def ThinImage(imgPath):
img = Image.open(imgPath).convert('L')
imgArray = numpy.asarray(img)
imgBinary = pymorph.neg(pymorph.binary(imgArray))
img = pymorph.thin(imgBinary)
iPrune = pymorph.thin(img,pymorph.endpoints('homotopic'),10)
Image.fromarray(pymorph.gray(pymorph.neg(iPrune))).save('thinned.png','PNG')
print "Saving"
def medial_points(thresh, filepath = '.'):
#leggo l'immagine e la trasformo in 0 o 255
(th, im_bw) = cv2.threshold(thresh, 240, 255, cv2.THRESH_BINARY_INV)
#plt.imshow(cv2.cvtColor(im_bw,cv2.COLOR_GRAY2RGB))
#plt.show()
#points = punti dei muri
points = []
for y in xrange(0,im_bw.shape[1]):
for x in xrange(im_bw.shape[0]):
if im_bw[x, y] == (0):
points.append([y,x])
points = np.asarray(points)
#distance transform
# distanceMap = ndimage.distance_transform_edt(im_bw)
# plt.title('8.distance map')
# plt.imshow(distanceMap)
# plt.show()
#disegno distance transforme
distanceMap = dsg.disegna_distance_transform(im_bw, filepath = filepath)#plot
#medial axis sulla distance transform
skel, distance = medial_axis(distanceMap, return_distance=True)
#elimino parte delle imprecisioni della skeletonization
b3 = m.thin(skel, m.endpoints('homotopic'), 15)
#elimino i punti isolati del medial axis, sono rumore
for riga in xrange(b3.shape[0]):
for col in xrange(b3.shape[1]):
if b3[riga][col]==True and isolato(b3,riga,col):
b3[riga][col]=False
#disegno medial axis
# plt.title('9.medial axis')
# plt.plot(points[:,0],points[:,1],'.')
# plt.imshow(b3,cmap='Greys')
# plt.show()
#disegno medial axis
dsg.disegna_medial_axis(points, b3, filepath = filepath)
punti_medial = []
#salvo in una lista i punti che fanno parte del medial axis
for riga in xrange(b3.shape[0]):
for col in xrange(b3.shape[1]):
if (b3[riga][col]==True):
punti_medial.append((col,riga))
punti_medial = np.asarray(punti_medial)
flip_vertici(punti_medial,b3.shape[0]-1)
return punti_medial
def process(self, im):
#single pixel restructure element
elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]])
print "starting img process.. binarizing"
b1 = im > 205 #binarize
print "removing single pixels"
#remove single pixels
singpix = mahotas.morph.hitmiss(b1, elem)
b1 = (b1 - singpix) > 0
print "closing holes"
b1 = m.close_holes(b1)
print "thinning"
#b2 = m.thin(b1) #thin
b2 = self.shitthin(b1) #thin
print "pruning"
b3 = m.thin(b2, m.endpoints('homotopic'), 8)
#remove single pixels
singpix = mahotas.morph.hitmiss(b3, elem)
b3 = b3 - singpix
b3 = b3 > 0
struct = np.ndarray((4, 3, 3), dtype=np.uint8)
struct[0, :, :] = [[1, 2, 1], [0, 1, 0], [0, 1, 0]]
for i in range(1, 4):
print "gen %i structure.." % (i)
struct[i, :, :] = np.rot90(struct[0], i)
#struct = struct == 1
print "using struct for branch summing:"
print struct
b4 = np.zeros((301, 398), dtype=bool)
for i in range(0, 4):
b4 = b4 | mahotas.morph.hitmiss(b3, struct[i])
b4 = b4 > 0
imgout = m.overlay(b1, b2, b4)
mahotas.imsave("thresh.png", imgout)
return b4
def process(self, im):
# single pixel restructure element
elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]])
print "starting img process.. binarizing"
b1 = im > 205 # binarize
print "removing single pixels"
# remove single pixels
singpix = mahotas.morph.hitmiss(b1, elem)
b1 = (b1 - singpix) > 0
print "closing holes"
b1 = m.close_holes(b1)
print "thinning"
