def grey_processing(inputImg):
# fp = [[0, 1, 0], [1, 1, 1], [0, 1, 0]]
fp = np.ones((3, 3))
data = nd.median_filter(inputImg, size=7)
data = nd.grey_closing(data, footprint=fp)
data = nd.grey_opening(data,footprint=fp)
return data
def segment_lungs(pixel_array):
img_gray_open = ndi.grey_opening(pixel_array, size=10, mode='wrap')
if DEBUG:
plot(img_gray_open, 'Gray Opening')
elavation_map = sobel(img_gray_open)
if DEBUG:
plot(elavation_map, 'Elevation Map')
markers = extract_markers(img_gray_open)
if DEBUG:
plot(markers, 'Markers')
watersheded = morphology.watershed(elavation_map, markers)
if DEBUG:
plot(watersheded, 'Watershed')
external_contour = ndi.binary_fill_holes(watersheded - 1)
if DEBUG:
plot(external_contour, 'External Contour')
watersheded_no_contour = (watersheded - external_contour)
if DEBUG:
plot(watersheded_no_contour, 'Watershed (No Contour)')
holes_filled = ndi.binary_fill_holes(watersheded_no_contour - 1)
if DEBUG:
plot(holes_filled, 'Watershed (No Contour + Holes Filled)')
removed_noise = morphology.remove_small_objects(holes_filled, 300)
if DEBUG:
plot(removed_noise, 'Removed Noise')
return removed_noise
def mpls_baseline(intensities,
smoothness_param=100,
deriv_order=1,
window_length=100):
'''Perform morphological weighted penalized least squares baseline removal.
* paper: DOI: 10.1039/C3AN00743J (Paper) Analyst, 2013, 138, 4483-4492
* Matlab code: https://code.google.com/p/mpls/
smoothness_param: Relative importance of smoothness of the predicted response.
deriv_order: Polynomial order of the difference of penalties.
window_length: size of the structuring element for the open operation.
'''
Xbg = grey_opening(intensities, window_length)
# find runs of equal values in Xbg
flat = (np.diff(Xbg) != 0).astype(np.int8)
run_idx, = np.where(np.diff(flat))
# local minimums between flat runs
bounds = run_idx[1:-1] if len(run_idx) % 2 == 0 else run_idx[1:]
bounds = bounds.reshape((-1, 2)) + (1, 2)
min_idxs = np.array([np.argmin(Xbg[s:t]) for s, t in bounds], dtype=int)
min_idxs += bounds[:, 0]
# create the weight vector by setting 1 at each local min
w = np.zeros_like(intensities)
w[min_idxs] = 1
# make sure we stick to the ends
w[0] = 5
w[-1] = 5
# run one iteration of smoothing
smoother = WhittakerSmoother(Xbg,
smoothness_param,
deriv_order=deriv_order)
return smoother.smooth(w)
def open_image(image_filtered):
"""open_image example ndimage.grey_opening
"""
c = ndimage.grey_opening(np.abs(image_filtered), size=(5, 5, 5))
new_image = nib.Nifti1Image(normalise(c), affine)
new_image.set_data_dtype(np.float32)
nib.save(new_image, 'image_open.nii.gz')
def test_opening(self):
self.image.opening(3)
original_image = retina_grayscale.Retina_grayscale(
None, _image_path, 1)
assert_array_equal(
self.image.np_image,
