config_folder = sys.argv[1] + '/'
assert os.path.exists(
config_folder), 'directory %s does not exist' % config_folder
config_files = [config_file for config_file in os.listdir(config_folder)]
modulo_base = 1
if len(sys.argv) > 2:
modulo_base = max(1, int(sys.argv[2]))
print('[Data] modulo base %d' % modulo_base)
modulo_index = 0
if len(sys.argv) > 3:
modulo_index = max(0, int(sys.argv[3]))
print('[Data] modulo index %d' % modulo_index)
config = utils.read_json(config_folder + config_files[-1])
watertight_directory = config['watertight_directory'] + '/'
simplified_directory = config['simplified_directory'] + '/'
utils.makedir(simplified_directory)
off_files = utils.read_ordered_directory(watertight_directory)
for n in range(len(off_files)):
if (n - modulo_index) % modulo_base == 0:
os.system('meshlabserver -i %s/%d.off -o %s/%d.off -s %s' %
(watertight_directory, n, simplified_directory, n,
config['simplification_script']))
modulo_base = 1
if len(sys.argv) > 2:
modulo_base = max(1, int(sys.argv[2]))
print('[Data] modulo base %d' % modulo_base)
modulo_index = 0
if len(sys.argv) > 3:
modulo_index = max(0, int(sys.argv[3]))
print('[Data] modulo index %d' % modulo_index)
scaled_directory = config['scaled_directory'] + '/'
assert os.path.exists(
scaled_directory), 'directory %s does not exist' % scaled_directory
scaled_files = utils.read_ordered_directory(scaled_directory)
n_files_expected = len(scaled_files)
depth_directory = config['depth_directory'] + '/'
while not os.path.exists(depth_directory):
print('[Data] waiting for %s' % depth_directory)
time.sleep(10)
watertight_directory = config['watertight_directory'] + '/'
tsdf_directory = config['tsdf_directory'] + '/'
utils.makedir(watertight_directory)
utils.makedir(tsdf_directory)
timer = Timer()
Rs = watertight_render.get_views(config['watertight_rendering']['n_views'])
dtype=np.float64)
size = np.array([image_height, image_width], dtype=np.int32)
mesh_center = (config['mesh_center_x'], config['mesh_center_y'],
config['mesh_center_z'])
znf = np.array([config['z_near'], config['z_far']], dtype=float)
height = config['height']
width = config['width']
depth = config['depth']
suffix = config['suffix']
off_directory = config['multiplied_directory'] + '/' + set + '/'
depth_file = common.filename(config, 'depth_file')
angles_file = common.filename(config, 'render_orientation_file')
off_files = utils.read_ordered_directory(off_directory)
n_files = len(off_files)
print('[Data] found %d off files in %s' % (n_files, off_directory))
n_observations = config['n_observations']
depth_maps = np.zeros(
(n_files, n_observations, image_height, image_width))
rotations = np.zeros((n_files, n_observations, 3))
for n in range(n_files):
base_mesh = Mesh.from_off(off_files[n])
for k in range(n_observations):
mesh = base_mesh.copy()
rotations[n, k, 0] = (
np.random.random() *
config_folder = sys.argv[1] + '/'
assert os.path.exists(
config_folder), 'directory %s does not exist' % config_folder
config_files = ['test.json']
for config_file in config_files:
print('[Data] reading ' + config_folder + config_file)
config = utils.read_json(config_folder + config_file)
multiplier = config['multiplier']
vis_directory = common.dirname(config, 'vis_dir')
if not os.path.isdir(vis_directory):
os.makedirs(vis_directory)
depth_directory = config['depth_directory']
depth_files = utils.read_ordered_directory(depth_directory)
N = 30
for i in range(N):
n = i * (len(depth_files) * multiplier // N) // multiplier
print('[Data] visualizing %d/%d' % ((n + 1), len(depth_files)))
depths = utils.read_hdf5(depth_files[n])
height = 3
width = 6
fig = plt.figure(figsize=(width * 1.6, height * 1.6))
gs = matplotlib.gridspec.GridSpec(height, width)
gs.update(wspace=0.025, hspace=0.025)
print('[Data] Usage python 3_simplify.py config_folder')
exit(1)
config_folder = sys.argv[1] + '/'
assert os.path.exists(
config_folder), 'directory %s does not exist' % config_folder
# Rescale all models in place (as this wasn't accounted for and there shouldn't be any more directories).
config_files = [config_file for config_file in os.listdir(config_folder)]
for config_file in config_files:
config = utils.read_json(config_folder + config_file)
padding = config['padding']
simplified_directory = config['simplified_directory'] + '/'
off_files = utils.read_ordered_directory(simplified_directory)
for n in range(len(off_files)):
mesh = Mesh.from_off(off_files[n])
# Get extents of model.
min, max = mesh.extents()
total_min = np.min(np.array(min))
total_max = np.max(np.array(max))
# Set the center (although this should usually be the origin already).
centers = ((min[0] + max[0]) / 2, (min[1] + max[1]) / 2,
(min[2] + max[2]) / 2)
