def export_one_scan(scan_name, output_filename_prefix):
mesh_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '_vh_clean_2.ply')
agg_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '.aggregation.json')
seg_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '_vh_clean_2.0.010000.segs.json')
meta_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '.txt') # includes axisAlignment info for the train set scans.
mesh_vertices, semantic_labels, instance_labels, instance_bboxes, instance2semantic = \
export(mesh_file, agg_file, seg_file, meta_file, LABEL_MAP_FILE, None)
mask = np.logical_not(np.in1d(semantic_labels, DONOTCARE_CLASS_IDS))
mesh_vertices = mesh_vertices[mask,:]
semantic_labels = semantic_labels[mask]
instance_labels = instance_labels[mask]
num_instances = len(np.unique(instance_labels))
print('Num of instances: ', num_instances)
bbox_mask = np.in1d(instance_bboxes[:,-1], OBJ_CLASS_IDS)
instance_bboxes = instance_bboxes[bbox_mask,:]
print('Num of care instances: ', instance_bboxes.shape[0])
N = mesh_vertices.shape[0]
if N > MAX_NUM_POINT:
choices = np.random.choice(N, MAX_NUM_POINT, replace=False)
mesh_vertices = mesh_vertices[choices, :]
semantic_labels = semantic_labels[choices]
instance_labels = instance_labels[choices]
np.save(output_filename_prefix+'_vert.npy', mesh_vertices)
np.save(output_filename_prefix+'_sem_label.npy', semantic_labels)
np.save(output_filename_prefix+'_ins_label.npy', instance_labels)
np.save(output_filename_prefix+'_bbox.npy', instance_bboxes)
def export_one_scan(scan_name,
output_filename_prefix,
max_num_point,
label_map_file,
scannet_dir,
test_mode=False):
mesh_file = osp.join(scannet_dir, scan_name, scan_name + '_vh_clean_2.ply')
agg_file = osp.join(scannet_dir, scan_name,
scan_name + '.aggregation.json')
seg_file = osp.join(scannet_dir, scan_name,
scan_name + '_vh_clean_2.0.010000.segs.json')
# includes axisAlignment info for the train set scans.
meta_file = osp.join(scannet_dir, scan_name, f'{scan_name}.txt')
mesh_vertices, semantic_labels, instance_labels, unaligned_bboxes, \
aligned_bboxes, instance2semantic, axis_align_matrix = export(
mesh_file, agg_file, seg_file, meta_file, label_map_file, None,
test_mode)
if not test_mode:
mask = np.logical_not(np.in1d(semantic_labels, DONOTCARE_CLASS_IDS))
mesh_vertices = mesh_vertices[mask, :]
semantic_labels = semantic_labels[mask]
instance_labels = instance_labels[mask]
num_instances = len(np.unique(instance_labels))
print(f'Num of instances: {num_instances}')
bbox_mask = np.in1d(unaligned_bboxes[:, -1], OBJ_CLASS_IDS)
unaligned_bboxes = unaligned_bboxes[bbox_mask, :]
bbox_mask = np.in1d(aligned_bboxes[:, -1], OBJ_CLASS_IDS)
aligned_bboxes = aligned_bboxes[bbox_mask, :]
assert unaligned_bboxes.shape[0] == aligned_bboxes.shape[0]
print(f'Num of care instances: {unaligned_bboxes.shape[0]}')
if max_num_point is not None:
max_num_point = int(max_num_point)
N = mesh_vertices.shape[0]
if N > max_num_point:
choices = np.random.choice(N, max_num_point, replace=False)
mesh_vertices = mesh_vertices[choices, :]
if not test_mode:
semantic_labels = semantic_labels[choices]
instance_labels = instance_labels[choices]
np.save(f'{output_filename_prefix}_vert.npy', mesh_vertices)
if not test_mode:
np.save(f'{output_filename_prefix}_sem_label.npy', semantic_labels)
np.save(f'{output_filename_prefix}_ins_label.npy', instance_labels)
