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extract_skel.py
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extract_skel.py
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from absl import flags, app
import sys
from nnutils.vis_utils import draw_skeleton_2d, get_bone_skeleton_association, get_skeleton, get_skeleton_numpy, get_skeleton_numpy_vis_v1
sys.path.insert(0,'third_party')
import numpy as np
import cv2
import trimesh
import os
import torch
from utils.io import get_bones_mesh, save_vid, str_to_frame, save_bones
from nnutils.train_utils import v2s_trainer
from nnutils.geom_utils import correct_bones, get_interpolated_skinning_weights, load_skeleton, obj_to_cam, tensor2array, vec_to_sim3, obj_to_cam
from ext_utils.flowlib import cat_imgflo
opts = flags.FLAGS
def save_output(rendered_seq, aux_seq, seqname, skeleton, save_flo, bone_to_skeleton_pairs):
save_dir = '%s/'%(opts.model_path.rsplit('/',1)[0]+'-rendering')
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
length = len(aux_seq['mesh'])
mesh_rest = aux_seq['mesh_rest']
len_max = (mesh_rest.vertices.max(0) - mesh_rest.vertices.min(0)).max()
mesh_rest.export('%s/mesh-rest.obj'%save_dir)
if 'mesh_rest_skin' in aux_seq.keys():
aux_seq['mesh_rest_skin'].export('%s/mesh-rest-skin.obj'%save_dir)
# save canonical skeleton
canonical_skeleton = get_skeleton_numpy(skeleton.joint_centers.detach().cpu().numpy(), skeleton.joint_connections)
canonical_skeleton.export('%s/skeleton-rest.obj'%save_dir)
flo_gt_vid = []
flo_p_vid = []
for i in range(length):
impath = aux_seq['impath'][i]
seqname = impath.split('/')[-2]
save_prefix = '%s/%s'%(save_dir,seqname)
idx = int(impath.split('/')[-1].split('.')[-2])
mesh = aux_seq['mesh'][i]
rtk = aux_seq['rtk'][i]
if 'skeleton' in aux_seq.keys() and len(aux_seq['skeleton'])>0:
skeleton_path = '%s-skeleton-%05d.obj'%(save_prefix, idx)
skel_as_mesh = get_skeleton_numpy_vis_v1(aux_seq['skeleton'][i], skeleton.joint_connections)
skel_as_mesh.export(skeleton_path)
if 'bone' in aux_seq.keys() and len(aux_seq['bone'])>0:
bones = aux_seq['bone'][i]
bone_path = '%s-bone-%05d.obj'%(save_prefix, idx)
save_bones(bones, len_max/5, bone_path)
ends = np.zeros((bones.shape[0],3))
for bone_idx in range(bones.shape[0]):
pair_stats = bone_to_skeleton_pairs[bone_idx]
parent_joint_idx, child_joint_idx = pair_stats[0][0], pair_stats[0][1]
ends[bone_idx,:] = aux_seq['skeleton'][i][parent_joint_idx] + pair_stats[1][1] * (aux_seq['skeleton'][i][child_joint_idx] - aux_seq['skeleton'][i][parent_joint_idx])
bone_skeleton_association = get_bone_skeleton_association(bones[:,:3], ends)
bone_skeleton_association = trimesh.util.concatenate([skel_as_mesh, bone_skeleton_association])
bone_skeleton_association.export('%s-bone_skeleton_association-%05d.obj'%(save_prefix, idx))
mesh.export('%s-mesh-%05d.obj'%(save_prefix, idx))
np.savetxt('%s-cam-%05d.txt' %(save_prefix, idx), rtk)
img_gt = rendered_seq['img'][i]
flo_gt = rendered_seq['flo'][i]
mask_gt = rendered_seq['sil'][i][...,0]
flo_gt[mask_gt<=0] = 0
img_gt[mask_gt<=0] = 1
if save_flo: img_gt = cat_imgflo(img_gt, flo_gt)
else: img_gt*=255
cv2.imwrite('%s-img-gt-%05d.jpg'%(save_prefix, idx), img_gt[...,::-1])
flo_gt_vid.append(img_gt)
img_p = rendered_seq['img_coarse'][i]
flo_p = rendered_seq['flo_coarse'][i]
mask_gt = cv2.resize(mask_gt, flo_p.shape[:2][::-1]).astype(bool)
flo_p[mask_gt<=0] = 0
img_p[mask_gt<=0] = 1
if save_flo: img_p = cat_imgflo(img_p, flo_p)
else: img_p*=255
cv2.imwrite('%s-img-p-%05d.jpg'%(save_prefix, idx), img_p[...,::-1])
flo_p_vid.append(img_p)
flo_gt = cv2.resize(flo_gt, flo_p.shape[:2])
flo_err = np.linalg.norm( flo_p - flo_gt ,2,-1)
flo_err_med = np.median(flo_err[mask_gt])
flo_err[~mask_gt] = 0.
