def set_attrs(self, file):
source_attrs = file['source_attrs']
_filename = filename(file['src_path'])
self.source = {
'name': _filename,
'date': unix_time(source_attrs.st_atime),
'size': convert_file_size(source_attrs.st_size),
'path': dst_path(file['src_path'])
}
target_attrs = file['target_attrs']
self.target = {
'name': _filename,
'date': unix_time(target_attrs.st_atime),
'size': convert_file_size(target_attrs.st_size),
'path': file['dst_path']
}
from skimage import morphology
if __name__ == '__main__':
if len(sys.argv) < 2:
print('[Data] Usage python 1_post_process.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)
outputs = utils.read_hdf5(common.filename(config, 'output_file'))
outputs = np.squeeze(outputs)
print('[Data] read ' + common.filename(config, 'output_file'))
filled = np.zeros(outputs.shape)
for n in range(outputs.shape[0]):
labels, num_labels = morphology.label(outputs[n],
background=1,
connectivity=1,
return_num=True)
outside_label = labels[0][0][0]
filled[n][labels != outside_label] = 1
#filled[n][labels == outside_label] = 0
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, 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
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)
set = config_file[:-5]
filled = utils.read_hdf5(common.filename(config, 'filled_file'))
print('[Data] read ' + common.filename(config, 'filled_file'))
inputs = utils.read_hdf5(common.filename(config, 'input_file'))
print('[Data] read ' + common.filename(config, 'input_file'))
space = utils.read_hdf5(common.filename(config, 'space_file'))
print('[Data] read ' + common.filename(config, 'space_file'))
print('[Data] filled: %s' % ' x '.join(map(str, filled.shape)))
print('[Data] inputs: %s' % ' x '.join(map(str, inputs.shape)))
print('[Data] space: %s' % ' x '.join(map(str, space.shape)))
if config['sdf']:
sdfs = utils.read_hdf5(common.filename(config, 'sdf_file'))
print('[Data] reading ' + common.filename(config, 'sdf_file'))
config['focal_length_x'], config['focal_length_y'],
config['principal_point_x'], config['principal_point_y']
],
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):
num = int(argv[1])
assert num > 0
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']
inputs = read_hdf5(common.filename(config, 'input_file', '.h5', num))
inputs = inputs.reshape(
(inputs.shape[0] * n_observations, 1, inputs.shape[2],
inputs.shape[3], inputs.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))
exit(1)
config_file = sys.argv[1]
assert os.path.exists(config_file)
config = utils.read_json(config_file)
set = ntpath.basename(config_file)[:-5]
print('[Data] processing ' + set)
split = []
with open(config ['split_file'], 'r') as f:
split = f.readlines()
split = [int(line.strip()) for line in split if line.strip() != '']
bounding_box_directory = common.filename(config, 'bounding_box_directory', '', set)
label_directory = config['label_directory']
multiplier = config['multiplier']
if not os.path.exists(bounding_box_directory):
os.makedirs(bounding_box_directory)
num_bounding_boxes = 0
for i in split:
bounding_box_file = label_directory + '/%06d.txt' % i
bounding_boxes = BoundingBox.from_kitti(bounding_box_file)
multiplied_bounding_boxes = []
for bounding_box in bounding_boxes:
if bounding_box.type.lower() == config['object_type']:
for j in range(multiplier):
'[Error] $BLENDER --background --python 12_3_visualize_binvox.py -- 1>/dev/null config_folder',
LogLevel.ERROR)
exit()
config_file = argv[0]
assert os.path.exists(config_file), 'file %s does not exist' % config_file
config = read_json(config_file)
set = ntpath.basename(config_file)[:-5]
height = config['height']
width = config['width']
depth = config['depth']
scale = 1. / max(height, width, depth)
space = read_hdf5(common.filename(config, 'part_space_file', '_f.h5', set))
log(space.shape)
vis_directory = common.filename(config, 'vis_dir', '', set)
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])
import common
if __name__ == '__main__':
if len(sys.argv) < 2:
print('Usage!')
