def erosion(image, selem=None, out=None, shift_x=False, shift_y=False):
"""Return greyscale morphological erosion of an image.
Morphological erosion sets a pixel at (i,j) to the minimum over all pixels
in the neighborhood centered at (i,j). Erosion shrinks bright regions and
enlarges dark regions.
Parameters
----------
image : ndarray
Image array.
selem : ndarray, optional
The neighborhood expressed as an array of 1's and 0's.
If None, use cross-shaped structuring element (connectivity=1).
out : ndarrays, optional
The array to store the result of the morphology. If None is
passed, a new array will be allocated.
shift_x, shift_y : bool, optional
shift structuring element about center point. This only affects
eccentric structuring elements (i.e. selem with even numbered sides).
Returns
-------
eroded : array, same shape as `image`
The result of the morphological erosion.
Notes
-----
For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the
lower algorithm complexity makes the `skimage.filter.rank.minimum`
function more efficient for larger images and structuring elements.
Examples
--------
>>> # Erosion shrinks bright regions
>>> import numpy as np
>>> from skimage.morphology import square
>>> bright_square = np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> erosion(bright_square, square(3))
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], dtype=uint8)
"""
selem = np.array(selem)
selem = _shift_selem(selem, shift_x, shift_y)
if out is None:
out = np.empty_like(image)
ndi.grey_erosion(image, footprint=selem, output=out)
return out
def fan_structure(median=11):
"""function to analyse fan sub structure by setting non-fan pixels to 0 and histo-equalizing the remaining img with only fans. Also
median-filtering is done to reduce noise."""
xcoords=[388, 449,497]
ycoords =[142, 254, 118]
x2 = [403,590]
y2 = [286,254]
x3 = [403,459]
y3 = [286,375]
x4 = [1372,1420]
y4 = [610,680]
x5 = [1372,1467]
y5 = [610,590]
x6 = [1321,1422]
y6 = [612,750]
x7 = [1321,1439]
y7 = [612,585]
fig = plt.figure()
ax = fig.add_subplot(111)
data = get_data('ESP_011931_0945')
data = nd.grey_erosion(data,footprint=np.ones((3,3)))
data = nd.grey_erosion(data,footprint=np.ones((3,3)))
data = nd.grey_dilation(data,footprint=np.ones((3,3)))
data = nd.grey_dilation(data,footprint=np.ones((3,3)))
threshold=0.045
fans = data < threshold
data = data*255/data.max()
intfans = np.zeros(data.shape, dtype=np.uint16)
intfans[fans] = np.round(data[fans])
filtered = nd.median_filter(intfans,median)
equ = hist_equal(filtered)
im = ax.imshow(equ,cmap = cm.spectral,aspect='equal')
ax.set_title('Fans within fans in Ithaca, filtered, opened and hist-equalized')
ax.set_xlabel('0.5 m/pixel')
# fig.savefig('Fans_within_fans.png')
# cb =plt.colorbar(im,shrink=0.75)
# cb.set_label('I/F')
plt.plot(xcoords[:-1],ycoords[:-1],[xcoords[0],xcoords[2]],[ycoords[0],ycoords[2]],
color='white',
hold=True,
scalex=False,scaley=False)
plt.plot(x2,y2,color='white',hold=True,scalex=False,scaley=False)
plt.plot(x3,y3,color='white',hold=True,scalex=False,scaley=False)
plt.plot(x4,y4,color='white',hold=True,scalex=False,scaley=False)
plt.plot(x5,y5,color='white',hold=True,scalex=False,scaley=False)
plt.plot(x6,y6,color='white',hold=True,scalex=False,scaley=False)
plt.plot(x7,y7,color='white',hold=True,scalex=False,scaley=False)
# plt.close(fig)
plt.show()
def rolling_ball_filter(data, ball_radius, spacing=None, top=False, **kwargs):
"""Rolling ball filter implemented with morphology operations
This implenetation is very similar to that in ImageJ and uses a top hat transform
with a ball shaped structuring element
https://en.wikipedia.org/wiki/Top-hat_transform
Parameters
----------
data : ndarray type uint8
image data (assumed to be on a regular grid)
ball_radius : float
the radius of the ball to roll
spacing : int or sequence
the spacing of the image data
top : bool
whether to roll the ball on the top or bottom of the data
kwargs : key word arguments
these are passed to the ndimage morphological operations
Returns
-------
data_nb : ndarray
data with background subtracted as uint8
bg : ndarray
background that was subtracted from the data
"""
data = data.astype(np.int16)
ndim = data.ndim
if spacing is None:
spacing = _normalize_sequence(1, ndim)
else:
spacing = _normalize_sequence(spacing, ndim)
radius = np.asarray(_normalize_sequence(ball_radius, ndim))
mesh = np.array(np.meshgrid(*[np.arange(-r, r + s, s) for r, s in zip(radius, spacing)], indexing="ij"))
structure = 2 * np.sqrt(2 - ((mesh / radius.reshape(-1, *((1,) * ndim)))**2).sum(0))
structure[~np.isfinite(structure)] = 0
if not top:
# ndi.white_tophat(y, structure=structure, output=background)
background = ndi.grey_erosion(data, structure=structure, **kwargs)
background = ndi.grey_dilation(background, structure=structure, **kwargs)
else:
# ndi.black_tophat(y, structure=structure, output=background)
background = ndi.grey_dilation(data, structure=structure, **kwargs)
background = ndi.grey_erosion(background, structure=structure, **kwargs)
data_corr = data - background
data_corr[data_corr<0] = 0
return data_corr.astype(np.uint8), background.astype(np.uint8)
def artifact_mask(imdata, airdata):
"""Computes a mask of artifacts found in the air region"""
if not np.issubdtype(airdata.dtype, np.integer):
airdata[airdata < .95] = 0
airdata[airdata > 0.] = 1
bg_img = imdata * airdata
# Find the background threshold (the most frequently occurring value
# excluding 0)
hist, bin_edges = np.histogram(bg_img[bg_img > 0], bins=128)
bg_threshold = np.mean(bin_edges[np.argmax(hist)])
# Apply this threshold to the background voxels to identify voxels
# contributing artifacts.
qi1_img = np.zeros_like(bg_img)
qi1_img[bg_img > bg_threshold] = bg_img[bg_img > bg_threshold]
# Create a structural element to be used in an opening operation.
struc = nd.generate_binary_structure(3, 2)
# Perform an a grayscale erosion operation.
qi1_img = nd.grey_erosion(qi1_img, structure=struc).astype(np.float32)
# Binarize and binary dilation
qi1_img[qi1_img > 0.] = 1
qi1_img[qi1_img < 1.] = 0
qi1_img = nd.binary_dilation(qi1_img, structure=struc).astype(np.uint8)
return qi1_img
def edge_matrix(labels, connectivity=1):
"""Generate a COO matrix containing the coordinates of edge pixels.
Parameters
----------
labels : array of int
An array of labeled pixels (or voxels).
connectivity : int in {1, ..., labels.ndim}
The square connectivity for considering neighborhood.
Returns
-------
edges : sparse.coo_matrix
A COO matrix where (i, j) indicate neighboring labels and the
corresponding data element is the linear index of the edge pixel
in the labels array.
"""
conn = ndi.generate_binary_structure(labels.ndim, connectivity)
eroded = ndi.grey_erosion(labels, footprint=conn).ravel()
dilated = ndi.grey_dilation(labels, footprint=conn).ravel()
labels = labels.ravel()
boundaries0 = np.flatnonzero(eroded != labels)
boundaries1 = np.flatnonzero(dilated != labels)
labels_small = np.concatenate((eroded[boundaries0], labels[boundaries1]))
labels_large = np.concatenate((labels[boundaries0], dilated[boundaries1]))
n = np.max(labels_large) + 1
data = np.concatenate((boundaries0, boundaries1))
sparse_graph = sparse.coo_matrix((data, (labels_small, labels_large)),
dtype=np.int_, shape=(n, n))
return sparse_graph
def multi_label_edge_detection(data):
f = nd.generate_binary_structure(len(data.shape), 1)
bound = (nd.grey_erosion(data,footprint=f) != nd.grey_dilation(data,footprint=f)) - \
(nd.binary_dilation(data.astype(np.bool)) - data.astype(np.bool)) # the unwanted thick bounds
data=bound.astype(data.dtype)
return data
def adjustImage(self, img_size, pixel_arr, black, white, brightness, outline, invert, binary):
mask = pixel_arr > white
pixel_arr[mask] = 255
mask = pixel_arr < black
pixel_arr[mask] = 0
if invert:
pixel_arr = 255 - pixel_arr
if outline:
pixel_arr = pixel_arr / ((2**8-1.0)/(2**16-1.0))
pixel_arr = pixel_arr.reshape(img_size)
eroded_arr = ndimage.grey_erosion(pixel_arr, mode='constant', size=(3,3))
outline_arr = pixel_arr - eroded_arr
outline_arr = outline_arr.reshape((reduce(lambda x, y: x * y, outline_arr.shape), ))
pixel_arr = outline_arr
if binary:
mask = pixel_arr > 0
pixel_arr[mask] = 255
#pixel_arr[numpy.invert(mask)] = 0
pixel_arr = pixel_arr * brightness
if brightness > 1.0:
mask = pixel_arr > 255
pixel_arr[mask] = 255
return pixel_arr
def generateData(self):
input = self.getInput(0).getData()/255
fill = ndimage.grey_erosion(input, size = (3,3))
output = input - fill
self.getOutput(0).setData(output*255)
self.getOutput(1).setData(input*255)
def findendsjunctions(img, disp=None):
if disp is None:
disp = 0
# Create a look up table to find junctions.
# lut = ndimage.grey_erosion(junction(img), size=(3,3))
junctions = ndimage.grey_erosion(img, size=(3, 3))
# Row and column coordinates of junction points in the image.
rjcj = np.where(junctions)
ends = ndimage.grey_erosion(img, size=(3, 3))
# Row and column coordinates of end points in the image.
rece = np.where(ends)
# Display image with the junctions and endings marked.
if disp:
plt.imshow(img)
def multi_label_edge_detection(data):
"""Detect the edge in the image with multi-labels."""
f = nd.generate_binary_structure(len(data.shape), 1)
# the unwanted thick bounds
bound = (nd.grey_erosion(data, footprint=f) != nd.grey_dilation(data, footprint=f)) - (
nd.binary_dilation(data.astype(np.bool)) - data.astype(np.bool)
)
data = bound.astype(data.dtype)
return data
def apply(array, **kwargs):
"""
Apply a set of standard filter to array data:
Call: apply(array-data, <list of key=value arguments>)
The list of key-value define the filtering to be done and should be given in
the order to be process. Possible key-value are:
* smooth: gaussian filtering, value is the sigma parameter (scalar or tuple)
* uniform: uniform filtering (2)
* max: maximum filtering (1)
* min: minimum filtering (1)
* median: median filtering (1)
* dilate: grey dilatation (1)
* erode: grey erosion (1)
* close: grey closing (1)
* open: grey opening (1)
* linear_map: call linear_map(), value is the tuple (min,max) (3)
* normalize: call normalize(), value is the method (3)
* adaptive: call adaptive(), value is the sigma (3)
* adaptive_: call adaptive(), with uniform kernel (3)
The filtering is done using standard scipy.ndimage functions.
(1) The value given (to the key) is the width of the the filter:
the distance from the center pixel (the size of the filter is thus 2*value+1)
The neighborhood is an (approximated) boolean circle (up to discretization)
(2) Same as (*) but the neighborhood is a complete square
(3) See doc of respective function
"""
for key in kwargs:
value = kwargs[key]
if key not in ('smooth','uniform'):
fp = _kernel.distance(array.ndim*(2*value+1,))<=value # circular filter
if key=='smooth' : array = _nd.gaussian_filter(array, sigma=value)
elif key=='uniform': array = _nd.uniform_filter( array, size=2*value+1)
elif key=='max' : array = _nd.maximum_filter( array, footprint=fp)
elif key=='min' : array = _nd.minimum_filter( array, footprint=fp)
elif key=='median' : array = _nd.median_filter( array, footprint=fp)
elif key=='dilate' : array = _nd.grey_dilation( array, footprint=fp)
elif key=='erode' : array = _nd.grey_erosion( array, footprint=fp)
elif key=='open' : array = _nd.grey_opening( array, footprint=fp)
elif key=='close' : array = _nd.grey_closing( array, footprint=fp)
elif key=='linear_map': array = linear_map(array, min=value[0], max=value[1])
elif key=='normalize' : array = normalize( array, method = value)
elif key=='adaptive' : array = adaptive( array, sigma = value, kernel='gaussian')
elif key=='adaptive_' : array = adaptive( array, sigma = value, kernel='uniform')
else:
print '\033[031mUnrecognized filter :', key
return array
def outline_segments(self, mask_background=False):
"""
Outline the labeled segments.
The "outlines" represent the pixels *just inside* the segments,
leaving the background pixels unmodified.
Parameters
----------
mask_background : bool, optional
Set to `True` to mask the background pixels (labels = 0) in
the returned image. This is useful for overplotting the
segment outlines on an image. The default is `False`.
Returns
-------
boundaries : 2D `~numpy.ndarray` or `~numpy.ma.MaskedArray`
An image with the same shape of the segmentation image
containing only the outlines of the labeled segments. The
pixel values in the outlines correspond to the labels in the
segmentation image. If ``mask_background`` is `True`, then
a `~numpy.ma.MaskedArray` is returned.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[0, 0, 0, 0, 0, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 0, 0, 0, 0, 0]])
>>> segm.outline_segments()
array([[0, 0, 0, 0, 0, 0],
[0, 2, 2, 2, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 2, 2, 2, 0],
[0, 0, 0, 0, 0, 0]])
"""
from scipy.ndimage import grey_erosion, grey_dilation
# mode='constant' ensures outline is included on the image borders
selem = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
eroded = grey_erosion(self.data, footprint=selem, mode='constant',
cval=0.)
dilated = grey_dilation(self.data, footprint=selem, mode='constant',
cval=0.)
outlines = ((dilated != eroded) & (self.data != 0)).astype(int)
outlines *= self.data
if mask_background:
outlines = np.ma.masked_where(outlines == 0, outlines)
return outlines
def gradient(self):
Type=self.getEdgeType()
Size=self.image_temp.shape
EdgeImage=np.zeros(Size)
if (Type=='S'):
EdgeImage=(ndimage.grey_dilation(self.image_temp,footprint=np.ones([3,3]))-ndimage.grey_erosion(self.image_temp,footprint=np.ones([3,3])))/2
if (Type=='I'):
EdgeImage=(self.image_temp-ndimage.grey_erosion(self.image_temp,footprint=np.ones([3,3])))/2
if (Type=='E'):
EdgeImage=(ndimage.grey_dilation(self.image_temp,footprint=np.ones([3,3]))-self.image_temp)/2
self.showView4(EdgeImage)
def calcSupNp(preprob, posp, lungs, imscan, pat, midx, psp, dictSubP, dimtabx):
'''calculate the number of reticulation and HC in subpleural'''
print 'number of subpleural for : ', pat, psp
imgngray = np.copy(lungs)
np.putmask(imgngray, imgngray == 1, 0)
np.putmask(imgngray, imgngray > 0, 1)
# subErosion= in mm
#avgPixelSpacing=0.734 in mm/ pixel
subErosionPixel = int(round(2 * subErosion / avgPixelSpacing))
erosion = ndimage.grey_erosion(
imgngray, size=(
subErosionPixel, subErosionPixel))
np.putmask(erosion, erosion > 0, 1)
mask_inv = np.bitwise_not(erosion)
subpleurmask = np.bitwise_and(imgngray, mask_inv)
ill = 0
for ll in posp:
xpat = ll[0]
ypat = ll[1]
proba = preprob[ill]
prec, mprobai = maxproba(proba)
classlabel = fidclass(prec, classif)
if xpat >= midx:
pospr = 1
pospl = 0
else:
pospr = 0
pospl = 1
if classlabel == pat and mprobai > thrprobaUIP:
tabpatch = np.zeros((dimtabx, dimtabx), np.uint8)
tabpatch[ypat:ypat + dimpavy, xpat:xpat + dimpavx] = 1
tabsubpl = np.bitwise_and(subpleurmask, tabpatch)
area = tabsubpl.sum()
# check if area above threshold
targ = float(area) / pxy
if targ > thrpatch:
dictSubP[pat]['all'] = (
dictSubP[pat]['all'][0] + pospl,
dictSubP[pat]['all'][1] + pospr)
dictSubP[pat][psp] = (
dictSubP[pat][psp][0] + pospl,
dictSubP[pat][psp][1] + pospr)
ill += 1
return dictSubP
def fit_background(self, image, subscriber=0):
poldegree = int(self.paramPoldegree.value)
swidth = int(self.paramSwidth.value)
sheight = int(self.paramSheight.value)
threshold = int(self.paramThreshold.value)
mediansize = int(self.paramMediansize.value)
medianruns = int(self.paramMedianruns.value)
darksize = int(self.paramDarksize.value)
darkruns = int(self.paramDarkruns.value)
brightsize = int(self.paramBrightsize.value)
brightruns = int(self.paramBrightruns.value)
dopreview = self.paramDopreview.value
data = image.data
# Median:
for run in xrange(medianruns):
data = ndimage.median_filter(data, size=mediansize)
# Suspend dark spots:
for run in xrange(darkruns):
data = 255 - ndimage.grey_erosion(255 - data, size=darksize)
# Suspend features:
for run in xrange(brightruns):
data = ndimage.grey_erosion(data, size=brightsize)
# Fit background:
if not dopreview:
data = self.fit(data, poldegree, swidth, sheight, threshold)
longname = "FitBackground"
result = DataContainer.FieldContainer(
data,
copy.deepcopy(image.unit),
copy.deepcopy(image.error),
copy.deepcopy(image.mask),
copy.deepcopy(image.dimensions),
longname,
image.shortname,
copy.deepcopy(image.attributes),
False,
)
result.seal()
return result
def PreProcess(im):
im=NMode(im)
im1 = ndimage.grey_erosion(im, size=(10,10))
scipy.misc.imsave("eroded.jpg",im1)
im1= Image.open("eroded.jpg")
im=ImageOps.equalize(im,0)
im=ImageChops.difference(im1, im)
#print ("image height %d and width %d\n"%(imh,imw))
im=GBinarization(im)#binarize the image
return im
def calib(extra=False):
global bins,refe,mall,dall,nomal
#calibration plate
sele=(array(mall)<20)*(array(dall)>700)
from scipy import ndimage
sele2=ndimage.grey_erosion(sele,2)
ia,ib=where(sele2>0.5)
nomal=mean([zzall[ia[i]][ib[i]] for i in range(100)],0)
if extra:
#testing individual lines:
ps=array([sum(ia<i) for i in range(30,41)])
koral=[mean([zzall[i+30][b] for b in ib[ps[i]:ps[i+1]]],0) for i in range(10)]
return nomal,koral
return nomal
def erode(im):
"""`scipy.ndimage.grey_erosion` with size set to 3 on each axis.