np.save(f'{output_filename_prefix}_unaligned_bbox.npy',
unaligned_bboxes)
np.save(f'{output_filename_prefix}_aligned_bbox.npy', aligned_bboxes)
np.save(f'{output_filename_prefix}_axis_align_matrix.npy',
axis_align_matrix)
def export_one_scan(scan_name, output_filename_prefix):
labels_file = os.path.join(SCANNET_DIR, scan_name,
scan_name + '_vh_clean_2.labels.ply')
mesh_file = os.path.join(SCANNET_DIR, scan_name,
scan_name + '_vh_clean_2.ply')
agg_file = os.path.join(SCANNET_DIR, scan_name,
scan_name + '.aggregation.json')
seg_file = os.path.join(SCANNET_DIR, scan_name,
scan_name + '_vh_clean_2.0.010000.segs.json')
coords, colors, sem_labels, instance_labels = export(
scan_name, mesh_file, agg_file, seg_file, labels_file, LABEL_MAP_FILE,
None)
num_instances = len(np.unique(instance_labels))
print('Num of instances: ', num_instances)
print("Shape of points: {}".format(coords.shape))
torch.save((coords, colors, sem_labels, instance_labels),
output_filename_prefix + '_inst_nostuff.pth')
def export_one_scan(model, scan_name, output_filename_prefix):
mesh_file = os.path.join(SCANNET_DIR, scan_name,
scan_name + '_vh_clean_2.ply')
seg_file = os.path.join(SCANNET_DIR, scan_name,
scan_name + '_vh_clean_2.0.010000.segs.json')
if (model == 'votenet'):
meta_file = os.path.join(
SCANNET_DIR, scan_name, scan_name +
'.txt') # includes axisAlignment info for the train set scans.
agg_file = os.path.join(SCANNET_DIR, scan_name,
scan_name + '_vh_clean.aggregation.json')
mesh_vertices, aligned_vertices, semantic_labels, instance_labels, instance_bboxes, aligned_instance_bboxes = export(
mesh_file, agg_file, seg_file, meta_file, LABEL_MAP_FILE, None)
mask = np.logical_not(np.in1d(semantic_labels, DONOTCARE_CLASS_IDS))
mesh_vertices = mesh_vertices[mask, :]
aligned_vertices = aligned_vertices[mask, :]
semantic_labels = semantic_labels[mask]
instance_labels = instance_labels[mask]
if instance_bboxes.shape[0] > 1:
num_instances = len(np.unique(instance_labels))
print('Num of instances: ', num_instances)
bbox_mask = np.in1d(instance_bboxes[:, -2],
OBJ_CLASS_IDS) # match the mesh2cap
instance_bboxes = instance_bboxes[bbox_mask, :]
aligned_instance_bboxes = aligned_instance_bboxes[bbox_mask, :]
print('Num of care instances: ', instance_bboxes.shape[0])
else:
print("No semantic/instance annotation for test scenes")
N = mesh_vertices.shape[0]
if N > MAX_NUM_POINT:
choices = np.random.choice(N, MAX_NUM_POINT, replace=False)
mesh_vertices = mesh_vertices[choices, :]
aligned_vertices = aligned_vertices[choices, :]
semantic_labels = semantic_labels[choices]
instance_labels = instance_labels[choices]
print("Shape of points: {}".format(mesh_vertices.shape))
np.save(output_filename_prefix + '_vert.npy', mesh_vertices)
np.save(output_filename_prefix + '_aligned_vert.npy', aligned_vertices)
np.save(output_filename_prefix + '_sem_label.npy', semantic_labels)
np.save(output_filename_prefix + '_ins_label.npy', instance_labels)
np.save(output_filename_prefix + '_bbox.npy', instance_bboxes)
np.save(output_filename_prefix + '_aligned_bbox.npy',
aligned_instance_bboxes)
elif (model == 'pointgroup'):
TEST_SCAN_NAMES = sorted(
[line.rstrip() for line in open('meta_data/scannetv2_test.txt')])
if (scan_name in TEST_SCAN_NAMES):
vertices = scannet_utils.read_mesh_vertices_rgb(mesh_file)
coords = np.ascontiguousarray(vertices[:, :3] -
vertices[:, :3].mean(0))
colors = np.ascontiguousarray(vertices[:, 3:6]) / 127.5 - 1