# b2 = m.thin(b1) #thin
b2 = self.shitthin(b1) # thin
print "pruning"
b3 = m.thin(b2, m.endpoints("homotopic"), 8)
# remove single pixels
singpix = mahotas.morph.hitmiss(b3, elem)
b3 = b3 - singpix
b3 = b3 > 0
struct = np.ndarray((4, 3, 3), dtype=np.uint8)
struct[0, :, :] = [[1, 2, 1], [0, 1, 0], [0, 1, 0]]
for i in range(1, 4):
print "gen %i structure.." % (i)
struct[i, :, :] = np.rot90(struct[0], i)
# struct = struct == 1
print "using struct for branch summing:"
print struct
b4 = np.zeros((301, 398), dtype=bool)
for i in range(0, 4):
b4 = b4 | mahotas.morph.hitmiss(b3, struct[i])
b4 = b4 > 0
imgout = m.overlay(b1, b2, b4)
mahotas.imsave("thresh.png", imgout)
return b4
def critical_points(metricMap):
'''
ottengo i critical points dal medial axis
'''
if cv2.__version__[0] == '3':
flag = cv2.IMREAD_GRAYSCALE
elif cv2.__version__[0] == '2':
flag = cv2.CV_LOAD_IMAGE_GRAYSCALE
else:
raise EnvironmentError(
'Opencv Version Error. You should have OpenCv 2.* or 3.*')
im_gray = cv2.imread(metricMap, flag)
#leggo l'immagine e la trasformo in 0 o 255
(thresh, im_bw) = cv2.threshold(im_gray, 240, 255, cv2.THRESH_BINARY_INV)
(th, im_bw) = cv2.threshold(im_bw, 240, 255, cv2.THRESH_BINARY_INV)
#points = punti dei muri
points = []
for y in xrange(0, im_bw.shape[1]):
for x in xrange(im_bw.shape[0]):
if im_bw[x, y] == (0):
points.append([y, x])
points = np.asarray(points)
#distance map
distanceMap = get_distance_transforme(im_bw)
#medial axis sulla distance transform
skel, distance = medial_axis(distanceMap, return_distance=True)
#elimino parte delle imprecisioni della skeletonization
#b3 = m.thin(skel, m.endpoints('homotopic'), 15)
b3 = m.thin(skel, m.endpoints('homotopic'), 500)
#---
import copy
b4 = copy.copy(b3)
#ottengo solo critical points
#critical_points = []
for riga in xrange(b4.shape[0]):
for col in xrange(b4.shape[1]):
if b4[riga][col] == True:
#controllo se in un intorno di quel punto c'è una frontiera
#creo intorno del pixel
start_x = riga - 17 #15
end_x = riga + 17
start_y = col - 17
end_y = col + 17
count = 0
for x in xrange(start_x, end_x):
for y in xrange(start_y, end_y):
if im_bw[x, y] <= (
200
): #se almeno una componente rgb di un pixel nell'introno del pixel cha fa parte del medial axis e' un pixel della frontiera
count += 1
if count <= 5:
b4[riga][col] = False
# else:
# #è un critical_points
# critical_points.append([riga, col])
critical_points = []
#salvo in una lista i critical points che fanno parte del medial axis
for riga in xrange(b4.shape[0]):
for col in xrange(b4.shape[1]):
if (b4[riga][col] == True):
critical_points.append((col, riga))
critical_points = np.asarray(critical_points)
flip_vertici(critical_points, b4.shape[0] - 1)
#------
return distanceMap, points, b3, b4, critical_points
import pymorph as m
import mahotas
from numpy import where, reshape
image = mahotas.imread('B.png') # Load image
b1 = image[:,:,0] < 100 # Make a binary image from the thresholded red channel
b2 = m.erode(b1, m.sedisk(4)) # Erode to enhance contrast of the bridge
b3 = m.open(b2,m.sedisk(4)) # Remove the bridge
b4 = b2-b3 # Bridge plus small noise
b5 = m.areaopen(b4,1000) # Remove small areas leaving only a thinned bridge
b6 = m.dilate(b3)*b5 # Extend the non-bridge area slightly and get intersection with the bridge.
