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import os
import torch
import numpy as np
from scipy.spatial.transform import Rotation as R
from torch.utils.data import Dataset, DataLoader, Subset
class TrumansDataset(Dataset):
def __init__(self, folder, device, mesh_grid, batch_size=1, seq_len=32, step=1, nb_voxels=32, train=True, load_scene=True, load_action=True, no_objects=False, **kwargs):
self.device = device
self.train = train
self.load_scene = load_scene
self.load_action = load_action
# self.body_pose = np.load(os.path.join(folder, 'human_pose.npy'))
# self.transl = np.load(os.path.join(folder, 'human_transl.npy'))
# self.global_orient = np.load(os.path.join(folder, 'human_orient.npy'))
# self.motion_ind = np.load(os.path.join(folder, 'idx_start.npy'))
# self.joints = np.load(os.path.join(folder, 'human_joints.npy'))
# self.file_blend = np.load(os.path.join(folder, 'file_blend.npy'))
self.seq_len=seq_len
self.step = step
self.batch_size = batch_size
# if self.load_action:
# self.action_label = np.load(os.path.join(folder, 'action_label.npy')).astype(np.float32)
if self.load_scene:
self.mesh_grid = mesh_grid
self.nb_voxels = nb_voxels
self.no_objects = no_objects
self.nb_voxels = nb_voxels
self.scene_occ = []
self.scene_dict = {}
self.scene_folder = os.path.join(folder, 'Scene')
# self.scene_flag = np.load(os.path.join(folder, 'scene_flag.npy'))
if not no_objects:
# self.object_flag = np.load(os.path.join(folder, 'object_flag.npy'))
# self.object_mat = np.load(os.path.join(folder, 'object_mat.npy'))
self.object_occ = {}
self.object_folder = os.path.join(folder, 'Object')
for file in sorted(os.listdir(self.object_folder)):
print(f"Loading object occupied coordinates {file}")
obj_name = file.replace('.npy', '')
self.object_occ[obj_name] = torch.from_numpy(np.load(os.path.join(self.object_folder, file))).to(device)
for sid, file in enumerate(sorted(os.listdir(self.scene_folder))):
# if scene_name != '' and scene_name not in file:
# continue
print(f"{sid} Loading Scene Mesh {file}")
scene_occ = np.load(os.path.join(self.scene_folder, file))
scene_occ = torch.from_numpy(scene_occ).to(device=device, dtype=bool)
self.scene_occ.append(scene_occ)
self.scene_dict[file] = sid
self.scene_occ = torch.stack(self.scene_occ)
self.scene_grid_np = np.array([-3, 0, -4, 3, 2, 4, 300, 100, 400])
self.scene_grid_torch = torch.tensor([-3, 0, -4, 3, 2, 4, 300, 100, 400]).to(device)
self.batch_id = torch.linspace(0, batch_size - 1, batch_size).tile((nb_voxels ** 3, 1)).T\
.reshape(-1, 1).to(device=device, dtype=torch.long)
self.batch_id_obj = torch.linspace(0, batch_size - 1, batch_size).tile((9000, 1)).T \
.reshape(-1, 1).to(device=device, dtype=torch.long)
# TODO CHANGE STEP
norm = np.load(os.path.join(folder, 'norm.npy'), allow_pickle=True).item()[f'{seq_len, 3}']
self.min = norm[0].astype(np.float32)
self.max = norm[1].astype(np.float32)
self.min_torch = torch.tensor(self.min).to(device)
self.max_torch = torch.tensor(self.max).to(device)
def add_object_points(self, points, occ):
points = points.reshape(-1, 3)
voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:])
voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size)
voxel = voxel.to(dtype=torch.long)
# voxel = rearrange(voxel, 'b p c -> (b p) c')
lb = torch.all(voxel >= 0, dim=-1)
ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1)
in_bound = torch.logical_and(lb, ub)
# voxel = torch.cat([self.batch_id_obj, voxel], dim=-1)
voxel = voxel[in_bound]
occ[0, voxel[:, 0], voxel[:, 1], voxel[:, 2]] = True
def get_occ_for_points(self, points, obj_locs, scene_flag):
#TODO
