def mv1pmf_skel(dataset, check_repro=True, args=None):
MIN_CONF_THRES = args.thres2d
no_img = not (args.vis_det or args.vis_repro)
dataset.no_img = no_img
kp3ds = []
start, end = args.start, min(args.end, len(dataset))
kpts_repro = None
for nf in tqdm(range(start, end), desc='triangulation'):
images, annots = dataset[nf]
check_keypoints(annots['keypoints'],
WEIGHT_DEBUFF=1,
min_conf=MIN_CONF_THRES)
keypoints3d, kpts_repro = simple_recon_person(annots['keypoints'],
dataset.Pall)
if check_repro:
keypoints3d, kpts_repro = check_repro_error(
keypoints3d,
kpts_repro,
annots['keypoints'],
P=dataset.Pall,
MAX_REPRO_ERROR=args.MAX_REPRO_ERROR)
# keypoints3d, kpts_repro = robust_triangulate(annots['keypoints'], dataset.Pall, config=config, ret_repro=True)
kp3ds.append(keypoints3d)
if args.vis_det:
dataset.vis_detections(images, annots, nf, sub_vis=args.sub_vis)
if args.vis_repro:
dataset.vis_repro(images, kpts_repro, nf=nf, sub_vis=args.sub_vis)
# smooth the skeleton
if args.smooth3d > 0:
kp3ds = smooth_skeleton(kp3ds, args.smooth3d)
for nf in tqdm(range(len(kp3ds)), desc='dump'):
dataset.write_keypoints3d(kp3ds[nf], nf + start)
def check_repro_error(keypoints3d, kpts_repro, keypoints2d, P,
MAX_REPRO_ERROR):
square_diff = (keypoints2d[:, :, :2] - kpts_repro[:, :, :2])**2
conf = keypoints3d[None, :, -1:]
conf = (keypoints3d[None, :, -1:] > 0) * (keypoints2d[:, :, -1:] > 0)
dist = np.sqrt((((kpts_repro[..., :2] - keypoints2d[..., :2]) *
conf)**2).sum(axis=-1))
vv, jj = np.where(dist > MAX_REPRO_ERROR)
if vv.shape[0] > 0:
keypoints2d[vv, jj, -1] = 0.
keypoints3d, kpts_repro = simple_recon_person(keypoints2d, P)
return keypoints3d, kpts_repro