# Scales all dimensions equally.
sizes = (total_max - total_min, total_max - total_min,
total_max - total_min)
print('[Data] \'-> writing ' + common.filename(to_config, key))
data = np.concatenate(tuple(data), axis=0)
utils.write_hdf5(common.filename(to_config, key), data)
key = 'off_gt_dir'
count = 0
for key in dir_keys:
for from_config_folder in config_folders:
from_config = utils.read_json(from_config_folder + config_file)
from_dir = common.dirname(from_config, key)
to_dir = common.dirname(to_config, key)
utils.makedir(to_dir)
files = utils.read_ordered_directory(from_dir)
for i in range(len(files)):
from_file = files[i]
to_file = to_dir + '/%d.%s' % (count, from_file[-3:])
if i == 0:
print('[Data] +')
print('[Data] |- ' + from_file)
print('[Data] \'-> ' + to_file)
shutil.copy(from_file, to_file)
count += 1
# 1 Assemble all HDF5 files.
hdf5_file_keys = [
'depth_file',
'render_orientation_file',
assert os.path.exists(config_folder), 'directory %s does not exist' % config_folder
config_files = [config_file for config_file in os.listdir(config_folder)]
config = utils.read_json(config_folder + config_files[-1])
index = -1
if len(sys.argv) > 2:
index = int(sys.argv[2])
if index >= 0:
depths = utils.read_hdf5(config['depth_directory'] + '/%d.hdf5' % index)
for i in range(depths.shape[0]):
pyplot.clf()
pyplot.imshow(depths[i], interpolation='none')
pyplot.savefig('%d_%d_depth.png' % (index, i))
print('[Data] wrote %d_%d_depth.png' % (index, i))
else:
n = 0
depth_files = utils.read_ordered_directory(config['depth_directory'])
for depth_file in depth_files:
depths = utils.read_hdf5(depth_file)
for i in range(depths.shape[0]):
pyplot.clf()
pyplot.imshow(depths[i], interpolation='none')
pyplot.savefig('%d_%d_depth.png' % (n, i))
print('[Data] wrote %d_%d_depth.png' % (n, i))
n += 1
import common
if __name__ == '__main__':
if len(sys.argv) < 2:
print('[Data] Usage python create_cuboids.py config_folder')
exit(1)
config_file = sys.argv[1]
assert os.path.exists(config_file)
config = utils.read_json(config_file)
dataset = ntpath.basename(config_file)[:-5]
orig_dir = common.filename(config, 'bounding_box_txt_directory', '',
dataset)
gt_dir = common.filename(config, 'velodyne_gt_txt_directory', '', dataset)
gt_files = utils.read_ordered_directory(gt_dir)
for i in range(len(gt_files)):
orig_file = orig_dir + '/' + ntpath.basename(gt_files[i])
orig_point_cloud = PointCloud.from_txt(orig_file)
gt_point_cloud = PointCloud.from_txt(gt_files[i])
print('[Data] read ' + orig_file)
print('[Data] read ' + gt_files[i])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
xx = gt_point_cloud.points[:, 0]
yy = gt_point_cloud.points[:, 1]
zz = gt_point_cloud.points[:, 2]
config_file for config_file in config_files
if config_file[:-5] in config_sets
]
for config_file in config_files:
config = utils.read_json(config_folder + config_file)
raw_directory = config['raw_directory'] + '/'
assert os.path.exists(
raw_directory), 'directory %s does not exist' % raw_directory
# The models file was crafted based on MatLab indexing; originally, the first file
# in the list got the corresponding number, 1-based.
# This means that the filter indices in the configuration file are 1-based.
filenames = utils.read_ordered_directory(raw_directory)
print('[Data] found %d files' % len(filenames))
scaled_directory = config['scaled_directory'] + '/'
utils.makedir(scaled_directory)
padding = config['padding']
j = 0
for i in range(len(filenames)):
if config['limit'] > 0 and i >= config['limit']:
print('[Data] processed %d models, reached limit' % (i + 1))
break
in_file = filenames[i]
for k in range(n_observations):
perm = np.random.permutation(n_observations)
perm = perm[:i]
print('[Data] perm ' + ', '.join(map(str, perm)))
i_inputs[k] = np.sum(inputs[perm], axis=0)
i_inputs[k] = np.clip(i_inputs[k], 0, 1)
i_space[k] = np.sum(space[perm], axis=0)
i_space[k] = np.clip(i_space[k], 0, 1)
i_sdf_inputs[k] = np.min(sdf_inputs[perm], axis=0)
i_tsdf_inputs[k] = np.min(tsdf_inputs[perm], axis=0)
i_ltsdf_inputs[k] = np.min(ltsdf_inputs[perm], axis=0)
# Also fuse the actual point clouds!
txt_directories = utils.read_ordered_directory(
common.dirname(config, 'txt_gt_dir'))
txt_directory = common.dirname(config, 'txt_gt_dir',
i) + '%d/' % k
utils.makedir(txt_directory)
for n in range(N):
point_cloud = PointCloud()
print('[Data] +')
for j in range(perm.shape[0]):
txt_file = txt_directories[perm[j]] + '/%d.txt' % n
print('[Data] | read ' + txt_file)
point_cloud_j = PointCloud.from_txt(txt_file)
point_cloud.points = np.concatenate(
(point_cloud.points, point_cloud_j.points), axis=0)
txt_file = txt_directory + '%d.txt' % n