in lib/dataset/coco.py [0:0]
def evaluate(self, cfg, preds, output_dir, all_boxes, img_path,
*args, **kwargs):
res_folder = os.path.join(output_dir, 'results')
if not os.path.exists(res_folder):
os.makedirs(res_folder)
res_file = os.path.join(
res_folder, 'keypoints_%s_results.json' % self.image_set)
# person x (keypoints)
_kpts = []
for idx, kpt in enumerate(preds):
_kpts.append({
'keypoints': kpt,
'center': all_boxes[idx][0:2],
'scale': all_boxes[idx][2:4],
'area': all_boxes[idx][4],
'score': all_boxes[idx][5],
'image': int(img_path[idx][-16:-4])
})
# image x person x (keypoints)
kpts = defaultdict(list)
for kpt in _kpts:
kpts[kpt['image']].append(kpt)
# rescoring and oks nms
num_joints = self.num_joints
in_vis_thre = self.in_vis_thre
oks_thre = self.oks_thre
oks_nmsed_kpts = []
for img in kpts.keys():
img_kpts = kpts[img]
for n_p in img_kpts:
box_score = n_p['score']
kpt_score = 0
valid_num = 0
for n_jt in range(0, num_joints):
t_s = n_p['keypoints'][n_jt][2]
if t_s > in_vis_thre:
kpt_score = kpt_score + t_s
valid_num = valid_num + 1
if valid_num != 0:
kpt_score = kpt_score / valid_num
# rescoring
n_p['score'] = kpt_score * box_score
keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))],
oks_thre)
if len(keep) == 0:
oks_nmsed_kpts.append(img_kpts)
else:
oks_nmsed_kpts.append([img_kpts[_keep] for _keep in keep])
self._write_coco_keypoint_results(
oks_nmsed_kpts, res_file)
if 'test' not in self.image_set:
info_str = self._do_python_keypoint_eval(
res_file, res_folder)
name_value = OrderedDict(info_str)
return name_value, name_value['AP']
else:
return {'Null': 0}, 0