def clean_mask(mask):
# first erode to get rid of noise
mask = erode(mask, sedisk(2))
# then dilate more to capture a slightly larger area
mask = dilate(mask, sedisk(16))
return mask
def create_shad(matte, target):
"""
Creates a shadowed image given target (to be shadowed) and matte.
The matte can be smaller that the target in which case it will be used at
some random location.
"""
# first get a bounding box of the shadow matte
mask = np.array(matte < 1, dtype=int)
mask = dilate(erode(mask, sedisk(3)), sedisk(3))
left, upper, right, lower = PIL.Image.fromarray(mask).getbbox()
# now cut it out
mh, mw = matte.shape[:2]
matte_bbox = matte[upper:lower, left:right]
# import pdb; pdb.set_trace()
# get new dimensions
mh, mw = matte_bbox.shape[:2]
th, tw = target.shape[:2]
new_matte = np.ones(target.shape)
# get random position to insert the matte
matte_x = matte_y = 0
if mh < th:
matte_y = (th - mh) * np.random.random()
if mw < tw:
matte_x = (tw - mw) * np.random.random()
new_matte[matte_y:matte_y + mh, matte_x:matte_x + mw] = matte_bbox
return new_matte * target
def _get_line_filter(segment_size, variation): """Computes the filters that can be used to enhance vertical lines in an Image. Args: segment_size: Size of the segment variatoin: Variation in horizontal axes if user wants not exact vertical lines. Returns: filters saved in 3D matrices, each 3rd dimension includes a filter """ smalldisk = pymorph.sedisk(1); bigdisk = pymorph.sedisk(2); horizontal_filter = numpy.zeros((variation*2+1, variation*2+1, segment_size)) horizontal_surrounding = numpy.zeros((variation*2+1, variation*2+1, segment_size)) index = -1 # Generates the filters for each direction of lines for variation_index in range(-variation, variation + 1): index = index + 1; points = bresenham(variation + variation_index,0, variation - variation_index, segment_size - 1) tmp = numpy.zeros((variation*2+1)*segment_size).reshape((variation*2+1, segment_size)) for point_ind in range(0, len(points)): tup_point = points[point_ind] tmp[tup_point[0], tup_point[1]] = 1 tmp_filter = pymorph.dilate(pymorph.binary(tmp), smalldisk) tmp_surrounding = pymorph.subm(pymorph.dilate(pymorph.binary(tmp), bigdisk) , \ pymorph.dilate(pymorph.binary(tmp), smalldisk)) horizontal_filter[index,:,:] = tmp_filter horizontal_surrounding[index,:,:] = tmp_surrounding return horizontal_filter, horizontal_surrounding
def run_one(fname, outname):
N = 1
im = img.open(fname)
#im = im.filter(ImageFilter.BLUR)
im = im.resize((600, 600), img.ANTIALIAS)
im = im.convert('L')
im = invert(im)
x = np.asarray(im)
y = x
size = 3
for i in range(N):
y = morph.dilate(y, morph.sedisk(size))
y = morph.close(y, morph.sedisk(size))
jm = img.fromarray(y)
jm = invert(jm)
jm = jm.resize((400, 400), img.ANTIALIAS)
jm.save(outname)
def GradBasedSegmentation(im):
blur = nd.gaussian_filter(im, 16)
rmax = pymorph.regmax(blur)
T = mahotas.thresholding.otsu(blur)
bImg0 = im > T
# bImg01=nd.binary_closing(bImg0,iterations=2)
bImg01 = pymorph.close(bImg0, pymorph.sedisk(3))
bImg = pymorph.open(bImg01, pymorph.sedisk(4))
# bImg=nd.binary_opening(bImg01,iterations=3)
b = pymorph.edgeoff(bImg)
d = distanceTranform(b)
seeds, nr_nuclei = nd.label(rmax)
lab = mahotas.cwatershed(d, seeds)
return lab
def GradBasedSegmentation(im):
blur=nd.gaussian_filter(im, 16)
rmax = pymorph.regmax(blur)
T = mahotas.thresholding.otsu(blur)
bImg0=im>T
#bImg01=nd.binary_closing(bImg0,iterations=2)
bImg01=pymorph.close(bImg0, pymorph.sedisk(3))
bImg=pymorph.open(bImg01, pymorph.sedisk(4))
#bImg=nd.binary_opening(bImg01,iterations=3)
b=pymorph.edgeoff(bImg)
d=distanceTranform(b)
seeds,nr_nuclei = nd.label(rmax)
lab=mahotas.cwatershed(d,seeds)
return lab
def morph_sharp(im):
img = numpy.zeros(im.shape, dtype=numpy.int32)
# Choose a disk structuring element (3x3 disk)
# Function could be modified to pass structuring element in
se = pymorph.sedisk(r=1, dim=2)
# Apply grayscale erosion
Ie = pymorph.erode(im, se)
# Apply grayscale dilation
Id = pymorph.dilate(im, se)
for i in range(0, im.shape[0]):
for j in range(0, im.shape[1]):
# Compute differences between original image and processed
da = Id[i][j] - im[i][j]
db = im[i][j] - Ie[i][j]
if da < db:
img[i][j] = Id[i][j]
elif da > db:
img[i][j] = Ie[i][j]
else:
img[i][j] = im[i][j]
return img
def morph_sharp(im):
img = numpy.zeros(im.shape,dtype=numpy.int32)
# Choose a disk structuring element (3x3 disk)
# Function could be modified to pass structuring element in
se = pymorph.sedisk(r=1,dim=2)
