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"""
Locality aware nms.
This code is refered from: https://github.com/songdejia/EAST/blob/master/locality_aware_nms.py
"""
import numpy as np
from shapely.geometry import Polygon
def intersection(g, p):
"""
Intersection.
"""
g = Polygon(g[:8].reshape((4, 2)))
p = Polygon(p[:8].reshape((4, 2)))
g = g.buffer(0)
p = p.buffer(0)
if not g.is_valid or not p.is_valid:
return 0
inter = Polygon(g).intersection(Polygon(p)).area
union = g.area + p.area - inter
if union == 0:
return 0
else:
return inter / union
def intersection_iog(g, p):
"""
Intersection_iog.
"""
g = Polygon(g[:8].reshape((4, 2)))
p = Polygon(p[:8].reshape((4, 2)))
if not g.is_valid or not p.is_valid:
return 0
inter = Polygon(g).intersection(Polygon(p)).area
# union = g.area + p.area - inter
union = p.area
if union == 0:
print("p_area is very small")
return 0
else:
return inter / union
def weighted_merge(g, p):
"""
Weighted merge.
"""
g[:8] = (g[8] * g[:8] + p[8] * p[:8]) / (g[8] + p[8])
g[8] = g[8] + p[8]
return g
def standard_nms(S, thres):
"""
Standard nms.
"""
order = np.argsort(S[:, 8])[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
inds = np.where(ovr <= thres)[0]
order = order[inds + 1]
return S[keep]
def standard_nms_inds(S, thres):
"""
Standard nms, retun inds.
"""
order = np.argsort(S[:, 8])[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
inds = np.where(ovr <= thres)[0]
order = order[inds + 1]
return keep
def nms(S, thres):
"""
nms.
"""
order = np.argsort(S[:, 8])[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
inds = np.where(ovr <= thres)[0]
order = order[inds + 1]
return keep
def soft_nms(boxes_in, Nt_thres=0.3, threshold=0.8, sigma=0.5, method=2):
"""
soft_nms
:para boxes_in, N x 9 (coords + score)
:para threshould, eliminate cases min score(0.001)
:para Nt_thres, iou_threshi
:para sigma, gaussian weght
:method, linear or gaussian
"""
boxes = boxes_in.copy()
N = boxes.shape[0]
if N is None or N < 1:
return np.array([])
pos, maxpos = 0, 0
weight = 0.0
inds = np.arange(N)
tbox, sbox = boxes[0].copy(), boxes[0].copy()
for i in range(N):
maxscore = boxes[i, 8]
maxpos = i
tbox = boxes[i].copy()
ti = inds[i]
pos = i + 1
# get max box
while pos < N:
if maxscore < boxes[pos, 8]:
maxscore = boxes[pos, 8]
maxpos = pos
pos = pos + 1
# add max box as a detection
boxes[i, :] = boxes[maxpos, :]
inds[i] = inds[maxpos]
# swap
boxes[maxpos, :] = tbox
inds[maxpos] = ti
tbox = boxes[i].copy()
pos = i + 1
# NMS iteration
while pos < N:
sbox = boxes[pos].copy()
ts_iou_val = intersection(tbox, sbox)
if ts_iou_val > 0:
if method == 1:
if ts_iou_val > Nt_thres:
weight = 1 - ts_iou_val
else:
weight = 1
elif method == 2:
weight = np.exp(-1.0 * ts_iou_val**2 / sigma)
else:
if ts_iou_val > Nt_thres:
weight = 0
else:
weight = 1
boxes[pos, 8] = weight * boxes[pos, 8]
# if box score falls below thresold, discard the box by
# swaping last box update N
if boxes[pos, 8] < threshold:
boxes[pos, :] = boxes[N - 1, :]
inds[pos] = inds[N - 1]
N = N - 1
pos = pos - 1
pos = pos + 1
return boxes[:N]
def nms_locality(polys, thres=0.3):
"""
locality aware nms of EAST
:param polys: a N*9 numpy array. first 8 coordinates, then prob
:return: boxes after nms
"""
S = []
p = None
for g in polys:
if p is not None and intersection(g, p) > thres:
p = weighted_merge(g, p)
else:
if p is not None:
S.append(p)
p = g
if p is not None:
S.append(p)
if len(S) == 0:
return np.array([])
return standard_nms(np.array(S), thres)
if __name__ == "__main__":
# 343,350,448,135,474,143,369,359
print(Polygon(np.array([[343, 350], [448, 135], [474, 143], [369, 359]])).area)
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