def calcoverlaps(BBGT, bb):
overlaps = []
for index, GT in enumerate(BBGT):
overlap = polyiou.iou_poly(polyiou.VectorDouble(BBGT[index]), polyiou.VectorDouble(bb))
overlaps.append(overlap)
return overlaps
def obb_iou(self, dt, gt):
'''
gt is a list of GT category_id = 1 of img_id = 1
dt is a list of candidates category_id = 1 of img_id = 1
'''
if len(gt) == 0:
return []
total_iou_array = []
for bb in dt:
bb = np.array(bb)
# bb_xmin = np.min(bb[0::2])
# bb_ymin = np.min(bb[1::2])
# bb_xmax = np.max(bb[0::2])
# bb_ymax = np.max(bb[1::2])
# # 1. calculate the overlaps between hbbs, if the iou between hbbs are 0, the iou between obbs are 0, too.
BBGT = np.array(gt)
# BBGT_xmin = np.min(BBGT[:, 0::2], axis=1)
# BBGT_ymin = np.min(BBGT[:, 1::2], axis=1)
# BBGT_xmax = np.max(BBGT[:, 0::2], axis=1)
# BBGT_ymax = np.max(BBGT[:, 1::2], axis=1)
# ixmin = np.maximum(BBGT_xmin, bb_xmin)
# iymin = np.maximum(BBGT_ymin, bb_ymin)
# ixmax = np.minimum(BBGT_xmax, bb_xmax)
# iymax = np.minimum(BBGT_ymax, bb_ymax)
# iw = np.maximum(ixmax - ixmin + 1., 0.)
# ih = np.maximum(iymax - iymin + 1., 0.)
# inters = iw * ih
# # union
# uni = ((bb_xmax - bb_xmin + 1.) * (bb_ymax - bb_ymin + 1.) +
# (BBGT_xmax - BBGT_xmin + 1.) *
# (BBGT_ymax - BBGT_ymin + 1.) - inters)
# overlaps = inters / uni
# candidate_iou_list = overlaps
candidate_iou_list = []
for bbgt in BBGT:
bbgt = bbgt.astype(float)
bb = bb.astype(float)
overlap = polyiou.iou_poly(polyiou.VectorDouble(bbgt), polyiou.VectorDouble(bb))
candidate_iou_list.append(overlap)
if total_iou_array == []:
total_iou_array = np.array([candidate_iou_list])
else:
total_iou_array = np.vstack( (total_iou_array, candidate_iou_list) )
return total_iou_array
def _calculate_ious_one_batch(self, pred_bboxes, target_bboxes):
"""
根据pred_bboxes: (num_obj, 8)和target_bboxes: (num_obj, 8), 计算ious: (num_obj, )的list
"""
num_obj = pred_bboxes.shape[0]
ious_one_batch = []
for i in range(num_obj):
iou = polyiou.iou_poly(polyiou.VectorDouble(pred_bboxes[i]), polyiou.VectorDouble(target_bboxes[i]))
ious_one_batch.append(iou)
return ious_one_batch
def py_cpu_nms_poly(dets, thresh): # 多边形的计算
"""
根据阈值 计算nms
:param dets: 多条检测记录
:param thresh: nms 阈值
:return:
"""
scores = dets[:, 8] # 第9个字段是分数
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([dets[i][0], dets[i][1],
dets[i][2], dets[i][3],
dets[i][4], dets[i][5],
dets[i][6], dets[i][7]])
polys.append(tm_polygon)
order = scores.argsort()[::-1] # 从大到小的sorce的下标
keep = [] # nms算法
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
for j in range(order.size - 1):
iou = polyiou.iou_poly(polys[i], polys[order[j + 1]])
ovr.append(iou)
ovr = np.array(ovr)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep # 保留的下标
def py_cpu_nms_poly(dets, thresh):
scores = dets[:, 8]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([dets[i][0], dets[i][1],
dets[i][2], dets[i][3],
dets[i][4], dets[i][5],
dets[i][6], dets[i][7]])
polys.append(tm_polygon)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
for j in range(order.size - 1):
iou = polyiou.iou_poly(polys[i], polys[order[j + 1]])
ovr.append(iou)
ovr = np.array(ovr)
# print('ovr: ', ovr)
# print('thresh: ', thresh)
try:
if math.isnan(ovr[0]):
pdb.set_trace()
except:
pass
inds = np.where(ovr <= thresh)[0]
# print('inds: ', inds)
order = order[inds + 1]
return keep
def py_cpu_nms_poly(dets, thresh):
scores = dets[:, 8]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([
dets[i][0], dets[i][1], dets[i][2], dets[i][3], dets[i][4],
dets[i][5], dets[i][6], dets[i][7]
])
polys.append(tm_polygon)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
