:param img_mean: Camera-relative co-ordinates and image features
*[batch_size, timesteps, height, width, 3 + feat]*
:type: img_mean: array
:param cam_rel_mat: The pose of the camera relative to the current agent pose, in matrix form
*[batch_size, timesteps, 3, 4]*
:type cam_rel_mat: array
:param img_var: Image depth and feature variance values, assumed all zero if None.
*[batch_size, timesteps, height, width, 1 + feat]*
:type: img_var: array, optional
:param validity_mask: Validity mask, for which pixels should be considered. Assumed all valid if None
*[batch_size, timesteps, height, width, 1]*
:type validity_mask: array, optional
:param pose_mean: The pose of the camera relative to the current agent pose, in rotation vector pose form.
Inferred from cam_rel_mat if None. *[batch_size, timesteps, 6]*
:type pose_mean: array, optional
:param pose_cov: The convariance of the camera relative pose, in rotation vector form. Assumed all zero if None.
*[batch_size, timesteps, 6, 6]*
:type pose_cov: array, optional
"""
img_mean = _pad_to_batch_n_time_dims(img_mean, 5)
cam_rel_mat = _pad_to_batch_n_time_dims(cam_rel_mat, 4)
self['img_mean'] = img_mean
self['cam_rel_mat'] = cam_rel_mat
if img_var is None:
img_var = ivy.zeros_like(img_mean)
else:
img_var = _pad_to_batch_n_time_dims(img_var, 5)
self['img_var'] = img_var
if validity_mask is None:
validity_mask = ivy.ones_like(img_mean[..., 0:1])
else:
validity_mask = _pad_to_batch_n_time_dims(validity_mask, 5)
self['validity_mask'] = validity_mask
if pose_mean is None:
pose_mean = ivy_mech.mat_pose_to_rot_vec_pose(cam_rel_mat)
else:
pose_mean = _pad_to_batch_n_time_dims(pose_mean, 3)
self['pose_mean'] = pose_mean
if pose_cov is None:
pose_cov = ivy.tile(ivy.expand_dims(ivy.zeros_like(pose_mean), -1), (1, 1, 1, 6))
else:
pose_cov = _pad_to_batch_n_time_dims(pose_cov, 4)
self['pose_cov'] = pose_cov
def create_uniform_pixel_coords_image(image_dims, batch_shape=None, normalized=False, dev_str=None):
"""
Create image of homogeneous integer :math:`xy` pixel co-ordinates :math:`\mathbf{X}\in\mathbb{Z}^{h×w×3}`, stored
as floating point values. The origin is at the top-left corner of the image, with :math:`+x` rightwards, and
:math:`+y` downwards. The final homogeneous dimension are all ones. In subsequent use of this image, the depth of
each pixel can be represented using this same homogeneous representation, by simply scaling each 3-vector by the
depth value. The final dimension therefore always holds the depth value, while the former two dimensions hold depth
scaled pixel co-ordinates.\n
`[reference] <localhost:63342/ivy/docs/source/references/mvg_textbook.pdf#page=172>`_
deduction from top of page 154, section 6.1, equation 6.1
:param image_dims: Image dimensions.
:type image_dims: sequence of ints.
:param batch_shape: Shape of batch. Assumed no batch dimensions if None.
:type batch_shape: sequence of ints, optional
:param normalized: Whether to normalize x-y pixel co-ordinates to the range 0-1.
:type normalized: bool
:param dev_str: device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
:type dev_str: str, optional
:return: Image of homogeneous pixel co-ordinates *[batch_shape,height,width,3]*
"""
# shapes as lists
batch_shape = [] if batch_shape is None else batch_shape
batch_shape = list(batch_shape)
image_dims = list(image_dims)
# other shape specs
num_batch_dims = len(batch_shape)
flat_shape = [1] * num_batch_dims + image_dims + [3]
tile_shape = batch_shape + [1] * 3
# H x W x 1
pixel_x_coords = _ivy.cast(_ivy.reshape(_ivy.tile(_ivy.arange(image_dims[1], dev_str=dev_str), [image_dims[0]]),
(image_dims[0], image_dims[1], 1)), 'float32')
if normalized:
pixel_x_coords = pixel_x_coords / (float(image_dims[1]) + MIN_DENOMINATOR)
# W x H x 1
pixel_y_coords_ = _ivy.cast(_ivy.reshape(_ivy.tile(_ivy.arange(image_dims[0], dev_str=dev_str), [image_dims[1]]),
(image_dims[1], image_dims[0], 1)), 'float32')
# H x W x 1
pixel_y_coords = _ivy.transpose(pixel_y_coords_, (1, 0, 2))
if normalized:
pixel_y_coords = pixel_y_coords / (float(image_dims[0]) + MIN_DENOMINATOR)
# H x W x 1
ones = _ivy.ones_like(pixel_x_coords, dev_str=dev_str)