Parameters
----------
im : np.ndarray, arbitrary type and shape.
The input image.
Returns
-------
out : np.ndarray, same type and shape as `im`
The eroded image.
"""
return nd.grey_erosion(im, size=[3] * im.ndim)
def fast_rag(labels, connectivity=1):
"""Build a data-free region adjacency graph quickly.
Parameters
----------
labels : array of int
Image pre-segmentation or segmentation
connectivity : int in {1, ..., labels.ndim}, optional
Use square connectivity equal to `connectivity`. See
`scipy.ndimage.generate_binary_structure` for more.
Returns
-------
g : networkx Graph
A graph where nodes represent regions in `labels` and edges
indicate adjacency.
Examples
--------
>>> labels = np.array([1, 1, 5, 5], dtype=np.int_)
>>> fast_rag(labels).edges()
[(1, 5)]
>>> labels = np.array([[1, 1, 1, 2, 2],
... [1, 1, 1, 2, 2],
... [3, 3, 4, 4, 4],
... [3, 3, 4, 4, 4]], dtype=np.int_)
>>> sorted(fast_rag(labels).edges())
[(1, 2), (1, 3), (1, 4), (2, 4), (3, 4)]
"""
conn = ndi.generate_binary_structure(labels.ndim, connectivity)
eroded = ndi.grey_erosion(labels, footprint=conn)
dilated = ndi.grey_dilation(labels, footprint=conn)
boundaries0 = (eroded != labels)
boundaries1 = (dilated != labels)
labels_small = np.concatenate((eroded[boundaries0], labels[boundaries1]))
labels_large = np.concatenate((labels[boundaries0], dilated[boundaries1]))
n = np.max(labels_large) + 1
# use a dummy broadcast array as data for RAG
data = np.broadcast_to(np.ones((1,), dtype=np.int_),
labels_small.shape)
sparse_graph = sparse.coo_matrix((data, (labels_small, labels_large)),
dtype=np.int_, shape=(n, n)).tocsr()
rag = nx.from_scipy_sparse_matrix(sparse_graph, edge_attribute='count')
return rag
def morphop(im, operation='open', radius='5'):
"""Perform a morphological operation with spherical structuring element.
Parameters
----------
im : array, shape (M, N[, P])
2D or 3D grayscale image.
operation : string, optional
The operation to perform. Choices are 'opening', 'closing',
'erosion', and 'dilation'. Imperative verbs also work, e.g.
'dilate'.
radius : int, optional
The radius of the structuring element (disk or ball) used.
Returns
-------
imout : array, shape (M, N[, P])
The transformed image.
Raises
------
ValueError : if the image is not 2D or 3D.
"""
if im.ndim == 2:
selem = skmorph.disk(radius)
elif im.ndim == 3:
selem = skmorph.ball(radius)
else:
raise ValueError("Image input to 'morphop' should be 2D or 3D"
", got %iD" % im.ndim)
if operation.startswith('open'):
imout = nd.grey_opening(im, footprint=selem)
elif operation.startswith('clos'):
imout = nd.grey_closing(im, footprint=selem)
elif operation.startswith('dila'):
imout = nd.grey_dilation(im, footprint=selem)
elif operation.startswith('ero'):
imout = nd.grey_erosion(im, footprint=selem)
return imout
def remove_merged_boundaries(labels, connectivity=1):
"""Remove boundaries in a label field when they separate the same region.
By convention, the boundary label is 0, and labels are positive.
Parameters
----------
labels : array of int
The label field to be processed.
connectivity : int in {1, ..., labels.ndim}, optional
The morphological connectivity for considering neighboring voxels.
Returns
-------
labels_out : array of int
The same label field, with unnecessary boundaries removed.
Examples
--------
>>> labels = np.array([[1, 0, 1], [0, 1, 0], [2, 0, 3]], np.int)
>>> remove_merged_boundaries(labels)
array([[1, 1, 1],
[0, 1, 0],
[2, 0, 3]])
"""
boundary = 0
labels_out = labels.copy()
is_boundary = (labels == boundary)
labels_complement = labels.copy()
labels_complement[is_boundary] = labels.max() + 1
se = nd.generate_binary_structure(labels.ndim, connectivity)
smaller_labels = nd.grey_erosion(labels_complement, footprint=se)
bigger_labels = nd.grey_dilation(labels, footprint=se)
merged = is_boundary & (smaller_labels == bigger_labels)
labels_out[merged] = smaller_labels[merged]
return labels_out
def process8mm(filenames, outputpath):
files = filenames.split(',')
# Select the midrange image for figuring out the cropping
filename = files[1]
if 3 != len(files):
logger.error("Need three filenames")
sys.exit(1)
ofiles = [os.path.isfile("%s/%s" % (outputpath, os.path.basename(xx))) for xx in files]
if [True, True, True] == ofiles:
logger.debug("Output files aready exist for %s" % filenames)
return
if options.whitecount:
for file in filenames:
whiteCount(file)
imp = PILImage.open(filename).convert('L')
im = scipy.misc.fromimage(imp, flatten = True).astype(numpy.uint8)
#(fcWidth, fcHeight) = im.shape
(fcHeight, fcWidth) = im.shape
#im = im[:,:400]
im = im[:,:300]
im1 = ndimage.grey_erosion(im, size=(25, 25))
im1[im1 < 100] = 0
im1[im1 >= 100] = 255
if options.debug and eroded_dir is not None:
scipy.misc.imsave('eroded/%s' % os.path.basename(filename), im1)
im1Image = scipy.misc.toimage(im1)
(spLeftX, spCenterY) = find8mmSprocket(im1Image, filename)
spLeftX = 152
logger.debug( "%s leftX %u centerY %u" % (filename, spLeftX, spCenterY))
if (0, 0) == (spLeftX, spCenterY):
logger.error("Cannot process %s" % filename)
return
pxPerMm = 393
frameOriginX = int(spLeftX + (1.53 * pxPerMm))
frameOriginY = int(spCenterY - (1.69 * pxPerMm))
# FUDGE FACTOR for misaligned images
#frameOriginY -= (122 + (.455 * pxPerMm))
# frameOriginY -= ((.465 * pxPerMm))
frameWidth = int(4.57 * pxPerMm)
frameHeight = int(3.39 * pxPerMm)
if frameWidth % 2 == 1:
frameWidth += 1
# crop and save
# if ((frameOriginX + frameWidth) > fcWidth) or ((frameOriginY + frameHeight) > fcHeight):
# logger.error("Crop tile out of bounds %u x %u > %u x %u" % (frameOriginX + frameWidth, fcWidth, frameOriginY + frameHeight, fcHeight))
# return
if options.debug and bw_dir is not None:
bwd = ImageDraw.Draw(imp)
bwd.line((spLeftX, spCenterY, fcWidth, spCenterY), fill=0)
bwd.line((spLeftX, spCenterY - (1.64 * pxPerMm),
spLeftX, spCenterY + (1.64 * pxPerMm)), fill = 0)
bwd.rectangle((frameOriginX, frameOriginY,
frameOriginX + frameWidth,
frameOriginY + frameHeight), fill=192)
imp.save('%s/%s' % (bw_dir, os.path.basename(filename)))
for iFile in files:
try:
fullColor = PILImage.open(iFile)
fullColor = fullColor.crop((int(frameOriginX), int(frameOriginY),
int(frameOriginX + frameWidth),
int(frameOriginY + frameHeight)))
fullColor.save('%s/%s' % (outputpath, os.path.basename(iFile)))
except:
logger.error("Did not save %s/%s" % (outputpath, os.path.basename(iFile)))
def erosion(image, footprint=None, out=None, shift_x=False, shift_y=False):
"""Return grayscale morphological erosion of an image.
Morphological erosion sets a pixel at (i,j) to the minimum over all pixels
in the neighborhood centered at (i,j). Erosion shrinks bright regions and
enlarges dark regions.
Parameters
----------
image : ndarray
Image array.
footprint : ndarray, optional
The neighborhood expressed as an array of 1's and 0's.
If None, use cross-shaped footprint (connectivity=1). This can also
be a sequence of 2-tuples where the first element of each 2-tuple is a
footprint and the second element as an integer describing the number of
times it should be iterated.
out : ndarrays, optional
The array to store the result of the morphology. If None is
passed, a new array will be allocated.
shift_x, shift_y : bool, optional
shift footprint about center point. This only affects
eccentric footprints (i.e. footprint with even numbered
sides).
Returns
-------
eroded : array, same shape as `image`
The result of the morphological erosion.
Notes
-----
For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the
lower algorithm complexity makes the `skimage.filters.rank.minimum`
function more efficient for larger images and footprints.
Examples
--------
>>> # Erosion shrinks bright regions
>>> import numpy as np
>>> from skimage.morphology import square
>>> bright_square = np.array([[0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]], dtype=np.uint8)
>>> erosion(bright_square, square(3))
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], dtype=uint8)
"""
if out is None:
out = np.empty_like(image)
if _footprint_is_sequence(footprint):
footprints = tuple(
(_shift_footprint(fp, shift_x, shift_y), n) for fp, n in footprint)
return _iterate_gray_func(ndi.grey_erosion, image, footprints, out)
footprint = np.array(footprint)
footprint = _shift_footprint(footprint, shift_x, shift_y)
ndi.grey_erosion(image, footprint=footprint, output=out)
return out
# Example from https://ilovesymposia.com/2017/03/12/scipys-new-lowlevelcallable-is-a-game-changer/
import contextlib
import time
from scipy import ndimage as ndi
import numpy as np
image = np.random.random((2048, 2048))
footprint = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=bool)
@contextlib.contextmanager
def timing():
start = time.time()
yield
delta = time.time() - start
print(f"Execution time: {delta:.03}s")
with timing():
ndi.grey_erosion(image, footprint=footprint)
with timing():
ndi.generic_filter(image, np.min, footprint=footprint)
def rag_boundary(labels, edge_map, connectivity=2):
""" Comouter RAG based on region boundaries
Given an image's initial segmentation and its edge map this method
constructs the corresponding Region Adjacency Graph (RAG). Each node in the
RAG represents a set of pixels within the image with the same label in
`labels`. The weight between two adjacent regions is the average value
in `edge_map` along their boundary.
labels : ndarray
The labelled image.
edge_map : ndarray
This should have the same shape as that of `labels`. For all pixels
along the boundary between 2 adjacent regions, the average value of the
corresponding pixels in `edge_map` is the edge weight between them.
connectivity : int, optional
Pixels with a squared distance less than `connectivity` from each other
are considered adjacent. It can range from 1 to `labels.ndim`. Its
behavior is the same as `connectivity` parameter in
`scipy.ndimage.filters.generate_binary_structure`.
Examples
--------
>>> from skimage import data, segmentation, filters, color
>>> from skimage.future import graph
>>> img = data.chelsea()
>>> labels = segmentation.slic(img)
>>> edge_map = filters.sobel(color.rgb2gray(img))
>>> rag = graph.rag_boundary(labels, edge_map)
"""
conn = ndi.generate_binary_structure(labels.ndim, connectivity)
eroded = ndi.grey_erosion(labels, footprint=conn)
dilated = ndi.grey_dilation(labels, footprint=conn)
boundaries0 = (eroded != labels)
boundaries1 = (dilated != labels)
labels_small = np.concatenate((eroded[boundaries0], labels[boundaries1]))
labels_large = np.concatenate((labels[boundaries0], dilated[boundaries1]))
n = np.max(labels_large) + 1
# use a dummy broadcast array as data for RAG
ones = as_strided(np.ones((1, ), dtype=np.float),
shape=labels_small.shape,
strides=(0, ))
count_matrix = sparse.coo_matrix((ones, (labels_small, labels_large)),
dtype=np.int_,
shape=(n, n)).tocsr()
data = np.concatenate((edge_map[boundaries0], edge_map[boundaries1]))
data_coo = sparse.coo_matrix((data, (labels_small, labels_large)))
graph_matrix = data_coo.tocsr()
graph_matrix.data /= count_matrix.data
rag = RAG()
rag.add_weighted_edges_from(_edge_generator_from_csr(graph_matrix),
weight='weight')
rag.add_weighted_edges_from(_edge_generator_from_csr(count_matrix),
weight='count')
for n in rag.nodes():
rag.node[n].update({'labels': [n]})
return rag
def connected_components(self, frame):
sing_frame = ndimage.grey_erosion(frame[:, :, 1], size=(2, 2))
blur_radius = .35
sing_frame = ndimage.gaussian_filter(sing_frame, blur_radius)
labeled, self.nr_objects = ndimage.label(sing_frame)
return labeled[:, :, np.newaxis]
import matplotlib.pyplot as plt
import scipy.ndimage as ndi
import numpy as np
img = np.zeros((16, 16))
img[4:-4, 4:-4] = 1
img = ndi.distance_transform_bf(img)
dilation = ndi.grey_dilation(img, size=(3, 3), structure=np.ones((3, 3)))
erosion = ndi.grey_erosion(img, size=(3, 3), structure=np.ones((3, 3)))
output = [img, dilation, erosion]
titles = ['Original', 'Dilation', 'Erosion']
for i in range(3):
print(output[i])
plt.subplot(1, 3, i + 1)
plt.imshow(output[i], interpolation='nearest', cmap='spectral')
plt.title(titles[i])
plt.axis('off')
plt.show()
def _create_fernsehbild_rgba(self, ct_alpha_def,
erosion_size=5, gaussion_filter_sigma=3,
dark_transparency_factor=3.0,
contrast_optimization_expr=None,
backup_orig_data=False):
"""
"""
if contrast_optimization_expr is None:
contrast_optimization_expr = "hist_equalize(inputdata, 8, 254)"
ct_chn = self["CloudType"]
ct_data = ct_chn.cloudtype
ct_alpha = np.ones(ct_data.shape)
for ct in range(len(ct_alpha_def)):
if ct_alpha_def[ct] < 1.0:
ct_alpha[(ct_data == ct)] = ct_alpha_def[ct]
# mask already masked data
ct_alpha[ct_data.mask] = 0.0
# ct_mask = ct_alpha < 0.01
# shrink alpha mask to ensure that smoothed edges are inside mask
import scipy.ndimage as ndi
ct_alpha = ndi.grey_erosion(
ct_alpha, size=(erosion_size, erosion_size)).astype(
ct_alpha.dtype)
self.check_channels("HRV", 0.85, 10.8)
if not self._dwd_channel_preparation(["HRV", 0.85, 10.8],
backup_orig_data=backup_orig_data):
return None
# get combination of HRV and VIS008 channel data
hrvc_chn = self._dwd_get_hrvc_channel()
img_type = self._dwd_get_image_type()
if img_type is None:
return None
# extract the clouds for hrvis channel
hrvc_clouds = hrvc_chn.data.copy()
# hrvc_clouds.mask[ct_mask] = True
median = np.ma.median(hrvc_clouds)
mean = np.ma.mean(hrvc_clouds)
comp = hrvc_clouds.compressed()
max_value = np.percentile(comp, 97)
LOGGER.debug("HRVIS median: {0}, mean: {1}, diff: {2}, min: {3}, max: {4}".
format(median, mean, abs(median - mean),
hrvc_clouds.min(), max_value))
# execute contrast optimization function (i.e. histogram equalisation)
hrvc_clouds = select_range_and_scale(hrvc_clouds, 0, 100, 255)
d = eval(contrast_optimization_expr, globals(), {'inputdata': hrvc_clouds})
d.mask = False
day_img = geo_image.GeoImage(d,
self.area,
get_first(self.time_slot),
fill_value=0,
mode="L",
crange=(0, 255))
# day_img.enhance(stretch="histogram")
# extract the clouds for infrared channel
ir_clouds = self[10.8].data.copy()
# ir_clouds.mask[ct_mask] = True
median = np.ma.median(ir_clouds)
mean = np.ma.mean(ir_clouds)
max_value = np.ma.max(ir_clouds)
LOGGER.debug("IR median: {0}, mean: {1}, diff: {2}, min: {3}, max: {4}".
