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estimater.py
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estimater.py
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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from Utils import *
from datareader import *
import itertools
from learning.training.predict_score import *
from learning.training.predict_pose_refine import *
import yaml
class FoundationPose:
def __init__(self, model_pts, model_normals, symmetry_tfs=None, mesh=None, scorer:ScorePredictor=None, refiner:PoseRefinePredictor=None, glctx=None, debug=0, debug_dir='/home/bowen/debug/novel_pose_debug/'):
self.gt_pose = None
self.ignore_normal_flip = True
self.debug = debug
self.debug_dir = debug_dir
os.makedirs(debug_dir, exist_ok=True)
self.reset_object(model_pts, model_normals, symmetry_tfs=symmetry_tfs, mesh=mesh)
self.make_rotation_grid(min_n_views=40, inplane_step=60)
self.glctx = glctx
if scorer is not None:
self.scorer = scorer
else:
self.scorer = ScorePredictor()
if refiner is not None:
self.refiner = refiner
else:
self.refiner = PoseRefinePredictor()
self.pose_last = None # Used for tracking; per the centered mesh
def reset_object(self, model_pts, model_normals, symmetry_tfs=None, mesh=None):
max_xyz = mesh.vertices.max(axis=0)
min_xyz = mesh.vertices.min(axis=0)
self.model_center = (min_xyz+max_xyz)/2
if mesh is not None:
self.mesh_ori = mesh.copy()
mesh = mesh.copy()
mesh.vertices = mesh.vertices - self.model_center.reshape(1,3)
model_pts = mesh.vertices
self.diameter = compute_mesh_diameter(model_pts=mesh.vertices, n_sample=10000)
self.vox_size = max(self.diameter/20.0, 0.003)
logging.info(f'self.diameter:{self.diameter}, vox_size:{self.vox_size}')
self.dist_bin = self.vox_size/2
self.angle_bin = 20 # Deg
pcd = toOpen3dCloud(model_pts, normals=model_normals)
pcd = pcd.voxel_down_sample(self.vox_size)
self.max_xyz = np.asarray(pcd.points).max(axis=0)
self.min_xyz = np.asarray(pcd.points).min(axis=0)
self.pts = torch.tensor(np.asarray(pcd.points), dtype=torch.float32, device='cuda')
self.normals = F.normalize(torch.tensor(np.asarray(pcd.normals), dtype=torch.float32, device='cuda'), dim=-1)
logging.info(f'self.pts:{self.pts.shape}')
self.mesh_path = None
self.mesh = mesh
if self.mesh is not None:
self.mesh_path = f'/tmp/{uuid.uuid4()}.obj'
self.mesh.export(self.mesh_path)
self.mesh_tensors = make_mesh_tensors(self.mesh)
if symmetry_tfs is None:
self.symmetry_tfs = torch.eye(4).float().cuda()[None]
else:
self.symmetry_tfs = torch.as_tensor(symmetry_tfs, device='cuda', dtype=torch.float)
logging.info("reset done")
def get_tf_to_centered_mesh(self):
tf_to_center = torch.eye(4, dtype=torch.float, device='cuda')
tf_to_center[:3,3] = -torch.as_tensor(self.model_center, device='cuda', dtype=torch.float)
return tf_to_center
def to_device(self, s='cuda:0'):
for k in self.__dict__:
self.__dict__[k] = self.__dict__[k]
if torch.is_tensor(self.__dict__[k]) or isinstance(self.__dict__[k], nn.Module):
logging.info(f"Moving {k} to device {s}")