ndimage.grey_opening(original_image.np_image, size=(3, 3)))
def MMF(Y,rn,bg):
stru_len_op = int(bg)
stru_len_clo = int(bg*1.5)
stru_ele_op = np.linspace(0,0,stru_len_op)
stru_ele_clo = np.linspace(0,0,stru_len_clo)
#triangular wave
tri_wave = []
amp = 1.0
width = 1
samp = rn
asym = 0.5
points = 1
while points <= samp:
Xi = 0.1*points
if 0 <= Xi and Xi <= width*asym:
tri_wave.append(amp*Xi/(width*asym))
elif Xi > width*asym and Xi <width:
tri_wave.append(amp*(width-Xi)/(width*(1-asym)))
else:
tri_wave.append(0)
points += 1
#low-pass
op_flat = nd.grey_opening(Y,size = (stru_len_op),structure = stru_ele_op)
clo_flat = nd.grey_closing(op_flat,size = (stru_len_clo),structure = stru_ele_clo)
reducing = []
for reduce in range(len(Y)):
reducing.append(Y[reduce] - clo_flat[reduce])
op_tri = nd.grey_opening(reducing,size = (rn),structure = tri_wave)
clo_tri = nd.grey_closing(reducing,size = (rn),structure = tri_wave)
after_stru_ele =np.linspace(0,0,rn)
op_than_clo = nd.grey_closing(op_tri,size = (rn),structure = after_stru_ele)
clo_than_op = nd.grey_opening(clo_tri,size = (rn),structure = after_stru_ele)
plusing = []
for plus in range(len(op_than_clo)):
plusing.append((op_than_clo[plus]+clo_than_op[plus])/2.0)
return plusing, clo_flat
def run_FreeCAD_ImageT(self):
from scipy import ndimage
fn = self.getData('image')
import matplotlib.image as mpimg
img = mpimg.imread(fn)
(sa, sb, sc) = img.shape
red = 0.005 * (self.getData("red") + 100)
green = 0.005 * (self.getData("green") + 100)
blue = 0.005 * (self.getData("blue") + 100)
#blue=0
say("rgb", red, green, blue)
# andere filtre
#img = ndimage.sobel(img)
#img = ndimage.laplace(img)
im2 = img[:, :, 0] * red + img[:, :, 1] * green + img[:, :, 2] * blue
im2 = np.round(im2)
if self.getData('invert'):
im2 = 1 - im2
#im2 = ndimage.sobel(im2)
ss = int((self.getData('maskSize') + 100) / 20)
say("ss", ss)
if ss != 0:
mode = self.getData('mode')
say("mode", mode)
if mode == 'closing':
im2 = ndimage.grey_closing(im2, size=(ss, ss))
elif mode == 'opening':
im2 = ndimage.grey_opening(im2, size=(ss, ss))
elif mode == 'erosion':
im2 = ndimage.grey_erosion(im2, size=(ss, ss))
elif mode == 'dilitation':
im2 = ndimage.grey_dilation(im2, footprint=np.ones((ss, ss)))
else:
say("NO MODE")
nonzes = np.where(im2 == 0)
pts = [
FreeCAD.Vector(sb + -x, sa - y)
for y, x in np.array(nonzes).swapaxes(0, 1)
]
h = 10
pts = [
FreeCAD.Vector(
sb + -x, sa - y,
(red * img[y, x, 0] + green * img[y, x, 1] + blue * img[y, x, 2]) *
h) for y, x in np.array(nonzes).swapaxes(0, 1)
]
colors = [img[y, x] for y, x in np.array(nonzes).swapaxes(0, 1)]
say("len pts", len(pts))
self.setData("Points_out", pts)
def test_grey_opening_operation_sparse_input_struct_zeros(self):
struct = np.zeros((3, 3, 3))
print("\n test_grey_opening_operation_sparse_input_struct_zeros...")
