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 rbbox_overlaps_cy(boxes_np, query_boxes_np):
# TODO: first calculate the hbb overlaps, for overlaps > 0, calculate the obb overlaps
polys_np = RotBox2Polys(boxes_np).astype(np.float)
query_polys_np = RotBox2Polys(query_boxes_np).astype(np.float)
h_bboxes_np = poly2bbox(polys_np).astype(np.float)
h_query_bboxes_np = poly2bbox(query_polys_np).astype(np.float)
# hious
ious = bbox_overlaps_cython(h_bboxes_np, h_query_bboxes_np)
import pdb
# pdb.set_trace()
inds = np.where(ious > 0)
for index in range(len(inds[0])):
box_index = inds[0][index]
query_box_index = inds[1][index]
box = polys_np[box_index]
query_box = query_polys_np[query_box_index]
# calculate obb iou
# import pdb
# pdb.set_trace()
overlap = polyiou.iou_poly(polyiou.VectorDouble(box), polyiou.VectorDouble(query_box))
ious[box_index][query_box_index] = overlap
return ious
def calcoverlaps(BBGT_keep, bb):
overlaps = []
for index, GT in enumerate(BBGT_keep):
overlap = polyiou.iou_poly(polyiou.VectorDouble(BBGT_keep[index]), polyiou.VectorDouble(bb))
overlaps.append(overlap)
return overlaps
def py_cpu_nms_poly_fast3_np(dets, thresh):
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]
])
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])
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 rbbox_overlaps_cy_warp_fast(rbboxes, query_boxes):
# TODO: first calculate the hbb overlaps, for overlaps > 0, calculate the obb overlaps
# import pdb
# pdb.set_trace()
box_device = query_boxes.device
query_boxes_np = query_boxes.detach().cpu().numpy().astype(np.float)
# polys_np = RotBox2Polys(boxes_np)
# TODO: change it to only use pos gt_masks
# polys_np = mask2poly(gt_masks)
# polys_np = np.array(Tuplelist2Polylist(polys_np)).astype(np.float)
polys_np = RotBox2Polys(rbboxes).astype(np.float)
query_polys_np = RotBox2Polys(query_boxes_np)
overlaps = []
for box, qbox in zip(polys_np, query_polys_np):
overlaps.append(
polyiou.iou_poly(polyiou.VectorDouble(box),
polyiou.VectorDouble(qbox)))
return torch.from_numpy(np.array(overlaps)).to(box_device)
def rbbox_overlaps_cy_warp(rbboxes, query_boxes):
# TODO: first calculate the hbb overlaps, for overlaps > 0, calculate the obb overlaps
# import pdb
# pdb.set_trace()
box_device = query_boxes.device
query_boxes_np = query_boxes.cpu().numpy().astype(np.float)
# polys_np = RotBox2Polys(boxes_np)
# TODO: change it to only use pos gt_masks
# polys_np = mask2poly(gt_masks)
# polys_np = np.array(Tuplelist2Polylist(polys_np)).astype(np.float)
polys_np = RotBox2Polys(rbboxes).astype(np.float)
query_polys_np = RotBox2Polys(query_boxes_np)
h_bboxes_np = poly2bbox(polys_np)
h_query_bboxes_np = poly2bbox(query_polys_np)
# hious
ious = bbox_overlaps_cython(h_bboxes_np, h_query_bboxes_np)
import pdb
# pdb.set_trace()
inds = np.where(ious > 0)
for index in range(len(inds[0])):
box_index = inds[0][index]
query_box_index = inds[1][index]
box = polys_np[box_index]
query_box = query_polys_np[query_box_index]
# calculate obb iou
# import pdb
# pdb.set_trace()
overlap = polyiou.iou_poly(polyiou.VectorDouble(box),