for j in range(order.size - 1):
iou_test = polyiou.iou_poly(polys[i], polys[order[j + 1]])
ovr.append(iou_test.iou)
ovr = np.array(ovr)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
def py_cpu_nms_poly(dets, thresh):
scores = dets[:, 8]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([dets[i][0], dets[i][1],
dets[i][2], dets[i][3],
dets[i][4], dets[i][5],
dets[i][6], dets[i][7]])
polys.append(tm_polygon)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
for j in range(order.size - 1):
iou = polyiou.iou_poly(polys[i], polys[order[j + 1]])
ovr.append(iou)
if (iou != iou):
print('poly i:', polys[i], 'polys j + 1:', polys[j + 1])
print('det i:', dets[i], 'dets j + 1:', dets[order[j + 1]])
pdb.set_trace()
if (np.sum(ovr != ovr) > 0):
print('before')
pdb.set_trace()
ovr2 = np.array(ovr)
if (np.sum(ovr2 != ovr2) > 0):
print('after')
pdb.set_trace()
inds = np.where(ovr2 <= thresh)[0]
order = order[inds + 1]
return keep
def py_cpu_nms_poly(dets, thresh):
"""
任意四点poly nms.取出nms后的边框的索引
@param dets: shape(detection_num, [poly, confidence1]) 原始图像中的检测出的目标数量
@param thresh:
@return:
keep: 经nms后的目标边框的索引
"""
scores = dets[:, 8]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([
dets[i][0], dets[i][1], dets[i][2], dets[i][3], dets[i][4],
dets[i][5], dets[i][6], dets[i][7]
])
polys.append(tm_polygon)
# argsort将元素小到大排列 返回索引值 [::-1]即从后向前取元素
order = scores.argsort()[::-1] # 取出元素的索引值 顺序为从大到小
keep = []
while order.size > 0:
ovr = []
i = order[0] # 取出当前剩余置信度最大的目标边框的索引
keep.append(i)
for j in range(order.size - 1): # 求出置信度最大poly与其他所有poly的IoU
iou = polyiou.iou_poly(polys[i], polys[order[j + 1]])
ovr.append(iou)
ovr = np.array(ovr)
inds = np.where(ovr <= thresh)[0] # 找出iou小于阈值的索引
order = order[inds + 1]
return keep
def py_cpu_nms_poly(dets, thresh):
if dets.shape[0] == 0:
return []
scores = dets[:, 8]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([
dets[i][0], dets[i][1], dets[i][2], dets[i][3], dets[i][4],
dets[i][5], dets[i][6], dets[i][7]
])
polys.append(tm_polygon)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
if order.size == 1: # in case of np.where warning
return keep
ovr = []
for _j in range(1, order.size):
j = order[_j]
iou = polyiou.iou_poly(polys[i], polys[j])
ovr.append(iou)
if np.isnan(iou):
# BUG: when the poly points are collinear, the calculation will get nan
print dets[i, :], dets[j, :]
ovr = np.array(ovr)
#np.seterr(all='raise')
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
def calcoverlaps(BBGT, bb):
overlaps = []
for index, GT in enumerate(BBGT):
# gtpoly = shgeo.Polygon([(BBGT[index, 0], BBGT[index, 1]),
# (BBGT[index, 2], BBGT[index,3]),
# (BBGT[index, 4], BBGT[index, 5]),
# (BBGT[index, 6], BBGT[index, 7])])
# detpoly = shgeo.Polygon([(bb[0], bb[1]),
# (bb[2], bb[3]),
# (bb[4], bb[5]),
# (bb[6], bb[7])])
# overlap = calc_iou(gtpoly, detpoly)
overlap = polyiou.iou_poly(polyiou.VectorDouble(BBGT[index]), polyiou.VectorDouble(bb))
# overlap = polygon_iou(BBGT[index], bb)
overlaps.append(overlap)
return overlaps
def nms_overlap_all(bboxes, nms_thresh):
score = bboxes[:, -1]
order = score.argsort()[::-1] # reverse vector
out_boxes = np.zeros(bboxes.shape[0])
for i in range(bboxes.shape[0]):
_i = order[i]
if out_boxes[_i] == 1:
continue
for j in range(i + 1, bboxes.shape[0]):
_j = order[j]
if out_boxes[_j] == 1:
continue
overlap = polyiou.iou_poly(polyiou.VectorDouble(bboxes[_i][0:8]), polyiou.VectorDouble(bboxes[_j][0:8]))
if overlap > nms_thresh:
# if polygon_iou(bboxes[_i][0:8], bboxes[_j][0:8]) > nms_thresh:
# print(box_i, box_j, bbox_iou(box_i, box_j, x1y1x2y2=False))
out_boxes[_j] = 1
return np.where(out_boxes == 0)[0]