format(median, mean, abs(median - mean),
ir_clouds.min(), max_value))
median = np.ma.median(ir_clouds)
# execute contrast optimization function (i.e. histogram equalisation)
ir_clouds = select_range_and_scale(ir_clouds, 40, -87.5, 255)
d = eval(contrast_optimization_expr, globals(), {'inputdata': ir_clouds})
d.mask = False
night_img = geo_image.GeoImage(d,
self.area,
get_first(self.time_slot),
fill_value=0,
mode="L",
crange=(0, 255))
# night_img.enhance(stretch="histogram")
if img_type == IMAGETYPES.DAY_ONLY:
img = day_img
if img_type == IMAGETYPES.NIGHT_ONLY:
img = night_img
if img_type == IMAGETYPES.DAY_NIGHT:
alpha_data =\
self._dwd_get_day_night_alpha_channel().data.astype(np.float64)\
/ 255.0
# create day image
day_img.putalpha(alpha_data)
day_img.enhance(inverse=(False, True))
# create night image
night_img.putalpha(alpha_data)
blend(night_img, day_img)
img = night_img
img.convert("L")
if gaussion_filter_sigma is not None:
# smooth alpha channel
ct_alpha = ndi.gaussian_filter(ct_alpha, gaussion_filter_sigma)
if dark_transparency_factor is not None:
# add transparency to dark image areas
ct_alpha = np.minimum(ct_alpha, img.channels[0] *
dark_transparency_factor)
img.convert("RGBA")
img.putalpha(ct_alpha)
img.fill_value = None
# img = geo_image.GeoImage(ct_alpha*255.0,
# self.area,
# self.time_slot,
# fill_value=0,
# mode="L",
# crange=(0, 255))
return img
def run(self, workspace):
'''Run the module on the image set'''
seed_objects_name = self.seed_objects_name.value
skeleton_name = self.image_name.value
seed_objects = workspace.object_set.get_objects(seed_objects_name)
labels = seed_objects.segmented
labels_count = np.max(labels)
label_range = np.arange(labels_count, dtype=np.int32) + 1
skeleton_image = workspace.image_set.get_image(
skeleton_name, must_be_binary=True)
skeleton = skeleton_image.pixel_data
if skeleton_image.has_mask:
skeleton = skeleton & skeleton_image.mask
try:
labels = skeleton_image.crop_image_similarly(labels)
except:
labels, m1 = cpo.size_similarly(skeleton, labels)
labels[~m1] = 0
#
# The following code makes a ring around the seed objects with
# the skeleton trunks sticking out of it.
#
# Create a new skeleton with holes at the seed objects
# First combine the seed objects with the skeleton so
# that the skeleton trunks come out of the seed objects.
#
# Erode the labels once so that all of the trunk branchpoints
# will be within the labels
#
#
# Dilate the objects, then subtract them to make a ring
#
my_disk = morph.strel_disk(1.5).astype(int)
dilated_labels = grey_dilation(labels, footprint=my_disk)
seed_mask = dilated_labels > 0
combined_skel = skeleton | seed_mask
closed_labels = grey_erosion(dilated_labels,
footprint=my_disk)
seed_center = closed_labels > 0
combined_skel = combined_skel & (~seed_center)
#
# Fill in single holes (but not a one-pixel hole made by
# a one-pixel image)
#
if self.wants_to_fill_holes:
def size_fn(area, is_object):
return (~ is_object) and (area <= self.maximum_hole_size.value)
combined_skel = morph.fill_labeled_holes(
combined_skel, ~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.skeletonize(combined_skel)
#
# The skeleton outside of the labels
#
outside_skel = combined_skel & (dilated_labels == 0)
#
# Associate all skeleton points with seed objects
#
dlabels, distance_map = propagate.propagate(np.zeros(labels.shape),
dilated_labels,
combined_skel, 1)
#
# Get rid of any branchpoints not connected to seeds
#
combined_skel[dlabels == 0] = False
#
# Find the branchpoints
#
branch_points = morph.branchpoints(combined_skel)
#
# Odd case: when four branches meet like this, branchpoints are not
# assigned because they are arbitrary. So assign them.
#
# . .
# B.
# .B
# . .
#
odd_case = (combined_skel[:-1, :-1] & combined_skel[1:, :-1] &
combined_skel[:-1, 1:] & combined_skel[1, 1])
branch_points[:-1, :-1][odd_case] = True
branch_points[1:, 1:][odd_case] = True
#
# Find the branching counts for the trunks (# of extra branches
# eminating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0, 0, 0, 1, 2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.endpoints(combined_skel)
#
# We use two ranges for classification here:
# * anything within one pixel of the dilated image is a trunk
# * anything outside of that range is a branch
#
nearby_labels = dlabels.copy()
nearby_labels[distance_map > 1.5] = 0
outside_labels = dlabels.copy()
outside_labels[nearby_labels > 0] = 0
#
# The trunks are the branchpoints that lie within one pixel of
# the dilated image.
#
if labels_count > 0:
trunk_counts = fix(scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0,), int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(scind.sum(branch_points, outside_labels,
label_range))
else:
branch_counts = np.zeros((0,), int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels, label_range))
else:
end_counts = np.zeros((0,), int)
#
# Calculate the distances
#
total_distance = morph.skeleton_length(
dlabels * outside_skel, label_range)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join((C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES,
skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
feature = "_".join((C_NEURON, F_TOTAL_NEURITE_LENGTH, skeleton_name))
m[seed_objects_name, feature] = total_distance
#
# Collect the graph information
#
if self.wants_neuron_graph:
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
intensity_image = workspace.image_set.get_image(
self.intensity_image_name.value)
edge_graph, vertex_graph = self.make_neuron_graph(
combined_skel, dlabels,
trunk_mask,
branch_points & ~trunk_mask,
end_points,
intensity_image.pixel_data)
image_number = workspace.measurements.image_set_number
edge_path, vertex_path = self.get_graph_file_paths(m, m.image_number)
workspace.interaction_request(
self, m.image_number, edge_path, edge_graph,
vertex_path, vertex_graph, headless_ok=True)
if self.show_window:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
workspace.display_data.intensity_image = intensity_image.pixel_data
#
# Make the display image
#
if self.show_window or self.wants_branchpoint_image:
branchpoint_image = np.zeros((skeleton.shape[0],
skeleton.shape[1],
3))
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
branch_mask = branch_points & (outside_labels != 0)
end_mask = end_points & (outside_labels != 0)
branchpoint_image[outside_skel, :] = 1
branchpoint_image[trunk_mask | branch_mask | end_mask, :] = 0
branchpoint_image[trunk_mask, 0] = 1
branchpoint_image[branch_mask, 1] = 1
branchpoint_image[end_mask, 2] = 1
branchpoint_image[dilated_labels != 0, :] *= .875
branchpoint_image[dilated_labels != 0, :] += .1
if self.show_window:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image,
parent_image=skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
def erode(img, selem):
"""腐蚀操作"""
selem = np.array(selem)
out = np.empty_like(img)
ndi.grey_erosion(img, footprint=selem, output=out)
return out
import cv2
import matplotlib.pyplot as plt
from scipy import ndimage
img = cv2.imread('fp.tif')
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.imshow(img, cmap='gray')
plt.title('Original Image')
plt.show()
img2 = ndimage.grey_erosion(img, size=(2, 2))
plt.imshow(img2, cmap='gray')
plt.title('2x2 Erosion')
plt.show()
img2 = ndimage.grey_erosion(img, size=(3, 3))
plt.imshow(img2, cmap='gray')
plt.title('3x3 Erosion')
plt.show()
img2 = ndimage.grey_erosion(img, size=(4, 4))
plt.imshow(img2, cmap='gray')
plt.title('4x4 Erosion')
plt.show()
def process(self, input=None, output_folder=None, progress_callback=None, filter=None,
correction_factor=2,
cutoff_cell_fusion=None,
restore_safe_cells=False,
_DEBUG=False,
_VISUAL_DEBUG=False, **kwargs):
start = timer()
# filename0 = path
# filename0_without_path = os.path.basename(filename0)
# filename0_without_ext = os.path.splitext(filename0_without_path)[0]
# parent_dir_of_filename0 = os.path.dirname(filename0)
# TA_output_filename = os.path.join(parent_dir_of_filename0, filename0_without_ext,
# 'handCorrection.tif') # TODO allow custom names here to allow ensemble methods
# non_TA_final_output_name = os.path.join(output_folder, filename0_without_ext + '.tif')
#
# filename_to_use_to_save = non_TA_final_output_name
# if TA_mode:
# filename_to_use_to_save = TA_output_filename
#
# if TA_mode:
# # try also to change path input name
# if os.path.exists(
# os.path.join(parent_dir_of_filename0, filename0_without_ext, 'raw_epyseg_output.tif')):
# path = os.path.join(parent_dir_of_filename0, filename0_without_ext, 'raw_epyseg_output.tif')
# img_orig = Img(path)
# print('analyzing', path, self.stop_now)
# try:
# if self.progress_callback is not None:
# self.progress_callback.emit((iii / len(list_of_files)) * 100)
# else:
# logger.info(str((iii / len(list_of_files)) * 100) + '%')
# except:
# traceback.print_exc()
# pass
# DO A DILATION OF SEEDS THEN AN EROSION TO JOIN CLOSE BY SEEDS
img_orig = input
img_has_seeds = True
# mask with several channels
if img_orig.has_c():
if restore_safe_cells:
img_seg = img_orig[..., 0].copy()
seeds_1 = img_orig[..., img_orig.shape[-1] - 1]
seeds_1 = Img.invert(seeds_1)
# seeds_1[seeds_1 >= 0.5] = 255
# seeds_1[seeds_1 < 0.5] = 0
seeds_1[seeds_1 >= 0.2] = 255 # TODO maybe be more stringent here
seeds_1[seeds_1 < 0.2] = 0
s = ndimage.generate_binary_structure(2, 1)
seeds_1 = ndimage.grey_dilation(seeds_1, footprint=s)
seeds_1 = ndimage.grey_dilation(seeds_1, footprint=s)
seeds_1 = ndimage.grey_dilation(seeds_1, footprint=s)
seeds_1 = ndimage.grey_erosion(seeds_1, footprint=s)
seeds_1 = ndimage.grey_erosion(seeds_1, footprint=s)
# seeds_1 = ndimage.grey_erosion(seeds_1, footprint=s)
# seeds_1 = ndimage.grey_erosion(seeds_1, footprint=s)
# for debug
if _DEBUG:
Img(seeds_1, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'wshed_seeds.tif')) # not bad
lab_seeds = label(seeds_1.astype(np.uint8), connectivity=2, background=0)
#
for region in regionprops(lab_seeds):
if region.area < 10:
for coordinates in region.coords:
lab_seeds[coordinates[0], coordinates[1]] = 0
if _DEBUG:
Img(seeds_1, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'wshed_seeds_deblobed.tif'))
img_orig[..., 3] = Img.invert(img_orig[..., 3])
img_orig[..., 4] = Img.invert(img_orig[..., 4])
# seems to work --> now need to do the projection
for c in range(1, img_orig.shape[-1] - 2):
img_orig[..., 0] += img_orig[..., 1]
img_orig[..., 0] /= img_orig.shape[-1] - 2
img_orig = img_orig[..., 0]
else:
# mask with single channel
img_has_seeds = False
if restore_safe_cells:
img_seg = img_orig.copy()
if restore_safe_cells:
if _DEBUG:
print(os.path.join(output_folder, 'extras', 'img_seg.tif'))
Img(img_seg, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'img_seg.tif'))
# for debug
if _DEBUG:
Img(img_orig, dimensions='hw').save(os.path.join(output_folder, 'extras', 'avg.tif'))
img_saturated = img_orig.copy()
if img_has_seeds:
img_saturated[img_saturated >= 0.5] = 255
img_saturated[img_saturated < 0.5] = 0
if restore_safe_cells:
# TODO maybe do a safe image
img_seg[img_seg >= 0.3] = 255
img_seg[img_seg < 0.3] = 0
secure_mask = img_seg
else:
img_saturated[img_saturated >= 0.3] = 255
img_saturated[img_saturated < 0.3] = 0
if restore_safe_cells:
img_seg[img_seg >= 0.95] = 255
img_seg[img_seg < 0.95] = 0
secure_mask = img_seg
# convert it to seeds and make sure they are all present in there
# if pixel is not labeled then read it
if restore_safe_cells:
labels_n_area_rescue_seeds = {}
rescue_seeds = label(Img.invert(secure_mask), connectivity=1, background=0)
for region in regionprops(rescue_seeds):
labels_n_area_rescue_seeds[region.label] = region.area
if _DEBUG:
Img(secure_mask, dimensions='hw').save(os.path.join(output_folder, 'extras', 'secure_mask.tif'))
# loop over those seeds to rescue
# for debug
if _DEBUG:
Img(img_saturated, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'handCorrection.tif'))
deblob = True
if deblob:
image_thresh = label(img_saturated, connectivity=2, background=0)
# for debug
if _DEBUG:
Img(image_thresh, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'before_deblobed.tif'))
# deblob
min_size = 200
for region in regionprops(image_thresh):
# take regions with large enough areas
if region.area < min_size:
for coordinates in region.coords:
image_thresh[coordinates[0], coordinates[1]] = 0
image_thresh[image_thresh > 0] = 255
img_saturated = image_thresh
# for debug
if _DEBUG:
Img(img_saturated, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'deblobed.tif'))
del image_thresh
# for debug
if _DEBUG:
Img(img_saturated, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'deblobed_out.tif'))
extra_dilations = True
if extra_dilations:
# do a dilation of 2 to close bonds
s = ndimage.generate_binary_structure(2, 1)
dilated = ndimage.grey_dilation(img_saturated, footprint=s)
dilated = ndimage.grey_dilation(dilated, footprint=s)
# Img(dilated, dimensions='hw').save(os.path.join(os.path.splitext(path)[0], 'filled_one_px_holes.tif'))
# other_seeds = label(invert(np.grey_dilation(dilated, footprint=s).astype(np.uint8)), connectivity=1, background=0)
labs = label(Img.invert(img_saturated.astype(np.uint8)), connectivity=1, background=0)
for region in regionprops(labs):
seeds = []
# exclude tiny cells form dilation because they may end up completely closed
if region.area >= 10 and region.area < 350:
for coordinates in region.coords:
dilated[coordinates[0], coordinates[1]] = 0
continue
else:
# pb when big cells around cause connections are not done
# preserve cells at edges because they have to e naturally smaller because they are cut
# put a size criterion too
if region.area < 100 and (
region.bbox[0] <= 1 or region.bbox[1] <= 1 or region.bbox[2] >= labs.shape[-2] - 2 or
region.bbox[
3] >= \
labs.shape[-1] - 2):
# edge cell detected --> removing dilation
for coordinates in region.coords:
dilated[coordinates[0], coordinates[1]] = 0
continue
img_saturated = dilated
# for debug
if _DEBUG:
Img(img_saturated, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'dilated_further.tif'))
del dilated
list_of_cells_to_dilate = []
labs = label(Img.invert(img_saturated.astype(np.uint8)), connectivity=1, background=0)
# c'est cette correction qui fixe bcp de choses mais recree aussi des choses qui n'existent pas... --> voir à quoi sont dus ces lignes blobs
# faudrait redeblober
if img_has_seeds:
for region in regionprops(labs, intensity_image=img_orig):
seeds = []
if not extra_dilations and region.area < 10:
continue
# if small and no associated seeds --> remove it ??? maybe or not
for coordinates in region.coords:
id = lab_seeds[coordinates[0], coordinates[1]]
if id != 0:
seeds.append(id)
seeds = set(seeds)
if len(seeds) >= 2:
# we may have found an undersegmented cell --> try segment it better
list_of_cells_to_dilate.append(region.label)
if len(list_of_cells_to_dilate) != 0:
props = regionprops(labs, intensity_image=img_orig)
for run in range(10):
something_changed = False # early stop
for region in props:
if region.label not in list_of_cells_to_dilate:
continue
# TODO recheck those values and wether it makes sense
threshold_values = [80 / 255, 60 / 255, 40 / 255, 30 / 255,
20 / 255,
10 / 255] # 160 / 255, 140 / 255, 120 / 255, 100 / 255, 1 / 255 , 2 / 255, , 5 / 255
try:
for threshold in threshold_values:
mask = region.image.copy()
image = region.image.copy()
image[region.intensity_image > threshold] = True
image[region.intensity_image <= threshold] = False
final = Img.invert(image.astype(np.uint8))
final[final < 255] = 0
final[mask == False] = 0
new_seeds = label(final, connectivity=1, background=0)
props2 = regionprops(new_seeds)
if len(props2) > 1: # cell was resplitted into smaller
for r in props2:
if r.area < 20:
raise Exception
region.image[mask == False] = False
region.image[mask == True] = True
region.image[new_seeds > 0] = False
something_changed = True
for coordinates in region.coords:
img_saturated[coordinates[0], coordinates[1]] = 255
region.image[mask == False] = False
region.image[mask == True] = True
del final
del new_seeds
except:
traceback.print_exc()
pass
if not something_changed:
# print('no more changes anymore --> quitting')
break
# for debug
if _DEBUG:
Img(img_saturated, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'saturated_mask4.tif'))
final_seeds = label(Img.invert(img_saturated), connectivity=1,
background=0) # keep like that otherwise creates tiny cells with erroneous wshed
# for debug
if _DEBUG:
Img(final_seeds, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'final_seeds_before.tif'))
final_seeds = label(Img.invert(img_saturated), connectivity=2, background=0) # is that needed ???