# BS x H x W x 3
return _ivy.tile(_ivy.reshape(_ivy.concatenate((pixel_x_coords, pixel_y_coords, ones), -1),
flat_shape), tile_shape)
def _get_dummy_obs(batch_size, num_frames, num_cams, image_dims, num_feature_channels, dev_str='cpu', ones=False,
empty=False):
uniform_pixel_coords =\
ivy_vision.create_uniform_pixel_coords_image(image_dims, [batch_size, num_frames], dev_str=dev_str)
img_meas = dict()
for i in range(num_cams):
validity_mask = ivy.ones([batch_size, num_frames] + image_dims + [1], dev_str=dev_str)
if ones:
img_mean = ivy.concatenate((uniform_pixel_coords[..., 0:2], ivy.ones(
[batch_size, num_frames] + image_dims + [1 + num_feature_channels], dev_str=dev_str)), -1)
img_var = ivy.ones(
[batch_size, num_frames] + image_dims + [3 + num_feature_channels], dev_str=dev_str)*1e-3
pose_mean = ivy.zeros([batch_size, num_frames, 6], dev_str=dev_str)
pose_cov = ivy.ones([batch_size, num_frames, 6, 6], dev_str=dev_str)*1e-3
else:
img_mean = ivy.concatenate((uniform_pixel_coords[..., 0:2], ivy.random_uniform(
1e-3, 1, [batch_size, num_frames] + image_dims + [1 + num_feature_channels], dev_str=dev_str)), -1)
img_var = ivy.random_uniform(
1e-3, 1, [batch_size, num_frames] + image_dims + [3 + num_feature_channels], dev_str=dev_str)
pose_mean = ivy.random_uniform(1e-3, 1, [batch_size, num_frames, 6], dev_str=dev_str)
pose_cov = ivy.random_uniform(1e-3, 1, [batch_size, num_frames, 6, 6], dev_str=dev_str)
if empty:
img_var = ivy.ones_like(img_var) * 1e12
validity_mask = ivy.zeros_like(validity_mask)
img_meas['dummy_cam_{}'.format(i)] =\
{'img_mean': img_mean,
'img_var': img_var,
'validity_mask': validity_mask,
'pose_mean': pose_mean,
'pose_cov': pose_cov,
'cam_rel_mat': ivy.identity(4, batch_shape=[batch_size, num_frames], dev_str=dev_str)[..., 0:3, :]}
if ones:
control_mean = ivy.zeros([batch_size, num_frames, 6], dev_str=dev_str)
control_cov = ivy.ones([batch_size, num_frames, 6, 6], dev_str=dev_str)*1e-3
else:
control_mean = ivy.random_uniform(1e-3, 1, [batch_size, num_frames, 6], dev_str=dev_str)
control_cov = ivy.random_uniform(1e-3, 1, [batch_size, num_frames, 6, 6], dev_str=dev_str)
return Container({'img_meas': img_meas,
'control_mean': control_mean,
'control_cov': control_cov,
'agent_rel_mat': ivy.identity(4, batch_shape=[batch_size, num_frames],
dev_str=dev_str)[..., 0:3, :]})
def make_coordinates_homogeneous(coords, batch_shape=None):
"""
Append ones to array of non-homogeneous co-ordinates to make them homogeneous.
:param coords: Array of non-homogeneous co-ordinates *[batch_shape,d]*
:type coords: array
:param batch_shape: Shape of batch. Inferred from inputs if None.
:type batch_shape: sequence of ints, optional
:return: Array of Homogeneous co-ordinates *[batch_shape,d+1]*
"""
if batch_shape is None:
batch_shape = coords.shape[:-1]
# shapes as list
batch_shape = list(batch_shape)
# BS x 1
ones = _ivy.ones_like(coords[..., 0:1])
# BS x (D+1)
return _ivy.concatenate((coords, ones), -1)
def sample_spline_path(anchor_points, anchor_vals, sample_points, order=3):
"""
Sample spline path, given sample locations for path defined by the anchor locations and points.
`[reference] <https://github.com/tensorflow/addons/blob/v0.11.2/tensorflow_addons/image/interpolate_spline.py>`_
:param anchor_points: Anchor locations between 0-1 (regular spacing not necessary) *[batch_shape,num_anchors,1]*
:type anchor_points: array
:param anchor_vals: Anchor points along the spline path, in path space *[batch_shape,num_anchors,path_dim]*
:type anchor_vals: array
:param sample_points: Sample locations between 0-1 *[batch_shape,num_samples,1]*
:type sample_points: array
:param order: Order of the spline path interpolation
:type order: float
:return: Spline path sampled at sample_locations, giving points in path space *[batch_shape,num_samples,path_dim]*
"""
# BS x N x PD, BS x 2 x PD
w, v = _fit_spline(anchor_points, anchor_vals, order)
# Kernel term
# BS x NS x N
pairwise_dists = _pairwise_distance(sample_points, anchor_points)
phi_pairwise_dists = _phi(pairwise_dists, order)
# BS x NS x PD
rbf_term = _ivy.matmul(phi_pairwise_dists, w)