v_output = vc.grey_opening(input_svar,
structure=struct,
make_float32=False)
d_output = ndimage.grey_opening(input_svar, structure=struct)
msgs = "test_grey_opening_operation_sparse_input_struct_zeros"
self.assertTrue((d_output == v_output).all(), msg=msgs)
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 opening(self, size_structure):
"""
dilates and erodes the stored image, by default the structure is a cross
:param size_structure: size of kernel to apply in the filter
"""
self._copy()
self.np_image = ndimage.grey_opening(self.np_image,
size=(size_structure,
size_structure))
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('Currently only support 2D&3D data')
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 remove_background(profiles, radius=20, light_background=True):
"""
Uses port of ImageJ rolling ball background subtraction
to estimate background and removes the background from the image
Parameters
------
profiles : Dictionary
Key is the well number and the value is a ndarray (2D) of the well
radius : float, optional
The radius of the rolling ball (default : 20)
light_background : Boolean
Whether the background is light or not (default : True)
Returns
------
newprofiles : Dictionary
Key is the well number and the value is a ndarray (2D) of the
background subtracted well
"""
# Make "spherical" structuring element
sz_ = 2 * radius + (radius + 1) % 2
xco, yco = np.meshgrid(range(sz_), range(sz_))
ballheight = float(radius**2) - (xco - radius)**2 - (yco - radius)**2
ballheight[ballheight < 0] = 0
ballheight = np.ma.masked_where(ballheight < 0, ballheight)
ballheight = np.sqrt(ballheight)
newprofiles = {}
if light_background:
for k, im1 in profiles.items():
imax = im1.max()
im2 = imax - im1
bg1 = ndi.grey_opening(im2, structure=ballheight, mode="reflect")
im2 -= bg1
newprofiles[k] = (im2 - imax)
else:
for k, im1 in profiles.items():
imin = im1.min()
im2 = im1 - imin
bg1 = ndi.grey_opening(im2, structure=ballheight, mode="reflect")
im2 -= bg1
newprofiles[k] = im2 - im2.min()
return newprofiles
def test_grey_opening_operation_sparse_input_default_value(self):
print("\n test_grey_opening_operation_sparse_input_default_value...")
v_output = vc.grey_opening(input_svar,
structure=structure,
make_float32=False)
d_output = ndimage.grey_opening(
input_svar,
structure=structure,
)
msgs = "test_grey_opening_operation_sparse_input_default_value"
self.assertTrue((d_output == v_output).all(), msg=msgs)
def __test_grey_opening_operation(self,input_var):
print("\n grey_opening Voxel testing...")
start_time = t.time()
v_output = vc.grey_opening(input_var,structure=structure,no_of_blocks=PL[0],fakeghost=PL[1],make_float32=False)
print("grey_opening Voxel testing time taken: ",(t.time() - start_time)," sec")
#print("\n grey_opening Default testing...")
start_time = t.time()
d_output = ndimage.grey_opening(input_var,structure=structure)
print("grey_opening Default testing time taken: ",(t.time() - start_time)," sec")
msgs = "grey_opening_operation_FAIL_with parameters: ",PL
self.assertTrue((d_output==v_output).all(), msg=msgs)
def test_grey_opening_operation_dense_input_fakeghost_four(self):
print("\n test_grey_opening_operation_dense_input_fakeghost_four...")
v_output = vc.grey_opening(input_dvar,
structure=structure,
fakeghost=4,
make_float32=False)
d_output = ndimage.grey_opening(
input_dvar,
structure=structure,
)
msgs = "test_grey_opening_operation_dense_input_fakeghost_four"
self.assertTrue((d_output == v_output).all(), msg=msgs)
def test_grey_opening_operation_sparse_input_blocks_ten(self):
print("\n test_grey_opening_operation_sparse_input_blocks_ten...")
v_output = vc.grey_opening(input_svar,
structure=structure,
no_of_blocks=10,
make_float32=False)
d_output = ndimage.grey_opening(
input_svar,
structure=structure,
)
msgs = "test_grey_opening_operation_sparse_input_blocks_ten"
self.assertTrue((d_output == v_output).all(), msg=msgs)
def getROI(image):
image_resized = resize(image)
b, g, r = cv2.split(image_resized)
g = cv2.GaussianBlur(g, (15, 15), 0)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15))
g = ndimage.grey_opening(g, structure=kernel)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(g)
x0 = int(maxLoc[0]) - 110
y0 = int(maxLoc[1]) - 110
x1 = int(maxLoc[0]) + 110
y1 = int(maxLoc[1]) + 110
return image_resized[y0:y1, x0:x1]
def test_2d_ndimage_equivalence():
image = np.zeros((9, 9), np.uint8)
image[2:-2, 2:-2] = 128
image[3:-3, 3:-3] = 196
image[4, 4] = 255
opened = gray.opening(image)
closed = gray.closing(image)
footprint = ndi.generate_binary_structure(2, 1)
ndimage_opened = ndi.grey_opening(image, footprint=footprint)
ndimage_closed = ndi.grey_closing(image, footprint=footprint)
assert_array_equal(opened, ndimage_opened)
assert_array_equal(closed, ndimage_closed)
def opening(self):
"""Perform a grey opening: an erosion followed by a dilation."""
