def _report_metric(self, res_file, metrics, pck_thr=0.2, auc_nor=30):
"""Keypoint evaluation.
Args:
res_file (str): Json file stored prediction results.
metrics (str | list[str]): Metric to be performed.
Options: 'PCK', 'PCKh', 'AUC', 'EPE'.
pck_thr (float): PCK threshold, default as 0.2.
pckh_thr (float): PCKh threshold, default as 0.7.
auc_nor (float): AUC normalization factor, default as 30 pixel.
Returns:
List: Evaluation results for evaluation metric.
"""
info_str = []
with open(res_file, 'r') as fin:
preds = json.load(fin)
assert len(preds) == len(self.db)
outputs = []
gts = []
masks = []
threshold_bbox = []
for pred, item in zip(preds, self.db):
outputs.append(np.array(pred['keypoints'])[:, :-1])
gts.append(np.array(item['joints_3d'])[:, :-1])
masks.append((np.array(item['joints_3d_visible'])[:, 0]) > 0)
if 'PCK' in metrics:
bbox = np.array(item['bbox'])
bbox_thr = np.max(bbox[2:])
threshold_bbox.append(np.array([bbox_thr, bbox_thr]))
outputs = np.array(outputs)
gts = np.array(gts)
masks = np.array(masks)
threshold_bbox = np.array(threshold_bbox)
if 'PCK' in metrics:
_, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr,
threshold_bbox)
info_str.append(('PCK', pck))
if 'AUC' in metrics:
info_str.append(('AUC', keypoint_auc(outputs, gts, masks,
auc_nor)))
if 'EPE' in metrics:
info_str.append(('EPE', keypoint_epe(outputs, gts, masks)))
return info_str
def _report_metric(self, res_file, metrics, pck_thr=0.3):
"""Keypoint evaluation.
Args:
res_file (str): Json file stored prediction results.
metrics (str | list[str]): Metric to be performed.
Options: 'PCK', 'NME'.
pck_thr (float): PCK threshold, default: 0.3.
Returns:
dict: Evaluation results for evaluation metric.
"""
info_str = []
with open(res_file, 'r') as fin:
preds = json.load(fin)
assert len(preds) == len(self.db)
outputs = []
gts = []
masks = []
for pred, item in zip(preds, self.db):
outputs.append(np.array(pred['keypoints'])[:, :-1])
gts.append(np.array(item['joints_3d'])[:, :-1])
masks.append((np.array(item['joints_3d_visible'])[:, 0]) > 0)
outputs = np.array(outputs)
gts = np.array(gts)
masks = np.array(masks)
normalize_factor = self._get_normalize_factor(gts)
if 'PCK' in metrics:
_, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr,
normalize_factor)
info_str.append(('PCK', pck))
if 'NME' in metrics:
info_str.append(
('NME', keypoint_nme(outputs, gts, masks, normalize_factor)))
return info_str
def _report_metric(self, res_file, metrics):
"""Keypoint evaluation.
Report PCK, AUC or EPE.
"""
info_str = []
with open(res_file, 'r') as fin:
preds = json.load(fin)
assert len(preds) == len(self.db)
outputs = []
gts = []
for pred, item in zip(preds, self.db):
outputs.append(pred['keypoints'])
gts.append(item['joints_3d'])
outputs = np.array(outputs)[:, :, :-1]
gts = np.array(gts)[:, :, :-1]
if 'PCK' in metrics:
hit = 0
exist = 0
for pred, item in zip(preds, self.db):
bbox = np.array(item['bbox'])
threshold = np.max(bbox[2:]) * 0.2
h, _, e = keypoint_pck_accuracy(
np.array(pred['keypoints'])[None, :, :-1],
np.array(item['joints_3d'])[None, :, :-1], 1,
np.array([[threshold, threshold]]))
hit += len(h[h > 0])
exist += e
pck = hit / exist
info_str.append(('PCK', pck))
if 'AUC' in metrics:
info_str.append(('AUC', keypoint_auc(outputs, gts, 30)))
if 'EPE' in metrics:
info_str.append(('EPE', keypoint_epe(outputs, gts)))
return info_str
def _report_metric(self, res_file, metrics, pck_thr=0.2):
"""Keypoint evaluation.
Args:
res_file (str): Json file stored prediction results.
metrics (str | list[str]): Metric to be performed.
Options: 'PCK', 'PCKh', 'AUC', 'EPE'.
pck_thr (float): PCK threshold, default as 0.2.
pckh_thr (float): PCKh threshold, default as 0.7.
auc_nor (float): AUC normalization factor, default as 30 pixel.
Returns:
dict: Evaluation results for evaluation metric.
"""
info_str = []
with open(res_file, 'r') as fin:
preds = json.load(fin)
assert len(preds) == len(self.db)
outputs = []
gts = []
masks = []
threshold_bbox = []
threshold_torso = []
for pred, item in zip(preds, self.db):
outputs.append(np.array(pred['keypoints'])[:, :-1])
gts.append(np.array(item['joints_3d'])[:, :-1])
masks.append((np.array(item['joints_3d_visible'])[:, 0]) > 0)
if 'PCK' in metrics:
bbox = np.array(item['bbox'])
bbox_thr = np.max(bbox[2:])
threshold_bbox.append(np.array([bbox_thr, bbox_thr]))
if 'tPCK' in metrics:
torso_thr = np.linalg.norm(item['joints_3d'][4, :2] -
item['joints_3d'][5, :2])
if torso_thr < 1:
torso_thr = np.linalg.norm(
np.array(pred['keypoints'])[4, :2] -
np.array(pred['keypoints'])[5, :2])
warnings.warn('Torso Size < 1.')
threshold_torso.append(np.array([torso_thr, torso_thr]))
outputs = np.array(outputs)
gts = np.array(gts)
masks = np.array(masks)
threshold_bbox = np.array(threshold_bbox)
threshold_torso = np.array(threshold_torso)
if 'PCK' in metrics:
pck_p, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr,
threshold_bbox)
stats_names = [
'Head PCK', 'Sho PCK', 'Elb PCK', 'Wri PCK', 'Hip PCK',
'Knee PCK', 'Ank PCK', 'Mean PCK'
]
stats = [
pck_p[2], 0.5 * pck_p[3] + 0.5 * pck_p[4],
0.5 * pck_p[7] + 0.5 * pck_p[8],
0.5 * pck_p[11] + 0.5 * pck_p[12],
0.5 * pck_p[5] + 0.5 * pck_p[6],
0.5 * pck_p[9] + 0.5 * pck_p[10],
0.5 * pck_p[13] + 0.5 * pck_p[14], pck
]
info_str.extend(list(zip(stats_names, stats)))
if 'tPCK' in metrics:
pck_p, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr,
threshold_torso)
stats_names = [
'Head tPCK', 'Sho tPCK', 'Elb tPCK', 'Wri tPCK', 'Hip tPCK',
'Knee tPCK', 'Ank tPCK', 'Mean tPCK'
]
stats = [
pck_p[2], 0.5 * pck_p[3] + 0.5 * pck_p[4],
0.5 * pck_p[7] + 0.5 * pck_p[8],
0.5 * pck_p[11] + 0.5 * pck_p[12],
0.5 * pck_p[5] + 0.5 * pck_p[6],
0.5 * pck_p[9] + 0.5 * pck_p[10],
0.5 * pck_p[13] + 0.5 * pck_p[14], pck
]
info_str.extend(list(zip(stats_names, stats)))
return info_str
