config_folder = argv[0] + '/'
assert os.path.exists(
config_folder), 'directory %s does not exist' % config_folder
config_files = ['test.json']
for config_file in config_files:
config = read_json(config_folder + config_file)
height = config['height']
width = config['width']
depth = config['depth']
scale = 1. / max(height, depth, width)
n_observations = config['n_observations']
off_directory = common.dirname(config, 'reconstructed_dir')
txt_directory = common.dirname(config, 'txt_gt_dir')
import itertools
off_files = read_ordered_directory(off_directory)
n_files = len(off_files)
off_files = list(
itertools.chain.from_iterable(
itertools.repeat(x, n_observations) for x in off_files))
txt_files = []
for n in range(n_files):
for k in range(n_observations):
txt_files.append(txt_directory + '/%d/%d.txt' % (k, n))
vis_directory = common.dirname(config, 'vis_dir')
config_files = [
config_file for config_file in os.listdir(from_config_folder)
]
for config_file in config_files:
assert os.path.exists(to_config_folder + config_file)
for config_file in config_files:
from_config = utils.read_json(from_config_folder + config_file)
to_config = utils.read_json(to_config_folder + config_file)
for key in hdf5_file_keys:
from_file = common.filename(from_config, key)
to_file = common.filename(to_config, key)
print('[Data] copying')
print(' ' + from_file)
print(' ' + to_file)
shutil.copy(from_file, to_file)
for key in dir_keys:
from_dir = common.dirname(from_config, key)
to_dir = common.dirname(to_config, key)
# !
assert not os.path.exists(to_dir)
print('[Data] copying')
print(' ' + from_dir)
print(' ' + to_dir)
shutil.copytree(from_dir, to_dir)
def test_dirname_win_path():
win_dir = 'A:\\b\\c\\d\\'
result = common.dirname(win_dir)
assert result == 'A:\\b\\c\\d'
assert os.path.exists(
config_folder), 'directory %s does not exist' % config_folder
config_files = ['test.json']
for config_file in config_files:
config = read_json(config_folder + config_file)
height = config['height']
width = config['width']
depth = config['depth']
scale = 1. / max(height, depth, width)
multiplier = config['multiplier']
n_observations = config['n_observations']
txt_directory = common.dirname(config, 'txt_gt_dir')
n_files = len(read_ordered_directory(txt_directory + '/0/'))
txt_files = []
for n in range(n_files):
for k in range(n_observations):
txt_files.append(txt_directory + '/%d/%d.txt' % (k, n))
vis_directory = common.dirname(config, 'vis_dir')
if not os.path.isdir(vis_directory):
os.makedirs(vis_directory)
N = 30
log('[Data] %d samples' % len(txt_files))
for i in range(N):
n = i * (len(txt_files) // N)
def test_dirname_linux_path():
linux_dir = '/a/b/c/d/'
result = common.dirname(linux_dir)
assert result == '/a/b/c/d'
def test_dirname_win_file():
win_file = 'A:\\b\\c\\d.txt'
result = common.dirname(win_file)
assert result == 'A:\\b\\c'
if __name__ == '__main__':
if len(sys.argv) < 2:
print('[Data] Usage python 10_reconstruct.py config_folder')
exit(1)
config_folder = sys.argv[1] + '/'
assert os.path.exists(
config_folder), 'directory %s does not exist' % config_folder
for config_file in os.listdir(config_folder):
print('[Data] reading ' + config_folder + config_file)
config = utils.read_json(config_folder + config_file)
filled_file = common.filename(config, 'filled_file')
assert os.path.exists(
filled_file), 'file %s does not exist' % filled_file
filled = utils.read_hdf5(filled_file)
filled = filled.squeeze()
binvox_directory = common.dirname(config, 'binvox_dir')
utils.makedir(binvox_directory)
for n in range(filled.shape[0]):
model = binvox_rw.Voxels(filled[n] > 0.5, filled[n].shape,
(0, 0, 0), 1)
binvox_file = binvox_directory + str(n) + '.binvox'
with open(binvox_file, 'w') as fp:
model.write(fp)
print('[Validation] wrote ' + binvox_file)
print('[Data] read ' + common.filename(config, 'output_file'))
inputs = utils.read_hdf5(common.filename(config, 'input_file'))
inputs = inputs.reshape(
(inputs.shape[0] * n_observations, 1, inputs.shape[2],
inputs.shape[3], inputs.shape[4]))
print('[Data] read ' + common.filename(config, 'input_file'))