# Apply grayscale erosion
Ie = pymorph.erode(im,se)
# Apply grayscale dilation
Id = pymorph.dilate(im,se)
for i in range(0,im.shape[0]):
for j in range(0,im.shape[1]):
# Compute differences between original image and processed
da = Id[i][j] - im[i][j]
db = im[i][j] - Ie[i][j]
if da < db:
img[i][j] = Id[i][j]
elif da > db:
img[i][j] = Ie[i][j]
else:
img[i][j] = im[i][j]
return img
def test_sedisk():
se = pymorph.sedisk(4)
w,h = se.shape
assert w == h
X,Y = np.where(~se)
X -= w//2
Y -= h//2
assert np.all( X**2+Y**2 > (w//2)**2 )
def LowResSegmentation(image):
'''Perform a simple threshold after DoG filtering'''
noBack=RemoveModalBackground(image)
#print "from Segmeta noBack:",noBack.min(),noBack.mean()
blurLowRes=nd.filters.gaussian_filter(noBack,13)
blurHiRes=nd.filters.gaussian_filter(noBack,1)
midPass=pymorph.subm(blurHiRes,0.70*blurLowRes)
bin=(midPass>1.5*midPass.mean())
binLowRes=pymorph.open(bin,pymorph.sedisk(4))
return binLowRes
def LowResSegmentation(image):
"""Perform a simple threshold after DoG filtering"""
noBack = RemoveModalBackground(image)
# print "from Segmeta noBack:",noBack.min(),noBack.mean()
blurLowRes = nd.filters.gaussian_filter(noBack, 13)
blurHiRes = nd.filters.gaussian_filter(noBack, 1)
midPass = pymorph.subm(blurHiRes, 0.70 * blurLowRes)
bin = midPass > 1.5 * midPass.mean()
binLowRes = pymorph.open(bin, pymorph.sedisk(4))
return binLowRes
def test_opentransf():
f = np.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0],
[0, 1, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0]], bool)
ot = pymorph.opentransf(f, 'city-block')
for y in xrange(ot.shape[0]):
for x in xrange(ot.shape[1]):
r = ot[y, x]
t = f.copy()
for k in xrange(1, r + 1):
assert t[y, x]
t = pymorph.open(f, pymorph.sedisk(k, 2, 'city-block'))
assert not t[y, x]
def _get_line_filter(segment_size, variation):
"""Computes the filters that can be used to enhance vertical lines in
an Image.
Args:
segment_size: Size of the segment
variatoin: Variation in horizontal axes if user wants not exact
vertical lines.
Returns:
filters saved in 3D matrices, each 3rd dimension includes a filter
"""
smalldisk = pymorph.sedisk(1)
bigdisk = pymorph.sedisk(2)
horizontal_filter = numpy.zeros(
(variation * 2 + 1, variation * 2 + 1, segment_size))
horizontal_surrounding = numpy.zeros(
(variation * 2 + 1, variation * 2 + 1, segment_size))
index = -1
# Generates the filters for each direction of lines
for variation_index in range(-variation, variation + 1):
index = index + 1
points = bresenham(variation + variation_index, 0,
variation - variation_index, segment_size - 1)
tmp = numpy.zeros((variation * 2 + 1) * segment_size).reshape(
(variation * 2 + 1, segment_size))
for point_ind in range(0, len(points)):
tup_point = points[point_ind]
tmp[tup_point[0], tup_point[1]] = 1
tmp_filter = pymorph.dilate(pymorph.binary(tmp), smalldisk)
tmp_surrounding = pymorph.subm(pymorph.dilate(pymorph.binary(tmp), bigdisk) , \
pymorph.dilate(pymorph.binary(tmp), smalldisk))
horizontal_filter[index, :, :] = tmp_filter
horizontal_surrounding[index, :, :] = tmp_surrounding
return horizontal_filter, horizontal_surrounding
def _getLineFilter(self, segmentSize, variation):
smallDisk = pymorph.sedisk(1);
bigDisk = pymorph.sedisk(2);
horizontal_filter = numpy.zeros((variation*2+1,variation*2+1,segmentSize))
horizontal_surrounding = numpy.zeros((variation*2+1,variation*2+1,segmentSize))
index = -1
for i in range(-variation,variation+1):
index = index + 1;
# find the line between selected points
points = bresenham(variation+i,0,variation-i,segmentSize-1)
tmp = numpy.zeros((variation*2+1)*segmentSize).reshape((variation*2+1, segmentSize))
for l in range(0, len(points)):
tup_point = points[l]
tmp[tup_point[0], tup_point[1]] = 1
tmp_filter = pymorph.dilate(pymorph.binary(tmp), smallDisk)
tmp_surrounding = pymorph.subm(pymorph.dilate(pymorph.binary(tmp), bigDisk) , pymorph.dilate(pymorph.binary(tmp), smallDisk))
horizontal_filter[index,:,:] = tmp_filter
horizontal_surrounding[index,:,:] = tmp_surrounding
return horizontal_filter, horizontal_surrounding
def test_opentransf():
f = np.array([
[0,0,0,0,0,0,0,0],
[0,0,1,1,1,1,0,0],
[0,1,0,1,1,1,0,0],
[0,0,1,1,1,1,0,0],
[1,1,0,0,0,0,0,0]], bool)
ot = pymorph.opentransf( f, 'city-block')
for y in xrange(ot.shape[0]):
for x in xrange(ot.shape[1]):
r = ot[y,x]
t = f.copy()
for k in xrange(1, r+1):
assert t[y,x]
t = pymorph.open(f, pymorph.sedisk(k, 2, 'city-block'))
assert not t[y,x]
def _getBlobs(im, minsize=10, opendisk=0):
""" Find blobs in an image.