# for debug
if _DEBUG:
Img(final_seeds, dimensions='hw').save(
os.path.join(output_folder, 'extras', 'final_seeds_before2.tif'))
final_seeds[img_saturated == 255] = 0
final_wshed = watershed(img_orig, markers=final_seeds,
watershed_line=True)
final_wshed[final_wshed != 0] = 1 # remove all seeds
final_wshed[final_wshed == 0] = 255 # set wshed values to 255
final_wshed[final_wshed == 1] = 0 # set all other cell content to
# filename0 = os.path.basename(path)
# parent_path = os.path.dirname(os.path.dirname(path))
if filter is None or filter == 0:
# TODO maybe offer the choice between saving wshed on predict or on orig
# Img(final_wshed, dimensions='hw').save(os.path.join(output_folder, os.path.splitext(filename0)[
# 0]) + '.tif') # need put original name here TODO put image default name here
# print('saving', filename_to_use_to_save)
# Img(final_wshed.astype(np.uint8), dimensions='hw').save(filename_to_use_to_save)
return final_wshed.astype(np.uint8)
else:
if isinstance(filter, int):
filter_by_size = filter
else:
filter_by_size = None
avg_area = 0
count = 0
if _DEBUG:
Img(final_wshed, dimensions='hw').save(os.path.join(output_folder, 'extras', 'test_size_cells.tif'))
final_seeds = Img.invert(final_wshed)
final_seeds = label(final_seeds, connectivity=1, background=0)
if _VISUAL_DEBUG:
plt.imshow(final_seeds)
plt.show()
removed_seeds = []
keep_seeds = []
labels_n_bbox = {}
labels_n_area = {}
border_cells = []
ids_n_local_median = {}
correspondance_between_cur_seeds_and_safe_ones = {}
if isinstance(filter, str) and 'local' in filter:
rps = regionprops(final_seeds)
for region in rps:
labels_n_bbox[region.label] = region.bbox
labels_n_area[region.label] = region.area
if (region.bbox[0] <= 3 or region.bbox[1] <= 3 or region.bbox[2] >= final_seeds.shape[-2] - 5 or
region.bbox[
3] >= \
final_seeds.shape[-1] - 5):
border_cells.append(region.label)
if restore_safe_cells:
for coordinates in region.coords:
if rescue_seeds[coordinates[0], coordinates[1]] != 0: # do r
correspondance_between_cur_seeds_and_safe_ones[region.label] = rescue_seeds[
coordinates[0], coordinates[1]]
break
break
_, tiles = Img.get_2D_tiles_with_overlap(final_seeds, overlap=64, dimension_h=-2, dimension_w=-1)
for r in tiles:
for tile in r:
rps2 = regionprops(tile)
for region in rps2:
if self.stop_now:
return
if region.label in border_cells:
continue
if (region.bbox[0] <= 3 or region.bbox[1] <= 3 or region.bbox[2] >= final_seeds.shape[
-2] - 5 or
region.bbox[
3] >= \
final_seeds.shape[-1] - 5):
continue
area_of_neighboring_cells = []
for region2 in rps2:
if region2.label == region.label:
continue
# find all cells with
if self.rect_distance(region.bbox, region2.bbox) <= 1:
area_of_neighboring_cells.append(labels_n_area[region2.label])
if area_of_neighboring_cells:
median = statistics.median_low(area_of_neighboring_cells)
ids_n_local_median[
region.label] = median / correction_factor
if region.area <= median / correction_factor:
removed_seeds.append(region.label)
else:
keep_seeds.append(region.label)
removed_seeds = [x for x in removed_seeds if x not in keep_seeds]
# TODO offer the things below as an option --> prevent removal of sure seeds or something like that
if restore_safe_cells:
removed_seeds_to_restore = []
for region in regionprops(final_seeds):
if region.label in removed_seeds:
first = True
for coordinates in region.coords:
if first and rescue_seeds[coordinates[0], coordinates[1]] != 0:
percent_diff = min(labels_n_area[region.label], labels_n_area_rescue_seeds[
rescue_seeds[coordinates[0], coordinates[1]]]) / max(
labels_n_area[region.label], labels_n_area_rescue_seeds[
rescue_seeds[coordinates[0], coordinates[1]]])
if (percent_diff >= 0.7 and percent_diff < 1.0) or (
labels_n_area[region.label] <= 200 and (
percent_diff >= 0.3 and percent_diff < 1.0)):
if _DEBUG:
print('0 finally not removing seed, safe seed', region.label,
percent_diff,
labels_n_area[region.label],
labels_n_area_rescue_seeds[
rescue_seeds[coordinates[0], coordinates[1]]],
labels_n_area[region.label] / labels_n_area_rescue_seeds[
rescue_seeds[coordinates[0], coordinates[1]]],
region.centroid)
removed_seeds_to_restore.append(region.label)
break
break
removed_seeds = [x for x in removed_seeds if x not in removed_seeds_to_restore]
else:
areas = []
for region in regionprops(final_seeds):
if (region.bbox[0] <= 3 or region.bbox[1] <= 3 or region.bbox[2] >= final_seeds.shape[-2] - 5 or
region.bbox[3] >= final_seeds.shape[-1] - 5):
continue
avg_area += region.area
count += 1
areas.append(region.area)
avg_area /= count
median = statistics.median_low(areas)
if isinstance(filter, int):
filter_by_size = filter
elif 'avg' in filter:
filter_by_size = avg_area / correction_factor
elif 'median' in filter:
filter_by_size = median / correction_factor
# TODO maybe use stdev or alike to see if cell should really be removed
if _DEBUG:
print('filter cells below=', filter_by_size, 'avg cell area=', avg_area, 'median=',
median) # , 'median', median
if filter_by_size is not None and filter_by_size != 0:
if _VISUAL_DEBUG:
plt.imshow(final_seeds)
plt.show()
for region in regionprops(final_seeds):
labels_n_bbox[region.label] = region.bbox
labels_n_area[region.label] = region.area
if region.area < filter_by_size:
if (region.bbox[0] <= 2 or region.bbox[1] <= 2 or region.bbox[2] >= labs.shape[
-2] - 3 or
region.bbox[
3] >= \
labs.shape[
-1] - 3):
continue
removed_seeds.append(region.label)
if cutoff_cell_fusion is not None and cutoff_cell_fusion > 1:
cells_to_fuse = []
for idx, removed_seed in enumerate(removed_seeds):
current_cells_to_fuse = set()
closest_pair = None
smallest_distance = None
for idx2 in range(idx + 1, len(removed_seeds)):
removed_seed2 = removed_seeds[idx2]
if closest_pair is None:
if self.rect_distance(labels_n_bbox[removed_seed], labels_n_bbox[removed_seed2]) <= 1:
closest_pair = removed_seed2
smallest_distance = self.rect_distance(labels_n_bbox[removed_seed],
labels_n_bbox[removed_seed2])
elif self.rect_distance(labels_n_bbox[removed_seed],
labels_n_bbox[removed_seed2]) <= smallest_distance:
closest_pair = removed_seed2
smallest_distance = self.rect_distance(labels_n_bbox[removed_seed],
labels_n_bbox[removed_seed2])
if self.rect_distance(labels_n_bbox[removed_seed], labels_n_bbox[removed_seed2]) <= 1:
current_cells_to_fuse.add(removed_seed)
current_cells_to_fuse.add(removed_seed2)
if current_cells_to_fuse:
cells_to_fuse.append(current_cells_to_fuse)
cells_to_fuse = [frozenset(i) for i in cells_to_fuse]
cells_to_fuse = list(dict.fromkeys(cells_to_fuse))
cells_to_keep = []
if cutoff_cell_fusion is not None and cutoff_cell_fusion > 0:
superfuse = []
copy_of_cells_to_fuse = cells_to_fuse.copy()
for idx, fuse in enumerate(copy_of_cells_to_fuse):
current_fusion = set(fuse.copy())
changed = True
while changed:
changed = False
for idx2 in range(len(copy_of_cells_to_fuse) - 1, idx, -1):
fuse2 = copy_of_cells_to_fuse[idx2]
if idx2 == idx:
continue
if fuse2.intersection(current_fusion):
current_fusion.update(fuse2)
del copy_of_cells_to_fuse[idx2]
changed = True
superfuse.append(current_fusion)
for sf in superfuse:
if len(sf) > cutoff_cell_fusion:
for val in sf:
cells_to_keep.append(val)
seeds_to_fuse = []
cells_to_fuse = sorted(cells_to_fuse, key=len)
for fuse in cells_to_fuse:
cumulative_area = 0
for _id in fuse:
if _id in cells_to_keep:
if _id in removed_seeds:
removed_seeds.remove(_id)
continue
cumulative_area += labels_n_area[_id]
if filter_by_size is not None:
if cumulative_area >= filter_by_size: #: #1200: #filter_by_size: # need hack this to get local area
seeds_to_fuse.append(fuse)
for _id in fuse:
if _id in removed_seeds:
removed_seeds.remove(_id)
else:
if cumulative_area >= ids_n_local_median[_id]:
seeds_to_fuse.append(fuse)
for _id in fuse:
if _id in removed_seeds:
removed_seeds.remove(_id)
# need recolor all the seeds in there with the new seed stuff
for fuse in seeds_to_fuse:
for _id in fuse:
break
for region in regionprops(final_seeds):
if region.label in fuse:
for coordinates in region.coords:
final_seeds[coordinates[0], coordinates[1]] = _id
if _VISUAL_DEBUG:
plt.imshow(final_seeds)
plt.show()
for region in regionprops(final_seeds):
if region.label in removed_seeds:
for coordinates in region.coords:
final_seeds[coordinates[0], coordinates[1]] = 0
if _VISUAL_DEBUG:
plt.imshow(final_seeds)
plt.show()
if _VISUAL_DEBUG:
plt.imshow(final_seeds)
plt.show()
final_wshed = watershed(img_orig, markers=final_seeds, watershed_line=True)
final_wshed[final_wshed != 0] = 1 # remove all seeds
final_wshed[final_wshed == 0] = 255 # set wshed values to 255
final_wshed[final_wshed == 1] = 0 # set all other cell content to
if _VISUAL_DEBUG:
plt.imshow(final_wshed)
plt.show()
# print('saving', filename_to_use_to_save)
# Img(final_wshed.astype(np.uint8), dimensions='hw').save(filename_to_use_to_save)
duration = timer() - start
if _DEBUG:
print('final duration wshed in secs', duration)
return final_wshed.astype(np.uint8) # is indeed a 2D image
def fill_sinks2(self, four_way=False):
"""
Fill sinks method adapted from fill depressions/sinks in floating point array
Parameters:
----------
input_array : [ndarray] Input array to be filled
four_way : [bool] Searchs the 4 (True) or 8 (False) adjacent cells
Returns:
----------
[ndarray] Filled array
This algorithm has been adapted (with minor modifications) from the
Charles Morton slow fill algorithm (with ndimage and python 3 was not slow
at all).
References
----------
Soile, P., Vogt, J., and Colombo, R., 2003. Carving and Adaptive
Drainage Enforcement of Grid Digital Elevation Models.
Water Resources Research, 39(12), 1366
Soille, P., 1999. Morphological Image Analysis: Principles and
Applications, Springer-Verlag, pp. 173-174
"""
# Change nan values to a very low value
copyarr = np.copy(self._array)
nodata_pos = self.get_nodata_pos()
copyarr[nodata_pos] = -9999.
# Set h_max to a value larger than the array maximum to ensure
# that the while loop will terminate
h_max = copyarr.max() + 100
# Build mask of cells with data not on the edge of the image
# Use 3x3 square Structuring element
inside_mask = ndimage.morphology.binary_erosion(
np.isfinite(copyarr),
structure=np.array([[1, 1, 1], [1, 1, 1], [1, 1,
1]]).astype(np.bool))
# Initialize output array as max value test_array except edges
output_array = np.copy(copyarr)
output_array[inside_mask] = h_max
# Array for storing previous iteration
output_old_array = np.copy(copyarr)
output_old_array[:] = 0
# Cross structuring element
if four_way:
el = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]]).astype(np.bool)
else:
el = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]).astype(np.bool)
# Iterate until marker array doesn't change
while not np.array_equal(output_old_array, output_array):
output_old_array = np.copy(output_array)
output_array = np.maximum(
copyarr,
ndimage.grey_erosion(output_array, size=(3, 3), footprint=el))
# Put back nodata values and change type
if self._nodata:
output_array[nodata_pos] = self._nodata
# Create output filled DEM
filled_dem = DEM()
filled_dem.copy_layout(self)
filled_dem.set_array(output_array)
filled_dem.set_nodata(self._nodata)
return filled_dem
def get_connections(seg_file, syn_seg_file, pre_synaptic_probs):
'''Get the detected synaptic connections
:param seg_file: the .h5 segmentation
:param syn_seg_file: the .h5 segmentation of the synapses
:param pre_synaptic_probs: the .h5 probability maps classifying
voxels as pre-synaptic
'''
#
# The strategy:
#
# * sum the probability map within the synapse regions to get
# the average strength of the signal within each neuron
# * find only the border pixels of the segmentations
# * overlay with synapses to get only border pixels within synapses
# * use np.bincount to compute average x, y and z
#
with h5py.File(seg_file, "r") as fd:
seg_volume = fd[fd.keys()[0]][:]
with h5py.File(syn_seg_file, "r") as fd:
synseg_volume = fd[fd.keys()[0]][:]
############################################
#
# Find the neuron pairs.
#
############################################
z, y, x = np.where(synseg_volume > 0)
seg, synseg = seg_volume[z, y, x], synseg_volume[z, y, x]
matrix = coo_matrix((np.ones(len(z)), (synseg, seg)))
matrix.sum_duplicates()
synapse_labels, neuron_labels = matrix.nonzero()
counts = matrix.tocsr()[synapse_labels, neuron_labels].getA1()
#
# Order by synapse label and -count to get the neurons with
# the highest count first
#
order = np.lexsort((-counts, synapse_labels))
counts, neuron_labels, synapse_labels = \
[_[order] for _ in counts, neuron_labels, synapse_labels]
first = np.hstack(
[[True], synapse_labels[:-1] != synapse_labels[1:], [True]])
idx = np.where(first)[0]
per_synapse_counts = idx[1:] - idx[:-1]
#
# Get rid of counts < 2
#
mask = per_synapse_counts >= 2
idx = idx[:-1][mask]
#
# pick out the first and second most overlapping neurons and
# their synapse.
#
neuron_1 = neuron_labels[idx]
synapses = synapse_labels[idx]
neuron_2 = neuron_labels[idx+1]
###################################
#
# Determine polarity
#
###################################
with h5py.File(pre_synaptic_probs, "r") as fd:
probs = fd[fd.keys()[0]][:][z, y, x]
#
# Start by making a matrix to transform the map.
#
matrix = coo_matrix(
(np.arange(len(idx)*2) + 1,
(np.hstack((neuron_1, neuron_2)),
np.hstack((synapses, synapses)))),
shape=(np.max(seg)+1, np.max(synseg) + 1)).tocsr()
#
# Convert the neuron / synapse map to the mapping labels
#
mapping_labeling = matrix[seg, synseg].A1
#
# Score each synapse / label overlap on both the transmitter
# and receptor probabilities
#
areas = np.bincount(mapping_labeling)
transmitter_score = np.bincount(
mapping_labeling, probs, minlength=len(areas)) / areas
del probs
score_1 = transmitter_score[1:len(idx)+1]
score_2 = transmitter_score[len(idx)+1:]
#
# Flip the scores and neuron assignments if score_2 > score_1
#
flippers = score_2 > score_1
score_1[flippers], score_2[flippers] = \
score_2[flippers], score_1[flippers]
neuron_1[flippers], neuron_2[flippers] = \
neuron_2[flippers], neuron_1[flippers]
##########################################################
#
# Compute synapse centers
#
##########################################################
edge_z, edge_y, edge_x = np.where(
(grey_dilation(seg, size=3) != grey_erosion(seg_volume, size=3)) &\
(synseg_volume != 0))
areas = np.bincount(synseg_volume[edge_z, edge_y, edge_x])
xc, yc, zc = [np.bincount(synseg_volume[edge_z, edge_y, edge_x], _)
for _ in edge_x, edge_y, edge_z]
result = dict(neuron_1=neuron_1,
neuron_2=neuron_2,
synapse_center=dict(x=xc[synapses]/areas[synapses],
y=yc[synapses]/areas[synapses],
z=zc[synapses]/areas[synapses]))
return result
def run(self, workspace):
'''Run the module on the image set'''
seed_objects_name = self.seed_objects_name.value
skeleton_name = self.image_name.value
seed_objects = workspace.object_set.get_objects(seed_objects_name)
labels = seed_objects.segmented
labels_count = np.max(labels)
label_range = np.arange(labels_count,dtype=np.int32)+1
skeleton_image = workspace.image_set.get_image(
skeleton_name, must_be_binary = True)
skeleton = skeleton_image.pixel_data
if skeleton_image.has_mask:
skeleton = skeleton & skeleton_image.mask
try:
labels = skeleton_image.crop_image_similarly(labels)
except:
labels, m1 = cpo.size_similarly(skeleton, labels)
labels[~m1] = 0
#
# The following code makes a ring around the seed objects with
# the skeleton trunks sticking out of it.
#
# Create a new skeleton with holes at the seed objects
# First combine the seed objects with the skeleton so
# that the skeleton trunks come out of the seed objects.
#
# Erode the labels once so that all of the trunk branchpoints
# will be within the labels
#
#
# Dilate the objects, then subtract them to make a ring
#
my_disk = morph.strel_disk(1.5).astype(int)
dilated_labels = grey_dilation(labels, footprint=my_disk)
seed_mask = dilated_labels > 0
combined_skel = skeleton | seed_mask
closed_labels = grey_erosion(dilated_labels,
footprint = my_disk)
seed_center = closed_labels > 0
combined_skel = combined_skel & (~seed_center)
#
# Fill in single holes (but not a one-pixel hole made by
# a one-pixel image)
#
if self.wants_to_fill_holes:
def size_fn(area, is_object):
return (~ is_object) and (area <= self.maximum_hole_size.value)
combined_skel = morph.fill_labeled_holes(
combined_skel, ~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.skeletonize(combined_skel)
#
# The skeleton outside of the labels
#
outside_skel = combined_skel & (dilated_labels == 0)
#
# Associate all skeleton points with seed objects
#
dlabels, distance_map = propagate.propagate(np.zeros(labels.shape),
dilated_labels,
combined_skel, 1)
#
# Get rid of any branchpoints not connected to seeds
#
combined_skel[dlabels == 0] = False
#
# Find the branchpoints
#
branch_points = morph.branchpoints(combined_skel)
#
# Odd case: when four branches meet like this, branchpoints are not
# assigned because they are arbitrary. So assign them.
#
# . .
# B.
# .B
# . .
#
odd_case = (combined_skel[:-1,:-1] & combined_skel[1:,:-1] &
combined_skel[:-1,1:] & combined_skel[1,1])
branch_points[:-1,:-1][odd_case] = True
branch_points[1:,1:][odd_case] = True
#
# Find the branching counts for the trunks (# of extra branches
# eminating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0,0,0,1,2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.endpoints(combined_skel)
#
# We use two ranges for classification here:
# * anything within one pixel of the dilated image is a trunk
# * anything outside of that range is a branch
#
nearby_labels = dlabels.copy()
nearby_labels[distance_map > 1.5] = 0
outside_labels = dlabels.copy()
outside_labels[nearby_labels > 0] = 0
#
# The trunks are the branchpoints that lie within one pixel of
# the dilated image.
#
if labels_count > 0:
trunk_counts = fix(scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0,),int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(scind.sum(branch_points, outside_labels,
label_range))
else:
branch_counts = np.zeros((0,),int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels, label_range))
else:
end_counts = np.zeros((0,), int)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join((C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES,
skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
#
# Collect the graph information
#
if self.wants_neuron_graph:
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
intensity_image = workspace.image_set.get_image(
self.intensity_image_name.value)
edge_graph, vertex_graph = self.make_neuron_graph(
combined_skel, dlabels,
trunk_mask,
branch_points & ~trunk_mask,
end_points,
intensity_image.pixel_data)
#
# Add an image number column to both and change vertex index
# to vertex number (one-based)
#
image_number = workspace.measurements.image_set_number
vertex_graph = np.rec.fromarrays(
(np.ones(len(vertex_graph)) * image_number,
np.arange(1, len(vertex_graph) + 1),
vertex_graph['i'],
vertex_graph['j'],
vertex_graph['labels'],
vertex_graph['kind']),
names = ("image_number", "vertex_number", "i", "j",
"labels", "kind"))
edge_graph = np.rec.fromarrays(
(np.ones(len(edge_graph)) * image_number,
edge_graph["v1"],
edge_graph["v2"],
edge_graph["length"],
edge_graph["total_intensity"]),
names = ("image_number", "v1", "v2", "length",
"total_intensity"))
path = self.directory.get_absolute_path(m)
edge_file = m.apply_metadata(self.edge_file_name.value)
edge_path = os.path.abspath(os.path.join(path, edge_file))
vertex_file = m.apply_metadata(self.vertex_file_name.value)
vertex_path = os.path.abspath(os.path.join(path, vertex_file))
d = self.get_dictionary(workspace.image_set_list)
for file_path, table, fmt in (
(edge_path, edge_graph, "%d,%d,%d,%d,%.4f"),
(vertex_path, vertex_graph, "%d,%d,%d,%d,%d,%s")):
#
# Delete files first time through / otherwise append
#
if not d.has_key(file_path):
d[file_path] = True
if os.path.exists(file_path):
if workspace.frame is not None:
import wx
if wx.MessageBox(
"%s already exists. Do you want to overwrite it?" %
file_path, "Warning: overwriting file",
style = wx.YES_NO,
parent = workspace.frame) != wx.YES:
raise ValueError("Can't overwrite %s" % file_path)
os.remove(file_path)
fd = open(file_path, 'wt')
header = ','.join(table.dtype.names)
fd.write(header + '\n')
else:
fd = open(file_path, 'at')
np.savetxt(fd, table, fmt)
fd.close()
if workspace.frame is not None:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
#
# Make the display image
#
if workspace.frame is not None or self.wants_branchpoint_image:
branchpoint_image = np.zeros((skeleton.shape[0],
skeleton.shape[1],
3))
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
branch_mask = branch_points & (outside_labels != 0)
end_mask = end_points & (outside_labels != 0)
branchpoint_image[outside_skel,:] = 1
branchpoint_image[trunk_mask | branch_mask | end_mask,:] = 0
branchpoint_image[trunk_mask,0] = 1
branchpoint_image[branch_mask,1] = 1
branchpoint_image[end_mask, 2] = 1
branchpoint_image[dilated_labels != 0,:] *= .875
branchpoint_image[dilated_labels != 0,:] += .1
if workspace.frame:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image,
parent_image = skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
def __call__(self,
img: np.ndarray,
mode: Optional[str] = None,
radius: Optional[int] = None,
binary: Optional[bool] = None) -> np.ndarray:
"""
Apply the transform to `img`.