self.__dict__[k] = self.__dict__[k].to(s)
for k in self.mesh_tensors:
logging.info(f"Moving {k} to device {s}")
self.mesh_tensors[k] = self.mesh_tensors[k].to(s)
if self.refiner is not None:
self.refiner.model.to(s)
if self.scorer is not None:
self.scorer.model.to(s)
if self.glctx is not None:
self.glctx = dr.RasterizeCudaContext(s)
def make_rotation_grid(self, min_n_views=40, inplane_step=60):
cam_in_obs = sample_views_icosphere(n_views=min_n_views)
logging.info(f'cam_in_obs:{cam_in_obs.shape}')
rot_grid = []
for i in range(len(cam_in_obs)):
for inplane_rot in np.deg2rad(np.arange(0, 360, inplane_step)):
cam_in_ob = cam_in_obs[i]
R_inplane = euler_matrix(0,0,inplane_rot)
cam_in_ob = cam_in_ob@R_inplane
ob_in_cam = np.linalg.inv(cam_in_ob)
rot_grid.append(ob_in_cam)
rot_grid = np.asarray(rot_grid)
logging.info(f"rot_grid:{rot_grid.shape}")
rot_grid = mycpp.cluster_poses(30, 99999, rot_grid, self.symmetry_tfs.data.cpu().numpy())
rot_grid = np.asarray(rot_grid)
logging.info(f"after cluster, rot_grid:{rot_grid.shape}")
self.rot_grid = torch.as_tensor(rot_grid, device='cuda', dtype=torch.float)
logging.info(f"self.rot_grid: {self.rot_grid.shape}")
def generate_random_pose_hypo(self, K, rgb, depth, mask, scene_pts=None):
'''
@scene_pts: torch tensor (N,3)
'''
ob_in_cams = self.rot_grid.clone()
center = self.guess_translation(depth=depth, mask=mask, K=K)
ob_in_cams[:,:3,3] = torch.tensor(center, device='cuda', dtype=torch.float).reshape(1,3)
return ob_in_cams
def guess_translation(self, depth, mask, K):
vs,us = np.where(mask>0)
if len(us)==0:
logging.info(f'mask is all zero')
return np.zeros((3))
uc = (us.min()+us.max())/2.0
vc = (vs.min()+vs.max())/2.0
valid = mask.astype(bool) & (depth>=0.1)
if not valid.any():
logging.info(f"valid is empty")
return np.zeros((3))
zc = np.median(depth[valid])
center = (np.linalg.inv(K)@np.asarray([uc,vc,1]).reshape(3,1))*zc
if self.debug>=2:
pcd = toOpen3dCloud(center.reshape(1,3))
o3d.io.write_point_cloud(f'{self.debug_dir}/init_center.ply', pcd)
return center.reshape(3)
def register(self, K, rgb, depth, ob_mask, ob_id=None, glctx=None, iteration=5):
'''Copmute pose from given pts to self.pcd
@pts: (N,3) np array, downsampled scene points
'''
set_seed(0)
logging.info('Welcome')
if self.glctx is None:
if glctx is None:
self.glctx = dr.RasterizeCudaContext()
# self.glctx = dr.RasterizeGLContext()
else:
self.glctx = glctx
depth = erode_depth(depth, radius=2, device='cuda')
depth = bilateral_filter_depth(depth, radius=2, device='cuda')
if self.debug>=2:
xyz_map = depth2xyzmap(depth, K)
valid = xyz_map[...,2]>=0.1
pcd = toOpen3dCloud(xyz_map[valid], rgb[valid])
o3d.io.write_point_cloud(f'{self.debug_dir}/scene_raw.ply',pcd)
cv2.imwrite(f'{self.debug_dir}/ob_mask.png', (ob_mask*255.0).clip(0,255))
normal_map = None
valid = (depth>=0.1) & (ob_mask>0)
if valid.sum()<4:
logging.info(f'valid too small, return')
pose = np.eye(4)
pose[:3,3] = self.guess_translation(depth=depth, mask=ob_mask, K=K)
return pose
if self.debug>=2:
imageio.imwrite(f'{self.debug_dir}/color.png', rgb)