torch.save((coords, colors),
output_filename_prefix + '_pointgroup.pth')
else:
labels_file = os.path.join(SCANNET_DIR, scan_name,
scan_name + '_vh_clean_2.labels.ply')
agg_file = os.path.join(SCANNET_DIR, scan_name,
scan_name + '.aggregation.json')
vertices = scannet_utils.read_mesh_vertices_rgb(mesh_file)
coords = np.ascontiguousarray(vertices[:, :3] -
vertices[:, :3].mean(0))
colors = np.ascontiguousarray(vertices[:, 3:6]) / 127.5 - 1
sem_labels = scannet_utils.get_labels(labels_file, OBJ_CLASS_IDS)
segid_to_pointid, _ = read_segmentation(seg_file)
instance_segids = scannet_utils.get_instance_segids(
scan_name, agg_file)
instance_labels = np.ones(sem_labels.shape[0]) * -100
for i in range(len(instance_segids)):
segids = instance_segids[i]
pointids = []
for segid in segids:
pointids += segid_to_pointid[segid]
instance_labels[pointids] = i
assert (len(np.unique(sem_labels[pointids])) == 1)
N = len(sem_labels)
if N > MAX_NUM_POINT:
choices = np.random.choice(N, MAX_NUM_POINT, replace=False)
coords = coords[choices, :]
colors = colors[choices, :]
sem_labels = sem_labels[choices]
instance_labels = instance_labels[choices]
torch.save((coords, colors, sem_labels, instance_labels),
output_filename_prefix + '_pointgroup.pth')
def export_one_scan(scan_name, output_filename_prefix):
mesh_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '_vh_clean_2.ply')
print("###### mesh_file", mesh_file)
agg_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '_vh_clean.aggregation.json')
seg_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '_vh_clean_2.0.010000.segs.json')
meta_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '.txt') # includes axisAlignment info for the train set scans.
mesh_vertices, aligned_vertices, semantic_labels, instance_labels, instance_bboxes, aligned_instance_bboxes = export(mesh_file, agg_file, seg_file, meta_file, LABEL_MAP_FILE, None)
mask = np.logical_not(np.in1d(semantic_labels, DONOTCARE_CLASS_IDS))
mesh_vertices = mesh_vertices[mask,:]
aligned_vertices = aligned_vertices[mask,:]
semantic_labels = semantic_labels[mask]
instance_labels = instance_labels[mask]
if instance_bboxes.shape[0] > 1:
num_instances = len(np.unique(instance_labels))
print('Num of instances: ', num_instances)
# bbox_mask = np.in1d(instance_bboxes[:,-1], OBJ_CLASS_IDS)
bbox_mask = np.in1d(instance_bboxes[:,-2], OBJ_CLASS_IDS) # match the mesh2cap
instance_bboxes = instance_bboxes[bbox_mask,:]
aligned_instance_bboxes = aligned_instance_bboxes[bbox_mask,:]
print('Num of care instances: ', instance_bboxes.shape[0])
else:
print("No semantic/instance annotation for test scenes")
N = mesh_vertices.shape[0]
if N > MAX_NUM_POINT:
choices = np.random.choice(N, MAX_NUM_POINT, replace=False)
mesh_vertices = mesh_vertices[choices, :]
aligned_vertices = aligned_vertices[choices, :]
semantic_labels = semantic_labels[choices]
instance_labels = instance_labels[choices]
print("Shape of points: {}".format(mesh_vertices.shape))
np.save(output_filename_prefix+'_vert.npy', mesh_vertices)
np.save(output_filename_prefix+'_aligned_vert.npy', aligned_vertices)
np.save(output_filename_prefix+'_sem_label.npy', semantic_labels)
np.save(output_filename_prefix+'_ins_label.npy', instance_labels)
np.save(output_filename_prefix+'_bbox.npy', instance_bboxes)
np.save(output_filename_prefix+'_aligned_bbox.npy', aligned_instance_bboxes)