"""
self.mode = self.mode if mode is None else mode
self.radius = self.radius if radius is None else radius
self.binary = self.binary if binary is None else binary
input_ndim = img.squeeze().ndim # spatial ndim
if input_ndim == 2:
structure = ndi.generate_binary_structure(2, 1)
elif input_ndim == 3:
structure = ndi.generate_binary_structure(3, 1)
else:
raise ValueError(
f'Currently only support 2D&3D data, but got image with shape of {img.shape}'
)
channel_dim = None
if input_ndim != img.ndim:
channel_dim = img.shape.index(1)
img = img.squeeze()
if self.mode == 'closing':
if self.binary:
img = ndi.binary_closing(img,
structure=structure,
iterations=self.radius)
else:
for _ in range(self.radius):
img = ndi.grey_closing(img, footprint=structure)
elif self.mode == 'dilation':
if self.binary:
img = ndi.binary_dilation(img,
structure=structure,
iterations=self.radius)
else:
for _ in range(self.radius):
img = ndi.grey_dilation(img, footprint=structure)
elif self.mode == 'erosion':
if self.binary:
img = ndi.binary_erosion(img,
structure=structure,
iterations=self.radius)
else:
for _ in range(self.radius):
img = ndi.grey_erosion(img, footprint=structure)
elif self.mode == 'opening':
if self.binary:
img = ndi.binary_opening(img,
structure=structure,
iterations=self.radius)
else:
for _ in range(self.radius):
img = ndi.grey_opening(img, footprint=structure)
else:
raise ValueError(f'Unexpected keyword {self.mode}')
if channel_dim is not None:
return np.expand_dims(img, axis=channel_dim)
else:
return img
def run(self, workspace):
'''Run the module on the image set'''
seed_objects_name = self.seed_objects_name.value
skeleton_name = self.image_name.value
seed_objects = workspace.object_set.get_objects(seed_objects_name)
labels = seed_objects.segmented
labels_count = np.max(labels)
label_range = np.arange(labels_count, dtype=np.int32) + 1
skeleton_image = workspace.image_set.get_image(skeleton_name,
must_be_binary=True)
skeleton = skeleton_image.pixel_data
if skeleton_image.has_mask:
skeleton = skeleton & skeleton_image.mask
try:
labels = skeleton_image.crop_image_similarly(labels)
except:
labels, m1 = cpo.size_similarly(skeleton, labels)
labels[~m1] = 0
#
# The following code makes a ring around the seed objects with
# the skeleton trunks sticking out of it.
#
# Create a new skeleton with holes at the seed objects
# First combine the seed objects with the skeleton so
# that the skeleton trunks come out of the seed objects.
#
# Erode the labels once so that all of the trunk branchpoints
# will be within the labels
#
#
# Dilate the objects, then subtract them to make a ring
#
my_disk = morph.strel_disk(1.5).astype(int)
dilated_labels = grey_dilation(labels, footprint=my_disk)
seed_mask = dilated_labels > 0
combined_skel = skeleton | seed_mask
closed_labels = grey_erosion(dilated_labels, footprint=my_disk)
seed_center = closed_labels > 0
combined_skel = combined_skel & (~seed_center)
#
# Fill in single holes (but not a one-pixel hole made by
# a one-pixel image)
#
if self.wants_to_fill_holes:
def size_fn(area, is_object):
return (~is_object) and (area <= self.maximum_hole_size.value)
combined_skel = morph.fill_labeled_holes(combined_skel,
~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.skeletonize(combined_skel)
#
# The skeleton outside of the labels
#
outside_skel = combined_skel & (dilated_labels == 0)
#
# Associate all skeleton points with seed objects
#
dlabels, distance_map = propagate.propagate(np.zeros(labels.shape),
dilated_labels,
combined_skel, 1)
#
# Get rid of any branchpoints not connected to seeds
#
combined_skel[dlabels == 0] = False
#
# Find the branchpoints
#
branch_points = morph.branchpoints(combined_skel)
#
# Odd case: when four branches meet like this, branchpoints are not
# assigned because they are arbitrary. So assign them.
#
# . .
# B.
# .B
# . .
#
odd_case = (combined_skel[:-1, :-1] & combined_skel[1:, :-1]
& combined_skel[:-1, 1:] & combined_skel[1, 1])
branch_points[:-1, :-1][odd_case] = True
branch_points[1:, 1:][odd_case] = True
#
# Find the branching counts for the trunks (# of extra branches
# eminating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0, 0, 0, 1, 2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.endpoints(combined_skel)
#
# We use two ranges for classification here:
# * anything within one pixel of the dilated image is a trunk
# * anything outside of that range is a branch
#
nearby_labels = dlabels.copy()
nearby_labels[distance_map > 1.5] = 0
outside_labels = dlabels.copy()
outside_labels[nearby_labels > 0] = 0
#
# The trunks are the branchpoints that lie within one pixel of
# the dilated image.
#
if labels_count > 0:
trunk_counts = fix(
scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0, ), int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(
scind.sum(branch_points, outside_labels, label_range))
else:
branch_counts = np.zeros((0, ), int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels,
label_range))
else:
end_counts = np.zeros((0, ), int)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join(
(C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
#
# Collect the graph information
#
if self.wants_neuron_graph:
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
intensity_image = workspace.image_set.get_image(
self.intensity_image_name.value)
edge_graph, vertex_graph = self.make_neuron_graph(
combined_skel, dlabels, trunk_mask,
branch_points & ~trunk_mask, end_points,
intensity_image.pixel_data)
#
# Add an image number column to both and change vertex index
# to vertex number (one-based)
#
image_number = workspace.measurements.image_set_number
vertex_graph = np.rec.fromarrays(
(np.ones(len(vertex_graph)) * image_number,
np.arange(1,
len(vertex_graph) + 1), vertex_graph['i'],
vertex_graph['j'], vertex_graph['labels'],
vertex_graph['kind']),
names=("image_number", "vertex_number", "i", "j", "labels",
"kind"))
edge_graph = np.rec.fromarrays(
(np.ones(len(edge_graph)) * image_number, edge_graph["v1"],
edge_graph["v2"], edge_graph["length"],
edge_graph["total_intensity"]),
names=("image_number", "v1", "v2", "length",
"total_intensity"))
path = self.directory.get_absolute_path(m)
edge_file = m.apply_metadata(self.edge_file_name.value)
edge_path = os.path.abspath(os.path.join(path, edge_file))
vertex_file = m.apply_metadata(self.vertex_file_name.value)
vertex_path = os.path.abspath(os.path.join(path, vertex_file))
d = self.get_dictionary(workspace.image_set_list)
for file_path, table, fmt in ((edge_path, edge_graph,
"%d,%d,%d,%d,%.4f"),
(vertex_path, vertex_graph,
"%d,%d,%d,%d,%d,%s")):
#
# Delete files first time through / otherwise append
#
if not d.has_key(file_path):
d[file_path] = True
if os.path.exists(file_path):
if workspace.frame is not None:
import wx
if wx.MessageBox(
"%s already exists. Do you want to overwrite it?"
% file_path,
"Warning: overwriting file",
style=wx.YES_NO,
parent=workspace.frame) != wx.YES:
raise ValueError("Can't overwrite %s" %
file_path)
os.remove(file_path)
fd = open(file_path, 'wt')
header = ','.join(table.dtype.names)
fd.write(header + '\n')
else:
fd = open(file_path, 'at')
np.savetxt(fd, table, fmt)
fd.close()
if workspace.frame is not None:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
#
# Make the display image
#
if workspace.frame is not None or self.wants_branchpoint_image:
branchpoint_image = np.zeros(
(skeleton.shape[0], skeleton.shape[1], 3))
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
branch_mask = branch_points & (outside_labels != 0)
end_mask = end_points & (outside_labels != 0)
branchpoint_image[outside_skel, :] = 1
branchpoint_image[trunk_mask | branch_mask | end_mask, :] = 0
branchpoint_image[trunk_mask, 0] = 1
branchpoint_image[branch_mask, 1] = 1
branchpoint_image[end_mask, 2] = 1
branchpoint_image[dilated_labels != 0, :] *= .875
branchpoint_image[dilated_labels != 0, :] += .1
if workspace.frame:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image, parent_image=skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
def run(self, workspace):
objects = workspace.object_set.get_objects(self.objects_name.value)
assert isinstance(objects, cpo.Objects)
labels = objects.segmented
half_window_size = self.window_size.value
threshold = self.threshold.value
output_labels = objects.segmented.copy()
max_objects = np.max(output_labels)
indices = np.arange(max_objects+1)
# Get object slices
object_slices = morph.fixup_scipy_ndimage_result(scind.measurements.find_objects(output_labels))
# Calculate perimeters
perimeters = morph.calculate_perimeters(output_labels, indices)
# Find the neighbors
neighbors = np.zeros((max_objects+1, max_objects+1), bool) # neighbors[i,j] = True when object j is a neighbor of object i.
lmax = scind.grey_dilation(output_labels, footprint=np.ones((3,3), bool)) # lower pixel values will be replaced by adjacent larger pixel values
lbig = output_labels.copy()
lbig[lbig == 0] = np.iinfo(output_labels.dtype).max # set the background to be large so that it is ignored next
lmin = scind.grey_erosion(lbig, footprint=np.ones((3,3), bool)) # larger pixel values will be replaced by adjacent smaller pixel values
for i in range(1, max_objects+1):
object_bounds = (object_slices[i-1][0],object_slices[i-1][1])
object_map = output_labels[object_bounds] == i # The part of the slice that contains the object
lower_neighbors = np.unique(lmin[object_bounds][object_map])
higher_neighbors = np.unique(lmax[object_bounds][object_map])
for neighbor_list in [lower_neighbors, higher_neighbors]:
for j in range(0, len(neighbor_list)):
neighbors[i,neighbor_list[j]] = True
neighbors[i,i] = False
# Generate window dimensions for each location (necessary for the edges)
dim1_window = np.ones(output_labels.shape, int) * half_window_size
dim2_window = np.ones(output_labels.shape, int) * half_window_size
for i in range(0, half_window_size):
dim1_window[i:output_labels.shape[0]-i,0:output_labels.shape[1]] += 1
dim2_window[0:output_labels.shape[0],i:output_labels.shape[1]-i] += 1
# Loop over all objects
for k in range(1, max_objects+1):
if (perimeters[k] == 0) or not np.max(neighbors[k]): # Has been removed by a merge or has no neighbors
continue
k_bounds_array = object_slices[k-1]
k_dim1_bounds = k_bounds_array[0].indices(output_labels.shape[0])
k_dim2_bounds = k_bounds_array[1].indices(output_labels.shape[1])
# Loop over all neighbors of object k
l = 0
while l < max_objects:
l += 1
if k == l or (perimeters[l] == 0) or not neighbors[k,l]: # Has been removed by a merge or is not a neighbor of object k
continue
l_bounds_array = object_slices[l-1]
l_dim1_bounds = l_bounds_array[0].indices(output_labels.shape[0])
l_dim2_bounds = l_bounds_array[1].indices(output_labels.shape[1])
kl_dim1_min_bound = min(k_dim1_bounds[0], l_dim1_bounds[0])
kl_dim1_max_bound = max(k_dim1_bounds[1], l_dim1_bounds[1])
kl_dim2_min_bound = min(k_dim2_bounds[0], l_dim2_bounds[0])
kl_dim2_max_bound = max(k_dim2_bounds[1], l_dim2_bounds[1])
kl_bounds = (slice(kl_dim1_min_bound, kl_dim1_max_bound), slice(kl_dim2_min_bound, kl_dim2_max_bound))
kl_shape = (kl_dim1_max_bound - kl_dim1_min_bound, kl_dim2_max_bound - kl_dim2_min_bound)
#
# Find the vertices of object pair k, l
#
vertices = np.zeros(kl_shape, bool)
isAdjacent = np.zeros(output_labels.shape, bool)
isBorder = np.zeros(output_labels.shape, bool)
for i in range(-1,2):
ki_min = 0
ki_max = 0
li_min = 0
li_max = 0
if k_dim1_bounds[0] + i < 0:
ki_min = -i
else:
ki_min = k_dim1_bounds[0]
if l_dim1_bounds[0] + i < 0:
li_min = -i
else:
li_min = l_dim1_bounds[0]
if k_dim1_bounds[1] + i > output_labels.shape[0]:
ki_max = output_labels.shape[0] - i
else:
ki_max = k_dim1_bounds[1]
if l_dim1_bounds[1] + i > output_labels.shape[0]:
li_max = output_labels.shape[0] - i
else:
li_max = l_dim1_bounds[1]
ki_slice = slice(ki_min, ki_max)
li_slice = slice(li_min, li_max)
ki_test_slice = slice(ki_min+i, ki_max+i)
li_test_slice = slice(li_min+i, li_max+i)
for j in range(-1,2):
if i == j == 0:
continue
kj_min = 0
kj_max = 0
lj_min = 0
lj_max = 0
if k_dim2_bounds[0] + j < 0:
kj_min = -j
else:
kj_min = k_dim2_bounds[0]
if l_dim2_bounds[0] + j < 0:
lj_min = -j
else:
lj_min = l_dim2_bounds[0]
if k_dim2_bounds[1] + j > output_labels.shape[0]:
kj_max = output_labels.shape[0] - j
else:
kj_max = k_dim2_bounds[1]
if l_dim2_bounds[1] + j > output_labels.shape[0]:
lj_max = output_labels.shape[0] - j
else:
lj_max = l_dim2_bounds[1]
if (kj_min == kj_max or ki_min == ki_max) and (lj_min == lj_max or li_min == li_max):
continue
kj_slice = slice(kj_min, kj_max)
lj_slice = slice(lj_min, lj_max)
kj_test_slice = slice(kj_min+j, kj_max+j)
lj_test_slice = slice(lj_min+j, lj_max+j)
k_bounds = (ki_slice, kj_slice)
l_bounds = (li_slice, lj_slice)
k_test_bounds = (ki_test_slice, kj_test_slice)
l_test_bounds = (li_test_slice, lj_test_slice)
kl_mod_bounds = (slice(min(ki_min, li_min), max(ki_max, li_max)), slice(min(kj_min, lj_min), max(kj_max, lj_max)))
isAdjacentSlice = np.zeros(output_labels.shape, bool)
isAdjacentSlice[k_bounds] = np.logical_and(output_labels[k_bounds] == k,
output_labels[k_test_bounds] == l)
isAdjacentSlice[l_bounds] = np.logical_or(isAdjacentSlice[l_bounds],
np.logical_and(output_labels[l_bounds] == l,
output_labels[l_test_bounds] == k))
isAdjacent[kl_mod_bounds] = np.logical_or(isAdjacent[kl_mod_bounds],
isAdjacentSlice[kl_mod_bounds])
isBorderSlice = np.zeros(output_labels.shape, bool)
isBorderSlice[k_bounds] = np.logical_and(output_labels[k_bounds] == k,
np.logical_and(output_labels[k_test_bounds] != k,
output_labels[k_test_bounds] != l))
isBorderSlice[l_bounds] = np.logical_or(isBorderSlice[l_bounds],
np.logical_and(output_labels[l_bounds] == l,
np.logical_and(output_labels[l_test_bounds] != k,
output_labels[l_test_bounds] != l)))
isBorder[kl_mod_bounds] = np.logical_or(isBorder[kl_mod_bounds],
isBorderSlice[kl_mod_bounds])
vertices = np.logical_and(isAdjacent[kl_bounds], isBorder[kl_bounds])
#
# Calculate the maximum vertex score for the pair
#
'''+1 for every non-I/J labeled pixel in the window
+1 more for every non-I/J labeled pixel adjacent to it
Divided by the score that would result if the objects were flat
e.g. (vertex is starred)
0 0 0 0 0 0 0
0 0 0 0 0 0 0
1 1 0 0 0 0 0
1 1 1 *1* 2 2 0
1 1 1 1 2 2 2
1 1 1 1 2 2 2
1 1 1 2 2 2 2
If it were flat, it would be 7 * 3 = 21, so the divisor 7 * 6 = 42.