# Polynomial / linear term.
# BS x NS x 2
query_points_pad = _ivy.concatenate(
[sample_points, _ivy.ones_like(sample_points[..., :1])], -1)
# BS x NS x PD
linear_term = _ivy.matmul(query_points_pad, v)
return rbf_term + linear_term
def _convert_images_to_omni_observations(self, measurements,
uniform_sphere_pixel_coords,
holes_prior, batch_size,
num_timesteps, num_cams,
image_dims):
"""
Convert image to omni-directional measurements
:param measurements: perspective captured images and relative poses container
:param uniform_sphere_pixel_coords: Uniform sphere pixel coords *[batch_size, num_timesteps, oh, ow, 3]*
:param holes_prior: Prior for quantization holes *[batch_size, num_timesteps, oh, ow, 1+f]*
:param batch_size: Size of batch
:param num_timesteps: Number of frames
:param num_cams: Number of cameras
:param image_dims: Image dimensions
:return: *[batch_size, n, oh, ow, 3+f]* *[batch_size, n, oh, ow, 3+f]*
"""
# coords from all scene cameras wrt world
images_list = list()
images_var_list = list()
cam_rel_poses_list = list()
cam_rel_poses_cov_list = list()
cam_rel_mats_list = list()
validity_mask_list = list()
for key, item in measurements.to_iterator():
if key == 'img_mean':
# B x N x 1 x H x W x (3+f)
images_list.append(ivy.expand_dims(item, 2))
elif key == 'img_var':
# B x N x 1 x H x W x (3+f)
images_var_list.append(ivy.expand_dims(item, 2))
elif key == 'pose_mean':
# B x N x 1 x 6
cam_rel_poses_list.append(ivy.expand_dims(item, 2))
elif key == 'pose_cov':
# B x N x 1 x 6 x 6
cam_rel_poses_cov_list.append(ivy.expand_dims(item, 2))
elif key == 'cam_rel_mat':
# B x N x 1 x 3 x 4
cam_rel_mats_list.append(ivy.expand_dims(item, 2))
elif key == 'validity_mask':
validity_mask_list.append(ivy.expand_dims(item, 2))
else:
raise Exception('Invalid image key: {}'.format(key))
# B x N x C x H x W x (3+f)
images = ivy.concatenate(images_list, 2)
# B x N x C x H x W x (3+f)
var_to_project = ivy.concatenate(images_var_list, 2)
# B x N x C x 6
cam_to_cam_poses = ivy.concatenate(cam_rel_poses_list, 2)
# B x N x C x 3 x 4
cam_to_cam_mats = ivy.concatenate(cam_rel_mats_list, 2)
# B x N x C x 6 x 6
cam_to_cam_pose_covs = ivy.concatenate(cam_rel_poses_cov_list, 2)
# B x N x C x 1
validity_masks = ivy.concatenate(validity_mask_list, 2) > 0
# B x N x OH x OW x (3+f)
holes_prior_var = ivy.ones(
[batch_size, num_timesteps] + self._sphere_img_dims +
[3 + self._feat_dim],
dev_str=self._dev_str) * 1e12
# reset invalid regions to prior
# B x N x C x H x W x (3+f)
images = ivy.where(
validity_masks, images,
ivy.concatenate(
(images[..., 0:2],
ivy.zeros_like(images[..., 2:], dev_str=self._dev_str)), -1))
# B x N x C x H x W x (3+f)
var_to_project = ivy.where(
validity_masks, var_to_project,
ivy.ones_like(var_to_project, dev_str=self._dev_str) * 1e12)
# B x N x OH x OW x (3+f) # B x N x OH x OW x (3+f)
return self._frame_to_omni_frame_projection(
cam_to_cam_poses, cam_to_cam_mats, uniform_sphere_pixel_coords,
images[..., 0:3], images[..., 3:], cam_to_cam_pose_covs,
var_to_project, holes_prior, holes_prior_var, batch_size,
num_timesteps, num_cams, image_dims)
def main(f=None):
# Framework Setup #
# ----------------#
# choose random framework
f = choose_random_framework() if f is None else f
set_framework(f)
# Orientation #
# ------------#
# rotation representations
# 3
rot_vec = ivy.array([0., 1., 0.])
# 3 x 3
rot_mat = ivy_mech.rot_vec_to_rot_mat(rot_vec)
# 3
euler_angles = ivy_mech.rot_mat_to_euler(rot_mat, 'zyx')
# 4
quat = ivy_mech.euler_to_quaternion(euler_angles)
# 4
axis_and_angle = ivy_mech.quaternion_to_axis_angle(quat)
# 3
rot_vec_again = axis_and_angle[..., :-1] * axis_and_angle[..., -1:]
# Pose #
# -----#
# pose representations
# 3
position = ivy.ones_like(rot_vec)
# 6
rot_vec_pose = ivy.concatenate((position, rot_vec), 0)
# 3 x 4
mat_pose = ivy_mech.rot_vec_pose_to_mat_pose(rot_vec_pose)
# 6
euler_pose = ivy_mech.mat_pose_to_euler_pose(mat_pose)
# 7
quat_pose = ivy_mech.euler_pose_to_quaternion_pose(euler_pose)
# 6
rot_vec_pose_again = ivy_mech.quaternion_pose_to_rot_vec_pose(quat_pose)
# Position #
# ---------#
# conversions of positional representation
# 3
cartesian_coord = ivy.random_uniform(0., 1., (3, ))
# 3
polar_coord = ivy_mech.cartesian_to_polar_coords(cartesian_coord)
# 3
cartesian_coord_again = ivy_mech.polar_to_cartesian_coords(polar_coord)
# cartesian co-ordinate frame-of-reference transformations
# 3 x 4
trans_mat = ivy.random_uniform(0., 1., (3, 4))
# 4
cartesian_coord_homo = ivy_mech.make_coordinates_homogeneous(
cartesian_coord)
# 3
trans_cartesian_coord = ivy.matmul(
trans_mat, ivy.expand_dims(cartesian_coord_homo, -1))[:, 0]
# 4
trans_cartesian_coord_homo = ivy_mech.make_coordinates_homogeneous(
trans_cartesian_coord)
# 4 x 4
trans_mat_homo = ivy_mech.make_transformation_homogeneous(trans_mat)
# 3 x 4
inv_trans_mat = ivy.inv(trans_mat_homo)[0:3]
# 3
cartesian_coord_again = ivy.matmul(
inv_trans_mat, ivy.expand_dims(trans_cartesian_coord_homo, -1))[:, 0]
# message
print('End of Run Through Demo!')
def cuboid_signed_distances(cuboid_ext_mats,
cuboid_dims,
query_positions,
batch_shape=None):
"""
Return the signed distances of a set of query points from the cuboid surfaces.\n
`[reference] <https://www.iquilezles.org/www/articles/distfunctions/distfunctions.htm>`_
:param cuboid_ext_mats: Extrinsic matrices of the cuboids *[batch_shape,num_cuboids,3,4]*
:type cuboid_ext_mats: array
:param cuboid_dims: Dimensions of the cuboids, in the order x, y, z *[batch_shape,num_cuboids,3]*
:type cuboid_dims: array
:param query_positions: Points for which to query the signed distances *[batch_shape,num_points,3]*
:type query_positions: array
:param batch_shape: Shape of batch. Assumed no batches if None.