def py_cpu_nms_poly_fast(dets, thresh):
# TODO: test it
try:
obbs = dets[:, 0:-1]
except:
print('fail index')
pdb.set_trace()
x1 = np.min(obbs[:, 0::2], axis=1)
y1 = np.min(obbs[:, 1::2], axis=1)
x2 = np.max(obbs[:, 0::2], axis=1)
y2 = np.max(obbs[:, 1::2], axis=1)
scores = dets[:, 8]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
polys = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([
dets[i][0], dets[i][1], dets[i][2], dets[i][3], dets[i][4],
dets[i][5], dets[i][6], dets[i][7]
])
polys.append(tm_polygon)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
ovr = []
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)
h = np.maximum(0.0, yy2 - yy1)
hbb_inter = w * h
hbb_ovr = hbb_inter / (areas[i] + areas[order[1:]] - hbb_inter)
h_inds = np.where(hbb_ovr > 0)[0]
tmp_order = order[h_inds + 1]
for j in range(tmp_order.size):
iou = polyiou.iou_poly(polys[i], polys[tmp_order[j]])
hbb_ovr[h_inds[j]] = iou
try:
if math.isnan(ovr[0]):
pdb.set_trace()
except:
pass
inds = np.where(hbb_ovr <= thresh)[0]
order = order[inds + 1]
return keep
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]
ex_x = np.vstack((dets[:, 0], dets[:, 2], dets[:, 4], dets[:, 6]))
ex_y = np.vstack((dets[:, 1], dets[:, 3], dets[:, 5], dets[:, 7]))
x1 = np.amin(ex_x, axis=0)
y1 = np.amin(ex_y, axis=0)
x2 = np.amax(ex_x, axis=0)
y2 = np.amax(ex_y, axis=0)
scores = dets[:, 8]
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
'''
if dets.shape[0] == 0:
return []
scores = dets[:, 8]
polys = []
areas = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([
dets[i][0], dets[i][1], dets[i][2], dets[i][3], dets[i][4],
dets[i][5], dets[i][6], dets[i][7]
])
polys.append(tm_polygon)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
for j in range(order.size - 1):
iou = polyiou.iou_poly(polys[i], polys[order[j + 1]])
ovr.append(iou)
ovr = np.array(ovr)
#print 'ovr is',ovr
inds = np.where(ovr <= thresh)[0]
#print 'inds is',inds
order = order[inds + 1]
#print 'order is',order
return keep
def py_cpu_nms_poly_fast(dets, thresh):
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
# obbs = dets[:, 0:-1]
# x1 = np.min(obbs[:, 0::2], axis=1)
# y1 = np.min(obbs[:, 1::2], axis=1)
# x2 = np.max(obbs[:, 0::2], axis=1)
# y2 = np.max(obbs[:, 1::2], axis=1)
# scores = dets[:, 8]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
polys = []
for i in range(len(dets)):
# tm_polygon = polyiou.VectorDouble([dets[i][0], dets[i][1],
# dets[i][2], dets[i][3],
# dets[i][4], dets[i][5],
# dets[i][6], dets[i][7]])
tm_polygon = polyiou.VectorDouble([x1[i], y1[i],
x2[i], y1[i],
x2[i], y2[i],
x1[i], y2[i]])
polys.append(tm_polygon)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
# if order.size == 0:
# break
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)
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
hbb_inter = w * h
hbb_ovr = hbb_inter / (areas[i] + areas[order[1:]] - hbb_inter)
# h_keep_inds = np.where(hbb_ovr == 0)[0]
h_inds = np.where(hbb_ovr > 0)[0]
tmp_order = order[h_inds + 1]
for j in range(tmp_order.size):
iou = polyiou.iou_poly(polys[i], polys[tmp_order[j]])
hbb_ovr[h_inds[j]] = iou
# ovr.append(iou)
# ovr_index.append(tmp_order[j])
# ovr = np.array(ovr)
# ovr_index = np.array(ovr_index)
# print('ovr: ', ovr)
# print('thresh: ', thresh)
try:
if math.isnan(ovr[0]):
pdb.set_trace()
except:
pass
inds = np.where(hbb_ovr <= thresh)[0]
# order_obb = ovr_index[inds]
# print('inds: ', inds)
# order_hbb = order[h_keep_inds + 1]
order = order[inds + 1]
# pdb.set_trace()
# order = np.concatenate((order_obb, order_hbb), axis=0).astype(np.int)
return keep