polyiou.VectorDouble(query_box))
ious[box_index][query_box_index] = overlap
return torch.from_numpy(ious).to(box_device)
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)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
def py_cpu_nms_poly_fast_np(dets, iou_threshold):
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)
# 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
def py_cpu_nms_poly_fast(dets, iou_threshold):
# TODO: check the type numpy()
if dets.shape[0] == 0:
keep = dets.new_zeros(0, dtype=torch.long)
keep = keep.cpu().numpy()
device = dets.device
if isinstance(dets, torch.Tensor):
dets = dets.cpu().numpy().astype(np.float64)
if isinstance(iou_threshold, torch.Tensor):
iou_threshold = iou_threshold.cpu().numpy().astype(np.float64)
else:
device = dets.device
if isinstance(dets, torch.Tensor):
dets = dets.cpu().numpy().astype(np.float64)
if isinstance(iou_threshold, torch.Tensor):
iou_threshold = iou_threshold.cpu().numpy().astype(np.float64)
obbs = dets[:, 0:-1]
# 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)
# 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 <= iou_threshold)[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 torch.from_numpy(dets[keep, :]).to(device), torch.from_numpy(
np.array(keep)).to(device)
def soft_py_cpu_nms_poly(dets, iou_thr, method=1, sigma=0.5, min_score=0.001):
# TODO: 1. check the correctness
# TODO: 2. optimize it
if dets.shape[0] == 0:
keep = dets.new_zeros(0, dtype=torch.long)
keep = keep.cpu().numpy()
device = dets.device
if isinstance(dets, torch.Tensor):
dets.dets.cpu().numpy().astype(np.float64)
if isinstance(iou_thr, torch.Tensor):
iou_thr = iou_thr.cpu().numpy().astype(np.float64)
else:
device = dets.device
if isinstance(dets, torch.Tensor):
dets = dets.cpu().numpy().astype(np.float64)
if isinstance(iou_thr, torch.Tensor):
iou_thr = iou_thr.cpu().numpy().astype(np.float64)
N = dets.shape[0]
inds = np.arange(N)
for i in range(N):
maxscore = dets[i, 8]
maxpos = i
tx1, ty1, tx2, ty2, tx3, ty3, tx4, ty4 = \
dets[i, 0], dets[i, 1], dets[i, 2], dets[i,
3], dets[i, 4], dets[i, 5], dets[i, 6], dets[i, 7]
ts = dets[i, 8]
ti = inds[i]
pos = i + 1
# get max box
while pos < N:
if maxscore < dets[pos, 8]:
maxscore = dets[pos, 8]
maxpos = pos
pos = pos + 1
# add max poly as a detection
dets[i, 0] = dets[maxpos, 0]
dets[i, 1] = dets[maxpos, 1]
dets[i, 2] = dets[maxpos, 2]
dets[i, 3] = dets[maxpos, 3]
dets[i, 4] = dets[maxpos, 4]
dets[i, 5] = dets[maxpos, 5]
dets[i, 6] = dets[maxpos, 6]
dets[i, 7] = dets[maxpos, 7]
dets[i, 8] = dets[maxpos, 8]
inds[i] = inds[maxpos]
# swap ith poly with position of max poly
dets[maxpos, 0] = tx1
dets[maxpos, 1] = ty1
dets[maxpos, 2] = tx2
dets[maxpos, 3] = ty2
dets[maxpos, 4] = tx3
dets[maxpos, 5] = ty3
dets[maxpos, 6] = tx4
dets[maxpos, 7] = ty4
dets[maxpos, 8] = ts
inds[maxpos] = ti
tx1 = dets[i, 0]
ty1 = dets[i, 1]
tx2 = dets[i, 2]
ty2 = dets[i, 3]
tx3 = dets[i, 4]
ty3 = dets[i, 5]
tx4 = dets[i, 6]
ty4 = dets[i, 7]
ts = dets[i, 8]
