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# Name: gen_dataset.py
# Purpose: Script to generate data for skeleton and connectivity predition stage
# Change dataset_folder to the folder where you put the downloaded pre-processed data
# RigNet Copyright 2020 University of Massachusetts
# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
#-------------------------------------------------------------------------------
import os
import shutil
import numpy as np
import open3d as o3d
from multiprocessing import Pool
from utils.io_utils import mkdir_p
from utils.rig_parser import Info
from geometric_proc.common_ops import calc_surface_geodesic, get_bones
def get_tpl_edges(remesh_obj_v, remesh_obj_f):
edge_index = []
for v in range(len(remesh_obj_v)):
face_ids = np.argwhere(remesh_obj_f == v)[:, 0]
neighbor_ids = []
for face_id in face_ids:
for v_id in range(3):
if remesh_obj_f[face_id, v_id] != v:
neighbor_ids.append(remesh_obj_f[face_id, v_id])
neighbor_ids = list(set(neighbor_ids))
neighbor_ids = [np.array([v, n])[np.newaxis, :] for n in neighbor_ids]
if len(neighbor_ids) == 0:
continue
neighbor_ids = np.concatenate(neighbor_ids, axis=0)
edge_index.append(neighbor_ids)
edge_index = np.concatenate(edge_index, axis=0)
return edge_index
def get_geo_edges(surface_geodesic, remesh_obj_v):
edge_index = []
surface_geodesic += 1.0 * np.eye(len(surface_geodesic)) # remove self-loop edge here
for i in range(len(remesh_obj_v)):
geodesic_ball_samples = np.argwhere(surface_geodesic[i, :] <= 0.06).squeeze(1)
if len(geodesic_ball_samples) > 10:
geodesic_ball_samples = np.random.choice(geodesic_ball_samples, 10, replace=False)
edge_index.append(np.concatenate((np.repeat(i, len(geodesic_ball_samples))[:, np.newaxis],
geodesic_ball_samples[:, np.newaxis]), axis=1))
edge_index = np.concatenate(edge_index, axis=0)
return edge_index
def genDataset(process_id):
global dataset_folder
print("process ID {:d}".format(process_id))
if process_id < 6:
model_list = np.loadtxt(os.path.join(dataset_folder, 'train_final.txt'), dtype=int)
model_list = model_list[365*process_id: 365*(process_id+1)]
split_name = 'train'
elif process_id == 6:
model_list = np.loadtxt(os.path.join(dataset_folder, 'val_final.txt'), dtype=int)
split_name = 'val'
elif process_id == 7:
model_list = np.loadtxt(os.path.join(dataset_folder, 'test_final.txt'), dtype=int)
split_name = 'test'
mkdir_p(os.path.join(dataset_folder, split_name))
for model_id in model_list:
remeshed_obj_filename = os.path.join(dataset_folder, 'obj_remesh/{:d}.obj'.format(model_id))
info_filename = os.path.join(dataset_folder, 'rig_info_remesh/{:d}.txt'.format(model_id))
remeshed_obj = o3d.io.read_triangle_mesh(remeshed_obj_filename)
remesh_obj_v = np.asarray(remeshed_obj.vertices)
if not remeshed_obj.has_vertex_normals():
remeshed_obj.compute_vertex_normals()
remesh_obj_vn = np.asarray(remeshed_obj.vertex_normals)
remesh_obj_f = np.asarray(remeshed_obj.triangles)
rig_info = Info(info_filename)
#vertices
vert_filename = os.path.join(dataset_folder, '{:s}/{:d}_v.txt'.format(split_name, model_id))
input_feature = np.concatenate((remesh_obj_v, remesh_obj_vn), axis=1)
np.savetxt(vert_filename, input_feature, fmt='%.6f')
#topology edges
edge_index = get_tpl_edges(remesh_obj_v, remesh_obj_f)
graph_filename = os.path.join(dataset_folder, '{:s}/{:d}_tpl_e.txt'.format(split_name, model_id))