config_file = sys.argv[1]
assert os.path.exists(config_file)
print('[Data] reading ' + config_file)
config = utils.read_json(config_file)
dataset = ntpath.basename(config_file)[:-5]
inputs = utils.read_hdf5(
common.filename(config, 'input_file', '_f.h5', dataset))
print('[Data] read ' +
common.filename(config, 'input_file', '_f.h5', dataset))
inputs_sdf = utils.read_hdf5(
common.filename(config, 'input_sdf_file', '_f.h5', dataset))
print('[Data] read ' +
common.filename(config, 'input_sdf_file', '_f.h5', dataset))
inputs_tsdf = utils.read_hdf5(
common.filename(config, 'input_tsdf_file', '_f.h5', dataset))
print('[Data] read ' +
common.filename(config, 'input_tsdf_file', '_f.h5', dataset))
inputs_ltsdf = utils.read_hdf5(
common.filename(config, 'input_ltsdf_file', '_f.h5', dataset))
print('[Data] read ' +
common.filename(config, 'input_ltsdf_file', '_f.h5', dataset))
assert os.path.exists(config_folder +
config_file), 'file %s not found' % (
config_folder + config_file)
print('[Data] found ' + config_folder + config_file)
for config_file in config_files:
to_config = utils.read_json(to_config_folder + config_file)
for key in hdf5_file_keys:
data = []
print('[Data] +')
for from_config_folder in config_folders:
from_config = utils.read_json(from_config_folder + config_file)
print('[Data] | reading ' + common.filename(from_config, key))
data.append(utils.read_hdf5(common.filename(from_config, key)))
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)
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
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)
set = config_file[:-5]
depths = utils.read_hdf5(common.filename(config, 'depth_file'))
print('[Data] read ' + common.filename(config, 'depth_file'))
print('[Data] depths: %s' % ' x '.join(map(str, depths.shape)))
for i in range(min(25, depths.shape[0])):
n = i
print('[Data] visualizing %s %d/%d' % (set,
(n + 1), depths.shape[0]))
pyplot.clf()
pyplot.imshow(depths[n][0], interpolation='none')
pyplot.savefig('%s_%d_depth.png' % (set, n))
print('[Data] Usage python 1_post_process.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)]
for config_file in config_files:
print('[Data] reading ' + config_folder + config_file)
config = utils.read_json(config_folder + config_file)
if config['synthetic_sdf']:
truncation = config['truncation']
filled_file = common.filename(config, 'filled_file')
filled = utils.read_hdf5(filled_file)
print('[Data] read ' + filled_file)
sdfs = np.zeros((filled.shape))
sdfs = np.squeeze(sdfs)
for n in range(filled.shape[0]):
positive_df = ndimage.distance_transform_edt(1 - filled[n])
negative_df = ndimage.distance_transform_edt(filled[n])
sdf = np.zeros(filled[n].shape)
mask = (filled[n] == 1)
sdf[mask] = -negative_df[mask]
mask = (filled[n] == 0)
sdf[mask] = positive_df[mask]
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
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)
set = config_file[:-5]
assert ('sdf' in config.keys() and config['sdf']) or ('synthetic_sdf' in config.keys() and config['synthetic_sdf'])
input_sdfs = utils.read_hdf5(common.filename(config, 'input_sdf_file'))
print('[Data] read ' + common.filename(config, 'input_sdf_file'))
input_ltsdfs = utils.read_hdf5(common.filename(config, 'input_ltsdf_file'))
print('[Data] read ' + common.filename(config, 'input_ltsdf_file'))
output_sdfs = utils.read_hdf5(common.filename(config, 'sdf_file'))
print('[Data] reading ' + common.filename(config, 'sdf_file'))
output_ltsdfs = utils.read_hdf5(common.filename(config, 'ltsdf_file'))
print('[Data] reading ' + common.filename(config, 'ltsdf_file'))
print('[Data] input_sdfs: %s' % ' x '.join(map(str, input_sdfs.shape)))
print('[Data] input_ltsdfs: %s' % ' x '.join(map(str, input_ltsdfs.shape)))
print('[Data] output_sdfs: %s' % ' x '.join(map(str, output_sdfs.shape)))
print('[Data] output_ltsdfs: %s' % ' x '.join(map(str, output_ltsdfs.shape)))
for i in range(min(25, output_sdfs.shape[0])):
if __name__ == '__main__':
if len(sys.argv) < 2:
print('[Experiments] use python visualize_experiment.py config_file')