# points_new = points.reshape(-1, 3)
# center_xz = points_new[:, [0, 2]].mean(axis=0)
# if torch.norm(center_xz) > 0.:
# occ_for_points = torch.load('occ_for_points_at_clear_space.pt').to(points.device)
# return occ_for_points
if isinstance(scene_flag, str):
for k, v in self.scene_dict.items():
if scene_flag in k:
scene_flag = [v]
break
batch_size = points.shape[0]
seq_len = points.shape[1]
points = points.reshape(-1, 3)
voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:])
voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size)
voxel = voxel.to(dtype=torch.long)
# voxel = rearrange(voxel, 'b p c -> (b p) c')
lb = torch.all(voxel >= 0, dim=-1)
ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1)
in_bound = torch.logical_and(lb, ub)
voxel[torch.logical_not(in_bound)] = 0
voxel = torch.cat([self.batch_id, voxel], dim=1)
occ = self.scene_occ[scene_flag]
#TODO
# occ[:] = False
# occ[:, :, 0, :] = True
# import cv2
# img = occ[0, :, 10, :].detach().cpu().numpy()
# im = np.zeros((300, 400))
# im[img] = 255
# cv2.imwrite('gray.jpg', im.T)
if obj_locs:
for obj_name, obj_loc in obj_locs.items():
obj_points = self.object_occ[obj_name].clone()
obj_points[:, 0] += obj_loc['x']
obj_points[:, 2] += obj_loc['z']
# import pdb
# pdb.set_trace()
self.add_object_points(obj_points, occ)
occ_for_points = occ[voxel[:, 0], voxel[:, 1], voxel[:, 2], voxel[:, 3]]
occ_for_points[torch.logical_not(in_bound)] = True
occ_for_points = occ_for_points.reshape(batch_size, seq_len, -1)
# torch.save(occ_for_points, 'occ_for_points_at_clear_space.pt')
# occ_for_points = torch.ones(batch_size, seq_len, 22).to('cuda')
return occ_for_points
def create_meshgrid(self, batch_size=1):
bbox = self.mesh_grid
size = (self.nb_voxels, self.nb_voxels, self.nb_voxels)
x = torch.linspace(bbox[0], bbox[1], size[0])
y = torch.linspace(bbox[2], bbox[3], size[1])
z = torch.linspace(bbox[4], bbox[5], size[2])
xx, yy, zz = torch.meshgrid(x, y, z, indexing='ij')
grid = torch.stack([xx, yy, zz], dim=-1).reshape(-1, 3)
grid = grid.repeat(batch_size, 1, 1)
# aug_z = 0.75 + torch.rand(batch_size, 1) * 0.35
# grid[:, :, 2] = grid[:, :, 2] * aug_z
return grid
@staticmethod
def combine_mesh(vert_list, face_list):
assert len(vert_list) == len(face_list)
verts = None
faces = None
for v, f in zip(vert_list, face_list):
if verts is None:
verts = v
faces = f
else:
f = f + verts.shape[0]
verts = torch.cat([verts, v])
faces = torch.cat([faces, f])
return verts, faces
@staticmethod
def transform_mesh(vert_list, trans_mats):
assert len(vert_list) == len(trans_mats)
vert_list_new = []
for v, m in zip(vert_list, trans_mats):
v = v @ m[:3, :3].T + m[:3, 3]
vert_list_new.append(v)
vert_list_new = torch.stack(vert_list_new)
return vert_list_new
def __len__(self):
return len(self.motion_ind)
def normalize(self, data):
shape_orig = data.shape
data = data.reshape((-1, 3))
# data = (data - self.mean) / self.std
data = -1. + 2. * (data - self.min) / (self.max - self.min)
data = data.reshape(shape_orig)
return data
def normalize_torch(self, data):
shape_orig = data.shape
data = data.reshape((-1, 3))
# data = (data - self.mean) / self.std
data = -1. + 2. * (data - self.min_torch) / (self.max_torch - self.min_torch)
data = data.reshape(shape_orig)
return data
def denormalize(self, data):
shape_orig = data.shape
data = data.reshape((-1, 3))
# data = data * self.std + self.mean
data = (data + 1.) * (self.max - self.min) / 2. + self.min
data = data.reshape(shape_orig)
return data
def denormalize_torch(self, data):
shape_orig = data.shape
data = data.reshape((-1, 3))
# data = data * self.std + self.mean
import pdb
data = (data + 1.) * (self.max_torch - self.min_torch) / 2. + self.min_torch
data = data.reshape(shape_orig)
return data |