:param im: input image
:type im: numpy array
:param minsize: minimal size of detected blobs
:type minsize: int
:param opendisk: disk size for opening operation (ommitted when opendisk <= 0)
:type opendisk: int
:return: identifiers for areas with blobs > minsize and labeled image (lac,lab)
"""
if opendisk > 0:
im = m.open(im.astype(np.int), m.sedisk(opendisk))
n = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
lab, nr = labeled.label(im, n)
sizes = np.bincount(np.reshape(lab, (lab.shape[0] * lab.shape[1])))
lac = np.nonzero(sizes > minsize)[0]
return lac, lab
def morph_toggleCE(im):
img = numpy.zeros(im.shape, dtype=numpy.int32)
se = pymorph.sedisk(r=1, dim=2)
Ie = pymorph.erode(im, se)
Id = pymorph.dilate(im, se)
for i in range(0, im.shape[0]):
for j in range(0, im.shape[1]):
da = Id[i][j] - im[i][j]
db = im[i][j] - Ie[i][j]
if da < db:
img[i][j] = Id[i][j]
else:
img[i][j] = Ie[i][j]
return img
def morph_CE(im):
img = numpy.zeros(im.shape, dtype=numpy.int32)
se = pymorph.sedisk(r=1, dim=2)
# Top-hat by opening
THo = pymorph.openth(im, se)
# Top-hat by closing
THc = pymorph.closeth(im, se)
for i in range(0, im.shape[0]):
for j in range(0, im.shape[1]):
newPixel = im[i][j] + THo[i][j] - THc[i][j]
if newPixel > 255:
img[i][j] = 255
else:
img[i][j] = newPixel
return img
def morph_CE(im):
img = numpy.zeros(im.shape,dtype=numpy.int32)
se = pymorph.sedisk(r=1,dim=2)
# Top-hat by opening
THo = pymorph.openth(im,se)
# Top-hat by closing
THc = pymorph.closeth(im,se)
for i in range(0,im.shape[0]):
for j in range(0,im.shape[1]):
newPixel = im[i][j] + THo[i][j] - THc[i][j]
if newPixel > 255:
img[i][j] = 255
else:
img[i][j] = newPixel
return img
def morph_toggleCE(im):
img = numpy.zeros(im.shape,dtype=numpy.int32)
se = pymorph.sedisk(r=1,dim=2)
Ie = pymorph.erode(im,se)
Id = pymorph.dilate(im,se)
for i in range(0,im.shape[0]):
for j in range(0,im.shape[1]):
da = Id[i][j] - im[i][j]
db = im[i][j] - Ie[i][j]
if da < db:
img[i][j] = Id[i][j]
else:
img[i][j] = Ie[i][j]
return img
def watershedSegment(image, diskSize=20):
gradmag = gradientMagnitudue(image)
## compute foreground markers
# open image to create flat regions at cell centers
se_disk = pymorph.sedisk(diskSize)
image_opened = mahotas.open(image, se_disk);
# define foreground markers as regional maxes of cells
# this step is slow!
foreground_markers = mahotas.regmax(image_opened)
## compute background markers
# Threshold the image, cast it to the right datatype, and then calculate the distance image
image_black_white = image_opened > mahotas.otsu(image_opened)
image_black_white = image_black_white.astype('uint16')
# note the inversion here- a key difference from the matlab algorithm
# matlab distance is to nearest non-zero pixel
# python distance is to nearest 0 pixel
image_distance = pymorph.to_uint16(nd.distance_transform_edt(np.logical_not(image_black_white)))
eight_conn = pymorph.sebox()
distance_markers = mahotas.label(mahotas.regmin(image_distance, eight_conn))[0]
image_dist_wshed, image_dist_wshed_lines =mahotas.cwatershed(image_distance, distance_markers, eight_conn, return_lines=True)
background_markers = image_distance_watershed_lines - image_black_white
all_markers = np.logical_or(foreground_markers, background_markers)
# impose a min on the gradient image. assumes int64
gradmag2 = imimposemin(gradmag.astype(int), all_markers, eight_conn)
# call watershed
segmented_cells, segmented_cell_lines = mahotas.cwatershed(gradmag2, mahotas.label(all_markers)[0], eight_conn, return_lines=True)
# seperate watershed regions
segmented_cells[gradientMagnitudue(segmented_cells) > 0] = 0
return segmented_cells > 0, segmented_cells
def alternative_solution(self,
a,
orientation='coronal',
linethickness=10,
outimg=False):
'''
Paramenters
-----------
a: original image in graylevel
'''
H, W = a.shape
if orientation == 'coronal':
# UL = mm.limits(a)[1] # upper limit
UL = 255
b = 1 - iacircle(a.shape, H / 3, (1.4 * H / 3, W / 2)) # Circle
b = b[0:70, W / 2 - 80:W / 2 + 80] # Rectangle
# if outimg:
# b_ = 0 * a; b_[0:70, W / 2 - 80:W / 2 + 80] = UL * b # b_ only for presentation
# b_[:, W / 2 - linethickness / 2:W / 2 + linethickness / 2] = UL # b_ only for presentation
c = a + 0
c[:, W / 2 - linethickness / 2:W / 2 + linethickness / 2] = UL
c[0:70, W / 2 - 80:W / 2 +
80] = (1 - b) * c[0:70, W / 2 - 80:W / 2 + 80] + b * UL
c[0:40, W / 2 - 70:W / 2 + 70] = UL
d = mm.open(c, mm.img2se(mm.binary(np.ones((20, 10)))))
e = mm.close(d, mm.seline(5))
f = mm.close_holes(e)
g = mm.subm(f, d)
h = mm.close_holes(g)
i = mm.areaopen(h, 1000)
j1, j2 = iaotsu(i)
# j = i > j1
ret, j = cv2.threshold(cv2.GaussianBlur(i, (7, 7), 0), j1, 255,
cv2.THRESH_BINARY)
k = mm.open(j, mm.seline(20, 90))
l = mm.areaopen(k, 1000)
# m = mm.label(l)
res = np.vstack(
[np.hstack([c, d, e, f, g]),
np.hstack([h, i, j, k, l])])
cv2.imshow('Result', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
################################
# l_ = mm.blob(k,'AREA','IMAGE')
# l = l_ == max(ravel(l_))
# m = mm.open(l, mm.sedisk(3)) # VERIFICAR O MELHOR ELEMENTO ESTRUTURANTE AQUI
# n = mm.label(m)
if outimg:
if not os.path.isdir('outimg'):
os.mkdir('outimg')
def N(x):
# y = uint8(ianormalize(x, (0, 255)) + 0.5)
y = (ianormalize(x, (0, 255)) + 0.5).astype(np.uint8)
