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run_linemod.py
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run_linemod.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 *
import json,uuid,joblib,os,sys
import scipy.spatial as spatial
from multiprocessing import Pool
import multiprocessing
from functools import partial
from itertools import repeat
import itertools
from datareader import *
from estimater import *
code_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(f'{code_dir}/mycpp/build')
import yaml
def get_mask(reader, i_frame, ob_id, detect_type):
if detect_type=='box':
mask = reader.get_mask(i_frame, ob_id)
H,W = mask.shape[:2]
vs,us = np.where(mask>0)
umin = us.min()
umax = us.max()
vmin = vs.min()
vmax = vs.max()
valid = np.zeros((H,W), dtype=bool)
valid[vmin:vmax,umin:umax] = 1
elif detect_type=='mask':
mask = reader.get_mask(i_frame, ob_id)
if mask is None:
return None
valid = mask>0
elif detect_type=='detected':
mask = cv2.imread(reader.color_files[i_frame].replace('rgb','mask_cosypose'), -1)
valid = mask==ob_id
else:
raise RuntimeError
return valid
def run_pose_estimation_worker(reader, i_frames, est:FoundationPose=None, debug=0, ob_id=None, device='cuda:0'):
torch.cuda.set_device(device)
est.to_device(device)
est.glctx = dr.RasterizeCudaContext(device=device)
result = NestDict()
for i, i_frame in enumerate(i_frames):
logging.info(f"{i}/{len(i_frames)}, i_frame:{i_frame}, ob_id:{ob_id}")
video_id = reader.get_video_id()
color = reader.get_color(i_frame)
depth = reader.get_depth(i_frame)
id_str = reader.id_strs[i_frame]
H,W = color.shape[:2]
debug_dir =est.debug_dir
ob_mask = get_mask(reader, i_frame, ob_id, detect_type=detect_type)
if ob_mask is None:
logging.info("ob_mask not found, skip")
result[video_id][id_str][ob_id] = np.eye(4)
return result
est.gt_pose = reader.get_gt_pose(i_frame, ob_id)
pose = est.register(K=reader.K, rgb=color, depth=depth, ob_mask=ob_mask, ob_id=ob_id)
logging.info(f"pose:\n{pose}")
if debug>=3:
m = est.mesh_ori.copy()
tmp = m.copy()
tmp.apply_transform(pose)
tmp.export(f'{debug_dir}/model_tf.obj')
result[video_id][id_str][ob_id] = pose
return result
def run_pose_estimation():
wp.force_load(device='cuda')
reader_tmp = LinemodReader(f'{opt.linemod_dir}/lm_test_all/test/000002', split=None)
debug = opt.debug
use_reconstructed_mesh = opt.use_reconstructed_mesh
debug_dir = opt.debug_dir
res = NestDict()
glctx = dr.RasterizeCudaContext()
mesh_tmp = trimesh.primitives.Box(extents=np.ones((3)), transform=np.eye(4)).to_mesh()
est = FoundationPose(model_pts=mesh_tmp.vertices.copy(), model_normals=mesh_tmp.vertex_normals.copy(), symmetry_tfs=None, mesh=mesh_tmp, scorer=None, refiner=None, glctx=glctx, debug_dir=debug_dir, debug=debug)
for ob_id in reader_tmp.ob_ids:
ob_id = int(ob_id)
if use_reconstructed_mesh:
mesh = reader_tmp.get_reconstructed_mesh(ob_id, ref_view_dir=opt.ref_view_dir)
else:
mesh = reader_tmp.get_gt_mesh(ob_id)
symmetry_tfs = reader_tmp.symmetry_tfs[ob_id]
args = []
video_dir = f'{opt.linemod_dir}/lm_test_all/test/{ob_id:06d}'
reader = LinemodReader(video_dir, split=None)
video_id = reader.get_video_id()
est.reset_object(model_pts=mesh.vertices.copy(), model_normals=mesh.vertex_normals.copy(), symmetry_tfs=symmetry_tfs, mesh=mesh)
for i in range(len(reader.color_files)):
args.append((reader, [i], est, debug, ob_id, "cuda:0"))
outs = []
for arg in args:
out = run_pose_estimation_worker(*arg)
outs.append(out)
for out in outs:
for video_id in out:
for id_str in out[video_id]:
for ob_id in out[video_id][id_str]:
res[video_id][id_str][ob_id] = out[video_id][id_str][ob_id]
with open(f'{opt.debug_dir}/linemod_res.yml','w') as ff:
yaml.safe_dump(make_yaml_dumpable(res), ff)
if __name__=='__main__':
parser = argparse.ArgumentParser()
code_dir = os.path.dirname(os.path.realpath(__file__))
parser.add_argument('--linemod_dir', type=str, default="/mnt/9a72c439-d0a7-45e8-8d20-d7a235d02763/DATASET/LINEMOD", help="linemod root dir")
parser.add_argument('--use_reconstructed_mesh', type=int, default=0)
parser.add_argument('--ref_view_dir', type=str, default="/mnt/9a72c439-d0a7-45e8-8d20-d7a235d02763/DATASET/YCB_Video/bowen_addon/ref_views_16")
parser.add_argument('--debug', type=int, default=0)
parser.add_argument('--debug_dir', type=str, default=f'{code_dir}/debug')
opt = parser.parse_args()
set_seed(0)
detect_type = 'mask' # mask / box / detected
run_pose_estimation()