exit(1)
config_folder = sys.argv[1] + '/'
assert os.path.exists(config_folder)
config_files = ['training_inference.json', 'test.json']
for config_file in config_files:
config = utils.read_json(config_folder + config_file)
print('[Data] read ' + config_folder + config_file)
n_observations = config['n_observations']
statistics_file = common.filename(config, 'statistics_file', '.txt')
with open(statistics_file, 'w') as f:
targets = utils.read_hdf5(common.filename(config, 'output_file'))
targets = np.repeat(targets, n_observations, axis=0)
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(
from mesh import Mesh
from point_cloud import PointCloud
import ntpath
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]
config_file = sys.argv[1]
dataset = ntpath.basename(config_file)[:-5]
base_directory = dataset + '/'
if not os.path.exists(base_directory):
os.makedirs(base_directory)
config = utils.read_json(config_file)
#statistics = utils.read_hdf5('/work/data/shapenet_3dop/real_space_statistics_training_prior.h5')
#statistics = 1 - statistics
#print('[Data] read statistics')
inputs = utils.read_hdf5(
common.filename(config, 'input_file', '_f.h5', dataset))
print('[Data] read ' +
common.filename(config, 'input_file', '_f.h5', dataset))
space = utils.read_hdf5(
common.filename(config, 'part_space_file', '_f.h5', dataset))
print('[Data] read ' +
common.filename(config, 'part_space_file', '_f.h5', dataset))
#statistics = statistics.reshape(1, 1, statistics.shape[0], statistics.shape[1], statistics.shape[2])
#statistics = np.repeat(statistics, space.shape[0], axis=0)
#print(space.shape, statistics.shape)
#invalid_space = space*statistics
points = []
print('[Data] Usage python 1_post_process.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)
# First we make a minor fix for the space.
# We do not allow voxels marked as free space which are also occupied,
# but during voxelization this usually happens!
space_file = common.filename(config, 'space_file')
if os.path.exists(space_file):
space = utils.read_hdf5(space_file)
input_file = common.filename(config, 'input_file')
input = utils.read_hdf5(input_file)
space[input == 1] = 0
if len(space.shape) < 5:
space = np.expand_dims(space, axis=1)
utils.write_hdf5(space_file, space)
print('[Data] wrote ' + space_file)
keys = [
'input', 'space', 'output', 'filled', 'sdf', 'tsdf', 'ltsdf',
'input_sdf', 'input_tsdf', 'input_ltsdf'
import binvox_rw
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)
if __name__ == '__main__':
if len(sys.argv) < 2:
print('[Data] Usage python 1_post_process.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)]
for config_file in config_files:
print('[Data] reading ' + config_folder + config_file)
config = utils.read_json(config_folder + config_file)
truncation = config['truncation']
sdfs = utils.read_hdf5(common.filename(config, 'sdf_file'))
tsdfs = sdfs.copy()
tsdfs[tsdfs > truncation] = truncation
tsdfs[tsdfs < -truncation] = -truncation
ltsdfs = tsdfs.copy()
ltsdfs[ltsdfs > 0] = np.log(ltsdfs[ltsdfs > 0] + 1)
ltsdfs[ltsdfs < 0] = -np.log(np.abs(ltsdfs[ltsdfs < 0]) + 1)
tsdf_file = common.filename(config, 'tsdf_file')
ltsdf_file = common.filename(config, 'ltsdf_file')
utils.write_hdf5(tsdf_file, tsdfs)
print('[Data] wrote ' + tsdf_file)
utils.write_hdf5(ltsdf_file, ltsdfs)
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)
print('[Data] reading ' + config_file)
config = utils.read_json(config_file)
set = ntpath.basename(config_file)[:-5]
# ['input_file', 'input_sdf_file', 'full_space_file', 'part_space_file']
size = 0
inputs_file = common.filename(config, 'input_file', '.h5', set)
inputs = utils.read_hdf5(inputs_file)
print('[Data] read ' + inputs_file)
inputs_sdf_file = common.filename(config, 'input_sdf_file', '.h5', set)
inputs_sdf = utils.read_hdf5(inputs_sdf_file)
print('[Data] read ' + inputs_sdf_file)
if config['gt']:
inputs_combined_gt_file = common.filename(config,
'input_combined_gt_file',
'.h5', set)
inputs_combined_gt = utils.read_hdf5(inputs_combined_gt_file)