cv2.imwrite(f'{self.debug_dir}/depth.png', (depth*1000).astype(np.uint16))
valid = xyz_map[...,2]>=0.1
pcd = toOpen3dCloud(xyz_map[valid], rgb[valid])
o3d.io.write_point_cloud(f'{self.debug_dir}/scene_complete.ply',pcd)
self.H, self.W = depth.shape[:2]
self.K = K
self.ob_id = ob_id
self.ob_mask = ob_mask
poses = self.generate_random_pose_hypo(K=K, rgb=rgb, depth=depth, mask=ob_mask, scene_pts=None)
poses = poses.data.cpu().numpy()
logging.info(f'poses:{poses.shape}')
center = self.guess_translation(depth=depth, mask=ob_mask, K=K)
poses = torch.as_tensor(poses, device='cuda', dtype=torch.float)
poses[:,:3,3] = torch.as_tensor(center.reshape(1,3), device='cuda')
add_errs = self.compute_add_err_to_gt_pose(poses)
logging.info(f"after viewpoint, add_errs min:{add_errs.min()}")
xyz_map = depth2xyzmap(depth, K)
poses, vis = self.refiner.predict(mesh=self.mesh, mesh_tensors=self.mesh_tensors, rgb=rgb, depth=depth, K=K, ob_in_cams=poses.data.cpu().numpy(), normal_map=normal_map, xyz_map=xyz_map, glctx=self.glctx, mesh_diameter=self.diameter, iteration=iteration, get_vis=self.debug>=2)
if vis is not None:
imageio.imwrite(f'{self.debug_dir}/vis_refiner.png', vis)
scores, vis = self.scorer.predict(mesh=self.mesh, rgb=rgb, depth=depth, K=K, ob_in_cams=poses.data.cpu().numpy(), normal_map=normal_map, mesh_tensors=self.mesh_tensors, glctx=self.glctx, mesh_diameter=self.diameter, get_vis=self.debug>=2)
if vis is not None:
imageio.imwrite(f'{self.debug_dir}/vis_score.png', vis)
add_errs = self.compute_add_err_to_gt_pose(poses)
logging.info(f"final, add_errs min:{add_errs.min()}")
ids = torch.as_tensor(scores).argsort(descending=True)
logging.info(f'sort ids:{ids}')
scores = scores[ids]
poses = poses[ids]
logging.info(f'sorted scores:{scores}')
best_pose = poses[0]@self.get_tf_to_centered_mesh()
self.pose_last = poses[0]
self.best_id = ids[0]
self.poses = poses
self.scores = scores
return best_pose.data.cpu().numpy()
def compute_add_err_to_gt_pose(self, poses):
'''
@poses: wrt. the centered mesh
'''
return -torch.ones(len(poses), device='cuda', dtype=torch.float)
def track_one(self, rgb, depth, K, iteration, extra={}):
if self.pose_last is None:
logging.info("Please init pose by register first")
raise RuntimeError
logging.info("Welcome")
depth = torch.as_tensor(depth, device='cuda', dtype=torch.float)
depth = erode_depth(depth, radius=2, device='cuda')
depth = bilateral_filter_depth(depth, radius=2, device='cuda')
logging.info("depth processing done")
xyz_map = depth2xyzmap_batch(depth[None], torch.as_tensor(K, dtype=torch.float, device='cuda')[None], zfar=np.inf)[0]
pose, vis = self.refiner.predict(mesh=self.mesh, mesh_tensors=self.mesh_tensors, rgb=rgb, depth=depth, K=K, ob_in_cams=self.pose_last.reshape(1,4,4).data.cpu().numpy(), normal_map=None, xyz_map=xyz_map, mesh_diameter=self.diameter, glctx=self.glctx, iteration=iteration, get_vis=self.debug>=2)
logging.info("pose done")
if self.debug>=2:
extra['vis'] = vis
self.pose_last = pose
return (pose@self.get_tf_to_centered_mesh()).data.cpu().numpy().reshape(4,4)