def disk(N):
"""Get circle morphology."""
y, x = np.ogrid[-N:N + .1, -N:N + .1]
return np.asarray(x**2 + y**2 < N**2, dtype=np.uint8)
# obtain and subtract background lighting level
kernel = disk(self.lsize)
if not cv2:
background = ndimage.grey_opening(self.im, structure=kernel)
else:
background = cv2.dilate(cv2.erode(self.im, kernel), kernel)
I2 = self.im - background
return I2
def dtm_krauss_2015(dsm, ps, minf_r):
nx, ny = dsm.shape
scale = 20.0 / ps
nnx = int(nx / scale + 0.5)
nny = int(ny / scale + 0.5)
scale_int = int(scale + 0.5)
print scale
print scale_int
minf = filters.minimum_filter(dsm, minf_r)
dwn = cv2.resize(minf, (nny, nnx), interpolation=cv2.INTER_NEAREST)
# dwn = scipy.misc.imresize(minf,(nnx,nny),interp='cubic')
dwn_o = ndimage.grey_opening(dwn, 5)
dwn_g = filters.gaussian_filter(dwn_o, 2.5)
# return scipy.misc.imresize(dwn_g,(nx,ny),interp='cubic')
return cv2.resize(dwn_g, (ny, nx), interpolation=cv2.INTER_CUBIC)
def test_2d_ndimage_equivalence():
image = np.zeros((9, 9), np.uint8)
image[2:-2, 2:-2] = 128
image[3:-3, 3:-3] = 196
image[4, 4] = 255
opened = grey.opening(image)
closed = grey.closing(image)
selem = ndi.generate_binary_structure(2, 1)
ndimage_opened = ndi.grey_opening(image, footprint=selem)
ndimage_closed = ndi.grey_closing(image, footprint=selem)
testing.assert_array_equal(opened, ndimage_opened)
testing.assert_array_equal(closed, ndimage_closed)
def dtm_krauss_2015(dsm,ps,minf_r):
nx,ny = dsm.shape
scale = 20.0/ps
nnx = int(nx/scale+0.5)
nny = int(ny/scale+0.5)
scale_int = int(scale+0.5)
print scale
print scale_int
minf = filters.minimum_filter(dsm,minf_r)
dwn = cv2.resize(minf,(nny,nnx),interpolation = cv2.INTER_NEAREST)
# dwn = scipy.misc.imresize(minf,(nnx,nny),interp='cubic')
dwn_o = ndimage.grey_opening(dwn,5)
dwn_g = filters.gaussian_filter(dwn_o,2.5)
# return scipy.misc.imresize(dwn_g,(nx,ny),interp='cubic')
return cv2.resize(dwn_g,(ny,nx),interpolation = cv2.INTER_CUBIC)
def __operationTask(self,input_var):
'''
perform respective moephological operation on input block.
Parameters
----------
input_var : type: 3d numpy array, ith block.
Returns
-------
output : type: 3d array, output of operation, ith block array.