return y
adwrite('outimg/a.png', N(a))
adwrite('outimg/b.png', N(b_))
adwrite('outimg/c.png', N(c))
adwrite('outimg/d.png', N(d))
adwrite('outimg/e.png', N(e))
adwrite('outimg/f.png', N(f))
adwrite('outimg/g.png', N(g))
adwrite('outimg/h.png', N(h))
adwrite('outimg/i.png', N(i))
adwrite('outimg/j.png', N(j))
adwrite('outimg/k.png', N(k))
adwrite('outimg/l.png', N(l))
adwrite('outimg/m.png', N(m))
# adwrite('outimg/n.png', N(n))
return m
else:
b = mm.areaopen(a, 500)
c = mm.close(b, mm.sebox(3))
d = mm.close_holes(c)
e = mm.subm(d, c)
f = mm.areaopen(e, 1000)
# g = f > 5
ret, g = cv2.threshold(cv2.GaussianBlur(f, (5, 5), 0), 3, 255,
cv2.THRESH_BINARY)
# ret, g = cv2.threshold(
# cv2.GaussianBlur(f, (7, 7), 0),
# 5, 255,
# cv2.THRESH_BINARY_INV)
h = mm.asf(g, 'CO', mm.sedisk(5))
i = mm.close_holes(h)
res = np.vstack(
[np.hstack([a, b, c, d, e]),
np.hstack([f, g, h, i, a])])
cv2.imshow('Result', res)
cv2.waitKey(0)
cv2.destroyAllWindows()
if outimg:
if not os.path.isdir('outimg'):
os.mkdir('outimg')
def N(x):
y = (ianormalize(x, (0, 255)) + 0.5).astype(np.uint8)
return y
adwrite('outimg/a.png', N(a))
adwrite('outimg/b.png', N(b))
adwrite('outimg/c.png', N(c))
adwrite('outimg/d.png', N(d))
adwrite('outimg/e.png', N(e))
adwrite('outimg/f.png', N(f))
adwrite('outimg/g.png', N(g))
adwrite('outimg/h.png', N(h))
adwrite('outimg/i.png', N(i))
return i
def tentDetection_MM(strInputFile,
maxTentArea,
strOutputFile,
strShape='box',
iThresh_coeff=0):
# five step to do this
# 1. opening-determine the square structure element (6-60 m2/resolution)
# 2. opening by reconstruction
# 3. top-hat by reconstruction
# 4. lower threshold
# 5. double threshold
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif (nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# opening
objOpen = pymm.open(objnImg, objStructureElement)
# opening by reconstruction
objOpenRec = pymm.openrec(objOpen, objStructureElement,
objStructureElement)
objtophat = pymm.openrecth(objnImg, objStructureElement,
objStructureElement)
# objtophat = pymm.subm(objnImg, objOpenRec)
# objTent = pymm.threshad(objtophat, 0.25 * objnImg, 0.40 * objnImg)
# y = mean + k*std
(minValue, maxValue, meanValue,
stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2,
stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3,
stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, threshad, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
'''
def tentDetection_wt_mm(strInputFile, maxTentArea, strOutputFile, strShape='box', iThresh_coeff=0):
import pywt
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif(nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# decomposition until 1 level
wp = pywt.WaveletPacket2D(data=objnImg, wavelet='db4', mode='sym', maxlevel=1)
# iMaxLevel = wp.maxlevel()
# top-hat
wp['h'].data = pymm.openrecth(pymm.to_int32(wp['h'].data), objStructureElement, objStructureElement)
wp['v'].data = pymm.openrecth(pymm.to_int32(wp['v'].data), objStructureElement, objStructureElement)
wp['d'].data = pymm.openrecth(pymm.to_int32(wp['d'].data), objStructureElement, objStructureElement)
wp['a'].data = 0.5 * wp['a'].data
# reconstruction
wp.reconstruct(update=True)
# top-hat for reconstructed image
objtophat = pymm.openrecth(pymm.to_int32(wp.data), objStructureElement, objStructureElement)
# y = mean + k*std
(minValue, maxValue, meanValue, stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2, stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3, stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, stdValue, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
def tentDetection_MM(strInputFile, maxTentArea, strOutputFile, strShape='box', iThresh_coeff=0):
# five step to do this
# 1. opening-determine the square structure element (6-60 m2/resolution)
# 2. opening by reconstruction
# 3. top-hat by reconstruction
# 4. lower threshold
# 5. double threshold
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif(nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# opening
objOpen = pymm.open(objnImg, objStructureElement)
# opening by reconstruction
objOpenRec = pymm.openrec(objOpen, objStructureElement, objStructureElement)
objtophat = pymm.openrecth(objnImg, objStructureElement, objStructureElement)
# objtophat = pymm.subm(objnImg, objOpenRec)
# objTent = pymm.threshad(objtophat, 0.25 * objnImg, 0.40 * objnImg)
# y = mean + k*std
(minValue, maxValue, meanValue, stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2, stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3, stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, threshad, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
'''
def tentDetection_wt_mm(strInputFile,
maxTentArea,
strOutputFile,
strShape='box',
iThresh_coeff=0):
import pywt
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif (nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# decomposition until 1 level
wp = pywt.WaveletPacket2D(data=objnImg,
wavelet='db4',
mode='sym',
maxlevel=1)
# iMaxLevel = wp.maxlevel()
# top-hat
wp['h'].data = pymm.openrecth(pymm.to_int32(wp['h'].data),
objStructureElement, objStructureElement)
wp['v'].data = pymm.openrecth(pymm.to_int32(wp['v'].data),
objStructureElement, objStructureElement)