:type batch_shape: sequence of ints, optional
:return: The distances of the query points from the closest cuboid surface *[batch_shape,num_points,1]*
"""
if batch_shape is None:
batch_shape = cuboid_ext_mats.shape[:-3]
# shapes as list
batch_shape = list(batch_shape)
num_batch_dims = len(batch_shape)
batch_dims_for_trans = list(range(num_batch_dims))
num_cuboids = cuboid_ext_mats.shape[-3]
num_points = query_positions.shape[-2]
# BS x 3 x NP
query_positions_trans = _ivy.transpose(
query_positions,
batch_dims_for_trans + [num_batch_dims + 1, num_batch_dims])
# BS x 1 x NP
ones = _ivy.ones_like(query_positions_trans[..., 0:1, :])
# BS x 4 x NP
query_positions_trans_homo = _ivy.concatenate(
(query_positions_trans, ones), -2)
# BS x NCx3 x 4
cuboid_ext_mats_flat = _ivy.reshape(cuboid_ext_mats, batch_shape + [-1, 4])
# BS x NCx3 x NP
rel_query_positions_trans_flat = _ivy.matmul(cuboid_ext_mats_flat,
query_positions_trans_homo)
# BS x NC x 3 x NP
rel_query_positions_trans = _ivy.reshape(
rel_query_positions_trans_flat,
batch_shape + [num_cuboids, 3, num_points])
# BS x NC x NP x 3
rel_query_positions = _ivy.transpose(
rel_query_positions_trans, batch_dims_for_trans +
[num_batch_dims, num_batch_dims + 2, num_batch_dims + 1])
q = _ivy.abs(rel_query_positions) - _ivy.expand_dims(cuboid_dims / 2, -2)
q_max_clipped = _ivy.maximum(q, 1e-12)
# BS x NC x NP x 1
q_min_clipped = _ivy.minimum(_ivy.reduce_max(q, -1, keepdims=True), 0.)
q_max_clipped_len = _ivy.reduce_sum(q_max_clipped**2, -1,
keepdims=True)**0.5
sdfs = q_max_clipped_len + q_min_clipped
# BS x NP x 1
return _ivy.reduce_min(sdfs, -3)
def _kalman_filter_on_measurement_sequence(
self, prev_fused_val, prev_fused_variance, hole_prior,
hole_prior_var, meas, meas_vars, uniform_sphere_pixel_coords,
agent_rel_poses, agent_rel_pose_covs, agent_rel_mats, batch_size,
num_timesteps):
"""
Perform kalman filter on measurement sequence
:param prev_fused_val: Fused value from previous timestamp *[batch_size, oh, ow, (3+f)]*
:param prev_fused_variance: Fused variance from previous timestamp *[batch_size, oh, ow, (3+f)]*
:param hole_prior: Prior for holes in quantization *[batch_size, oh, ow, (1+f)]*
:param hole_prior_var: Prior variance for holes in quantization *[batch_size, oh, ow, (3+f)]*
:param meas: Measurements *[batch_size, num_timesteps, oh, ow, (3+f)]*
:param meas_vars: Measurement variances *[batch_size, num_timesteps, oh, ow, (3+f)]*
:param uniform_sphere_pixel_coords: Uniform sphere pixel co-ordinates *[batch_size, oh, ow, 3]*
:param agent_rel_poses: Relative poses of agents to the previous step *[batch_size, num_timesteps, 6]*
:param agent_rel_pose_covs: Agent relative pose covariances *[batch_size, num_timesteps, 6, 6]*
:param agent_rel_mats: Relative transformations matrix of agents to the previous step
*[batch_size, num_timesteps, 3, 4]*
:param batch_size: Size of batch
:param num_timesteps: Number of frames
:return: list of *[batch_size, oh, ow, (3+f)]*, list of *[batch_size, oh, ow, (3+f)]*
"""
fused_list = list()
fused_variances_list = list()
for i in range(num_timesteps):
# project prior from previous frame #
# ----------------------------------#
# B x OH x OW x (3+F)
prev_prior = prev_fused_val
prev_prior_variance = prev_fused_variance
# B x 3 x 4
agent_rel_mat = agent_rel_mats[:, i]
# B x 6
agent_rel_pose = agent_rel_poses[:, i]
# B x 6 x 6
agent_rel_pose_cov = agent_rel_pose_covs[:, i]
# B x OH x OW x (3+F) B x OH x OW x (3+F)
fused_projected, fused_projected_variance = self._omni_frame_to_omni_frame_projection(
agent_rel_pose, agent_rel_mat, uniform_sphere_pixel_coords,
prev_prior[..., 0:2], prev_prior[..., 2:3],
prev_prior[..., 3:], agent_rel_pose_cov, prev_prior_variance,
hole_prior, hole_prior_var, batch_size)
# reset prior
# B x OH x OW x (3+F)
prior = fused_projected
prior_var = fused_projected_variance
# per-pixel fusion with measurements #
# -----------------------------------#
# extract slice for frame
# B x OH x OW x (3+F)
measurement = meas[:, i]