space = utils.read_hdf5(common.filename(config, 'space_file'))
space = space.reshape(
(space.shape[0] * n_observations, 1, space.shape[2],
space.shape[3], space.shape[4]))
print('[Data] read ' + common.filename(config, 'space_file'))
points = []
point_dir = common.dirname(config, 'txt_gt_dir') + '/'
for k in range(n_observations):
k_point_dir = point_dir + '/%d/' % k
for txt_file in os.listdir(k_point_dir):
point_cloud = PointCloud.from_txt(k_point_dir + txt_file)
points.append(point_cloud.points.shape[0])
points = np.array(points)
occupied = np.squeeze(
np.sum(np.sum(np.sum(targets, axis=4), axis=3), axis=2))
observed_points = np.squeeze(
np.sum(np.sum(np.sum(inputs, axis=4), axis=3), axis=2))
observed_space = np.squeeze(
np.sum(np.sum(np.sum(space, axis=4), axis=3), axis=2))
def test_dirname_win_file():
win_file = 'A:\\b\\c\\d.txt'
result = common.dirname(win_file)
assert result == 'A:\\b\\c'
def test_dirname_win_path():
win_dir = 'A:\\b\\c\\d\\'
result = common.dirname(win_dir)
assert result == 'A:\\b\\c\\d'
def test_dirname_linux_path():
linux_dir = '/a/b/c/d/'
result = common.dirname(linux_dir)
assert result == '/a/b/c/d'
def test_dirname_linux_file():
linux_file = '/a/b/c/d.txt'
result = common.dirname(linux_file)
assert result == '/a/b/c'
from point_cloud import PointCloud
if __name__ == '__main__':
if len(sys.argv) < 2:
print('[Data] Usage python 13_ply_observations.py config_folder')
exit(1)
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)
if not (config_file.find('prior') > 0)
]
for config_file in config_files:
print('[Data] reading ' + config_folder + config_file)
config = utils.read_json(config_folder + config_file)
for k in range(config['n_observations']):
txt_directory = common.dirname(config, 'txt_gt_dir') + str(k) + '/'
assert os.path.exists(txt_directory)
ply_directory = common.dirname(config, 'ply_gt_dir') + str(k) + '/'
if not os.path.exists(ply_directory):
os.makedirs(ply_directory)
for filename in os.listdir(txt_directory):
point_cloud = PointCloud.from_txt(txt_directory + filename)
point_cloud.to_ply(ply_directory + filename[:-4] + '.ply')
print('[Data] wrote ' + ply_directory + filename[:-4] + '.ply')
config_files = ['test.json']
for config_file in config_files:
config = read_json(config_folder + config_file)
height = config['height']
width = config['width']
depth = config['depth']
scale = 1. / max(height, width, depth)
n_observations = config['n_observations']
space = read_hdf5(common.filename(config, 'space_file'))
space = space.reshape((space.shape[0] * n_observations, 1,
space.shape[2], space.shape[3], space.shape[4]))
vis_directory = common.dirname(config, 'vis_dir')
if not os.path.isdir(vis_directory):
os.makedirs(vis_directory)
voxel_size = 0.007
if height >= 32:
voxel_size = 0.0055
if height >= 48:
voxel_size = 0.004
log('[Data] voxel size ' + str(voxel_size))
N = 30
log('[Data] %d samples' % space.shape[0])
for i in range(N):
n = i * (space.shape[0] // N)
def test_dirname_linux_file():
linux_file = '/a/b/c/d.txt'
result = common.dirname(linux_file)
assert result == '/a/b/c'
import mcubes
if __name__ == '__main__':
if len(sys.argv) < 2:
print('[Data] Usage python 10_reconstruct.py config_folder')
exit(1)
config_folder = sys.argv[1] + '/'
assert os.path.exists(
config_folder), 'directory %s does not exist' % config_folder
for config_file in os.listdir(config_folder):
print('[Data] reading ' + config_folder + config_file)
config = utils.read_json(config_folder + config_file)
sdf_file = common.filename(config, 'sdf_file')
assert os.path.exists(sdf_file), 'file %s does not exist' % sdf_file
sdfs = utils.read_hdf5(sdf_file)
sdfs = sdfs.squeeze()
reconstructed_directory = common.dirname(config, 'reconstructed_dir')
utils.makedir(reconstructed_directory)
for n in range(sdfs.shape[0]):
vertices, triangles = mcubes.marching_cubes(
-sdfs[n].transpose(1, 0, 2), 0)
off_file = reconstructed_directory + '/%d.off' % n
mcubes.export_off(vertices, triangles, off_file)
print('[Data] wrote %s' % off_file)
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