wp['d'].data = pymm.openrecth(pymm.to_int32(wp['d'].data),
objStructureElement, objStructureElement)
wp['a'].data = 0.5 * wp['a'].data
# reconstruction
wp.reconstruct(update=True)
# top-hat for reconstructed image
objtophat = pymm.openrecth(pymm.to_int32(wp.data), objStructureElement,
objStructureElement)
# y = mean + k*std
(minValue, maxValue, meanValue,
stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2,
stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3,
stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, stdValue, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
def correction_light(I, method, show_light, mask=None):
"""Corrige la derive eclairement
:I: array_like ou iplimage
:method: 'polynomial' or 'frequency'
:show_light: option affiche correction (true|false)
:mask: array de zone non interet
:returns: iplimage 32bit
"""
from progress import *
import Tkinter
if type(I) == cv.iplimage:
if I.nChannels == 3:
if method == 'None':
I = RGB2L(I)
I = cv2array(I)[:, :, 0]
I = pymorph.hmin(I, 15, pymorph.sedisk(3))
I = array2cv(I)
cv.EqualizeHist(I, I)
return I
I = RGB2L(I)
I_32bit = cv.CreateImage(cv.GetSize(I), cv.IPL_DEPTH_32F, 1)
cv.ConvertScale(I, I_32bit, 3000.0, 0.0)
I = cv.CloneImage(I_32bit)
I = cv2array(I)[:, :, 0]
elif len(I.shape) == 3:
I = (I[:, :, 0] + I[:, :, 0] + I[:, :, 0])\
/ 3.0 # A modifier: non utiliser dans notre cas
elif method == 'None':
I = array2cv(I)
cv.EqualizeHist(I, I)
return I
I = np.log(I + 10 ** (-6))
(H, W) = np.shape(I)
I_out = I * 0 + 10 ** (-6)
if method == 'polynomial':
## I = M.A avec A coeff. du polynome
I_flat = I.flatten()
degree = 3
print("modification degree 3")
#degree du polynome
nb_coeff = (degree + 1) * (degree + 2) / 2 # nombre coefficient
[yy, xx] = np.meshgrid(np.arange(W, dtype=np.float64),
np.arange(H, dtype=np.float64))
if mask is not None:
xx[mask] = 0
yy[mask] = 0
# Creation de M
try:
M = np.zeros((H * W, nb_coeff), dtype=np.float64)
except MemoryError:
print MemoryError
return MemoryError
i, j = 0, 0 # i,j degree de x,y
#Bar progression
bar = Tkinter.Tk(className='Correcting Light...')
m = Meter(bar, relief='ridge', bd=3)
m.pack(fill='x')
m.set(0.0, 'Starting correction...')
for col in np.arange(nb_coeff):
M[:, col] = (xx.flatten() ** i) * (yy.flatten() ** j)
i += 1
m.set(0.5 * float(col) / (nb_coeff - 1))
if i + j == degree + 1:
i = 0
j += 1
# Resolution au sens des moindres carree: pseudo-inverse
try:
M = pl.pinv(M)
A = np.dot(M, I_flat)
except ValueError:
return ValueError
# Calcul de la surface
i, j = 0, 0
surface = np.zeros((H, W), dtype=np.float64)
for cmpt in np.arange(nb_coeff):
surface += A[cmpt] * (xx ** i) * (yy ** j) # forme quadratique
i += 1
m.set(0.5 + 0.5 * float(cmpt) / (nb_coeff - 1))
if i + j == degree + 1:
i = 0
j += 1
bar.destroy()
I_out = np.exp(I / surface)
light = surface
elif method == 'frequency':
Rx, Ry = 2, 2
# zero padding
N = [H, W]
filtre = np.zeros((N[1], N[0]))
centre_x = round(N[0] / 2)
centre_y = round(N[1] / 2)
print("FFT2D...")
I_fourier = pl.fftshift(pl.fft2(I, N))
# Gaussian filter
[xx, yy] = np.meshgrid(np.arange(N[0], dtype=np.float),
np.arange(N[1], dtype=np.float))
filtre = np.exp(-2 * ((xx - centre_x) ** 2 + (yy - centre_y) ** 2) /
(Rx ** 2 + Ry ** 2))
filtre = pl.transpose(filtre)
I_fourier = I_fourier * filtre
print("IFFT2D...")
I_out = (np.abs(pl.ifft2(pl.ifftshift(I_fourier), N)))[0:H, 0:W]
light = I_out
I_out = np.exp(I / I_out)
else:
light = I * 0
I_out = I
# Display Light
if show_light:
light = ((light - light.min()) * 3000.0 /
light.max()).astype('float32')
light = array2cv(light)
fig = pl.figure()
pl.imshow(light)
fig.show()
I_out = (I_out - I_out.min()) * 3000.0 / I_out.max()
I_out = I_out.astype('uint8')
#chapeau haut de forme
I_out = pymorph.hmin(I_out, 25, pymorph.sedisk(3))
#Conversion en iplimage et ajustement contraste
gr = array2cv(I_out)
cv.EqualizeHist(gr, gr)
return gr
def watershedSegment(image, diskSize=20):
"""This routine implements the watershed example from
http://www.mathworks.com/help/images/examples/marker-controlled-watershed-segmentation.html,
but using pymorph and mahotas.
:param image: an image (2d numpy array) to be segemented
:param diskSize: an integer used as a size for a structuring element used
for morphological preprocessing.
:returns: tuple of binarized and labeled segmention masks
"""
def gradientMagnitudue(image):
sobel_x = nd.sobel(image.astype('double'), 0)
sobel_y = nd.sobel(image.astype('double'), 1)
return np.sqrt((sobel_x * sobel_x) + (sobel_y * sobel_y))
def imimposemin(image, mask, connectivity):
fm = image.copy()
fm[mask] = -9223372036854775800
fm[np.logical_not(mask)] = 9223372036854775800
fp1 = image + 1
g = np.minimum(fp1, fm)
j = infrec(fm, g)
return j
def infrec(f, g, Bc=None):
if Bc is None: Bc = pymorph.secross()
n = f.size
return fast_conditional_dilate(f, g, Bc, n)
def fast_conditional_dilate(f, g, Bc=None, n=1):
if Bc is None:
Bc = pymorph.secross()
f = pymorph.intersec(f, g)
for i in xrange(n):
prev = f
f = pymorph.intersec(mahotas.dilate(f, Bc), g)
if pymorph.isequal(f, prev):
break
return f
gradmag = gradientMagnitudue(image)
## compute foreground markers
# open image to create flat regions at cell centers
se_disk = pymorph.sedisk(diskSize)
image_opened = mahotas.open(image, se_disk)