'''
D=self.__operationArgumentDic
if self.__operation=="binary_closing":
return ndimage.binary_closing(input_var, structure=D["structure"], iterations=D["iterations"], output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"], brute_force=D["brute_force"])
elif self.__operation=="binary_dilation":
return ndimage.binary_dilation(input_var, structure=D["structure"], iterations=D["iterations"], output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"], brute_force=D["brute_force"])
elif self.__operation=="binary_erosion":
return ndimage.binary_erosion(input_var, structure=D["structure"], iterations=D["iterations"], output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"], brute_force=D["brute_force"])
elif self.__operation=="binary_fill_holes": #the output might be different then scipy.ndimage
return ndimage.binary_fill_holes(input_var, structure=D["structure"],output=D["output"], origin=D["origin"])
elif self.__operation=="binary_hit_or_miss":
return ndimage.binary_hit_or_miss(input_var, structure1=D["structure1"],structure2=D["structure2"],output=D["output"], origin1=D["origin1"], origin2=D["origin2"])
elif self.__operation=="binary_opening":
return ndimage.binary_opening(input_var, structure=D["structure"], iterations=D["iterations"], output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"], brute_force=D["brute_force"])
elif self.__operation=="binary_propagation":
return ndimage.binary_propagation(input_var, structure=D["structure"],output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"])
elif self.__operation=="black_tophat":
return ndimage.black_tophat(input_var, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"])
elif self.__operation=="grey_dilation":
return ndimage.grey_dilation(input_var, structure=D["structure"],size=D["size"], footprint=D["footprint"],output=D["output"], mode=D["mode"], cval=D["cval"], origin=D["origin"])
elif self.__operation=="grey_closing":
return ndimage.grey_closing(input_var, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"])
elif self.__operation=="grey_erosion":
return ndimage.grey_erosion(input_var, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"])
elif self.__operation=="grey_opening":
return ndimage.grey_opening(input_var, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"])
elif self.__operation=="morphological_gradient":
return ndimage.morphological_gradient(input_var, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"])
elif self.__operation=="morphological_laplace":
return ndimage.morphological_laplace(input_var, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"])
elif self.__operation=="white_tophat":
return ndimage.white_tophat(input_var, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"])
elif self.__operation=="multiply":
return input_var*D["scalar"]
else:
return input_var # no operation performed....
def test_grey_morph():
root = 'local_histos/'
for datablock in get_data():
data,x,y = datablock
data = get_uint_image(data)
print(x,y)
for i in range(0,30,4):
mdata = nd.grey_opening(data,(i,i))
R = mahotas.thresholding.rc(mdata)
gradient = get_grad_mag(mdata)
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(211)
ax1.imshow(gradient)
ax1.set_title(str(i)+', R: '+str(R))
ax2 = fig.add_subplot(212)
ax2.hist(gradient.flatten(),50)
plt.savefig(get_fname([root+'img',x,y,i,'.png']))
plt.close(fig)
def forward(self, labels: Tensor, *args) -> Tensor:
r"""Computes the Opening loss -- i.e. the MSE due to performing a greyscale opening operation.
:param labels: Predicted class probabilities
:param args: Extra inputs, in case user also provides input/output image values.
:return: Opening loss
"""
smooth_labels = labels.clone().detach().cpu().numpy()
for i in range(labels.shape[0]):
for j in range(labels.shape[1]):
smooth_labels[i, j] = grey_opening(smooth_labels[i, j],
self.radius)
smooth_labels = torch.from_numpy(smooth_labels.astype(np.float32))
if labels.device.type == 'cuda':
smooth_labels = smooth_labels.cuda()
return nn.MSELoss()(labels, smooth_labels.detach())
def epi_mask(in_file, out_file=None):
"""Use grayscale morphological operations to obtain a quick mask of EPI data."""