cv2.imwrite('%s-flo-err-%05d.jpg'%(save_prefix, idx),
128*flo_err/flo_err_med)
img_gt = rendered_seq['img'][i]
img_p = rendered_seq['img_coarse'][i]
img_gt = cv2.resize(img_gt, img_p.shape[:2][::-1])
img_err = np.power(img_gt - img_p,2).sum(-1)
img_err_med = np.median(img_err[mask_gt])
img_err[~mask_gt] = 0.
cv2.imwrite('%s-img-err-%05d.jpg'%(save_prefix, idx),
128*img_err/img_err_med)
upsample_frame = min(30, len(flo_p_vid))
save_vid('%s-img-p' %(save_prefix), flo_p_vid, upsample_frame=upsample_frame)
save_vid('%s-img-gt' %(save_prefix),flo_gt_vid,upsample_frame=upsample_frame)
def main(_):
trainer = v2s_trainer(opts, is_eval=True)
data_info = trainer.init_dataset()
trainer.define_model(data_info)
seqname=opts.seqname
dynamic_mesh = opts.flowbw or opts.lbs
idx_render = str_to_frame(opts.test_frames, data_info)
trainer.model.img_size = opts.render_size
chunk = opts.frame_chunk
bones_rst = trainer.model.bones
bones_rst, _ = correct_bones(trainer.model, bones_rst)
assert opts.skeleton_file != ''
if opts.skeleton_bone_residual > 0:
constructed_skeleton = load_skeleton(opts.skeleton_file, trainer.model.device, residual_update=True)
unchanged_skel = get_skeleton(constructed_skeleton.joint_centers, constructed_skeleton.joint_connections)
unchanged_skel.export((opts.model_path[:-17]+'canonical_skel_unchanged.obj'))
clipped_residuals = torch.tanh(trainer.model.skel_bone_residuals) * opts.skeleton_bone_residual
trainer.model.skeleton.update_skeleton_with_residuals(clipped_residuals)
learned_skel = get_skeleton(trainer.model.skeleton.joint_centers, trainer.model.skeleton.joint_connections)
learned_skel.export((opts.model_path[:-17]+'canonical_skel_learned.obj'))
bone_to_skeleton_pairs = get_interpolated_skinning_weights(trainer.model.skeleton, bones_rst)
draw_skeleton_2d(trainer.model.skeleton.joint_centers, trainer.model.skeleton.joint_connections, (opts.model_path[:-17]+'skeleton_2d.png'))
ends = np.zeros((bones_rst.shape[0],3))
joint_centers = trainer.model.skeleton.joint_centers.detach().cpu().numpy()
for bone_idx in range(bones_rst.shape[0]):
pair_stats = bone_to_skeleton_pairs[bone_idx]
parent_joint_idx, child_joint_idx = pair_stats[0][0], pair_stats[0][1]
ends[bone_idx,:] = joint_centers[parent_joint_idx] + pair_stats[1][1] * (joint_centers[child_joint_idx] - joint_centers[parent_joint_idx])
skel_as_mesh = get_skeleton_numpy(joint_centers, trainer.model.skeleton.joint_connections)
bone_skeleton_association = get_bone_skeleton_association(bones_rst[:,:3].detach().cpu().numpy(), ends)
bones_rst_mesh = get_bones_mesh(bones_rst.detach().cpu().numpy(),0.02)
bone_skeleton_association = trimesh.util.concatenate([skel_as_mesh, bone_skeleton_association, bones_rst_mesh])
bone_skeleton_association.export((opts.model_path[:-17]+'canonical_skel_bone_association.obj'))
for i in range(0, len(idx_render), chunk):
rendered_seq, aux_seq = trainer.eval_skel(idx_render=idx_render[i:i+chunk],
dynamic_mesh=dynamic_mesh, skeleton=trainer.model.skeleton, bone_to_skeleton_pairs=bone_to_skeleton_pairs)
rendered_seq = tensor2array(rendered_seq)
save_output(rendered_seq, aux_seq, seqname, trainer.model.skeleton, save_flo=opts.use_corresp, bone_to_skeleton_pairs=bone_to_skeleton_pairs)
if __name__ == '__main__':
app.run(main)