import numpy as np

cimport numpy as np

cdef inline np.float32_t max(np.float32_t a, np.float32_t b):

return a if a >= b else b

cdef inline np.float32_t min(np.float32_t a, np.float32_t b):

return a if a <= b else b

def nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh):

cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0]

cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1]

cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2]

cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3]

cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4]

cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1)

cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1]

cdef int ndets = dets.shape[0]

cdef np.ndarray[np.int_t, ndim=1] suppressed = \

np.zeros((ndets), dtype=np.int)

# nominal indices

cdef int _i, _j

# sorted indices

cdef int i, j

# temp variables for box i's (the box currently under consideration)

cdef np.float32_t ix1, iy1, ix2, iy2, iarea

# variables for computing overlap with box j (lower scoring box)

cdef np.float32_t xx1, yy1, xx2, yy2

cdef np.float32_t w, h

cdef np.float32_t inter, ovr

keep = []

for _i in range(ndets):

i = order[_i]

if suppressed[i] == 1:

continue

keep.append(i)

ix1 = x1[i]

iy1 = y1[i]

ix2 = x2[i]

iy2 = y2[i]

iarea = areas[i]

for _j in range(_i + 1, ndets):

j = order[_j]

if suppressed[j] == 1:

continue

xx1 = max(ix1, x1[j])

yy1 = max(iy1, y1[j])

xx2 = min(ix2, x2[j])

yy2 = min(iy2, y2[j])

w = max(0.0, xx2 - xx1 + 1)

h = max(0.0, yy2 - yy1 + 1)

inter = w * h

ovr = inter / (iarea + areas[j] - inter)

if ovr >= thresh:

suppressed[j] = 1

return keep

def soft_nms(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0):

cdef unsigned int N = boxes.shape[0]

cdef float iw, ih, box_area

cdef float ua

cdef int pos = 0

cdef float maxscore = 0

cdef int maxpos = 0

cdef float x1,x2,y1,y2,tx1,tx2,ty1,ty2,ts,area,weight,ov

for i in range(N):

maxscore = boxes[i, 4]

maxpos = i

tx1 = boxes[i,0]

ty1 = boxes[i,1]

tx2 = boxes[i,2]

ty2 = boxes[i,3]

ts = boxes[i,4]

pos = i + 1

# get max box

while pos < N:

if maxscore < boxes[pos, 4]:

maxscore = boxes[pos, 4]

maxpos = pos

pos = pos + 1

# add max box as a detection

boxes[i,0] = boxes[maxpos,0]

boxes[i,1] = boxes[maxpos,1]

boxes[i,2] = boxes[maxpos,2]

boxes[i,3] = boxes[maxpos,3]

boxes[i,4] = boxes[maxpos,4]

# swap ith box with position of max box

boxes[maxpos,0] = tx1

boxes[maxpos,1] = ty1

boxes[maxpos,2] = tx2

boxes[maxpos,3] = ty2

boxes[maxpos,4] = ts

tx1 = boxes[i,0]

ty1 = boxes[i,1]

tx2 = boxes[i,2]

ty2 = boxes[i,3]

ts = boxes[i,4]

pos = i + 1

# NMS iterations, note that N changes if detection boxes fall below threshold

while pos < N:

x1 = boxes[pos, 0]

y1 = boxes[pos, 1]

x2 = boxes[pos, 2]

y2 = boxes[pos, 3]

s = boxes[pos, 4]

area = (x2 - x1 + 1) * (y2 - y1 + 1)

iw = (min(tx2, x2) - max(tx1, x1) + 1)

if iw > 0:

ih = (min(ty2, y2) - max(ty1, y1) + 1)

if ih > 0:

ua = float((tx2 - tx1 + 1) * (ty2 - ty1 + 1) + area - iw * ih)

ov = iw * ih / ua #iou between max box and detection box

if method == 1: # linear

if ov > Nt:

weight = 1 - ov

else:

weight = 1

elif method == 2: # gaussian

weight = np.exp(-(ov * ov)/sigma)

else: # original NMS

if ov > Nt:

weight = 0

else:

weight = 1

boxes[pos, 4] = weight*boxes[pos, 4]

# if box score falls below threshold, discard the box by swapping with last box

# update N

if boxes[pos, 4] < threshold:

boxes[pos,0] = boxes[N-1, 0]

boxes[pos,1] = boxes[N-1, 1]

boxes[pos,2] = boxes[N-1, 2]

boxes[pos,3] = boxes[N-1, 3]

boxes[pos,4] = boxes[N-1, 4]