# define foreground markers as regional maxes of cells
# this step is slow!
foreground_markers = mahotas.regmax(image_opened)
## compute background markers
# Threshold the image, cast it to the right datatype, and then calculate the distance image
image_black_white = image_opened > mahotas.otsu(image_opened)
image_black_white = image_black_white.astype('uint16')
# note the inversion here- a key difference from the matlab algorithm
# matlab distance is to nearest non-zero pixel
# python distance is to nearest 0 pixel
image_distance = pymorph.to_uint16(
nd.distance_transform_edt(np.logical_not(image_black_white)))
eight_conn = pymorph.sebox()
distance_markers = mahotas.label(mahotas.regmin(image_distance,
eight_conn))[0]
image_dist_wshed, image_dist_wshed_lines = mahotas.cwatershed(
image_distance, distance_markers, eight_conn, return_lines=True)
background_markers = image_dist_wshed_lines - image_black_white
all_markers = np.logical_or(foreground_markers, background_markers)
# impose a min on the gradient image. assumes int64
gradmag2 = imimposemin(gradmag.astype(int), all_markers, eight_conn)
# call watershed
segmented_cells, segmented_cell_lines = mahotas.cwatershed(
gradmag2, mahotas.label(all_markers)[0], eight_conn, return_lines=True)
segmented_cells -= 1
# seperate watershed regions
segmented_cells[gradientMagnitudue(segmented_cells) > 0] = 0
return segmented_cells > 0, segmented_cells
def subset(data,
left=None,
top=None,
right=None,
bottom=None,
rows=None,
cols=None,
center=False,
mask_corners=False):
"""
Subsets a DataArray
Args:
data (DataArray): The ``xarray.DataArray`` to subset.
left (Optional[float]): The left coordinate.
top (Optional[float]): The top coordinate.
right (Optional[float]): The right coordinate.
bottom (Optional[float]): The bottom coordinate.
rows (Optional[int]): The number of output rows.
cols (Optional[int]): The number of output rows.
center (Optional[bool]): Whether to center the subset on ``left`` and ``top``.
mask_corners (Optional[bool]): Whether to mask corners (*requires ``pymorph``).
Returns:
``xarray.DataArray``
Example:
>>> import geowombat as gw
>>>
>>> with gw.open('image.tif', chunks=512) as ds:
>>>
>>> ds_sub = gw.subset(ds,
>>> left=-263529.884,
>>> top=953985.314,
>>> rows=2048,
>>> cols=2048)
"""
if isinstance(right, int) or isinstance(right, float):
cols = int((right - left) / data.gw.celly)
if not isinstance(cols, int):
logger.exception(
' The right coordinate or columns must be specified.')
if isinstance(bottom, int) or isinstance(bottom, float):
rows = int((top - bottom) / data.gw.celly)
if not isinstance(rows, int):
logger.exception(
' The bottom coordinate or rows must be specified.')
x_idx = np.linspace(math.ceil(left),
math.ceil(left) + (cols * abs(data.gw.cellx)),
cols) + abs(data.gw.cellxh)
y_idx = np.linspace(math.ceil(top),
math.ceil(top) - (rows * abs(data.gw.celly)),
rows) - abs(data.gw.cellyh)
if center:
y_idx += ((rows / 2.0) * abs(data.gw.celly))
x_idx -= ((cols / 2.0) * abs(data.gw.cellx))
ds_sub = data.sel(y=y_idx, x=x_idx, method='nearest')
if mask_corners:
if PYMORPH_INSTALLED:
try:
disk = da.from_array(
pymorph.sedisk(r=int(rows / 2.0))[:rows, :cols],
chunks=ds_sub.data.chunksize).astype('uint8')
ds_sub = ds_sub.where(disk == 1)
except:
logger.warning(
' Cannot mask corners without a square subset.')
else:
logger.warning(' Cannot mask corners without Pymorph.')
# Update the left and top coordinates
transform = list(data.transform)
transform[2] = x_idx[0]
transform[5] = y_idx[0]
# Align the coordinates to the target grid
dst_transform, dst_width, dst_height = aligned_target(
Affine(*transform), ds_sub.shape[1], ds_sub.shape[0], data.res)
ds_sub.attrs['transform'] = dst_transform
return ds_sub
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 addCell(self, eventTuple):
if self.maskOn:
if self.data.ndim == 2:
self.aveData = self.data.copy()
else:
self.aveData = self.data.mean(axis=2)
x, y = eventTuple
localValue = self.currentMask[x, y]
print str(self.mode) + " " + "x: " + str(x) + ", y: " + str(y) + ", mask val: " + str(localValue)
# ensure mask is uint16
self.currentMask = self.currentMask.astype("uint16")
sys.stdout.flush()
########## NORMAL MODE
if self.mode is None:
if localValue > 0 and localValue != self.currentMaskNumber:
print "we are altering mask at at %d, %d" % (x, y)
# copy the old mask
newMask = self.currentMask.copy()
# make a labeled image of the current mask
labeledCurrentMask = mahotas.label(newMask)[0]
roiNumber = labeledCurrentMask[x, y]
# set that ROI to zero
newMask[labeledCurrentMask == roiNumber] = self.currentMaskNumber
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask)
self.currentMask = self.listOfMasks[-1]
elif localValue > 0 and self.data.ndim == 3:
# update info panel
labeledCurrentMask = mahotas.label(self.currentMask.copy())[0]
roiNumber = labeledCurrentMask[x, y]
self.updateInfoPanel(ROI_number=roiNumber)
elif localValue == 0:
xmin = int(x - self.diskSize)
xmax = int(x + self.diskSize)
ymin = int(y - self.diskSize)
ymax = int(y + self.diskSize)
sub_region_image = self.aveData[xmin:xmax, ymin:ymax].copy()
# threshold = mahotas.otsu(self.data[xmin:xmax, ymin:ymax].astype('uint16'))
# do a gaussian_laplacian filter to find the edges and the center
g_l = nd.gaussian_laplace(
sub_region_image, 1
) # second argument is a free parameter, std of gaussian
g_l = mahotas.dilate(mahotas.erode(g_l >= 0))
g_l = mahotas.label(g_l)[0]
center = g_l == g_l[g_l.shape[0] / 2, g_l.shape[0] / 2]
# edges = mahotas.dilate(mahotas.dilate(mahotas.dilate(center))) - center
newCell = np.zeros_like(self.currentMask)
newCell[xmin:xmax, ymin:ymax] = center
newCell = mahotas.dilate(newCell)
if self.useNMF:
modes, thresh_modes, fit_data, this_cell, is_cell, nmf_limits = self.doLocalNMF(x, y, newCell)
for mode, mode_thresh, t, i in zip(modes, thresh_modes, this_cell, is_cell):
# need to place it in the right place
# have x and y
mode_width, mode_height = mode_thresh.shape
mode_thresh_fullsize = np.zeros_like(newCell)
mode_thresh_fullsize[
nmf_limits[0] : nmf_limits[1], nmf_limits[2] : nmf_limits[3]
