def match_poses(gts, gt_stats, errs, scene_id, visib_gt_min, error_threshs,
n_top):
# Organize the errors by image id and object id (for faster query)
errs_org = {}
for e in errs:
errs_org.setdefault(e['im_id'], {}). \
setdefault(e['obj_id'], []).append(e)
# Matching
matches = []
for im_id, gts_im in gts.items():
matches_im = []
for gt_id, gt in enumerate(gts_im):
valid = gt_stats[im_id][gt_id]['visib_fract'] >= visib_gt_min
matches_im.append({
'scene_id': scene_id,
'im_id': im_id,
'obj_id': gt['obj_id'],
'gt_id': gt_id,
'est_id': -1,
'score': -1,
'error': -1,
'error_norm': -1,
# 'stats': gt_stats[im_id][gt_id],
'valid': int(valid)
})
# Mask of valid GT poses (i.e. GT poses with sufficient visibility)
gt_valid_mask = [m['valid'] for m in matches_im]
# Treat estimates of each object separately
im_obj_ids = set([gt['obj_id'] for gt in gts_im])
for obj_id in im_obj_ids:
if im_id in errs_org.keys() and obj_id in errs_org[im_id].keys():
# Greedily match the estimated poses to the ground truth
# poses in the order of decreasing score
errs_im_obj = errs_org[im_id][obj_id]
ms = pose_matching.match_poses(errs_im_obj,
error_threshs[obj_id], n_top,
gt_valid_mask)
for m in ms:
g = matches_im[m['gt_id']]
g['est_id'] = m['est_id']
g['score'] = m['score']
g['error'] = m['error']
g['error_norm'] = m['error_norm']
matches += matches_im
return matches
} for gt_id, gt in enumerate(gts_im)]
# Mask of valid GT poses (i.e. GT poses with sufficient visibility)
gt_valid_mask = [m['valid'] for m in matches_im]
# Treat estimates of each object separately
im_obj_ids = set([gt['obj_id'] for gt in gts_im])
for obj_id in im_obj_ids:
if im_id in errs_org.keys()\
and obj_id in errs_org[im_id].keys():
# Greedily match the estimated poses to the ground truth
# poses in the order of decreasing score
errs_im_obj = errs_org[im_id][obj_id]
ms = pose_matching.match_poses(errs_im_obj,
error_threshs[obj_id],
n_top, gt_valid_mask)
for m in ms:
g = matches_im[m['gt_id']]
g['est_id'] = m['est_id']
g['score'] = m['score']
g['error'] = m['error']
g['error_norm'] = m['error_norm']
matches += matches_im
# Calculation of performance scores
#---------------------------------------------------------------------------
# Count the number of visible object instances in each image
insts = {
i: {j: defaultdict(lambda: 0)
