def evaluate(self, preds, output_dir, all_boxes, img_path):
res_folder = os.path.join(output_dir, 'results')
if not os.path.exists(res_folder):
try:
os.makedirs(res_folder)
except Exception:
logger.error('Fail to make {}'.format(res_folder))
# 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
if self.soft_nms:
keep = soft_oks_nms(
[img_kpts[i] for i in range(len(img_kpts))], oks_thre)
else:
keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))],
oks_thre)
oks_nmsed_kpts.append(img_kpts if len(keep) ==
0 else [img_kpts[_keep] for _keep in keep])
return oks_nmsed_kpts
def evaluate(self, cfg, preds, output_dir, all_boxes, img_path, *args, **kwargs):
rank = cfg.RANK
res_folder = os.path.join(output_dir, "results")
if not os.path.exists(res_folder):
try:
os.makedirs(res_folder)
except Exception:
logger.error("Fail to make {}".format(res_folder))
res_file = os.path.join(
res_folder, "keypoints_{}_results_{}.json".format(self.image_set, rank)
)
# 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
if self.soft_nms:
keep = soft_oks_nms(
[img_kpts[i] for i in range(len(img_kpts))], oks_thre
)
else:
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
def evaluate(self, cfg, preds, output_dir, all_boxes, img_path, *args,
**kwargs):
rank = cfg.RANK
res_folder = os.path.join(output_dir, 'results')
if not os.path.exists(res_folder):
try:
os.makedirs(res_folder)
except Exception:
logger.error('Fail to make {}'.format(res_folder))
res_file = os.path.join(
res_folder,
'keypoints_{}_results_{}.json'.format(self.image_set, rank))
# 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].split('/')[-1].replace('.jpg', ''))
})
# 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
if self.soft_nms:
keep = soft_oks_nms(
[img_kpts[i] for i in range(len(img_kpts))],
oks_thre,
sigmas=self.sigmas)
else:
keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))],
oks_thre,
sigmas=self.sigmas)
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 'val' 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
def rescore_and_save_result(self, output_file, preds, all_boxes, img_path,
orig_boxes):
assert output_file.endswith('.json') or output_file.endswith(
'.npy'), "Only json and numpy output is supported"
ensuredir(os.path.dirname(output_file))
# 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': img_path[idx],
'origbox': orig_boxes[idx]
})
# 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 = []
nmsed_kpts_by_frame = defaultdict(list)
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
if self.soft_nms:
keep = soft_oks_nms(
[img_kpts[i] for i in range(len(img_kpts))], oks_thre)
else:
keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))],
oks_thre)
if len(keep) == 0:
selected_kpts = img_kpts
else:
selected_kpts = [img_kpts[_keep] for _keep in keep]
oks_nmsed_kpts.append(selected_kpts)
nmsed_kpts_by_frame[img] = selected_kpts
self._write_keypoint_results(nmsed_kpts_by_frame, output_file)