N = N - 1

pos = pos - 1

pos = pos + 1

keep = [i for i in range(N)]

return keep

def soft_nms_with_points(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0):

cdef unsigned int N = boxes.shape[0]

cdef float iw, ih, box_area

cdef float ua

cdef int pos = 0

cdef float maxscore = 0

cdef int maxpos = 0

cdef float x1,x2,y1,y2,tx1,tx2,ty1,ty2,ts,area,weight,ov

for i in range(N):

maxscore = boxes[i, 4]

maxpos = i

tx1 = boxes[i,0]

ty1 = boxes[i,1]

tx2 = boxes[i,2]

ty2 = boxes[i,3]

ts = boxes[i,4]

ttx = boxes[i,5]

tty = boxes[i,6]

tlx = boxes[i,7]

tly = boxes[i,8]

tbx = boxes[i,9]

tby = boxes[i,10]

trx = boxes[i,11]

try_ = boxes[i,12]

pos = i + 1

# get max box

while pos < N:

if maxscore < boxes[pos, 4]:

maxscore = boxes[pos, 4]

maxpos = pos

pos = pos + 1

# add max box as a detection

boxes[i,0] = boxes[maxpos,0]

boxes[i,1] = boxes[maxpos,1]

boxes[i,2] = boxes[maxpos,2]

boxes[i,3] = boxes[maxpos,3]

boxes[i,4] = boxes[maxpos,4]

boxes[i,5] = boxes[maxpos,5]

boxes[i,6] = boxes[maxpos,6]

boxes[i,7] = boxes[maxpos,7]

boxes[i,8] = boxes[maxpos,8]

boxes[i,9] = boxes[maxpos,9]

boxes[i,10] = boxes[maxpos,10]

boxes[i,11] = boxes[maxpos,11]

boxes[i,12] = boxes[maxpos,12]

# swap ith box with position of max box

boxes[maxpos,0] = tx1

boxes[maxpos,1] = ty1

boxes[maxpos,2] = tx2

boxes[maxpos,3] = ty2

boxes[maxpos,4] = ts

boxes[maxpos,5] = ttx

boxes[maxpos,6] = tty

boxes[maxpos,7] = tlx

boxes[maxpos,8] = tly

boxes[maxpos,9] = tbx

boxes[maxpos,10] = tby

boxes[maxpos,11] = trx

boxes[maxpos,12] = try_

tx1 = boxes[i,0]

ty1 = boxes[i,1]

tx2 = boxes[i,2]

ty2 = boxes[i,3]

ts = boxes[i,4]

ttx = boxes[i,5]

tty = boxes[i,6]

tlx = boxes[i,7]

tly = boxes[i,8]

tbx = boxes[i,9]

tby = boxes[i,10]

trx = boxes[i,11]

try_ = boxes[i,12]

pos = i + 1

# NMS iterations, note that N changes if detection boxes fall below threshold

while pos < N:

x1 = boxes[pos, 0]

y1 = boxes[pos, 1]

x2 = boxes[pos, 2]

y2 = boxes[pos, 3]

s = boxes[pos, 4]

area = (x2 - x1 + 1) * (y2 - y1 + 1)

iw = (min(tx2, x2) - max(tx1, x1) + 1)

if iw > 0:

ih = (min(ty2, y2) - max(ty1, y1) + 1)

if ih > 0:

ua = float((tx2 - tx1 + 1) * (ty2 - ty1 + 1) + area - iw * ih)

ov = iw * ih / ua #iou between max box and detection box

if method == 1: # linear

if ov > Nt:

weight = 1 - ov

else:

weight = 1

elif method == 2: # gaussian

weight = np.exp(-(ov * ov)/sigma)

else: # original NMS

if ov > Nt:

weight = 0

else:

weight = 1

boxes[pos, 4] = weight*boxes[pos, 4]

# if box score falls below threshold, discard the box by swapping with last box

# update N

if boxes[pos, 4] < threshold:

boxes[pos,0] = boxes[N-1, 0]

boxes[pos,1] = boxes[N-1, 1]

boxes[pos,2] = boxes[N-1, 2]

boxes[pos,3] = boxes[N-1, 3]

boxes[pos,4] = boxes[N-1, 4]

boxes[pos,5] = boxes[N-1, 5]

boxes[pos,6] = boxes[N-1, 6]

boxes[pos,7] = boxes[N-1, 7]

boxes[pos,8] = boxes[N-1, 8]

boxes[pos,9] = boxes[N-1, 9]

boxes[pos,10] = boxes[N-1, 10]

boxes[pos,11] = boxes[N-1, 11]

boxes[pos,12] = boxes[N-1, 12]