] = mode_thresh
# need to add all modes belonging to this cell first,
# then remove the ones nearby.
if i:
if t:
valid_area = np.logical_and(
mahotas.dilate(
mahotas.dilate(mahotas.dilate(mahotas.dilate(newCell.astype(bool))))
),
mode_thresh_fullsize,
)
newCell = np.logical_or(newCell.astype(bool), valid_area)
else:
newCell = np.logical_and(
newCell.astype(bool), np.logical_not(mahotas.dilate(mode_thresh_fullsize))
)
newCell = mahotas.close_holes(newCell.astype(bool))
self.excludePixels(newCell, 2)
newCell = newCell.astype(self.currentMask.dtype)
# remove all pixels in and near current mask and filter for ROI size
newCell[mahotas.dilate(self.currentMask > 0)] = 0
newCell = self.excludePixels(newCell, 10)
newMask = (newCell * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask.copy())
self.currentMask = newMask.copy()
elif self.mode is "OGB":
# build structuring elements
se = pymorph.sebox()
se2 = pymorph.sedisk(self.cellRadius, metric="city-block")
seJunk = pymorph.sedisk(max(np.floor(self.cellRadius / 4.0), 1), metric="city-block")
seExpand = pymorph.sedisk(self.diskSize, metric="city-block")
# add a disk around selected point, non-overlapping with adjacent cells
dilatedOrignal = mahotas.dilate(self.currentMask.astype(bool), Bc=se)
safeUnselected = np.logical_not(dilatedOrignal)
# tempMask is
tempMask = np.zeros_like(self.currentMask, dtype=bool)
tempMask[x, y] = True
tempMask = mahotas.dilate(tempMask, Bc=se2)
tempMask = np.logical_and(tempMask, safeUnselected)
# calculate the area we should add to this disk based on % of a threshold
cellMean = self.aveData[tempMask == 1.0].mean()
allMeanBw = self.aveData >= (cellMean * float(self.contrastThreshold))
tempLabel = mahotas.label(np.logical_and(allMeanBw, safeUnselected).astype(np.uint16))[0]
connMeanBw = tempLabel == tempLabel[x, y]
connMeanBw = np.logical_and(np.logical_or(connMeanBw, tempMask), safeUnselected).astype(np.bool)
# erode and then dilate to remove sharp bits and edges
erodedMean = mahotas.erode(connMeanBw, Bc=seJunk)
dilateMean = mahotas.dilate(erodedMean, Bc=seJunk)
dilateMean = mahotas.dilate(dilateMean, Bc=seExpand)
modes, thresh_modes, fit_data, this_cell, is_cell, limits = self.doLocaNMF(x, y)
newCell = np.logical_and(dilateMean, safeUnselected)
newMask = (newCell * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask.copy())
self.currentMask = newMask.copy()
########## SQUARE MODE
elif self.mode is "square":
self.modeData.append((x, y))
if len(self.modeData) == 2:
square_mask = np.zeros_like(self.currentMask)
xstart = self.modeData[0][0]
ystart = self.modeData[0][1]
xend = self.modeData[1][0]
yend = self.modeData[1][1]
square_mask[xstart:xend, ystart:yend] = 1
# check if square_mask interfers with current mask, if so, abort
if np.any(np.logical_and(square_mask, self.currentMask)):
return None
# add square_mask to mask
newMask = (square_mask * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask)
self.currentMask = self.listOfMasks[-1]
# clear current mode data
self.clearModeData()
########## CIRCLE MODE
elif self.mode is "circle":
# make a strel and move it in place to make circle_mask
if self.diskSize < 1:
return None
if self.diskSize is 1:
se = np.ones((1, 1))
elif self.diskSize is 2:
se = pymorph.secross(r=1)
else:
se = pymorph.sedisk(r=(self.diskSize - 1))
se_extent = int(se.shape[0] / 2)
circle_mask = np.zeros_like(self.currentMask)
circle_mask[x - se_extent : x + se_extent + 1, y - se_extent : y + se_extent + 1] = se * 1.0
circle_mask = circle_mask.astype(bool)
# check if circle_mask interfers with current mask, if so, abort
if np.any(np.logical_and(circle_mask, mahotas.dilate(self.currentMask.astype(bool)))):
return None
# add circle_mask to mask
newMask = (circle_mask * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask)
self.currentMask = self.listOfMasks[-1]
########## POLY MODE
elif self.mode is "poly":
self.modeData.append((x, y))
sys.stdout.flush()
self.makeNewMaskAndBackgroundImage()
print('Filename: {} - Shape: {}'.format('IM ({}).jpg'.format(i + 1),
img.shape))
img_out = mm_segmentation.alternative_solution(img, optype,
opthickness)
cv2.imwrite('{}/lungIM ({}).png'.format(DIR_dest, i + 1), img_out)
# tmp = mm.gshow(img, img_out)
# tmp = colorize_segmentation(img, img_out)
# num = filename[:-4]
# adwrite(num + '_out.png', tmp)
# adwrite(num + '_out.png', img_out)
if optype == 'coronal' and opnmasks == 2:
u, i, j = iaunique(ravel(img_out))
tmp1 = mm.gradm(img_out == u[1], mm.sedisk(opthickness),
mmsedisk(opthickness))
tmp2 = mm.gradm(img_out == u[2], mm.sedisk(opthickness),
mmsedisk(opthickness))
s1 = sum(tmp1, 0)
s2 = sum(tmp2, 0)
s = mm.subm(s1, s2)
meio = len(s) / 2
if sum(s[0:meio]) > sum(s[meio::]):
tmpR = tmp1
tmpL = tmp2
else:
tmpR = tmp2
