def get_rgb_paths(split, category, modifier=''):
"""Reads a file containing a list of .npz files on which to run inference."""
t = time.time()
ensure_split_valid(split)
ensure_category_valid(category)
path = ('/ROOT_DIR/%s-%s%s.txt') % (category, split, modifier)
try:
files = file_util.readlines(path)
except:
raise ValueError('Failed to read filelist %s' % path)
tf.logging.info('Time loading filelist %0.2f', (time.time() - t))
return files
def get_npz_paths(split, category, modifier=''):
"""Returns the list of npz paths for the split's ground truth."""
t = time.time()
ensure_split_valid(split)
filelist = os.path.join(
path_util.get_path_to_ldif_root(),
'data/basedirs/%s-%s%s.txt' % (split, category, modifier))
try:
filenames = file_util.readlines(filelist)
except:
raise ValueError('Unable to read filelist %s.' % filelist)
tf.logging.info('Loaded filelist in %0.2fms.', (time.time() - t))
return filenames
def read_cam_file(path, verbose=False):
"""Reads a GAPS .cam file to 4x4 matrices.
Args:
path: filepath to a gaps .cam file with K cameras.
verbose: Boolean. Whether to print detailed file info.
Returns:
cam2world: Numpy array with shape [K, 4, 4].
xfov: Numpy array with shape [K]. The x field-of-view in GAPS' format (which
is the half-angle in radians).
"""
lines = file_util.readlines(path)
if verbose:
log.info('There are %i cameras in %s.' % (len(lines), path))
cam2worlds, xfovs = [], []
for i, l in enumerate(lines):
vals = [float(x) for x in l.split(' ') if x]
if len(vals) != 12:
raise ValueError(
'Failed reading %s: Expected 12 items on line %i, but read %i.'
% (path, i, len(vals)))
viewpoint = np.array(vals[0:3]).astype(np.float32)
towards = np.array(vals[3:6]).astype(np.float32)
up = np.array(vals[6:9]).astype(np.float32)
right = np.cross(towards, up)
right = right / np.linalg.norm(right)
xfov = vals[9]
# 11th is yfov but GAPS ignores it and recomputes.
# 12th is a 'score' that is irrelevant.
towards = towards / np.linalg.norm(towards)
up = np.cross(right, towards)
up = up / np.linalg.norm(up)
# aspect = float(height) / float(width)
# yf = math.atan(aspect * math.tan(xfov))
rotation = np.stack([right, up, -towards], axis=1)
rotation_4x4 = np.eye(4)
rotation_4x4[:3, :3] = rotation
cam2world = rotation_4x4.copy()
cam2world[:3, 3] = viewpoint
cam2worlds.append(cam2world)
xfovs.append(xfov)
cam2worlds = np.stack(cam2worlds, axis=0).astype(np.float32)
xfovs = np.array(xfovs, dtype=np.float32)
return cam2worlds, xfovs
def _load_v1_txt(path):
"""Parses a SIF V1 text file, returning numpy arrays.
Args:
path: string containing the path to the ASCII file.
Returns:
A tuple of 4 elements:
constants: A numpy array of shape (element_count). The constant
associated with each SIF element.
centers: A numpy array of shape (element_count, 3). The centers of the
SIF elements.
radii: A numpy array of shape (element_count, 3). The axis-aligned
radii of the gaussian falloffs.
rotations: A numpy array of shape (element_count, 3). The euler-angle
rotations of the SIF elements.
symmetry_count: An integer. The number of elements which are left-right
symmetric.
features: A numpy array of shape (element_count, implicit_len). The LDIF
neural features, if they are present.
"""
lines = file_util.readlines(path)
if lines[0] != 'SIF':
raise ValueError(
f'Could not parse {path} as a sif txt. First line was {lines[0]}')
shape_count, version, implicit_len = [int(x) for x in lines[1].split(' ')]
version += 1
if version != 1:
raise ValueError(
f'This function can only parse v1 files. This version: {version}.')
symmetry_count = 0
last_was_symmetric = True
constants = []
centers = []
radii = []
rotations = []
features = []
for row in lines[2:]:
elts = row.split(' ')
if len(elts) != 11 + implicit_len:
raise ValueError('Failed to parse the following row with '
f'implicit_len {implicit_len}: {row}')
explicit_params = np.array([float(x) for x in elts[:10]],
dtype=np.float32)
is_symmetric = bool(int(elts[10]))
if is_symmetric:
symmetry_count += 1
if not last_was_symmetric:
raise ValueError(
f'File not supported by parser: row {row} is '
'symmetric but follows an asymmetric element.')
constants.append(explicit_params[0])
centers.append(explicit_params[1:4])
radii.append(explicit_params[4:7])
rotations.append(explicit_params[7:10])
if implicit_len > 0:
implicit_params = np.array([float(x) for x in elts[11:]],
dtype=np.float32)
features.append(implicit_params)
constants = np.stack(constants)
centers = np.stack(centers)
radii = np.stack(radii)
rotations = np.stack(rotations)
features = np.stack(features) if features else None
# Radii have their sqrt stored for GAPS:
radii = radii * radii
return constants, centers, radii, rotations, symmetry_count, features
def get_evaluation_directories(split):
registry_path = f'{FLAGS.dataset_directory}/{split}.txt'
items_to_eval = file_util.readlines(registry_path)
return items_to_eval