print('[Data] read ' + inputs_combined_gt_file)
#inputs_sdf_combined_gt_file = common.filename(config, 'input_sdf_combined_gt_file', '.h5', set)
]
dir_keys = ['off_gt_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')
import common
if __name__ == '__main__':
if len(sys.argv) < 2:
print('[Data] Usage python 1_post_process.py config_folder')
exit(1)
config_file = sys.argv[1]
assert os.path.exists(config_file), 'file %s does not exist' % config_file
print('[Data] reading ' + config_file)
config = utils.read_json(config_file)
set = ntpath.basename(config_file)[:-5]
truncation = config['truncation']
input_sdfs = utils.read_hdf5(common.filename(config, 'input_sdf_file', '_f.h5', set))
input_tsdfs = input_sdfs.copy()
input_tsdfs[input_tsdfs > truncation] = truncation
input_tsdfs[input_tsdfs < -truncation] = -truncation
input_ltsdfs = input_tsdfs.copy()
input_ltsdfs[input_ltsdfs > 0] = np.log(input_ltsdfs[input_ltsdfs > 0] + 1)
input_ltsdfs[input_ltsdfs < 0] = - np.log(np.abs(input_ltsdfs[input_ltsdfs < 0]) + 1)
input_tsdf_file = common.filename(config, 'input_tsdf_file', '_f.h5', set)
input_ltsdf_file = common.filename(config, 'input_ltsdf_file', '_f.h5', set)
utils.write_hdf5(input_tsdf_file, input_tsdfs)
print('[Data] wrote ' + input_tsdf_file)
utils.write_hdf5(input_ltsdf_file, input_ltsdfs)
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)
set = config_file[:-5]
outputs = utils.read_hdf5(common.filename(config, 'filled_file'))
print('[Data] read ' + common.filename(config, 'filled_file'))
num = 2
inputs = utils.read_hdf5(filename(config, 'input_file', num))
print('[Data] read ' + filename(config, 'input_file', num))
space = utils.read_hdf5(filename(config, 'space_file', num))
print('[Data] read ' + filename(config, 'space_file', num))
print('[Data] outputs: %s' % ' x '.join(map(str, outputs.shape)))
print('[Data] inputs: %s' % ' x '.join(map(str, inputs.shape)))
print('[Data] space: %s' % ' x '.join(map(str, space.shape)))
n_observations = config['n_observations']
'[Error] $BLENDER --background --python 12_1_visualize_gt.py -- 1>/dev/null config_folder',
LogLevel.ERROR)
exit()
config_file = argv[0]
assert os.path.exists(config_file), 'file %s does not exist' % config_file
config = read_json(config_file)
set = ntpath.basename(config_file)[:-5]
height = config['height']
width = config['width']
depth = config['depth']
scale = 1. / max(height, depth, width)
txt_directory = common.filename(config, 'bounding_box_txt_directory', '',
set)
txt_files = read_ordered_directory(txt_directory)
vis_directory = common.filename(config, 'vis_dir', '', set)
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)
txt_file = txt_files[n]
camera_target = initialize()
txt_material = make_material('BRC_Material_Point_Cloud',
(0.65, 0.23, 0.25), 1, True)
'[Error] $BLENDER --background --python 12_3_visualize_binvox.py -- 1>/dev/null config_folder',
LogLevel.ERROR)
exit()
config_file = argv[0]
assert os.path.exists(config_file), 'file %s does not exist' % config_file
config = read_json(config_file)
set = ntpath.basename(config_file)[:-5]
height = config['height']
width = config['width']
depth = config['depth']
scale = 1. / max(height, width, depth)
inputs = read_hdf5(common.filename(config, 'input_file', '_f.h5', set))
log(inputs.shape)
vis_directory = common.filename(config, 'vis_dir', '', set)
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' % inputs.shape[0])
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, width, depth)
n_observations = config['n_observations']
filled = read_hdf5(common.filename(config, 'filled_file'))
filled = np.repeat(filled, n_observations, axis=0)
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' % filled.shape[0])
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, width, depth)
n_observations = config['n_observations']
inputs = 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]))
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))
import common
if __name__ == '__main__':
if len(sys.argv) < 2:
print('Usage!')