measurement_variance = meas_vars[:, i]
# fuse prior and measurement
# B x 2 x OH x OW x (3+F)
prior_and_meas = ivy.concatenate(
(ivy.expand_dims(prior, 1), ivy.expand_dims(measurement, 1)),
1)
prior_and_meas_variance = ivy.concatenate((ivy.expand_dims(
prior_var, 1), ivy.expand_dims(measurement_variance, 1)), 1)
# B x OH x OW x (3+F)
low_var_mask = ivy.reduce_sum(
ivy.cast(
prior_and_meas_variance <
ivy.expand_dims(hole_prior_var, 1) *
self._threshold_var_factor, 'int32'), 1) > 0
# B x 1 x OH x OW x (3+F) B x 1 x OH x OW x (3+F)
if self._with_depth_buffer:
# ToDo: make this more efficient
prior_low_var_mask = ivy.reduce_max(ivy.cast(
prior_var >= hole_prior_var * self._threshold_var_factor,
'int32'),
-1,
keepdims=True) == 0
meas_low_var_mask = ivy.reduce_max(ivy.cast(
measurement_variance >=
hole_prior_var * self._threshold_var_factor, 'int32'),
-1,
keepdims=True) == 0
neiter_low_var_mask = ivy.logical_and(
ivy.logical_not(prior_low_var_mask),
ivy.logical_not(meas_low_var_mask))
prior_w_large = ivy.where(prior_low_var_mask, prior,
ivy.ones_like(prior) * 1e12)
meas_w_large = ivy.where(meas_low_var_mask, measurement,
ivy.ones_like(measurement) * 1e12)
prior_and_meas_w_large = ivy.concatenate((ivy.expand_dims(
prior_w_large, 1), ivy.expand_dims(meas_w_large, 1)), 1)
fused_val_unsmoothed = ivy.reduce_min(prior_and_meas_w_large,
1,
keepdims=True)
fused_val_unsmoothed = ivy.where(neiter_low_var_mask, prior,
fused_val_unsmoothed)
# ToDo: solve this variance correspondence properly, rather than assuming the most certain
fused_variance_unsmoothed = ivy.reduce_min(
prior_and_meas_variance, 1, keepdims=True)
else:
fused_val_unsmoothed, fused_variance_unsmoothed = \
self._fuse_measurements_with_uncertainty(prior_and_meas, prior_and_meas_variance, 1)
# B x OH x OW x (3+F)
# This prevents accumulating certainty from duplicate re-projections from prior measurements
fused_variance_unsmoothed = ivy.where(
low_var_mask, fused_variance_unsmoothed[:, 0], hole_prior_var)
# B x OH x OW x (3+F)
fused_val = fused_val_unsmoothed[:, 0]
fused_variance = fused_variance_unsmoothed
low_var_mask = fused_variance < hole_prior_var
# B x OH x OW x (3+F) B x OH x OW x (3+F)
fused_val, fused_variance = self.smooth(fused_val, fused_variance,
low_var_mask,
self._smooth_mean,
self._smooth_kernel_size,
True, True, batch_size)
# append to list for returning
# B x OH x OW x (3+F)
fused_list.append(fused_val)
# B x OH x OW x (3+F)
fused_variances_list.append(fused_variance)
# update for next time step
prev_fused_val = fused_val
prev_fused_variance = fused_variance
# list of *[batch_size, oh, ow, (3+f)]*, list of *[batch_size, oh, ow, (3+f)]*
return fused_list, fused_variances_list
def render_implicit_features_and_depth(network_fn,
rays_o,
rays_d,
near,
far,
samples_per_ray,
render_variance=False,
batch_size_per_query=512 * 64,
inter_feat_fn=None,
v=None):
"""
Render an rgb-d image, given an implicit rgb and density function conditioned on xyz data.
:param network_fn: the implicit function.
:type network_fn: callable
:param rays_o: The camera center *[batch_shape,3]*
:type rays_o: array
:param rays_d: The rays in world space *[batch_shape,ray_batch_shape,3]*
:type rays_d: array
:param near: The near clipping plane values *[batch_shape,ray_batch_shape]*
:type near: array
:param far: The far clipping plane values *[batch_shape,ray_batch_shape]*
:type far: array
:param samples_per_ray: The number of stratified samples to use along each ray
:type samples_per_ray: int
:param render_variance: Whether to also render the feature variance. Default is False.
:type render_variance: bool, optional
:param batch_size_per_query: The maximum batch size for querying the implicit network. Default is 1024.
:type batch_size_per_query: int, optional
:param inter_feat_fn: Function to extract interpolated features from world-coords *[batch_shape,ray_batch_shape,3]*
:type inter_feat_fn: callable, optional
:param v: The container of trainable variables for the implicit model. default is to use internal variables.
