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# ------------------------------------------------------------------------------
# Adapted from https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
# Original licence: Copyright (c) Microsoft, under the MIT License.
# ------------------------------------------------------------------------------
import numpy as np
def nms(dets, thr):
"""Greedily select boxes with high confidence and overlap <= thr.
Args:
dets: [[x1, y1, x2, y2, score]].
thr: Retain overlap < thr.
Returns:
list: Indexes to keep.
"""
if len(dets) == 0:
return []
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while len(order) > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thr)[0]
order = order[inds + 1]
return keep
def oks_iou(g, d, a_g, a_d, sigmas=None, vis_thr=None):
"""Calculate oks ious.
Args:
g: Ground truth keypoints.
d: Detected keypoints.
a_g: Area of the ground truth object.
a_d: Area of the detected object.
sigmas: standard deviation of keypoint labelling.
vis_thr: threshold of the keypoint visibility.
Returns:
list: The oks ious.
"""
if sigmas is None:
sigmas = np.array([
.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07,
.87, .87, .89, .89
]) / 10.0
vars = (sigmas * 2)**2
xg = g[0::3]
yg = g[1::3]
vg = g[2::3]
ious = np.zeros(len(d), dtype=np.float32)
for n_d in range(0, len(d)):
xd = d[n_d, 0::3]
yd = d[n_d, 1::3]
vd = d[n_d, 2::3]
dx = xd - xg
dy = yd - yg
e = (dx**2 + dy**2) / vars / ((a_g + a_d[n_d]) / 2 + np.spacing(1)) / 2
if vis_thr is not None:
ind = list(vg > vis_thr) and list(vd > vis_thr)
e = e[ind]
ious[n_d] = np.sum(np.exp(-e)) / len(e) if len(e) != 0 else 0.0
return ious
def oks_nms(kpts_db, thr, sigmas=None, vis_thr=None, score_per_joint=False):
"""OKS NMS implementations.
Args:
kpts_db: keypoints.
thr: Retain overlap < thr.
sigmas: standard deviation of keypoint labelling.
vis_thr: threshold of the keypoint visibility.
score_per_joint: the input scores (in kpts_db) are per joint scores
Returns:
np.ndarray: indexes to keep.
"""
if len(kpts_db) == 0:
return []
if score_per_joint:
scores = np.array([k['score'].mean() for k in kpts_db])
else:
scores = np.array([k['score'] for k in kpts_db])
kpts = np.array([k['keypoints'].flatten() for k in kpts_db])
areas = np.array([k['area'] for k in kpts_db])
order = scores.argsort()[::-1]
keep = []
while len(order) > 0:
i = order[0]
keep.append(i)
oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]],
sigmas, vis_thr)
inds = np.where(oks_ovr <= thr)[0]
order = order[inds + 1]
keep = np.array(keep)
return keep
def _rescore(overlap, scores, thr, type='gaussian'):
"""Rescoring mechanism gaussian or linear.
Args:
overlap: calculated ious
scores: target scores.
thr: retain oks overlap < thr.
type: 'gaussian' or 'linear'
Returns:
np.ndarray: indexes to keep
"""
assert len(overlap) == len(scores)
assert type in ['gaussian', 'linear']
if type == 'linear':
inds = np.where(overlap >= thr)[0]
scores[inds] = scores[inds] * (1 - overlap[inds])
else:
scores = scores * np.exp(-overlap**2 / thr)
return scores
def soft_oks_nms(kpts_db,
thr,
max_dets=20,
sigmas=None,
vis_thr=None,
score_per_joint=False):
"""Soft OKS NMS implementations.
Args:
kpts_db
thr: retain oks overlap < thr.
max_dets: max number of detections to keep.
sigmas: Keypoint labelling uncertainty.
score_per_joint: the input scores (in kpts_db) are per joint scores
Returns:
np.ndarray: indexes to keep.
"""
if len(kpts_db) == 0:
return []
if score_per_joint:
scores = np.array([k['score'].mean() for k in kpts_db])
else:
scores = np.array([k['score'] for k in kpts_db])
kpts = np.array([k['keypoints'].flatten() for k in kpts_db])
areas = np.array([k['area'] for k in kpts_db])
order = scores.argsort()[::-1]
scores = scores[order]
keep = np.zeros(max_dets, dtype=np.intp)
keep_cnt = 0
while len(order) > 0 and keep_cnt < max_dets:
i = order[0]
oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]],
sigmas, vis_thr)
order = order[1:]
scores = _rescore(oks_ovr, scores[1:], thr)
tmp = scores.argsort()[::-1]
order = order[tmp]
scores = scores[tmp]
keep[keep_cnt] = i
keep_cnt += 1
keep = keep[:keep_cnt]
return keep
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