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# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Modified from py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from .cpu_nms import cpu_nms
from .gpu_nms import gpu_nms
def py_nms_wrapper(thresh):
def _nms(dets):
return nms(dets, thresh)
return _nms
def cpu_nms_wrapper(thresh):
def _nms(dets):
return cpu_nms(dets, thresh)
return _nms
def gpu_nms_wrapper(thresh, device_id):
def _nms(dets):
return gpu_nms(dets, thresh, device_id)
return _nms
def nms(dets, thresh):
"""
greedily select boxes with high confidence and overlap with current maximum <= thresh
rule out overlap >= thresh
:param dets: [[x1, y1, x2, y2 score]]
:param thresh: retain overlap < thresh
:return: indexes to keep
"""
if dets.shape[0] == 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 order.size > 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 <= thresh)[0]
order = order[inds + 1]
return keep
def oks_iou(g, d, a_g, a_d, sigmas=None, in_vis_thre=None):
if not isinstance(sigmas, np.ndarray):
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((d.shape[0]))
for n_d in range(0, d.shape[0]):
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 in_vis_thre is not None:
ind = list(vg > in_vis_thre) and list(vd > in_vis_thre)
e = e[ind]
ious[n_d] = np.sum(np.exp(-e)) / e.shape[0] if e.shape[0] != 0 else 0.0
return ious
def oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None):
"""
greedily select boxes with high confidence and overlap with current maximum <= thresh
rule out overlap >= thresh, overlap = oks
:param kpts_db
:param thresh: retain overlap < thresh
:return: indexes to keep
"""
if len(kpts_db) == 0:
return []
scores = np.array([kpts_db[i]['score'] for i in range(len(kpts_db))])
kpts = np.array([kpts_db[i]['keypoints'].flatten() for i in range(len(kpts_db))])
areas = np.array([kpts_db[i]['area'] for i in range(len(kpts_db))])
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]], sigmas, in_vis_thre)
inds = np.where(oks_ovr <= thresh)[0]
order = order[inds + 1]
return keep
def rescore(overlap, scores, thresh, type='gaussian'):
assert overlap.shape[0] == scores.shape[0]
if type == 'linear':
inds = np.where(overlap >= thresh)[0]
scores[inds] = scores[inds] * (1 - overlap[inds])
else:
scores = scores * np.exp(- overlap**2 / thresh)
return scores
def soft_oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None):
"""
greedily select boxes with high confidence and overlap with current maximum <= thresh
rule out overlap >= thresh, overlap = oks
:param kpts_db
:param thresh: retain overlap < thresh
:return: indexes to keep
"""
if len(kpts_db) == 0:
return []
scores = np.array([kpts_db[i]['score'] for i in range(len(kpts_db))])
kpts = np.array([kpts_db[i]['keypoints'].flatten() for i in range(len(kpts_db))])
areas = np.array([kpts_db[i]['area'] for i in range(len(kpts_db))])
order = scores.argsort()[::-1]
scores = scores[order]
# max_dets = order.size
max_dets = 20
keep = np.zeros(max_dets, dtype=np.intp)
keep_cnt = 0
while order.size > 0 and keep_cnt < max_dets:
i = order[0]
oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]], sigmas, in_vis_thre)
order = order[1:]
scores = rescore(oks_ovr, scores[1:], thresh)
tmp = scores.argsort()[::-1]
order = order[tmp]
scores = scores[tmp]
keep[keep_cnt] = i
keep_cnt += 1
keep = keep[:keep_cnt]
return keep
# kpts_db = kpts_db[:keep_cnt]
# return kpts_db
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