In the case that the window is rectangular (such as at the image edge),
we split the difference.
Generalized:
Vertex Score = (Sum of NotIJ) / [A * B - 0.5 * (A + B)]
'''
sum_not_IJ = np.zeros(kl_shape, int)
for i in range(0, kl_shape[0]):
for j in range(0,kl_shape[1]):
if not vertices[i,j]:
continue
window_bounds = (slice(i - half_window_size + kl_dim1_min_bound, i + half_window_size + kl_dim1_min_bound),
slice(j - half_window_size + kl_dim2_min_bound, j + half_window_size + kl_dim2_min_bound))
window_slice = output_labels[window_bounds]
sum_not_IJ[i,j] = count_nonzero(np.logical_and(window_slice != k,
window_slice != l))
# Should be np.count_nonzero, but it doesn't exist in the version that comes with
# CellProfiler 2.0 r11710
vertex_scores = sum_not_IJ / (dim1_window[kl_bounds] * dim2_window[kl_bounds] - 0.5 * (dim1_window[kl_bounds] + dim2_window[kl_bounds]))
merged = np.zeros(kl_shape, int)
merged[output_labels[kl_bounds] == k] = 1
merged[output_labels[kl_bounds] == l] = 1
merged_perimeter = morph.calculate_perimeters(merged, [1])
seam_length = (perimeters[k] + perimeters[l] - merged_perimeter) / 2
min_external_perimeter = min(perimeters[k], perimeters[l]) - seam_length
adjusted_min_external_perimeter = min_external_perimeter / np.pi
seam_fraction = seam_length / (adjusted_min_external_perimeter + seam_length)
max_vertex_score = np.max(vertex_scores)
score = (max_vertex_score + seam_fraction) / 2
#
# Join object pair with score above the threshold
#
if score >= threshold:
output_labels[output_labels == l] = k
# Calculate new perimeters
perimeters[k] = merged_perimeter
perimeters[l] = 0
# Reconfigure neighbors
neighbors[k] = np.logical_or(neighbors[k], neighbors[l])
for x in range(1, max_objects+1): # Is there an array method to do this?
if neighbors[x,l]:
neighbors[x,k] = True
neighbors[0:max_objects,l] = False
# Recalculate bounds
k_dim1_bounds = kl_bounds[0].indices(output_labels.shape[0])
k_dim2_bounds = kl_bounds[1].indices(output_labels.shape[1])
# Reset l to 0 so that we check all the neighbors against this new object
l = 0
else:
pass
output_objects = cpo.Objects()
output_objects.segmented = output_labels
if objects.has_small_removed_segmented:
output_objects.small_removed_segmented = \
copy_labels(objects.small_removed_segmented, output_labels)
if objects.has_unedited_segmented:
output_objects.unedited_segmented = \
copy_labels(objects.unedited_segmented, output_labels)
output_objects.parent_image = objects.parent_image
workspace.object_set.add_objects(output_objects, self.output_objects_name.value)
measurements = workspace.measurements
add_object_count_measurements(measurements,
self.output_objects_name.value,
np.max(output_objects.segmented))
add_object_location_measurements(measurements,
self.output_objects_name.value,
output_objects.segmented)
#
# Relate the output objects to the input ones and record
# the relationship.
#
children_per_parent, parents_of_children = \
objects.relate_children(output_objects)
measurements.add_measurement(self.objects_name.value,
FF_CHILDREN_COUNT %
self.output_objects_name.value,
children_per_parent)
measurements.add_measurement(self.output_objects_name.value,
FF_PARENT%self.objects_name.value,
parents_of_children)
if self.wants_outlines:
outlines = cellprofiler.cpmath.outline.outline(output_labels)
outline_image = cpi.Image(outlines.astype(bool))
workspace.image_set.add(self.outlines_name.value,
outline_image)
if workspace.frame is not None:
workspace.display_data.orig_labels = objects.segmented
workspace.display_data.output_labels = output_objects.segmented
def outline_segments(self, mask_background=False):
"""
Outline the labeled segments.
The "outlines" represent the pixels *just inside* the segments,
leaving the background pixels unmodified.
Parameters
----------
mask_background : bool, optional
Set to `True` to mask the background pixels (labels = 0) in
the returned array. This is useful for overplotting the
segment outlines. The default is `False`.
Returns
-------
boundaries : `~numpy.ndarray` or `~numpy.ma.MaskedArray`
An array with the same shape of the segmentation array
containing only the outlines of the labeled segments. The
pixel values in the outlines correspond to the labels in the
segmentation array. If ``mask_background`` is `True`, then
a `~numpy.ma.MaskedArray` is returned.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> segm = SegmentationImage([[0, 0, 0, 0, 0, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 0, 0, 0, 0, 0]])
>>> segm.outline_segments()
array([[0, 0, 0, 0, 0, 0],
[0, 2, 2, 2, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 2, 2, 2, 0],
[0, 0, 0, 0, 0, 0]])
"""
from scipy.ndimage import (generate_binary_structure, grey_dilation,
grey_erosion)
# mode='constant' ensures outline is included on the array borders
selem = generate_binary_structure(self._ndim, 1) # edge connectivity
eroded = grey_erosion(self.data,
footprint=selem,
mode='constant',
cval=0.)
dilated = grey_dilation(self.data,
footprint=selem,
mode='constant',
cval=0.)
outlines = ((dilated != eroded) & (self.data != 0)).astype(int)
outlines *= self.data
if mask_background:
outlines = np.ma.masked_where(outlines == 0, outlines)
return outlines
a1 = toNumpyArray(Image.open('../85.tiff').convert('L')).copy()
a2 = toNumpyArray(Image.open('../86.tiff').convert('L')).copy()
a1[a1>10] = 40
a1[a1<=10]= 0
a2[a2>10]=90
a2[a2<=10]=0
numpy.savetxt('a1.out', a1)
numpy.savetxt('a2.out', a2)
a1a2=numpy.logical_xor(a1,a2)
numpy.savetxt('a1a2.out',numpy.logical_xor(a1,a2))
print numpy.unique(numpy.logical_xor(a1,a2))
print numpy.unique(a1)
print numpy.unique(a2)
footprint = ndimage.generate_binary_structure(2, 1)
cnt1 = a1 - ndimage.grey_erosion(a1, footprint=footprint)
cnt2 = a2 - ndimage.grey_erosion(a2, footprint=footprint)
cnt = cnt1+cnt2
cnt[cnt==numpy.amax(a1)+numpy.amax(a2)]= numpy.amax(a1)
numpy.savetxt('cnt.out', cnt)
numpy.savetxt('ics.out', a1a2)
icscnt = a1+a2
icscnt[a1a2!=0] = 255
icscnt[icscnt!=255] = 0
icscnt[cnt!=0] = cnt[cnt!=0]
numpy.savetxt('start.out', icscnt)
print numpy.unique(icscnt)
w = icscnt
eros = erosion(signal, selem=ball(2)) plt.imshow(eros[251], vmax=5e-5) plt.show() plt.imshow(np.log(eros[251])) plt.show() fft = np.fft.fftshift(np.fft.fftn(np.fft.fftshift(eros))) out = (fft * np.conj(fft)).real plt.imshow(np.log(out[251])) plt.show() eros = ndi.grey_erosion(signal, footprint=ball(3)) eros2 = ndi.grey_erosion(image, footprint=disk(3)) opened = opening(image) closed = closing(image) opened = opening(signal) closed = closing(signal) gf = ndi.gaussian_filter(image, 1) opened = opening(gf) closed = closing(gf) eros = erosion(gf) # Finding local maxima # http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_peak_local_max.html
def _create_world_composite(items,
lon_limits=None,
erosion_size=20,
smooth_width=20):
# smooth_sigma = 4
img = None
for (path, area, timeslot) in items:
if not isinstance(area, AreaDefinition):
area = get_area_def(area)
next_img = read_image(path, area, timeslot)
if img is None:
img = next_img
else:
# scaled_smooth_sigma = smooth_sigma * (float(img.width) / 1000.0)
img_mask = reduce(np.ma.mask_or,
[chn.mask for chn in img.channels])
next_img_mask = reduce(np.ma.mask_or,
[chn.mask for chn in next_img.channels])
# Mask overlapping areas away
if lon_limits:
for sat in lon_limits:
if sat in path:
mask_limits = calc_pixel_mask_limits(
area, lon_limits[sat])
for lim in mask_limits:
next_img_mask[:, lim[0]:lim[1]] = 1
break
alpha = np.ones(next_img_mask.shape, dtype='float')
alpha[next_img_mask] = 0.0
if erosion_size is not None and smooth_width is not None:
scaled_erosion_size = erosion_size * (float(img.width) /
1000.0)
scaled_smooth_width = smooth_width * (float(img.width) /
1000.0)
# smooth_alpha = ndi.gaussian_filter(
# ndi.grey_erosion(alpha, size=(scaled_erosion_size,
# scaled_erosion_size)),
# scaled_smooth_sigma)
smooth_alpha = ndi.uniform_filter(
ndi.grey_erosion(alpha,
size=(scaled_erosion_size,
scaled_erosion_size)),
scaled_smooth_width)
smooth_alpha[img_mask] = alpha[img_mask]
else:
smooth_alpha = alpha
for i in range(0, min(len(img.channels), len(next_img.channels))):
chdata = next_img.channels[i].data * smooth_alpha + \
img.channels[i].data * (1 - smooth_alpha)
chmask = np.logical_and(img_mask, next_img_mask)
img.channels[i] = \
np.ma.masked_where(chmask, chdata)
return img
# %% # median filter # img_blur = cv2.medianBlur(img, 3) img_blur = ndimage.median_filter(img, 3) show(img_blur, 'blur') # %% # test with t=128 res = g(img_blur, 128, 256, 0) show(res, 'test') # %% t_ostu = ostu(img_blur) res_ostu = g(img_blur, t_ostu, 256, 0) show(res_ostu, 'ostu') # %% # erosion img_eroded = ndimage.grey_erosion(img, 1) show(img_eroded, 'erosion r = 1') show(g(img_eroded, 128, 256, 0), 'test_erosion r = 1') show(g(img_eroded, ostu(img_eroded), 256, 0), 'ostu_erosion r = 1') img_eroded = ndimage.grey_erosion(img, 3) show(img_eroded, 'erosion r = 3') show(g(img_eroded, 128, 256, 0), 'test_erosion r = 3') show(g(img_eroded, ostu(img_eroded), 256, 0), 'ostu_erosion r = 3') img_eroded = ndimage.grey_erosion(img, 5) show(img_eroded, 'erosion r = 5') show(g(img_eroded, 128, 256, 0), 'test_erosion r = 5') show(g(img_eroded, ostu(img_eroded), 256, 0), 'ostu_erosion r = 5') # %% img_dilated = ndimage.grey_dilation(img, 1) show(img_dilated, 'dilatation r = 1') show(g(img_dilated, 128, 256, 0), 'test_dilatation r = 1')
if counter == 0:
print snapPrefix
counter += 1
model.load_state_dict(saved_state_dict)
class_list = ['cow', 'horse']
pytorch_list = []
for class_ in class_list:
gt_path = args['--testGTpath'] + class_
img_list = next(os.walk(gt_path))[2]
path = sketch_root + class_
for i in img_list:
img = cv2.imread(path + '/' + i)
img = ndimage.grey_erosion(img[:, :, 0].astype(np.uint8),
size=(2, 2))
img = np.repeat(img[:, :, np.newaxis], 3, 2)
gt = cv2.imread(gt_path + '/' + i, 0)
output = model(
Variable(torch.from_numpy(img[np.newaxis, :].transpose(
0, 3, 1, 2)).float(),
volatile=True).cuda(gpu0))
interp = nn.UpsamplingBilinear2d(size=(321, 321))
if args['--visualize']:
output_temp = interp(output[3]).cpu().data[0].numpy()
output_temp = output_temp.transpose(1, 2, 0)
output_temp = np.argmax(output_temp, axis=2)
plt.subplot(1, 3, 1)
plt.imshow(img)
plt.subplot(1, 3, 2)
def main():
structure = np.ones((3, 3, 3))
#.............test1. dense.......
filename = 'gyroidUniform.npy'
input = np.load(filename, mmap_mode="r")
print(
"..............................dense.............................................."
)
#0.Nothing..............
print("\n nothing testing...")
output = vc.nothing(input, blockSize=50, fakeGhost=4, makeFloat32=False)
print("\nresult: ", (input == output).all())
print(output.dtype, input.dtype)
#1.grey_dilation..............
print("\ngrey_dilation VoxelProcessind")
output = vc.grey_dilation(input, structure=structure, makeFloat32=False)
print("\ngrey_dilation Default")
d = ndimage.grey_dilation(input, structure=structure)
print("\nresult: ", (d == output).all())
print(output.dtype, input.dtype)
#2.grey_erosion..............
print("\ngrey_erosion VoxelProcessind")
output = vc.grey_erosion(input,
makeFloat32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\ngrey_erosion Default")
d = ndimage.grey_erosion(input,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nresult: ", (d == output).all())
#3.grey_closing..............
print("\ngrey_closing VoxelProcessind")
output = vc.grey_closing(input,
makeFloat32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\ngrey_closing Default")
d = ndimage.grey_closing(input,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nresult: ", (d == output).all())
print(output.dtype, input.dtype)
#4.grey_opening..............
print("\ngrey_opening VoxelProcessind")
output = vc.grey_opening(input,
makeFloat32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\ngrey_opening Default")
d = ndimage.grey_opening(input,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nresult: ", (d == output).all())
#5.binary_closing..............
print("\nbinary_closing VoxelProcessind")
output = vc.binary_closing(input,
makeFloat32=False,
structure=None,
iterations=1,
output=None,
origin=0,
mask=None,
border_value=0,
brute_force=False)
print("\nbinary_closing Default")
d = ndimage.binary_closing(input,
structure=None,
iterations=1,
output=None,
origin=0,
mask=None,
border_value=0,
brute_force=False)
print("\nresult: ", (d == output).all())
print(output[151][151][151])
#6.binary_opening..............
print("\nbinary_opening VoxelProcessind")
output = vc.binary_opening(input,
structure=None,
iterations=1,
output=None,
origin=0,
mask=None,
border_value=0,
brute_force=False)
print("\nbinary_opening Default")
d = ndimage.binary_opening(input,
structure=None,
iterations=1,
output=None,
origin=0,
mask=None,
border_value=0,
brute_force=False)
print("\nresult: ", (d == output).all())
#7.binary_dilation..............
print("\nbinary_dilation VoxelProcessind")
output = vc.binary_dilation(input,
makeFloat32=False,
structure=structure,
iterations=1,
mask=None,
output=None,
border_value=0,
origin=0,
brute_force=False)
print("\nbinary_dilation Default")
d = ndimage.binary_dilation(input,
structure=structure,
iterations=1,
mask=None,
output=None,
border_value=0,
origin=0,
brute_force=False)
print("\nresult: ", (d == output).all())
#8.binary_erosion..............
print("\nbinary_erosion VoxelProcessind")
output = vc.binary_erosion(input,
makeFloat32=False,
structure=None,
iterations=1,
mask=None,
output=None,
border_value=0,
origin=0,
brute_force=False)
print("\nbinary_erosion Default")
d = ndimage.binary_erosion(input,
structure=None,
iterations=1,
mask=None,
output=None,
border_value=0,
origin=0,
brute_force=False)
print("\nresult: ", (d == output).all())
#9.binary_fill_holes..............
print("\nbinary_fill_holes VoxelProcessind")
output = vc.binary_fill_holes(input,
makeFloat32=False,
structure=None,
output=None,
origin=0)
print("\nbinary_fill_holes Default")
d = ndimage.binary_fill_holes(input, structure=None, output=None, origin=0)
print("\nresult: ", (d == output).all())
#10.binary_hit_or_miss..............
print("\nbinary_hit_or_miss VoxelProcessind")
output = vc.binary_hit_or_miss(input,
makeFloat32=False,
structure1=None,
structure2=None,
output=None,
origin1=0,
origin2=None)
print("\nbinary_hit_or_miss Default")
d = ndimage.binary_hit_or_miss(input,
structure1=None,
structure2=None,
output=None,
origin1=0,
origin2=None)
print("\nresult: ", (d == output).all())
#11.binary_propagation..............
print("\nbinary_propagation VoxelProcessind")
output = vc.binary_propagation(input,
makeFloat32=False,
structure=None,
mask=None,
output=None,
border_value=0,
origin=0)
print("\nbinary_propagation Default")
d = ndimage.binary_propagation(input,
structure=None,
mask=None,
output=None,
border_value=0,
origin=0)
print("\nresult: ", (d == output).all())
#12.black_tophat..............
print("\nblack_tophat VoxelProcessind")
output = vc.black_tophat(input,
makeFloat32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nblack_tophat Default")
d = ndimage.black_tophat(input,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nresult: ", (d == output).all())
#13.morphological_gradient..............
print("\nmorphological_gradient VoxelProcessind")
output = vc.morphological_gradient(input,
makeFloat32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nmorphological_gradient Default")
d = ndimage.morphological_gradient(input,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nresult: ", (d == output).all())
#14.morphological_laplace..............
print("\nmorphological_laplace VoxelProcessind")
output = vc.morphological_laplace(input,
makeFloat32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nmorphological_laplace Default")
d = ndimage.morphological_laplace(input,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nresult: ", (d == output).all())
#15.white_tophat..............
print("\nwhite_tophat VoxelProcessind")
output = vc.white_tophat(input,
makeFloat32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nwhite_tophat VoxelProcessind Default")
d = ndimage.white_tophat(input,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("\nresult: ", (d == output).all())
#16.intMultiply..............
print("\nintMultiply VoxelProcessind")
output = vc.intMultiply(input,
makeFloat32=False,
blockSize=50,
fakeGhost=1,
scalar=10)
print("\nintMultiply Default")
d = input * 10
print("\nresult: ", (d == output).all())
print(
"..............................Sparse.............................................."