:type v: ivy Container of variables
:return: The rendered feature *[batch_shape,ray_batch_shape,feat]* and depth *[batch_shape,ray_batch_shape,1]* values
"""
# shapes
batch_shape = list(near.shape)
flat_batch_size = np.prod(batch_shape)
num_sections = math.ceil(flat_batch_size * samples_per_ray /
batch_size_per_query)
# Compute 3D query points
# BS x SPR x 1
z_vals = ivy.expand_dims(stratified_sample(near, far, samples_per_ray), -1)
# BS x 1 x 3
rays_d = ivy.expand_dims(rays_d, -2)
rays_o = ivy.broadcast_to(rays_o, rays_d.shape)
# BS x SPR x 3
pts = rays_o + rays_d * z_vals
# (BSxSPR) x 3
pts_flat = ivy.reshape(pts, (flat_batch_size * samples_per_ray, 3))
# batch
# ToDo: use a more general batchify function, from ivy core
# num_sections size list of BSPQ x 3
pts_split = [
pts_flat[i * batch_size_per_query:min(
(i + 1) * batch_size_per_query, flat_batch_size * samples_per_ray)]
for i in range(num_sections)
]
if inter_feat_fn is not None:
# (BSxSPR) x IF
features = ivy.reshape(inter_feat_fn(pts),
(flat_batch_size * samples_per_ray, -1))
# num_sections size list of BSPQ x IF
feats_split =\
[features[i * batch_size_per_query:min((i + 1) * batch_size_per_query, flat_batch_size*samples_per_ray)]
for i in range(num_sections)]
else:
feats_split = [None] * num_sections
# Run network
# num_sections size list of tuple of (BSPQ x OF, BSPQ)
feats_n_densities = [
network_fn(pt, f, v=v) for pt, f in zip(pts_split, feats_split)
]
# BS x SPR x OF
feat = ivy.reshape(
ivy.concatenate([item[0] for item in feats_n_densities], 0),
batch_shape + [samples_per_ray, -1])
# FBS x SPR
densities = ivy.reshape(
ivy.concatenate([item[1] for item in feats_n_densities], 0),
batch_shape + [samples_per_ray])
# BS x (SPR+1)
z_vals_w_terminal = ivy.concatenate(
(z_vals[..., 0], ivy.ones_like(z_vals[..., -1:, 0]) * 1e10), -1)
# BS x SPR
depth_diffs = z_vals_w_terminal[..., 1:] - z_vals_w_terminal[..., :-1]
ray_term_probs = ray_termination_probabilities(densities, depth_diffs)
# BS x OF
feat = ivy.clip(
render_rays_via_termination_probabilities(ray_term_probs, feat,
render_variance), 0., 1.)
# BS x 1
depth = render_rays_via_termination_probabilities(ray_term_probs, z_vals,
render_variance)
if render_variance:
# BS x OF, BS x OF, BS x 1, BS x 1
return feat[0], feat[1], depth[0], depth[1]
# BS x OF, BS x 1
return feat, depth
def quantize_to_image(pixel_coords,
final_image_dims,
feat=None,
feat_prior=None,
with_db=False,
pixel_coords_var=1e-3,
feat_var=1e-3,
pixel_coords_prior_var=1e12,
feat_prior_var=1e12,
var_threshold=(1e-3, 1e12),
uniform_pixel_coords=None,
batch_shape=None,
dev_str=None):
"""
Quantize pixel co-ordinates with d feature channels (for depth, rgb, normals etc.), from
images :math:`\mathbf{X}\in\mathbb{R}^{input\_images\_shape×(2+d)}`, which may have been reprojected from a host of
different cameras (leading to non-integer pixel values), to a new quantized pixel co-ordinate image with the same
feature channels :math:`\mathbf{X}\in\mathbb{R}^{h×w×(2+d)}`, and with integer pixel co-ordinates.
Duplicates during the quantization are either probabilistically fused based on variance, or the minimum depth is
chosen when using depth buffer mode.
:param pixel_coords: Pixel co-ordinates *[batch_shape,input_size,2]*
:type pixel_coords: array
:param final_image_dims: Image dimensions of the final image.
:type final_image_dims: sequence of ints
:param feat: Features (i.e. depth, rgb, encoded), default is None. *[batch_shape,input_size,d]*
:type feat: array, optional
:param feat_prior: Prior feature image mean, default is None. *[batch_shape,input_size,d]*
:type feat_prior: array or float to fill with
:param with_db: Whether or not to use depth buffer in rendering, default is false
:type with_db: bool, optional
:param pixel_coords_var: Pixel coords variance *[batch_shape,input_size,2]*
:type pixel_coords_var: array or float to fill with
:param feat_var: Feature variance *[batch_shape,input_size,d]*
:type feat_var: array or float to fill with
:param pixel_coords_prior_var: Pixel coords prior variance *[batch_shape,h,w,2]*
:type pixel_coords_prior_var: array or float to fill with
:param feat_prior_var: Features prior variance *[batch_shape,h,w,3]*
:type feat_prior_var: array or float to fill with
:param var_threshold: Variance threshold, for projecting valid coords and clipping *[batch_shape,2+d,2]*
:type var_threshold: array or sequence of floats to fill with
:param uniform_pixel_coords: Homogeneous uniform (integer) pixel co-ordinate images, inferred from final_image_dims
if None *[batch_shape,h,w,3]*
:type uniform_pixel_coords: array, optional
:param batch_shape: Shape of batch. Assumed no batches if None.
:type batch_shape: sequence of ints, optional
:param dev_str: device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc. Same as x if None.
:type dev_str: str, optional
:return: Quantized pixel co-ordinates image with d feature channels (for depth, rgb, normals etc.) *[batch_shape,h,w,2+d]*,
maybe the quantized variance, *[batch_shape,h,w,2+d]*, and scatter counter image *[batch_shape,h,w,1]*
"""
