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extract.py
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extract.py
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from absl import flags, app
import sys
sys.path.insert(0,'third_party')
import numpy as np
import torch
import os
import glob
import pdb
import cv2
import trimesh
from scipy.spatial.transform import Rotation as R
import imageio
from utils.io import save_vid, str_to_frame, save_bones
from utils.colors import label_colormap
from nnutils.train_utils import v2s_trainer
from nnutils.geom_utils import obj_to_cam, tensor2array, vec_to_sim3, obj_to_cam
from ext_utils.util_flow import write_pfm
from ext_utils.flowlib import cat_imgflo
opts = flags.FLAGS
def save_output(rendered_seq, aux_seq, seqname, save_flo):
save_dir = '%s/'%(opts.model_path.rsplit('/',1)[0])
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)
if 'bone_rest' in aux_seq.keys():
bone_rest = aux_seq['bone_rest']
save_bones(bone_rest, len_max, '%s/bone-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]
# convert bones to meshes TODO: warp with a function
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, bone_path)
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)
# fps = 1./(5./len(flo_p_vid))
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 transform_shape(mesh,rtk):
"""
(deprecated): absorb rt into mesh vertices,
"""
vertices = torch.Tensor(mesh.vertices)
Rmat = torch.Tensor(rtk[:3,:3])
Tmat = torch.Tensor(rtk[:3,3])
vertices = obj_to_cam(vertices, Rmat, Tmat)
rtk[:3,:3] = np.eye(3)
rtk[:3,3] = 0.
mesh = trimesh.Trimesh(vertices.numpy(), mesh.faces)
return mesh, rtk
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)
# idx_render[0] += 50
# idx_render[0] += 374
# idx_render[0] += 292
# idx_render[0] += 10
# idx_render[0] += 340
# idx_render[0] += 440
# idx_render[0] += 540
# idx_render[0] += 640
# idx_render[0] += trainer.model.data_offset[4]-4 + 37
# idx_render[0] += 36
trainer.model.img_size = opts.render_size
chunk = opts.frame_chunk
for i in range(0, len(idx_render), chunk):
rendered_seq, aux_seq = trainer.eval(idx_render=idx_render[i:i+chunk],
dynamic_mesh=dynamic_mesh)
rendered_seq = tensor2array(rendered_seq)
save_output(rendered_seq, aux_seq, seqname, save_flo=opts.use_corresp)
#TODO merge the outputs
if __name__ == '__main__':
app.run(main)