)
input = random(400, 80000, density=0.3, dtype="float64")
input = input.todense()
input = np.array(input)
input = np.reshape(input, (400, 200, 400))
#0.Nothing..............
print("\n nothing testing...")
output = vc.nothing(input, makeFloat32=False)
print("\nresult: ", (input == output).all())
print(output.dtype, input.dtype)
#1.grey_dilation..............
print("\ngrey_dilation VoxelProcessind")
output = vc.grey_dilation(input, structure=structure, makeFloat32=False)
print("\ngrey_dilation Default")
d = ndimage.grey_dilation(input, structure=structure)
print("\nresult: ", (d == output).all())
print(output.dtype, input.dtype)
#2.grey_erosion..............
print("\ngrey_erosion VoxelProcessind")
output = vc.grey_erosion(input, makeFloat32=False, structure=structure)
print("\ngrey_erosion Default")
d = ndimage.grey_erosion(input, structure=structure)
print("\nresult: ", (d == output).all())
#3.grey_closing..............
print("\ngrey_closing VoxelProcessind")
output = vc.grey_closing(input, makeFloat32=False, structure=structure)
print("\ngrey_closing Default")
d = ndimage.grey_closing(input, structure=structure)
print("\nresult: ", (d == output).all())
print(output.dtype, input.dtype)
#4.grey_opening..............
print("\ngrey_opening VoxelProcessind")
output = vc.grey_opening(input, makeFloat32=False, structure=structure)
print("\ngrey_opening Default")
d = ndimage.grey_opening(input, structure=structure)
print("\nresult: ", (d == output).all())
#5.binary_closing..............
print("\nbinary_closing VoxelProcessind")
output = vc.binary_closing(input, makeFloat32=False)
print("\nbinary_closing Default")
d = ndimage.binary_closing(input)
print("\nresult: ", (d == output).all())
#6.binary_opening..............
print("\nbinary_opening VoxelProcessind")
output = vc.binary_opening(input, makeFloat32=False)
print("\nbinary_opening Default")
d = ndimage.binary_opening(input)
print("\nresult: ", (d == output).all())
#7.binary_dilation..............
print("\nbinary_dilation VoxelProcessind")
output = vc.binary_dilation(input, makeFloat32=False, structure=structure)
print("\nbinary_dilation Default")
d = ndimage.binary_dilation(input, structure=structure)
print("\nresult: ", (d == output).all())
#8.binary_erosion..............
print("\nbinary_erosion VoxelProcessind")
output = vc.binary_erosion(input, makeFloat32=False)
print("\nbinary_erosion Default")
d = ndimage.binary_erosion(input)
print("\nresult: ", (d == output).all())
#9.binary_fill_holes..............
print("\nbinary_fill_holes VoxelProcessind")
output = vc.binary_fill_holes(input, makeFloat32=False)
print("\nbinary_fill_holes Default")
d = ndimage.binary_fill_holes(input)
print("\nresult: ", (d == output).all())
#10.binary_hit_or_miss..............
print("\nbinary_hit_or_miss VoxelProcessind")
output = vc.binary_hit_or_miss(input, makeFloat32=False)
print("\nbinary_hit_or_miss Default")
d = ndimage.binary_hit_or_miss(input)
print("\nresult: ", (d == output).all())
#11.binary_propagation..............
print("\nbinary_propagation VoxelProcessind")
output = vc.binary_propagation(input, makeFloat32=False)
print("\nbinary_propagation Default")
d = ndimage.binary_propagation(input)
print("\nresult: ", (d == output).all())
#12.black_tophat..............
print("\nblack_tophat VoxelProcessind")
output = vc.black_tophat(input, makeFloat32=False, structure=structure)
print("\nblack_tophat Default")
d = ndimage.black_tophat(input, structure=structure)
print("\nresult: ", (d == output).all())
#13.morphological_gradient..............
print("\nmorphological_gradient VoxelProcessind")
output = vc.morphological_gradient(
input,
structure=structure,
makeFloat32=False,
)
print("\nmorphological_gradient Default")
d = ndimage.morphological_gradient(input, structure=structure)
print("\nresult: ", (d == output).all())
#14.morphological_laplace..............
print("\nmorphological_laplace VoxelProcessind")
output = vc.morphological_laplace(input,
structure=structure,
makeFloat32=False)
print("\nmorphological_laplace Default")
d = ndimage.morphological_laplace(input, structure=structure)
print("\nresult: ", (d == output).all())
#15.white_tophat..............
print("\nwhite_tophat VoxelProcessind")
output = vc.white_tophat(input, makeFloat32=False, structure=structure)
print("\nwhite_tophat VoxelProcessind Default")
d = ndimage.white_tophat(input, structure=structure)
print("\nresult: ", (d == output).all())
#16.intMultiply..............
print("\nintMultiply VoxelProcessind")
output = vc.intMultiply(input, makeFloat32=False, scalar=10)
print("\nintMultiply Default")
d = input * 10
print("\nresult: ", (d == output).all())
limiti_base = np.empty(4)
limiti_picco = np.empty(4)
#
for prova in N_prove:
flag_selezione_roi = True
for file_corrente in lista_file:
print(file_corrente[-21:-4])
mappa = np.load(file_corrente)[0, :, :]
if flag_selezione_roi:
(limiti_base, limiti_picco) = seleziona_cordinate_mista(mappa)
flag_selezione_roi = False # prendo solo la prima
if True:
mappa = ndimage.gaussian_filter(mappa, sigma=10)
footprint = np.matrix([[1, 1, 1], [1, 2, 1], [1, 1, 1]])
mappa = ndimage.grey_dilation(mappa, footprint=footprint)
mappa = ndimage.grey_erosion(mappa, footprint=footprint)
plt.imshow(mappa)
plt.show()
media_base = np.mean(mappa[limiti_base[0]:limiti_base[1],
limiti_base[2]:limiti_base[3]])
std_base = np.std(mappa[limiti_base[0]:limiti_base[1],
limiti_base[2]:limiti_base[3]])
media_picco = np.mean(mappa[limiti_picco[0]:limiti_picco[1],
limiti_picco[2]:limiti_picco[3]])
std_picco = np.std(mappa[limiti_picco[0]:limiti_picco[1],
limiti_picco[2]:limiti_picco[3]])
_, ax = plt.subplots()
ax.imshow(mappa[limiti_picco[0]:limiti_picco[1],
limiti_picco[2]:limiti_picco[3]],
cmap='inferno')
plt.show()
def run(self, workspace):
'''Run the module on the image set'''
seed_objects_name = self.seed_objects_name.value
skeleton_name = self.image_name.value
seed_objects = workspace.object_set.get_objects(seed_objects_name)
labels = seed_objects.segmented
labels_count = np.max(labels)
label_range = np.arange(labels_count, dtype=np.int32) + 1
skeleton_image = workspace.image_set.get_image(skeleton_name,
must_be_binary=True)
skeleton = skeleton_image.pixel_data
if skeleton_image.has_mask:
skeleton = skeleton & skeleton_image.mask
try:
labels = skeleton_image.crop_image_similarly(labels)
except:
labels, m1 = cpo.size_similarly(skeleton, labels)
labels[~m1] = 0
#
# The following code makes a ring around the seed objects with
# the skeleton trunks sticking out of it.
#
# Create a new skeleton with holes at the seed objects
# First combine the seed objects with the skeleton so
# that the skeleton trunks come out of the seed objects.
#
# Erode the labels once so that all of the trunk branchpoints
# will be within the labels
#
#
# Dilate the objects, then subtract them to make a ring
#
my_disk = morph.strel_disk(1.5).astype(int)
dilated_labels = grey_dilation(labels, footprint=my_disk)
seed_mask = dilated_labels > 0
combined_skel = skeleton | seed_mask
closed_labels = grey_erosion(dilated_labels, footprint=my_disk)
seed_center = closed_labels > 0
combined_skel = combined_skel & (~seed_center)
#
# Fill in single holes (but not a one-pixel hole made by
# a one-pixel image)
#
if self.wants_to_fill_holes:
def size_fn(area, is_object):
return (~is_object) and (area <= self.maximum_hole_size.value)
combined_skel = morph.fill_labeled_holes(combined_skel,
~seed_center, size_fn)
#
# Reskeletonize to make true branchpoints at the ring boundaries
#
combined_skel = morph.skeletonize(combined_skel)
#
# The skeleton outside of the labels
#
outside_skel = combined_skel & (dilated_labels == 0)
#
# Associate all skeleton points with seed objects
#
dlabels, distance_map = propagate.propagate(np.zeros(labels.shape),
dilated_labels,
combined_skel, 1)
#
# Get rid of any branchpoints not connected to seeds
#
combined_skel[dlabels == 0] = False
#
# Find the branchpoints
#
branch_points = morph.branchpoints(combined_skel)
#
# Odd case: when four branches meet like this, branchpoints are not
# assigned because they are arbitrary. So assign them.
#
# . .
# B.
# .B
# . .
#
odd_case = (combined_skel[:-1, :-1] & combined_skel[1:, :-1]
& combined_skel[:-1, 1:] & combined_skel[1, 1])
branch_points[:-1, :-1][odd_case] = True
branch_points[1:, 1:][odd_case] = True
#
# Find the branching counts for the trunks (# of extra branches
# eminating from a point other than the line it might be on).
#
branching_counts = morph.branchings(combined_skel)
branching_counts = np.array([0, 0, 0, 1, 2])[branching_counts]
#
# Only take branches within 1 of the outside skeleton
#
dilated_skel = scind.binary_dilation(outside_skel, morph.eight_connect)
branching_counts[~dilated_skel] = 0
#
# Find the endpoints
#
end_points = morph.endpoints(combined_skel)
#
# We use two ranges for classification here:
# * anything within one pixel of the dilated image is a trunk
# * anything outside of that range is a branch
#
nearby_labels = dlabels.copy()
nearby_labels[distance_map > 1.5] = 0
outside_labels = dlabels.copy()
outside_labels[nearby_labels > 0] = 0
#
# The trunks are the branchpoints that lie within one pixel of
# the dilated image.
#
if labels_count > 0:
trunk_counts = fix(
scind.sum(branching_counts, nearby_labels,
label_range)).astype(int)
else:
trunk_counts = np.zeros((0, ), int)
#
# The branches are the branchpoints that lie outside the seed objects
#
if labels_count > 0:
branch_counts = fix(
scind.sum(branch_points, outside_labels, label_range))
else:
branch_counts = np.zeros((0, ), int)
#
# Save the endpoints
#
if labels_count > 0:
end_counts = fix(scind.sum(end_points, outside_labels,
label_range))
else:
end_counts = np.zeros((0, ), int)
#
# Save measurements
#
m = workspace.measurements
assert isinstance(m, cpmeas.Measurements)
feature = "_".join((C_NEURON, F_NUMBER_TRUNKS, skeleton_name))
m.add_measurement(seed_objects_name, feature, trunk_counts)
feature = "_".join(
(C_NEURON, F_NUMBER_NON_TRUNK_BRANCHES, skeleton_name))
m.add_measurement(seed_objects_name, feature, branch_counts)
feature = "_".join((C_NEURON, F_NUMBER_BRANCH_ENDS, skeleton_name))
m.add_measurement(seed_objects_name, feature, end_counts)
#
# Collect the graph information
#
if self.wants_neuron_graph:
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
intensity_image = workspace.image_set.get_image(
self.intensity_image_name.value)
edge_graph, vertex_graph = self.make_neuron_graph(
combined_skel, dlabels, trunk_mask,
branch_points & ~trunk_mask, end_points,
intensity_image.pixel_data)
image_number = workspace.measurements.image_set_number
edge_path, vertex_path = self.get_graph_file_paths(
m, m.image_number)
workspace.interaction_request(self,
m.image_number,
edge_path,
edge_graph,
vertex_path,
vertex_graph,
headless_ok=True)
if self.show_window:
workspace.display_data.edge_graph = edge_graph
workspace.display_data.vertex_graph = vertex_graph
workspace.display_data.intensity_image = intensity_image.pixel_data
#
# Make the display image
#
if self.show_window or self.wants_branchpoint_image:
branchpoint_image = np.zeros(
(skeleton.shape[0], skeleton.shape[1], 3))
trunk_mask = (branching_counts > 0) & (nearby_labels != 0)
branch_mask = branch_points & (outside_labels != 0)
end_mask = end_points & (outside_labels != 0)
branchpoint_image[outside_skel, :] = 1
branchpoint_image[trunk_mask | branch_mask | end_mask, :] = 0
branchpoint_image[trunk_mask, 0] = 1
branchpoint_image[branch_mask, 1] = 1
branchpoint_image[end_mask, 2] = 1
branchpoint_image[dilated_labels != 0, :] *= .875
branchpoint_image[dilated_labels != 0, :] += .1
if self.show_window:
workspace.display_data.branchpoint_image = branchpoint_image
if self.wants_branchpoint_image:
bi = cpi.Image(branchpoint_image, parent_image=skeleton_image)
workspace.image_set.add(self.branchpoint_image_name.value, bi)
def apply_math_morphologie(input_image,
morph_type,
morph_op='binary',
size=3,
show_result=False):
"""
Apply a mathematical morphologie to a image.
Parameters
----------
input_image : nparray
Represents the image that you want to apply the morphologie
morph_type: str
Must be one of:
'erosion',
'dilation',
'opening',
'closing',
'propagation',
'reconstruction',
'open/close',
'full_reconstruction'
morph_op: str
'binary' or 'grey', representing the two types of supported image
size: int
The morphologie structure is a matrix of ones with sizeXsize
show_result: Boolean
If True, the result is plotted using matplotlib, default is False.
Returns
-------
nparray
The image after morphologia, as the same format of the input.
Note
----
Some configurations may not exit.
They are:
grey propagation
grey reconstruction
grey full_reconstruction
"""
if not morph_type in mathematical_morphologies_names:
raise (NotImplemented)
if not morph_op in mathematical_morphologies_options:
raise (NotImplemented)
if morph_op == 'binary':
if morph_type == 'erosion':
output_image = ndimage.binary_erosion(input_image,
structure=np.ones(
(size, size)))
elif morph_type == 'dilation':
output_image = ndimage.binary_dilation(input_image,
structure=np.ones(
(size, size)))
elif morph_type == 'opening':
output_image = ndimage.binary_opening(input_image,
structure=np.ones(
(size, size)))
elif morph_type == 'closing':
output_image = ndimage.binary_closing(input_image,
structure=np.ones(
(size, size)))
elif morph_type == 'propagation':
output_image = ndimage.binary_propagation(input_image,
structure=np.ones(
(size, size)))
elif morph_type == 'reconstruction':
eroded_img = ndimage.binary_erosion(input_image,
structure=np.ones(
(size, size)))
output_image = ndimage.binary_propagation(eroded_img,
structure=np.ones(
(size, size)),
mask=input_image)
elif morph_type == 'open/close':
open_img = ndimage.binary_opening(
input_image, structure=np.ones(
(size, size))) # Remove small white regions
output_image = ndimage.binary_closing(
open_img, structure=np.ones(
(size, size))) # Remove small black hole
elif morph_type == 'full_reconstruction':
eroded_img = ndimage.binary_erosion(input_image,
structure=np.ones(
(size, size)))
reconstruct_img = ndimage.binary_propagation(eroded_img,
structure=np.ones(
(size, size)),
mask=input_image)
tmp = np.logical_not(reconstruct_img)
eroded_tmp = ndimage.binary_erosion(tmp,
structure=np.ones(
(size, size)))
output_image = np.logical_not(
ndimage.binary_propagation(eroded_tmp,
structure=np.ones((size, size)),
mask=tmp))
elif morph_op == 'grey':
if morph_type == 'erosion':
output_image = ndimage.grey_erosion(input_image, size=size)
elif morph_type == 'dilation':
output_image = ndimage.grey_dilation(input_image, size=size)
elif morph_type == 'opening':
output_image = ndimage.grey_opening(input_image, size=size)
elif morph_type == 'closing':
output_image = ndimage.grey_closing(input_image, size=size)
elif morph_type == 'propagation':
raise (NotImplemented)
elif morph_type == 'reconstruction':
raise (NotImplemented)
elif morph_type == 'open/close':
open_img = ndimage.grey_opening(
input_image, size=size) # Remove small white regions
output_image = ndimage.grey_closing(
open_img, size=size) # Remove small black hole
elif morph_type == 'full_reconstruction':
raise (NotImplemented)
if show_result:
show_images_and_hists(
[input_image, output_image],
titles=[
'Input',
'Output Image - %s%s\n%s - %s' %
("Morph: ", morph_type, str(morph_op), str(size))
],
colorbar=True)
output_image = output_image.astype(input_image.dtype) # input format
return output_image
a += 0.25 * np.random.standard_normal(a.shape)
print("a======>", a)
mask = a >= 0.5
print("mask=====>", mask)
opened_mask = ndimage.binary_opening(mask)
print("opened_mask=====>", opened_mask)
closed_mask = ndimage.binary_closing(opened_mask)
print("closed_mask=====>", closed_mask)
a = np.zeros((7, 7), dtype=np.int)
a[1:6, 1:6] = 3
a[4, 4] = 2
a[2, 3] = 1
print("a=====>", a)
c = ndimage.grey_erosion(a, size=(3, 3))
print("c=====>", c)
print("==== 图像测量 ====")
x, y = np.indices((300, 300))
sig = np.sin(2 * np.pi * x / 50.) * np.sin(
2 * np.pi * y / 50.) * (1 + x * y / 50.**2)**2
mask = sig > 1
#现在我们查找图像中对象的各种信息：
labels, nb = ndimage.label(mask)
print("labels====>", labels)
print("nb====>", nb)
areas = ndimage.sum(mask, labels, range(1, labels.max() + 1))
print("areas====>", areas)
# <codecell>
import numpy as np
image = np.random.random((512, 512))
footprint = np.array([[0, 1, 0],
[1, 1, 1],
[0, 1, 0]], dtype=bool)
# <codecell>
from scipy import ndimage as ndi
%timeit ndi.grey_erosion(image, footprint=footprint)
# <codecell>
%timeit ndi.generic_filter(image, np.min, footprint=footprint)
# <codecell>
f'Slowdown is {825 / 2.85} times'
def rag_boundary(labels, edge_map, connectivity=2):
""" Comouter RAG based on region boundaries
Given an image's initial segmentation and its edge map this method
constructs the corresponding Region Adjacency Graph (RAG). Each node in the
RAG represents a set of pixels within the image with the same label in
`labels`. The weight between two adjacent regions is the average value
in `edge_map` along their boundary.
labels : ndarray
The labelled image.
edge_map : ndarray
This should have the same shape as that of `labels`. For all pixels
along the boundary between 2 adjacent regions, the average value of the
corresponding pixels in `edge_map` is the edge weight between them.
connectivity : int, optional
Pixels with a squared distance less than `connectivity` from each other
are considered adjacent. It can range from 1 to `labels.ndim`. Its
behavior is the same as `connectivity` parameter in
`scipy.ndimage.filters.generate_binary_structure`.