config_file = sys.argv[1]
assert os.path.exists(config_file)
print('[Data] reading ' + config_file)
config = utils.read_json(config_file)
dataset = ntpath.basename(config_file)[:-5]
inputs = utils.read_hdf5(
common.filename(config, 'input_file', '_f.h5', dataset))
print('[Data] read ' +
common.filename(config, 'input_file', '_f.h5', dataset))
#inputs_sdf = utils.read_hdf5(common.filename(config, 'input_sdf_file', '_f.h5', dataset))
#print('[Data] read ' + common.filename(config, 'input_sdf_file', '_f.h5', dataset))
#inputs_combined_gt = utils.read_hdf5(common.filename(config, 'input_combined_gt_file', '_f.h5', dataset))
#print('[Data] read ' + common.filename(config, 'input_combined_gt_file', '_f.h5', dataset))
#inputs_sdf_combined_gt = utils.read_hdf5(common.filename(config, 'input_sdf_combined_gt_file', '_f.h5', dataset))
#print('[Data] read ' + common.filename(config, 'input_sdf_combined_gt_file', '_f.h5', dataset))
#inputs_gt = utils.read_hdf5(common.filename(config, 'input_gt_file', '_f.h5', dataset))
#print('[Data] read ' + common.filename(config, 'input_gt_file', '_f.h5', dataset))
#inputs_sdf_gt = utils.read_hdf5(common.filename(config, 'input_sdf_gt_file', '_f.h5', dataset))
#print('[Data] read ' + common.filename(config, 'input_sdf_gt_file', '_f.h5', dataset))
#full_space_gt = utils.read_hdf5(common.filename(config, 'full_space_gt_file', '_f.h5', dataset))
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)
if __name__ == '__main__':
config_file = sys.argv[1]
dataset = ntpath.basename(config_file)[:-5]
base_directory = dataset + '/'
if not os.path.exists(base_directory):
os.makedirs(base_directory)
config = utils.read_json(config_file)
#statistics = utils.read_hdf5('/work/data/shapenet_3dop/real_space_statistics_training_prior.h5')
#statistics = 1 - statistics
#print('[Data] read statistics')
inputs = utils.read_hdf5(common.filename(config, 'input_file', '_f.h5', dataset))
print('[Data] read ' + common.filename(config, 'input_file', '_f.h5', dataset))
inputs_combined_gt = utils.read_hdf5(common.filename(config, 'input_combined_gt_file', '_f.h5', dataset))
print('[Data] read ' + common.filename(config, 'input_combined_gt_file', '_f.h5', dataset))
space = utils.read_hdf5(common.filename(config, 'part_space_file', '_f.h5', dataset))
print('[Data] read ' + common.filename(config, 'part_space_file', '_f.h5', dataset))
#statistics = statistics.reshape(1, 1, statistics.shape[0], statistics.shape[1], statistics.shape[2])
#statistics = np.repeat(statistics, space.shape[0], axis=0)
#print(space.shape, statistics.shape)
#invalid_space = space*statistics
points = []