# ToDo: make variance fully optional. If not specified,
# then do not compute and scatter during function call for better efficiency.
# config
if batch_shape is None:
batch_shape = pixel_coords.shape[:-2]
if dev_str is None:
dev_str = _ivy.dev_str(pixel_coords)
if feat is None:
d = 0
else:
d = feat.shape[-1]
min_depth_diff = _ivy.array([MIN_DEPTH_DIFF], dev_str=dev_str)
red = 'min' if with_db else 'sum'
# shapes as list
batch_shape = list(batch_shape)
final_image_dims = list(final_image_dims)
num_batch_dims = len(batch_shape)
# variance threshold
if isinstance(var_threshold, tuple) or isinstance(var_threshold, list):
ones = _ivy.ones(batch_shape + [1, 2 + d, 1])
var_threshold = _ivy.concatenate(
(ones * var_threshold[0], ones * var_threshold[1]), -1)
else:
var_threshold = _ivy.reshape(var_threshold,
batch_shape + [1, 2 + d, 2])
# uniform pixel coords
if uniform_pixel_coords is None:
uniform_pixel_coords =\
_ivy_svg.create_uniform_pixel_coords_image(final_image_dims, batch_shape, dev_str=dev_str)
uniform_pixel_coords = uniform_pixel_coords[..., 0:2]
# Extract Values #
feat_prior = _ivy.ones_like(feat) * feat_prior if isinstance(
feat_prior, float) else feat_prior
pixel_coords_var = _ivy.ones_like(pixel_coords) * pixel_coords_var\
if isinstance(pixel_coords_var, float) else pixel_coords_var
feat_var = _ivy.ones_like(feat) * feat_var if isinstance(
feat_var, float) else feat_var
pixel_coords_prior_var = _ivy.ones(batch_shape + final_image_dims + [2]) * pixel_coords_prior_var\
if isinstance(pixel_coords_prior_var, float) else pixel_coords_prior_var
feat_prior_var = _ivy.ones(batch_shape + final_image_dims + [d]) * feat_prior_var\
if isinstance(feat_prior_var, float) else feat_prior_var
# Quantize #
# BS x N x 2
quantized_pixel_coords = _ivy.reshape(
_ivy.cast(_ivy.round(pixel_coords), 'int32'), batch_shape + [-1, 2])
# Combine #
# BS x N x (2+D)
pc_n_feat = _ivy.reshape(_ivy.concatenate((pixel_coords, feat), -1),
batch_shape + [-1, 2 + d])
pc_n_feat_var = _ivy.reshape(
_ivy.concatenate((pixel_coords_var, feat_var), -1),
batch_shape + [-1, 2 + d])
# BS x H x W x (2+D)
prior = _ivy.concatenate((uniform_pixel_coords, feat_prior), -1)
prior_var = _ivy.concatenate((pixel_coords_prior_var, feat_prior_var), -1)
# Validity Mask #
# BS x N x 1
var_validity_mask = \
_ivy.reduce_sum(_ivy.cast(pc_n_feat_var < var_threshold[..., 1], 'int32'), -1, keepdims=True) == 2+d
bounds_validity_mask = _ivy.logical_and(
_ivy.logical_and(quantized_pixel_coords[..., 0:1] >= 0,
quantized_pixel_coords[..., 1:2] >= 0),
_ivy.logical_and(
quantized_pixel_coords[..., 0:1] <= final_image_dims[1] - 1,
quantized_pixel_coords[..., 1:2] <= final_image_dims[0] - 1))
validity_mask = _ivy.logical_and(var_validity_mask, bounds_validity_mask)
# num_valid_indices x len(BS)+2
validity_indices = _ivy.reshape(
_ivy.cast(_ivy.indices_where(validity_mask), 'int32'),
[-1, num_batch_dims + 2])
num_valid_indices = validity_indices.shape[-2]
if num_valid_indices == 0:
return _ivy.concatenate((uniform_pixel_coords[..., 0:2], feat_prior), -1), \
_ivy.concatenate((pixel_coords_prior_var, feat_prior_var), -1),\
_ivy.zeros_like(feat[..., 0:1], dev_str=dev_str)
# Depth Based Scaling #
mean_depth_min = None
mean_depth_range = None
pc_n_feat_wo_depth_range = None
pc_n_feat_wo_depth_min = None
var_vals_range = None
var_vals_min = None
if with_db:
# BS x N x 1
mean_depth = pc_n_feat[..., 2:3]
# BS x 1 x 1
mean_depth_min = _ivy.reduce_min(mean_depth, -2, keepdims=True)
mean_depth_max = _ivy.reduce_max(mean_depth, -2, keepdims=True)
mean_depth_range = mean_depth_max - mean_depth_min
# BS x N x 1
scaled_depth = (mean_depth - mean_depth_min) / (
mean_depth_range * min_depth_diff + MIN_DENOMINATOR)
if d == 1:
# BS x 1 x 1+D
pc_n_feat_wo_depth_min = _ivy.zeros(batch_shape + [1, 0],
dev_str=dev_str)
pc_n_feat_wo_depth_range = _ivy.ones(batch_shape + [1, 0],
dev_str=dev_str)
else:
# feat without depth
# BS x N x 1+D
pc_n_feat_wo_depth = _ivy.concatenate(
(pc_n_feat[..., 0:2], pc_n_feat[..., 3:]), -1)
# find the min and max of each value
# BS x 1 x 1+D
pc_n_feat_wo_depth_max = _ivy.reduce_max(
pc_n_feat_wo_depth, -2, keepdims=True) + 1
pc_n_feat_wo_depth_min = _ivy.reduce_min(
pc_n_feat_wo_depth, -2, keepdims=True) - 1
pc_n_feat_wo_depth_range = pc_n_feat_wo_depth_max - pc_n_feat_wo_depth_min
# BS x N x 1+D
normed_pc_n_feat_wo_depth = (pc_n_feat_wo_depth - pc_n_feat_wo_depth_min) / \
(pc_n_feat_wo_depth_range + MIN_DENOMINATOR)