#b6 is image of end of bridge, now find single points
b7 = m.thin(b6, m.endpoints('homotopic')) # Narrow regions to single points.
labelled = m.label(b7) # Label endpoints.
x1, y1 = reshape(where(labelled == 1),(1,2))[0]
x2, y2 = reshape(where(labelled == 2),(1,2))[0]
outputimage = m.overlay(b1, m.dilate(b7,m.sedisk(5)))
mahotas.imsave('output.png', outputimage)
def medial_points(thresh):
#leggo l'immagine e la trasformo in 0 o 255
(th, im_bw) = cv2.threshold(thresh, 240, 255, cv2.THRESH_BINARY_INV)
#plt.imshow(cv2.cvtColor(im_bw,cv2.COLOR_GRAY2RGB))
#plt.show()
#points = punti dei muri
points = []
for y in xrange(0, im_bw.shape[1]):
for x in xrange(im_bw.shape[0]):
if im_bw[x, y] == (0):
points.append([y, x])
points = np.asarray(points)
#distance transform
# distanceMap = ndimage.distance_transform_edt(im_bw)
# plt.title('8.distance map')
# plt.imshow(distanceMap)
# plt.show()
#disegno distance transforme
#distanceMap = dsg.disegna_distance_transform(im_bw, filepath = filepath)#plot
distanceMap = get_distance_transforme(im_bw)
#medial axis sulla distance transform
skel, distance = medial_axis(distanceMap, return_distance=True)
#elimino parte delle imprecisioni della skeletonization
#b3 = m.thin(skel, m.endpoints('homotopic'), 15)
b3 = m.thin(skel, m.endpoints('homotopic'), 500)
'''
#------
import copy
b4 = copy.copy(b3)
#ottengo solo critical points
#critical_points = []
for riga in xrange(b4.shape[0]):
for col in xrange(b4.shape[1]):
if b4[riga][col]==True:
#controllo se in un intorno di quel punto c'è una frontiera
#creo intorno del pixel
start_x = riga-17 #15
end_x = riga+17
start_y = col-17
end_y = col+17
count = 0
for x in xrange(start_x,end_x):
for y in xrange(start_y, end_y):
if im_bw[x, y] <= (200): #se almeno una componente rgb di un pixel nell'introno del pixel cha fa parte del medial axis e' un pixel della frontiera
count+=1
if count <= 5:
b4[riga][col]=False
# else:
# #è un critical_points
# critical_points.append([riga, col])
critical_points = []
#salvo in una lista i critical points che fanno parte del medial axis
for riga in xrange(b4.shape[0]):
for col in xrange(b4.shape[1]):
if (b4[riga][col]==True):
critical_points.append((col,riga))
critical_points = np.asarray(critical_points)
flip_vertici(critical_points,b4.shape[0]-1)
#------
'''
#elimino i punti isolati del medial axis, sono rumore
for riga in xrange(b3.shape[0]):
for col in xrange(b3.shape[1]):
if b3[riga][col] == True and isolato(b3, riga, col):
b3[riga][col] = False
#disegno medial axis
# plt.title('9.medial axis')
# plt.plot(points[:,0],points[:,1],'.')
# plt.imshow(b3,cmap='Greys')
# plt.show()
#disegno medial axis
#dsg.disegna_medial_axis(points, b3, filepath = filepath)#quiiiiiiiii
punti_medial = []
#salvo in una lista i punti che fanno parte del medial axis
for riga in xrange(b3.shape[0]):
for col in xrange(b3.shape[1]):
if (b3[riga][col] == True):
punti_medial.append((col, riga))
punti_medial = np.asarray(punti_medial)
flip_vertici(punti_medial, b3.shape[0] - 1)
return punti_medial, distanceMap, points, b3, distance