def iaareaopen(f,a,Bc=iasecross()):
a = -a
s = f.shape
g = np.zeros_like(f).ravel()
f1 = np.concatenate((f.ravel(),np.array([0])))
area = -np.ones((f1.size,), np.int32)
N = MT.iaNlut(s, MT.iase2off(Bc))
pontos = f1.nonzero()[0]
pontos = pontos[np.lexsort((np.arange(0,-len(pontos),-1),f1[pontos]))[::-1]]
for p in pontos:
for v in N[p]:
if f1[p] < f1[v] or (f1[p] == f1[v] and v < p):
rv = find_area(area, v)
if rv != p:
if area[rv] > a or f1[p] == f1[rv]:
area[p] = area[p] + area[rv]
area[rv] = p
else:
area[p] = a
for p in pontos[::-1]:
if area[p] >= 0:
g[p] = g[area[p]]
else:
if area[p] <= a:
g[p] = f1[p]
return g.reshape(s)
def ialabel_unionfind(img, Bc):
imshape = img.shape
imsize = img.size
# Offsets and neighbors
offsets = MT.iase2off(Bc, 'fw')
neighbors = MT.iaNlut(imshape, offsets)
parents = np.arange(imsize, dtype=int)
# Raster image and no-nzero pixels
img = img.ravel()
nonzero_nodes = np.nonzero(img)[0]
img = np.concatenate((img, np.array([0])))
O = np.zeros(imsize, dtype=int)
cur_label = 0
# pass 1: backward scan, forward neighbors
for p in nonzero_nodes[::-1]:
for nb in neighbors[p]:
if img[nb]:
Union(nb, p, parents)
# pass 2_1: root labeled
pbool = parents[nonzero_nodes] == nonzero_nodes
labels = np.arange(1, pbool.sum() + 1)
O[nonzero_nodes[pbool]] = labels
# pass 2_2: labeling root descendants
for p in nonzero_nodes[~pbool]:
O[p] = O[parents[p]]
return O.reshape(imshape)
def iaareaopen(f, a, Bc=iasecross()):
a = -a
s = f.shape
g = np.zeros_like(f).ravel()
f1 = np.concatenate((f.ravel(), np.array([0])))
area = -np.ones((f1.size,), np.int32)
N = MT.iaNlut(s, MT.iase2off(Bc))
pontos = f1.nonzero()[0]
pontos = pontos[np.lexsort((np.arange(0, -len(pontos), -1), f1[pontos]))[::-1]]
for p in pontos:
for v in N[p]:
if f1[p] < f1[v] or (f1[p] == f1[v] and v < p):
rv = find_area(area, v)
if rv != p:
if area[rv] > a or f1[p] == f1[rv]:
area[p] = area[p] + area[rv]
area[rv] = p
else:
area[p] = a
for p in pontos[::-1]:
if area[p] >= 0:
g[p] = g[area[p]]
else:
if area[p] <= a:
g[p] = f1[p]
return g.reshape(s)
def ialabelflat_unionfind(img, Bc, delta):
imshape = img.shape
imsize = img.size
# Offsets and neighbors
offsets = MT.iase2off(Bc, "fw")
neighbors = MT.iaNlut(imshape, offsets)
parents = np.arange(imsize, dtype=int)
# Raster image and no-nzero pixels
img = img.ravel()
nonzero_nodes = np.nonzero(img)[0]
img = np.concatenate((img, np.array([0])))
g = np.zeros(imsize, dtype=int)
cur_label = 0
# pass 1: backward scan, forward neighbors
for p in nonzero_nodes[::-1]:
v = img[p]
for nb in neighbors[p]:
if img[nb] and (abs(v - img[nb]) <= delta):
Union(nb, p, parents)
# pass 2_1: root labeled
pbool = parents[nonzero_nodes] == nonzero_nodes
labels = np.arange(1, pbool.sum() + 1)
g[nonzero_nodes[pbool]] = labels
# pass 2_2: labeling root descendants
for p in nonzero_nodes[~pbool]:
g[p] = g[parents[p]]
return g.reshape(imshape)
def iainfrec(m, f, Bc=iasecross()):
h,w = Bc.shape
hc,wc = h/2,w/2
B = Bc.copy()
off = np.transpose(B.nonzero()) - np.array([hc,wc])
i = off[:,0] * w + off[:,1]
Nids = MT.iaNlut(f.shape,off)
x,y = np.where(Nids==f.size)
Nids[x,y] = x
Nids_pos = Nids[:,i<0] #de acordo com a convenção em Vincent93
Nids_neg = Nids[:,i>0] #de acordo com a convenção em Vincent93
I = f.flatten()
J = m.flatten()
D = np.nonzero(J)[0]
V = np.zeros(f.size,np.bool) #para controle de inserção na fila
fila =[]
for p in D:
Jq = J[p]
for q in Nids_pos[p]:
Jq = max(Jq,J[q])
if (J[q] < J[p]) and (J[q] < I[q]) and ~V[p]:
fila.append(p)
V[p]=True
J[p] = min(Jq,I[p])
for p in D[::-1]:
Jq = J[p]
for q in Nids_neg[p]:
Jq = max(Jq,J[q])
if (J[q] < J[p]) and (J[q] < I[q]) and ~V[p]:
fila.append(p)
V[p]=True
J[p] = min(Jq,I[p])
while fila:
p = fila.pop(0)
for q in Nids[p]:
if J[q]<J[p] and I[q]!=J[q]:
J[q] = min(J[p],I[q])
fila.append(q)
return J.reshape(f.shape)
def iainfrec(m, f, Bc=iasecross()):
h, w = Bc.shape
hc, wc = h / 2, w / 2
B = Bc.copy()
off = np.transpose(B.nonzero()) - np.array([hc, wc])
i = off[:, 0] * w + off[:, 1]
Nids = MT.iaNlut(f.shape, off)
x, y = np.where(Nids == f.size)
Nids[x, y] = x
Nids_pos = Nids[:, i < 0] #de acordo com a convenção em Vincent93
Nids_neg = Nids[:, i > 0] #de acordo com a convenção em Vincent93
I = f.flatten()
J = m.flatten()
D = np.nonzero(J)[0]
V = np.zeros(f.size, np.bool) #para controle de inserção na fila
fila = []
for p in D:
Jq = J[p]
for q in Nids_pos[p]:
Jq = max(Jq, J[q])
if (J[q] < J[p]) and (J[q] < I[q]) and ~V[p]:
fila.append(p)
V[p] = True
J[p] = min(Jq, I[p])
for p in D[::-1]:
Jq = J[p]
for q in Nids_neg[p]:
Jq = max(Jq, J[q])
if (J[q] < J[p]) and (J[q] < I[q]) and ~V[p]:
fila.append(p)
V[p] = True
J[p] = min(Jq, I[p])
while fila:
p = fila.pop(0)
for q in Nids[p]:
if J[q] < J[p] and I[q] != J[q]:
J[q] = min(J[p], I[q])
fila.append(q)
return J.reshape(f.shape)