# combine with scaled depth
# BS x N x 1+D
pc_n_feat_wo_depth_scaled = normed_pc_n_feat_wo_depth + scaled_depth
# BS x N x (2+D)
pc_n_feat = _ivy.concatenate(
(pc_n_feat_wo_depth_scaled[..., 0:2], mean_depth,
pc_n_feat_wo_depth_scaled[..., 2:]), -1)
# scale variance
# BS x 1 x (2+D)
var_vals_max = _ivy.reduce_max(pc_n_feat_var, -2, keepdims=True) + 1
var_vals_min = _ivy.reduce_min(pc_n_feat_var, -2, keepdims=True) - 1
var_vals_range = var_vals_max - var_vals_min
# BS x N x (2+D)
normed_var_vals = (pc_n_feat_var - var_vals_min) / (var_vals_range +
MIN_DENOMINATOR)
pc_n_feat_var = normed_var_vals + scaled_depth
# ready for later reversal with full image dimensions
# BS x 1 x 1 x D
var_vals_min = _ivy.expand_dims(var_vals_min, -2)
var_vals_range = _ivy.expand_dims(var_vals_range, -2)
# Validity Pruning #
# num_valid_indices x (2+D)
pc_n_feat = _ivy.gather_nd(pc_n_feat,
validity_indices[..., 0:num_batch_dims + 1])
pc_n_feat_var = _ivy.gather_nd(pc_n_feat_var,
validity_indices[..., 0:num_batch_dims + 1])
# num_valid_indices x 2
quantized_pixel_coords = _ivy.gather_nd(
quantized_pixel_coords, validity_indices[..., 0:num_batch_dims + 1])
if with_db:
means_to_scatter = pc_n_feat
vars_to_scatter = pc_n_feat_var
else:
# num_valid_indices x (2+D)
vars_to_scatter = 1 / (pc_n_feat_var + MIN_DENOMINATOR)
means_to_scatter = pc_n_feat * vars_to_scatter
# Scatter #
# num_valid_indices x 1
counter = _ivy.ones_like(pc_n_feat[..., 0:1], dev_str=dev_str)
if with_db:
counter *= -1
# num_valid_indices x 2(2+D)+1
values_to_scatter = _ivy.concatenate(
(means_to_scatter, vars_to_scatter, counter), -1)
# num_valid_indices x (num_batch_dims + 2)
all_indices = _ivy.flip(quantized_pixel_coords, -1)
if num_batch_dims > 0:
all_indices = _ivy.concatenate(
(validity_indices[..., :-2], all_indices), -1)
# BS x H x W x (2(2+D) + 1)
quantized_img = _ivy.scatter_nd(
_ivy.reshape(all_indices, [-1, num_batch_dims + 2]),
_ivy.reshape(values_to_scatter, [-1, 2 * (2 + d) + 1]),
batch_shape + final_image_dims + [2 * (2 + d) + 1],
reduction='replace' if _ivy.backend == 'mxnd' else red)
# BS x H x W x 1
quantized_counter = quantized_img[..., -1:]
if with_db:
invalidity_mask = quantized_counter != -1
else:
invalidity_mask = quantized_counter == 0
if with_db:
# BS x H x W x (2+D)
quantized_mean_scaled = quantized_img[..., 0:2 + d]
quantized_var_scaled = quantized_img[..., 2 + d:2 * (2 + d)]
# BS x H x W x 1
quantized_depth_mean = quantized_mean_scaled[..., 2:3]
# BS x 1 x 1 x 1
mean_depth_min = _ivy.expand_dims(mean_depth_min, -2)
mean_depth_range = _ivy.expand_dims(mean_depth_range, -2)
# BS x 1 x 1 x (1+D)
pc_n_feat_wo_depth_min = _ivy.expand_dims(pc_n_feat_wo_depth_min, -2)
pc_n_feat_wo_depth_range = _ivy.expand_dims(pc_n_feat_wo_depth_range,
-2)
# BS x 1 x 1 x (2+D) x 2
var_threshold = _ivy.expand_dims(var_threshold, -3)
# BS x H x W x (1+D)
quantized_mean_wo_depth_scaled = _ivy.concatenate(
(quantized_mean_scaled[..., 0:2], quantized_mean_scaled[..., 3:]),
-1)
quantized_mean_wo_depth_normed = quantized_mean_wo_depth_scaled - (quantized_depth_mean - mean_depth_min) / \
(mean_depth_range * min_depth_diff + MIN_DENOMINATOR)
quantized_mean_wo_depth = quantized_mean_wo_depth_normed * pc_n_feat_wo_depth_range + pc_n_feat_wo_depth_min
prior_wo_depth = _ivy.concatenate((prior[..., 0:2], prior[..., 3:]),
-1)
quantized_mean_wo_depth = _ivy.where(invalidity_mask, prior_wo_depth,
quantized_mean_wo_depth)
# BS x H x W x (2+D)
quantized_mean = _ivy.concatenate(
(quantized_mean_wo_depth[..., 0:2], quantized_depth_mean,
quantized_mean_wo_depth[..., 2:]), -1)
# BS x H x W x (2+D)
quantized_var_normed = quantized_var_scaled - (quantized_depth_mean - mean_depth_min) / \
(mean_depth_range * min_depth_diff + MIN_DENOMINATOR)
quantized_var = _ivy.maximum(
quantized_var_normed * var_vals_range + var_vals_min,
var_threshold[..., 0])
quantized_var = _ivy.where(invalidity_mask, prior_var, quantized_var)
else:
# BS x H x W x (2+D)
quantized_sum_mean_x_recip_var = quantized_img[..., 0:2 + d]
quantized_var_wo_increase = _ivy.where(
invalidity_mask, prior_var,
(1 / (quantized_img[..., 2 + d:2 * (2 + d)] + MIN_DENOMINATOR)))
quantized_var = _ivy.maximum(
quantized_var_wo_increase * quantized_counter,
_ivy.expand_dims(var_threshold[..., 0], -2))
quantized_var = _ivy.where(invalidity_mask, prior_var, quantized_var)
quantized_mean = _ivy.where(
invalidity_mask, prior,
quantized_var_wo_increase * quantized_sum_mean_x_recip_var)
# BS x H x W x (2+D) BS x H x W x (2+D) BS x H x W x 1
return quantized_mean, quantized_var, quantized_counter