tmpL = tmp1
# adwrite(num + '_maskL.png', 255 * tmpL)
# adwrite(num + '_maskR.png', 255 * tmpR)
dapi = sio.imread(dapi_path)
#cy3 = sio.imread(cy3_path)
plb.imshow(dapi)
plb.show()
#==============================================================================
# Segmentation
#==============================================================================
small = nd.zoom(dapi, 0.5)
labelDAPI, n= nd.label(ex.LowResSegmentation(small))
obj = ex.extractParticles(small,labelDAPI)
ex.makeMosaic(obj,20,10000)
print n
#broken = pymorph.sedisk()
pixel_distance = 1
#se = np.array([[1,1,1],[1,1,1],[1,1,1]])
se = np.uint(pymorph.sedisk(pixel_distance)) #2+1+2=5
print se
print 'find neighbours at ',pixel_distance, 'from particles'
g = ng.findneighborhoods(labelDAPI, se)
print 'converting to networkx'
G = ng.convertToGraph(g, noBack=True)
#==============================================================================
# Graphic display
#==============================================================================
plb.subplot(121, frameon = False, xticks = [], yticks = [])
plb.imshow(small)
plb.subplot(122, frameon = False, xticks = [], yticks = [])
plb.imshow(labelDAPI)
plb.show()
#plb.subplot(133)
nx.draw_networkx(G)
dapi = sio.imread(dapi_path) #cy3 = sio.imread(cy3_path) plb.imshow(dapi) plb.show() #============================================================================== # Segmentation #============================================================================== small = nd.zoom(dapi, 0.5) labelDAPI, n = nd.label(ex.LowResSegmentation(small)) obj = ex.extractParticles(small, labelDAPI) ex.makeMosaic(obj, 20, 10000) print n #broken = pymorph.sedisk() pixel_distance = 1 #se = np.array([[1,1,1],[1,1,1],[1,1,1]]) se = np.uint(pymorph.sedisk(pixel_distance)) #2+1+2=5 print se print 'find neighbours at ', pixel_distance, 'from particles' g = ng.findneighborhoods(labelDAPI, se) print 'converting to networkx' G = ng.convertToGraph(g, noBack=True) #============================================================================== # Graphic display #============================================================================== plb.subplot(121, frameon=False, xticks=[], yticks=[]) plb.imshow(small) plb.subplot(122, frameon=False, xticks=[], yticks=[]) plb.imshow(labelDAPI) plb.show() #plb.subplot(133) nx.draw_networkx(G)
def watershedSegment(image, diskSize=20):
def gradientMagnitudue(image):
sobel_x = nd.sobel(image.astype('double'), 0)
sobel_y = nd.sobel(image.astype('double'), 1)
return np.sqrt((sobel_x * sobel_x) + (sobel_y * sobel_y))
def imimposemin(image, mask, connectivity):
fm = image.copy()
fm[mask] = -9223372036854775800
fm[np.logical_not(mask)] = 9223372036854775800
fp1 = image + 1
g = np.minimum(fp1, fm)
j = infrec(fm, g)
return j
def infrec(f, g, Bc=None):
if Bc is None: Bc = pymorph.secross()
n = f.size
return fast_conditional_dilate(f, g, Bc, n);
def fast_conditional_dilate(f, g, Bc=None, n=1):
if Bc is None:
Bc = pymorph.secross()
f = pymorph.intersec(f,g)
for i in xrange(n):
prev = f
f = pymorph.intersec(mahotas.dilate(f, Bc), g)
if pymorph.isequal(f, prev):
break
return f
gradmag = gradientMagnitudue(image)
## compute foreground markers
# open image to create flat regions at cell centers
se_disk = pymorph.sedisk(diskSize)
image_opened = mahotas.open(image, se_disk);
# define foreground markers as regional maxes of cells
# this step is slow!
foreground_markers = mahotas.regmax(image_opened)
## compute background markers
# Threshold the image, cast it to the right datatype, and then calculate the distance image
image_black_white = image_opened > mahotas.otsu(image_opened)
image_black_white = image_black_white.astype('uint16')
# note the inversion here- a key difference from the matlab algorithm
# matlab distance is to nearest non-zero pixel
# python distance is to nearest 0 pixel
image_distance = pymorph.to_uint16(nd.distance_transform_edt(np.logical_not(image_black_white)))
eight_conn = pymorph.sebox()
distance_markers = mahotas.label(mahotas.regmin(image_distance, eight_conn))[0]
image_dist_wshed, image_dist_wshed_lines = mahotas.cwatershed(image_distance, distance_markers, eight_conn, return_lines=True)
background_markers = image_dist_wshed_lines - image_black_white
all_markers = np.logical_or(foreground_markers, background_markers)
# impose a min on the gradient image. assumes int64
gradmag2 = imimposemin(gradmag.astype(int), all_markers, eight_conn)
# call watershed
segmented_cells, segmented_cell_lines = mahotas.cwatershed(gradmag2, mahotas.label(all_markers)[0], eight_conn, return_lines=True)
segmented_cells -= 1
# seperate watershed regions
segmented_cells[gradientMagnitudue(segmented_cells) > 0] = 0
return segmented_cells > 0, segmented_cells