def detect(nl_img, size, points):
thresh = LAT(nl_img)
wt = weight.bright2dark(thresh)
W = np.ones((size[0], size[1]+2))
for i in range(size[0]):
for j in range(1, size[1]+1):
W[i][j] = wt[i][j-1]
s1 = [0, points[0]]
s2 = [size[0]-1, points[1]]
D = fastsweeping(W, (size[0], size[1]+2), s1, s2)
res = gradientFlow(D, s1, s2)
return res
def detect(is_os, img, wt, size, points):
temp = np.zeros(size)
for j in range(size[1]):
for i in range(size[0] - 1, 0, -1):
if is_os[i - 1][j] > 0:
break
temp[i][j] = wt[i][j]
W = np.ones((size[0], size[1] + 2))
for i in range(size[0]):
for j in range(1, size[1] + 1):
W[i][j] = temp[i][j - 1]
s1 = [0, points[0]]
s2 = [size[0] - 1, points[1]]
D = fastsweeping(W, (size[0], size[1] + 2), s1, s2)
res = gradientFlow(D, s1, s2)
return res
def detect(ilm, onl_is, img, wt, size, points):
flag = 0
temp = np.zeros(size)
for j in range(size[1]):
for i in range(1, size[0]):
if ilm[i - 10][j] > 0:
flag = 1
if onl_is[i][j] > 0:
flag = 0
temp[i][j] = flag * wt[i][j]
W = np.ones((size[0], size[1] + 2))
for i in range(size[0]):
for j in range(1, size[1] + 1):
W[i][j] = temp[i][j - 1]
s1 = [0, points[0]]
s2 = [size[0] - 1, points[1]]
D = fastsweeping(W, (size[0], size[1] + 2), s1, s2)
res = gradientFlow(D, s1, s2)
return res