N = N - 1

pos = pos - 1

pos = pos + 1

keep = [i for i in range(N)]

return keep

def soft_nms_merge(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0, float weight_exp=6):

cdef unsigned int N = boxes.shape[0]

cdef float iw, ih, box_area

cdef float ua

cdef int pos = 0

cdef float maxscore = 0

cdef int maxpos = 0

cdef float x1,x2,y1,y2,tx1,tx2,ty1,ty2,ts,area,weight,ov

cdef float mx1,mx2,my1,my2,mts,mbs,mw

for i in range(N):

maxscore = boxes[i, 4]

maxpos = i

tx1 = boxes[i,0]

ty1 = boxes[i,1]

tx2 = boxes[i,2]

ty2 = boxes[i,3]

ts = boxes[i,4]

pos = i + 1

# get max box

while pos < N:

if maxscore < boxes[pos, 4]:

maxscore = boxes[pos, 4]

maxpos = pos

pos = pos + 1

# add max box as a detection

boxes[i,0] = boxes[maxpos,0]

boxes[i,1] = boxes[maxpos,1]

boxes[i,2] = boxes[maxpos,2]

boxes[i,3] = boxes[maxpos,3]

boxes[i,4] = boxes[maxpos,4]

mx1 = boxes[i, 0] * boxes[i, 5]

my1 = boxes[i, 1] * boxes[i, 5]

mx2 = boxes[i, 2] * boxes[i, 6]

my2 = boxes[i, 3] * boxes[i, 6]

mts = boxes[i, 5]

mbs = boxes[i, 6]

# swap ith box with position of max box

boxes[maxpos,0] = tx1

boxes[maxpos,1] = ty1

boxes[maxpos,2] = tx2

boxes[maxpos,3] = ty2

boxes[maxpos,4] = ts

tx1 = boxes[i,0]

ty1 = boxes[i,1]

tx2 = boxes[i,2]

ty2 = boxes[i,3]

ts = boxes[i,4]

pos = i + 1

# NMS iterations, note that N changes if detection boxes fall below threshold

while pos < N:

x1 = boxes[pos, 0]

y1 = boxes[pos, 1]

x2 = boxes[pos, 2]

y2 = boxes[pos, 3]

s = boxes[pos, 4]

area = (x2 - x1 + 1) * (y2 - y1 + 1)

iw = (min(tx2, x2) - max(tx1, x1) + 1)

if iw > 0:

ih = (min(ty2, y2) - max(ty1, y1) + 1)

if ih > 0:

ua = float((tx2 - tx1 + 1) * (ty2 - ty1 + 1) + area - iw * ih)

ov = iw * ih / ua #iou between max box and detection box

if method == 1: # linear

if ov > Nt:

weight = 1 - ov

else:

weight = 1

elif method == 2: # gaussian

weight = np.exp(-(ov * ov)/sigma)

else: # original NMS

if ov > Nt:

weight = 0

else:

weight = 1

mw = (1 - weight) ** weight_exp

mx1 = mx1 + boxes[pos, 0] * boxes[pos, 5] * mw

my1 = my1 + boxes[pos, 1] * boxes[pos, 5] * mw

mx2 = mx2 + boxes[pos, 2] * boxes[pos, 6] * mw

my2 = my2 + boxes[pos, 3] * boxes[pos, 6] * mw

mts = mts + boxes[pos, 5] * mw

mbs = mbs + boxes[pos, 6] * mw

boxes[pos, 4] = weight*boxes[pos, 4]

# if box score falls below threshold, discard the box by swapping with last box

# update N

if boxes[pos, 4] < threshold:

boxes[pos,0] = boxes[N-1, 0]

boxes[pos,1] = boxes[N-1, 1]

boxes[pos,2] = boxes[N-1, 2]

boxes[pos,3] = boxes[N-1, 3]

boxes[pos,4] = boxes[N-1, 4]

N = N - 1

pos = pos - 1

pos = pos + 1

boxes[i, 0] = mx1 / mts

boxes[i, 1] = my1 / mts

boxes[i, 2] = mx2 / mbs

boxes[i, 3] = my2 / mbs

keep = [i for i in range(N)]

return keep