pos = i + 1
# NMS iterations, note that N changes if detection polys fall below threshold
while pos < N:
x1, y1, x2, y2, x3, y3, x4, y4 = \
dets[pos, 0], dets[pos, 1], dets[pos, 2], dets[pos, 3], \
dets[pos, 4], dets[pos, 5], dets[pos, 6], dets[pos, 7]
s = dets[pos, -1]
# TODO:finish the area calculation
# area = ()
# finish the iou calculation
max_polygon = polyiou.VectorDouble(
[tx1, ty1, tx2, ty2, tx3, ty3, tx4, ty4])
pos_polygon = polyiou.VectorDouble(
[x1, y1, x2, y2, x3, y3, x4, y4])
ov = polyiou.iou_poly(max_polygon, pos_polygon)
if method == 1: # linear
if ov > iou_thr:
weight = 1 - ov
else:
weight = 1
elif method == 2: # gaussian
weight = np.exp(-(ov * ov) / sigma)
else: # original NMS
if ov > iou_thr:
weight = 0
else:
weight = 1
dets[pos, 8] = weight * dets[pos, 8]
# if det score falls below threshold, discard the poly by
# swapping with last poly update N
if dets[pos, 8] < min_score:
dets[pos, 0] = dets[N - 1, 0]
dets[pos, 1] = dets[N - 1, 1]
dets[pos, 2] = dets[N - 1, 2]
dets[pos, 3] = dets[N - 1, 3]
dets[pos, 4] = dets[N - 1, 4]
dets[pos, 5] = dets[N - 1, 5]
dets[pos, 6] = dets[N - 1, 6]
dets[pos, 7] = dets[N - 1, 7]
dets[pos, 8] = dets[N - 1, 8]
inds[pos] = inds[N - 1]
N = N - 1
pos = pos - 1
pos = pos + 1
return torch.from_numpy(dets[:N]).to(device), torch.from_numpy(
inds[:N]).to(device)
def soft_py_cpu_nms_poly_np(dets_in,
iou_thr,
method=1,
sigma=0.5,
min_score=0.05):
dets = dets_in.copy()
N = dets.shape[0]
inds = np.arange(N)
for i in range(N):
maxscore = dets[i, 8]
maxpos = i
tx1, ty1, tx2, ty2, tx3, ty3, tx4, ty4 = \
dets[i, 0], dets[i, 1], dets[i, 2], dets[i,
3], dets[i, 4], dets[i, 5], dets[i, 6], dets[i, 7]
ts = dets[i, 8]
ti = inds[i]
pos = i + 1
# get max box
while pos < N:
if maxscore < dets[pos, 8]:
maxscore = dets[pos, 8]
maxpos = pos
pos = pos + 1
# add max poly as a detection
dets[i, 0] = dets[maxpos, 0]
dets[i, 1] = dets[maxpos, 1]
dets[i, 2] = dets[maxpos, 2]
dets[i, 3] = dets[maxpos, 3]
dets[i, 4] = dets[maxpos, 4]
dets[i, 5] = dets[maxpos, 5]
dets[i, 6] = dets[maxpos, 6]
dets[i, 7] = dets[maxpos, 7]
dets[i, 8] = dets[maxpos, 8]
inds[i] = inds[maxpos]
# swap ith poly with position of max poly
dets[maxpos, 0] = tx1
dets[maxpos, 1] = ty1
dets[maxpos, 2] = tx2
dets[maxpos, 3] = ty2
dets[maxpos, 4] = tx3
dets[maxpos, 5] = ty3
dets[maxpos, 6] = tx4
dets[maxpos, 7] = ty4
dets[maxpos, 8] = ts
inds[maxpos] = ti
tx1 = dets[i, 0]
ty1 = dets[i, 1]
tx2 = dets[i, 2]
ty2 = dets[i, 3]
tx3 = dets[i, 4]
ty3 = dets[i, 5]
tx4 = dets[i, 6]
ty4 = dets[i, 7]
ts = dets[i, 8]
# hbb
txmin, tymin, txmax, tymax = min(tx1, tx2, tx3, tx4), \
min(ty1, ty2, ty3, ty4), \
max(tx1, tx2, tx3, tx4), \
max(ty1, ty2, ty3, ty4)
pos = i + 1
# NMS iterations, note that N changes if detection polys fall below threshold
while pos < N:
x1, y1, x2, y2, x3, y3, x4, y4 = \
dets[pos, 0], dets[pos, 1], dets[pos, 2], dets[pos, 3], \
dets[pos, 4], dets[pos, 5], dets[pos, 6], dets[pos, 7]
s = dets[pos, 8]
xmin, ymin, xmax, ymax = min(x1, x2, x3, x4), \
min(y1, y2, y3, y4), \
max(x1, x2, x3, x4), \