Examples
--------
>>> from skimage import data, segmentation, filters, color
>>> from skimage.future import graph
>>> img = data.chelsea()
>>> labels = segmentation.slic(img)
>>> edge_map = filters.sobel(color.rgb2gray(img))
>>> rag = graph.rag_boundary(labels, edge_map)
"""
conn = ndi.generate_binary_structure(labels.ndim, connectivity)
eroded = ndi.grey_erosion(labels, footprint=conn)
dilated = ndi.grey_dilation(labels, footprint=conn)
boundaries0 = (eroded != labels)
boundaries1 = (dilated != labels)
labels_small = np.concatenate((eroded[boundaries0], labels[boundaries1]))
labels_large = np.concatenate((labels[boundaries0], dilated[boundaries1]))
n = np.max(labels_large) + 1
# use a dummy broadcast array as data for RAG
ones = as_strided(np.ones((1,), dtype=np.float), shape=labels_small.shape,
strides=(0,))
count_matrix = sparse.coo_matrix((ones, (labels_small, labels_large)),
dtype=np.int_, shape=(n, n)).tocsr()
data = np.concatenate((edge_map[boundaries0], edge_map[boundaries1]))
data_coo = sparse.coo_matrix((data, (labels_small, labels_large)))
graph_matrix = data_coo.tocsr()
graph_matrix.data /= count_matrix.data
rag = RAG()
rag.add_weighted_edges_from(_edge_generator_from_csr(graph_matrix),
weight='weight')
rag.add_weighted_edges_from(_edge_generator_from_csr(count_matrix),
weight='count')
for n in rag.nodes():
rag.node[n].update({'labels': [n]})
return rag
d = ndimage.grey_dilation(input_var, structure=structure)
print("scipy grey_dilation: ", (t.time() - start_time), " sec")
print("\nresult: ", (d == output).all())
#2.grey_erosion..............
print("\ngrey_erosion VoxelProcessing")
start_time = t.time()
output = vc.grey_erosion(input_var,
no_of_blocks=7,
make_float32=False,
structure=structure)
print("vc grey_erosion: ", (t.time() - start_time), " sec")
print("\ngrey_erosion Default")
start_time = t.time()
d = ndimage.grey_erosion(
input_var,
structure=structure,
)
print("scipy grey_erosion: ", (t.time() - start_time), " sec")
print("\nresult: ", (d == output).all())
#3.grey_closing..............
print("\ngrey_closing VoxelProcessing")
start_time = t.time()
output = vc.grey_closing(input_var,
make_float32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
def soc_adaptation_iter(
modnet, backup_modnet, optimizer, image,
soc_semantic_scale=100.0, soc_detail_scale=1.0):
""" Self-Supervised sub-objective consistency (SOC) adaptation iteration of MODNet
This function fine-tunes MODNet for one iteration in an unlabeled dataset.
Note that SOC can only fine-tune a converged MODNet, i.e., MODNet that has been
trained in a labeled dataset.
Arguments:
modnet (torch.nn.Module): instance of MODNet
backup_modnet (torch.nn.Module): backup of the trained MODNet
optimizer (torch.optim.Optimizer): optimizer for self-supervised SOC
image (torch.autograd.Variable): input RGB image
its pixel values should be normalized
soc_semantic_scale (float): scale of the SOC semantic loss
NOTE: please adjust according to your dataset
soc_detail_scale (float): scale of the SOC detail loss
NOTE: please adjust according to your dataset
Returns:
soc_semantic_loss (torch.Tensor): loss of the semantic SOC
soc_detail_loss (torch.Tensor): loss of the detail SOC
Example:
import copy
import torch
from src.models.modnet import MODNet
from src.trainer import soc_adaptation_iter
bs = 1 # batch size
lr = 0.00001 # learn rate
epochs = 10 # total epochs
modnet = torch.nn.DataParallel(MODNet()).cuda()
modnet = LOAD_TRAINED_CKPT() # NOTE: please finish this function
optimizer = torch.optim.Adam(modnet.parameters(), lr=lr, betas=(0.9, 0.99))
dataloader = CREATE_YOUR_DATALOADER(bs) # NOTE: please finish this function
for epoch in range(0, epochs):
backup_modnet = copy.deepcopy(modnet)
for idx, (image) in enumerate(dataloader):
soc_semantic_loss, soc_detail_loss = \
soc_adaptation_iter(modnet, backup_modnet, optimizer, image)
"""
global blurer
# set the backup model to eval mode
backup_modnet.eval()
# set the main model to train mode and freeze its norm layers
modnet.train()
modnet.module.freeze_norm()
# clear the optimizer
optimizer.zero_grad()
# forward the main model
pred_semantic, pred_detail, pred_matte = modnet(image, False)
# forward the backup model
with torch.no_grad():
_, pred_backup_detail, pred_backup_matte = backup_modnet(image, False)
# calculate the boundary mask from `pred_matte` and `pred_semantic`
pred_matte_fg = (pred_matte.detach() > 0.1).float()
pred_semantic_fg = (pred_semantic.detach() > 0.1).float()
pred_semantic_fg = F.interpolate(pred_semantic_fg, scale_factor=16, mode='bilinear')
pred_fg = pred_matte_fg * pred_semantic_fg
n, c, h, w = pred_matte.shape
np_pred_fg = pred_fg.data.cpu().numpy()
np_boundaries = np.zeros([n, c, h, w])
for sdx in range(0, n):
sample_np_boundaries = np_boundaries[sdx, 0, ...]
sample_np_pred_fg = np_pred_fg[sdx, 0, ...]
side = int((h + w) / 2 * 0.05)
dilated = grey_dilation(sample_np_pred_fg, size=(side, side))
eroded = grey_erosion(sample_np_pred_fg, size=(side, side))
sample_np_boundaries[np.where(dilated - eroded != 0)] = 1
np_boundaries[sdx, 0, ...] = sample_np_boundaries
boundaries = torch.tensor(np_boundaries).float().cuda()
# sub-objectives consistency between `pred_semantic` and `pred_matte`
# generate pseudo ground truth for `pred_semantic`
downsampled_pred_matte = blurer(F.interpolate(pred_matte, scale_factor=1/16, mode='bilinear'))
pseudo_gt_semantic = downsampled_pred_matte.detach()
pseudo_gt_semantic = pseudo_gt_semantic * (pseudo_gt_semantic > 0.01).float()
# generate pseudo ground truth for `pred_matte`
pseudo_gt_matte = pred_semantic.detach()
pseudo_gt_matte = pseudo_gt_matte * (pseudo_gt_matte > 0.01).float()
# calculate the SOC semantic loss
soc_semantic_loss = F.mse_loss(pred_semantic, pseudo_gt_semantic) + F.mse_loss(downsampled_pred_matte, pseudo_gt_matte)
soc_semantic_loss = soc_semantic_scale * torch.mean(soc_semantic_loss)
# NOTE: using the formulas in our paper to calculate the following losses has similar results
# sub-objectives consistency between `pred_detail` and `pred_backup_detail` (on boundaries only)
backup_detail_loss = boundaries * F.l1_loss(pred_detail, pred_backup_detail)
backup_detail_loss = torch.sum(backup_detail_loss, dim=(1,2,3)) / torch.sum(boundaries, dim=(1,2,3))
backup_detail_loss = torch.mean(backup_detail_loss)
# sub-objectives consistency between pred_matte` and `pred_backup_matte` (on boundaries only)
backup_matte_loss = boundaries * F.l1_loss(pred_matte, pred_backup_matte)
backup_matte_loss = torch.sum(backup_matte_loss, dim=(1,2,3)) / torch.sum(boundaries, dim=(1,2,3))
backup_matte_loss = torch.mean(backup_matte_loss)
soc_detail_loss = soc_detail_scale * (backup_detail_loss + backup_matte_loss)
# calculate the final loss, backward the loss, and update the model
loss = soc_semantic_loss + soc_detail_loss
loss.backward()
optimizer.step()
return soc_semantic_loss, soc_detail_loss
def _raw_to_displayed(self, raw):
"""Determine displayed image from a saved raw image and a saved seed.
This function ensures that the 0 label gets mapped to the 0 displayed
pixel.
Parameters
----------
raw : array or int
Raw integer input image.
Returns
-------
image : array
Image mapped between 0 and 1 to be displayed.
"""
if (not self.show_selected_label
and self._color_mode == LabelColorMode.DIRECT):
u, inv = np.unique(raw, return_inverse=True)
image = np.array([
self._label_color_index[x] if x in self._label_color_index else
self._label_color_index[None] for x in u
])[inv].reshape(raw.shape)
elif (not self.show_selected_label
and self._color_mode == LabelColorMode.AUTO):
image = np.where(raw > 0, low_discrepancy_image(raw, self._seed),
0)
elif (self.show_selected_label
and self._color_mode == LabelColorMode.AUTO):
selected = self._selected_label
image = np.where(
raw == selected,
low_discrepancy_image(selected, self._seed),
0,
)
elif (self.show_selected_label
and self._color_mode == LabelColorMode.DIRECT):
selected = self._selected_label
if selected not in self._label_color_index:
selected = None
index = self._label_color_index
image = np.where(
raw == selected,
index[selected],
np.where(
raw != self._background_label,
index[None],
index[self._background_label],
),
)
else:
raise ValueError("Unsupported Color Mode")
if self.contour > 0 and raw.ndim == 2:
image = np.zeros_like(raw)
struct_elem = ndi.generate_binary_structure(raw.ndim, 1)
thickness = self.contour
thick_struct_elem = ndi.iterate_structure(struct_elem,
thickness).astype(bool)
boundaries = ndi.grey_dilation(
raw, footprint=struct_elem) != ndi.grey_erosion(
raw, footprint=thick_struct_elem)
image[boundaries] = raw[boundaries]
image = np.where(image > 0,
low_discrepancy_image(image, self._seed), 0)
elif self.contour > 0 and raw.ndim > 2:
warnings.warn("Contours are not displayed during 3D rendering")
return image
def _create_world_composite(items, lon_limits=None,
erosion_size=20,
smooth_width=20):
# smooth_sigma = 4
img = None
for (path, area, timeslot) in items:
if not isinstance(area, AreaDefinition):
area = get_area_def(area)
next_img = read_image(path, area, timeslot)
if img is None:
img = next_img
else:
# scaled_smooth_sigma = smooth_sigma * (float(img.width) / 1000.0)
img_mask = reduce(np.ma.mask_or,
[chn.mask for chn in img.channels])
next_img_mask = reduce(np.ma.mask_or,
[chn.mask for chn in next_img.channels])
# Mask overlapping areas away
if lon_limits:
for sat in lon_limits:
if sat in path:
mask_limits = calc_pixel_mask_limits(area,
lon_limits[sat])
for lim in mask_limits:
next_img_mask[:, lim[0]:lim[1]] = 1
break
alpha = np.ones(next_img_mask.shape, dtype='float')
alpha[next_img_mask] = 0.0
if erosion_size is not None and smooth_width is not None:
scaled_erosion_size = erosion_size * (float(img.width) /
1000.0)
scaled_smooth_width = smooth_width * (float(img.width) /
1000.0)
# smooth_alpha = ndi.gaussian_filter(
# ndi.grey_erosion(alpha, size=(scaled_erosion_size,
# scaled_erosion_size)),
# scaled_smooth_sigma)
smooth_alpha = ndi.uniform_filter(
ndi.grey_erosion(alpha, size=(scaled_erosion_size,
scaled_erosion_size)),
scaled_smooth_width)
smooth_alpha[img_mask] = alpha[img_mask]
else:
smooth_alpha = alpha
for i in range(0, min(len(img.channels), len(next_img.channels))):
chdata = next_img.channels[i].data * smooth_alpha + \
img.channels[i].data * (1 - smooth_alpha)
chmask = np.logical_and(img_mask, next_img_mask)
img.channels[i] = \
np.ma.masked_where(chmask, chdata)
return img
no_of_blocks=7,
make_float32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("vc grey_erosion: ", (t.time() - start_time), " sec")
print("\ngrey_erosion Default")
start_time = t.time()
d = ndimage.grey_erosion(input_var,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
cval=0.0,
origin=0)
print("scipy grey_erosion: ", (t.time() - start_time), " sec")
print("\nresult: ", (d == output).all())
#3.grey_closing..............
print("\ngrey_closing VoxelProcessing")
start_time = t.time()
output = vc.grey_closing(input_var,
make_float32=False,
size=None,
footprint=None,
structure=structure,
output=None,
mode='reflect',
def _raw_to_displayed(self, raw):
"""Determine displayed image from a saved raw image and a saved seed.
This function ensures that the 0 label gets mapped to the 0 displayed
pixel.
Parameters
----------
raw : array or int
Raw integer input image.
Returns
-------
image : array
Image mapped between 0 and 1 to be displayed.
"""
if raw.dtype == bool:
raw = raw.view(dtype=np.uint8)
if (not self.show_selected_label
and self._color_mode == LabelColorMode.DIRECT):
u, inv = np.unique(raw, return_inverse=True)
image = np.array([
self._label_color_index[x] if x in self._label_color_index else
self._label_color_index[None] for x in u
])[inv].reshape(raw.shape)
elif (not self.show_selected_label
and self._color_mode == LabelColorMode.AUTO):
try:
image = self._all_vals[raw]
except IndexError:
max_val = np.max(raw)
self._all_vals = low_discrepancy_image(np.arange(max_val + 1),
self._seed)
self._all_vals[0] = 0
image = self._all_vals[raw]
elif (self.show_selected_label
and self._color_mode == LabelColorMode.AUTO):
selected_color = low_discrepancy_image(self._selected_label,
self._seed)
if self.selected_label > len(self._all_vals):
self._all_vals = low_discrepancy_image(
np.arange(self.selected_label + 1), self._seed)
colors = np.zeros(len(self._all_vals))
colors[self.selected_label] = selected_color
image = colors[raw]
elif (self.show_selected_label
and self._color_mode == LabelColorMode.DIRECT):
selected = self._selected_label
if selected not in self._label_color_index:
selected = None
index = self._label_color_index
image = np.where(
raw == selected,
index[selected],
np.where(
raw != self._background_label,
index[None],
index[self._background_label],
),
)
else:
raise ValueError("Unsupported Color Mode")
if self.contour > 0 and raw.ndim == 2:
image = np.zeros_like(raw)
struct_elem = ndi.generate_binary_structure(raw.ndim, 1)
thickness = self.contour
thick_struct_elem = ndi.iterate_structure(struct_elem,
thickness).astype(bool)
boundaries = ndi.grey_dilation(
raw, footprint=struct_elem) != ndi.grey_erosion(
raw, footprint=thick_struct_elem)
image[boundaries] = raw[boundaries]
image = self._all_vals[image]
elif self.contour > 0 and raw.ndim > 2:
warnings.warn(
trans._(
"Contours are not displayed during 3D rendering",
deferred=True,
))
return image
import os, sys, scipy
from scipy import ndimage
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
inFile = ''
outFile = ''
if len(sys.argv) != 3:
print('Input format error!')
else:
inFile = sys.argv[1]
outFile = sys.argv[2]
#im = ndimage.imread(inFile)
#im = ndimage.binary_erosion(im).astype(np.float32)
#scipy.misc.imsave(outFile, im)
#im2 = Image.open(inFile)
#im2 = ndimage.binary_dilation(im2)
im = scipy.misc.imread(inFile, flatten=True).astype(np.uint8)
im2 = ndimage.grey_erosion(im, size=(100, 10))
scipy.misc.imsave(outFile, im2)
def f(image_config):
s1 = random.randrange(func_config.min_size, func_config.max_size + 1)
s2 = random.randrange(func_config.min_size, func_config.max_size + 1)
array = ndimage.grey_erosion(image_config.image, size = (s1, s2))
image = Image.fromarray(array)
image_config.image = image
def slow_fill(input_array, four_way=False):
"""
Slow flood fill depressions/sinks in floating point array
Parameters
----------
input_array : ndarray
Input array to be filled
four_way : bool, optional
If True, search 4 immediately adjacent cells (cross structuring element)
If False, search all 8 adjacent cells (square structuring element).
The Default is False.
Returns
-------
out : ndarray
Filled array
References
----------
Soile, P., Vogt, J., and Colombo, R., 2003. Carving and Adaptive Drainage Enforcement of Grid Digital Elevation Models. Water Resources Research, 39(12), 1366
Soille, P., 1999. Morphological Image Analysis: Principles and Applications, Springer-Verlag, pp. 173-174
"""
print('Slow Fill')
# Rename or copy input so that input_array is a local variable?
# input_array = np.copy(input_array)
# Set h_max to a value larger than the array maximum to ensure
# that the while loop will terminate
h_max = np.max(input_array * 2.0)
# Build mask of cells with data not on the edge of the image
# Use 3x3 square Structuring element
# Build Structuring element only using NumPy module
# Structuring element could also be built using SciPy ndimage module
# el = ndimage.generate_binary_structure(2,2).astype(np.int)
inside_mask = ndimage.binary_erosion(
np.isfinite(input_array),
structure=np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]).astype(np.bool))
# Initialize output array as max value test_array except edges
output_array = np.copy(input_array)
output_array[inside_mask] = h_max
# Array for storing previous iteration
output_old_array = np.copy(input_array)
output_old_array[:] = 0
# Cross structuring element
if four_way:
el = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]]).astype(np.bool)
# el = ndimage.generate_binary_structure(2, 1).astype(np.int)
else:
el = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]).astype(np.bool)
# el = ndimage.generate_binary_structure(2, 2).astype(np.int)
# Iterate until marker array doesn't change
while not np.array_equal(output_old_array, output_array):
output_old_array = np.copy(output_array)
output_array = np.maximum(
input_array,
ndimage.grey_erosion(output_array, size=(3, 3), footprint=el))
return output_array