np.savetxt(graph_filename, edge_index, fmt='%d')
# geodesic_edges
surface_geodesic = calc_surface_geodesic(remeshed_obj)
edge_index = get_geo_edges(surface_geodesic, remesh_obj_v)
graph_filename = os.path.join(dataset_folder, '{:s}/{:d}_geo_e.txt'.format(split_name, model_id))
np.savetxt(graph_filename, edge_index, fmt='%d')
# joints
joint_pos = rig_info.get_joint_dict()
joint_name_list = list(joint_pos.keys())
joint_pos_list = list(joint_pos.values())
joint_pos_list = [np.array(i) for i in joint_pos_list]
adjacent_matrix = rig_info.adjacent_matrix()
joint_filename = os.path.join(dataset_folder, '{:s}/{:d}_j.txt'.format(split_name, model_id))
adj_filename = os.path.join(dataset_folder, '{:s}/{:d}_adj.txt'.format(split_name, model_id))
np.savetxt(adj_filename, adjacent_matrix, fmt='%d')
np.savetxt(joint_filename, np.array(joint_pos_list), fmt='%.6f')
# pre_trained attn
shutil.copyfile(os.path.join(dataset_folder, 'pretrain_attention/{:d}.txt'.format(model_id)),
os.path.join(dataset_folder, '{:s}/{:d}_attn.txt'.format(split_name, model_id)))
# voxel
shutil.copyfile(os.path.join(dataset_folder, 'vox/{:d}.binvox'.format(model_id)),
os.path.join(dataset_folder, '{:s}/{:d}.binvox'.format(split_name, model_id)))
#skinning information
num_nearest_bone = 5
geo_dist = np.load(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_volumetric_geo.npy".format(model_id)))
bone_pos, bone_names, bone_isleaf = get_bones(rig_info)
input_samples = [] # mesh_vertex_id, (bone_id, 1 / D_g, is_leaf) * N
ground_truth_labels = [] # w_1, w_2, ..., w_N
for vert_remesh_id in range(len(remesh_obj_v)):
this_sample = [vert_remesh_id]
this_label = []
skin = rig_info.joint_skin[vert_remesh_id]
skin_w = {}
for i in np.arange(1, len(skin), 2):
skin_w[skin[i]] = float(skin[i + 1])
bone_id_near_to_far = np.argsort(geo_dist[vert_remesh_id, :])
for i in range(num_nearest_bone):
if i >= len(bone_id_near_to_far):
this_sample += [-1, 0, 0]
this_label.append(0.0)
continue
bone_id = bone_id_near_to_far[i]
this_sample.append(bone_id)
this_sample.append(1.0 / (geo_dist[vert_remesh_id, bone_id] + 1e-10))
this_sample.append(bone_isleaf[bone_id])
start_joint_name = bone_names[bone_id][0]
if start_joint_name in skin_w:
this_label.append(skin_w[start_joint_name])
del skin_w[start_joint_name]
else:
this_label.append(0.0)
input_samples.append(this_sample)
ground_truth_labels.append(this_label)
with open(os.path.join(dataset_folder, '{:s}/{:d}_skin.txt'.format(split_name, model_id)), 'w') as fout:
for i in range(len(bone_pos)):
fout.write('bones {:s} {:s} {:.6f} {:.6f} {:.6f} '
'{:.6f} {:.6f} {:.6f}\n'.format(bone_names[i][0], bone_names[i][1],
bone_pos[i, 0], bone_pos[i, 1], bone_pos[i, 2],
bone_pos[i, 3], bone_pos[i, 4], bone_pos[i, 5]))
for i in range(len(input_samples)):
fout.write('bind {:d} '.format(input_samples[i][0]))
for j in np.arange(1, len(input_samples[i]), 3):
fout.write('{:d} {:.6f} {:d} '.format(input_samples[i][j], input_samples[i][j + 1], input_samples[i][j + 2]))
fout.write('\n')
for i in range(len(ground_truth_labels)):
fout.write('influence ')
for j in range(len(ground_truth_labels[i])):
fout.write('{:.3f} '.format(ground_truth_labels[i][j]))
fout.write('\n')
if __name__ == '__main__':
dataset_folder = "/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/"
p = Pool(8)
p.map(genDataset, [0, 1, 2, 3, 4, 5, 6, 7])
#genDataset(0)
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