max(y1, y2, y3, y4)
iw = (min(txmax, xmax) - max(txmin, xmin) + 1)
if iw > 0:
ih = (min(tymax, ymax) - max(tymin, ymin) + 1)
if ih > 0:
max_polygon = polyiou.VectorDouble(
[tx1, ty1, tx2, ty2, tx3, ty3, tx4, ty4])
pos_polygon = polyiou.VectorDouble(
[x1, y1, x2, y2, x3, y3, x4, y4])
ov = polyiou.iou_poly(max_polygon, pos_polygon)
if ov > 0:
if method == 1: # linear
if ov > iou_thr:
weight = 1 - ov
else:
weight = 1
elif method == 2: # gaussian
weight = np.exp(-(ov * ov) / sigma)
else: # original NMS
if ov > iou_thr:
weight = 0
else:
weight = 1
dets[pos, 8] = weight * dets[pos, 8]
# if det score falls below threshold, discard the poly by
# swapping with last poly update N
if dets[pos, 8] < min_score:
dets[pos, 0] = dets[N - 1, 0]
dets[pos, 1] = dets[N - 1, 1]
dets[pos, 2] = dets[N - 1, 2]
dets[pos, 3] = dets[N - 1, 3]
dets[pos, 4] = dets[N - 1, 4]
dets[pos, 5] = dets[N - 1, 5]
dets[pos, 6] = dets[N - 1, 6]
dets[pos, 7] = dets[N - 1, 7]
dets[pos, 8] = dets[N - 1, 8]
inds[pos] = inds[N - 1]
N = N - 1
pos = pos - 1
pos = pos + 1
return inds[:N]
def BOXES_cpt(single_poly):
xmin, ymin, xmax, ymax = min(single_poly[0::2]), min(single_poly[1::2]), \
max(single_poly[0::2]), max(single_poly[1::2])
width, height = xmax - xmin, ymax - ymin
BOX = xmin, ymin, xmax, ymax
BOXP = np.array(single_poly).reshape(1, -1, 2)
BOXP = np.append(BOXP, BOXP[0, :2].reshape(2, 2)).reshape(-1, 2).tolist()
# print(BOXP)
box_lefttop = [xmin, ymin]
poly_1 = BOXP.pop(0)
poly_2 = BOXP.pop(0)
rotate_num = 0
while len(BOXP[0]) != 0:
rotate_num += 1
if poly_1[0] != box_lefttop[0]:
poly_1 = poly_2
poly_2 = BOXP.pop(0)
else:
break
# print(box_lefttop)
# print(poly_1, poly_2)
poly_3 = BOXP.pop(0)
poly_1 = np.array(poly_1)
poly_2 = np.array(poly_2)
poly_3 = np.array(poly_3)
vecter_1 = poly_2 - box_lefttop
vecter_2 = poly_2 - poly_1
Lx = np.sqrt(vecter_1.dot(vecter_1))
Ly = np.sqrt(vecter_2.dot(vecter_2))
if (Lx * Ly) == 0.0:
# print(Lx, Ly)
theta = np.pi / 2
else:
cos_theta = vecter_1.dot(vecter_2) / (Lx * Ly)
# theta = np.arccos(cos_theta)
if cos_theta > 1:
cos_theta = 1.0
# print(theta)
elif cos_theta < -1:
cos_theta = -1.0
theta = np.arccos(cos_theta)
poly_W = math.hypot(vecter_2[0], vecter_2[1])
vecter_3 = poly_3 - poly_2
poly_H = math.hypot(vecter_3[0], vecter_3[1])
poly_1 = poly_1.tolist() # must be list
# print(poly_1, obj['poly'])
# print(poly_W, poly_H, theta)
URBOX = poly_1[0], poly_1[1], poly_W, poly_H, theta
# print(single_poly, Nrotate(URBOX[:4], URBOX[4]))
URBOX_iou = polyiou.iou_poly(
polyiou.VectorDouble(single_poly),
polyiou.VectorDouble(Nrotate(URBOX[:4], URBOX[4])))
BBOX_iou = polyiou.iou_poly(polyiou.VectorDouble(single_poly),
polyiou.VectorDouble(dots4ToRec8(BOX)))
if BBOX_iou > URBOX_iou:
# print('exit!')
URBOX = xmin, ymin, width, height, 0.0
ORBOX = URBOX
else:
# print(URBOX_iou)
if not rotate_num % 2:
ORBOX = single_poly[0], single_poly[
1], poly_W, poly_H, theta + np.pi * (rotate_num / 2.0)
else:
ORBOX = single_poly[0], single_poly[
1], poly_H, poly_W, theta + np.pi * (rotate_num / 2.0)
return BOX, URBOX, ORBOX