from pathlib import Path
import nibabel as nb
import numpy as np
from scipy import ndimage
from skimage.morphology import ball
if out_file is None:
out_file = Path("mask.nii.gz").absolute()
img = nb.load(in_file)
data = img.get_fdata(dtype="float32")
# First open to blur out the skull around the brain
opened = ndimage.grey_opening(data, structure=ball(3))
# Second, close large vessels and the ventricles
closed = ndimage.grey_closing(opened, structure=ball(2))
# Window filter on percentile 30
closed -= np.percentile(closed, 30)
# Window filter on percentile 90 of data
maxnorm = np.percentile(closed[closed > 0], 90)
closed = np.clip(closed, a_min=0.0, a_max=maxnorm)
# Calculate index of center of masses
cm = tuple(
np.round(ndimage.measurements.center_of_mass(closed)).astype(int))
# Erode the picture of the brain by a lot
eroded = ndimage.grey_erosion(closed, structure=ball(5))
# Calculate the residual
wshed = opened - eroded
wshed -= wshed.min()
wshed = np.round(1e3 * wshed / wshed.max()).astype(np.uint16)
markers = np.zeros_like(wshed, dtype=int)
markers[cm] = 2
markers[0, 0, -1] = -1
# Run watershed
labels = ndimage.watershed_ift(wshed, markers)
hdr = img.header.copy()
hdr.set_data_dtype("uint8")
nb.Nifti1Image(
ndimage.binary_dilation(labels == 2, ball(2)).astype("uint8"),
img.affine, hdr).to_filename(out_file)
return out_file
def label_fusion(label, win=3):
"""Apply a morphological filtering on the label to remove isolated labels.
In case the input is a two channel label (2D ndarray of boolean of same
length) the labels of two channels are fused to remove
overlaping segments of speech.
:param label: input labels given in a 1D or 2D ndarray
:param win: parameter or the morphological filters
"""
channel_nb = len(label)
if channel_nb == 2:
overlap_label = numpy.logical_and(label[0], label[1])
label[0] = numpy.logical_and(label[0], ~overlap_label)
label[1] = numpy.logical_and(label[1], ~overlap_label)
for idx, lbl in enumerate(label):
cl = ndimage.grey_closing(lbl, size=win)
label[idx] = ndimage.grey_opening(cl, size=win)
return label
def detect_growth_markers(flow, wvd):
wvd_diff_raw = flow.diff(wvd) / get_time_diff_from_coord(
wvd.t)[:, np.newaxis, np.newaxis]
wvd_diff_smoothed = filtered_tdiff(flow, wvd_diff_raw)
s_struct = ndi.generate_binary_structure(2, 1)[np.newaxis, ...]
wvd_diff_filtered = ndi.grey_opening(
wvd_diff_smoothed, footprint=s_struct) * get_curvature_filter(wvd)
watershed_markers = flow.label(wvd_diff_filtered >= 0.5)
if isinstance(wvd, xr.DataArray):
watershed_markers = filter_labels_by_length_and_mask(
watershed_markers, wvd.data >= -5, 3)
else:
watershed_markers = filter_labels_by_length_and_mask(
watershed_markers, wvd >= -5, 3)
# marker_regions = flow.watershed(-wvd_diff_filtered,
# watershed_markers != 0,
# mask=wvd_diff_filtered<0.25,
# structure=ndi.generate_binary_structure(3,1))
marker_labels = flow.label(
ndi.binary_opening(wvd_diff_filtered >= 0.25, structure=s_struct))
# marker_labels = flow.label(ndi.binary_opening(marker_regions, structure=s_struct))
marker_labels = filter_labels_by_length_and_mask(marker_labels,
watershed_markers != 0, 3)
if isinstance(wvd, xr.DataArray):
marker_labels = filter_labels_by_length_and_mask(
marker_labels, wvd.data >= -5, 3)
else:
marker_labels = filter_labels_by_length_and_mask(
marker_labels, wvd >= -5, 3)
if isinstance(wvd, xr.DataArray):
wvd_diff_raw = xr.DataArray(wvd_diff_raw, wvd.coords, wvd.dims)
marker_labels = xr.DataArray(marker_labels, wvd.coords, wvd.dims)
return wvd_diff_smoothed, marker_labels
def grey_opening(img, params):
if params['footprint_shape'] == 'rectangle':
footprint = np.ones(
(params['footprint_size_y'], params['footprint_size_x']),
dtype=int)
elif params['footprint_shape'] == 'ellipse':
a = params['footprint_size_x'] / 2
b = params['footprint_size_y'] / 2
x, y = np.mgrid[-ceil(a):ceil(a) + 1, -ceil(b):ceil(b) + 1]
footprint = ((x / a)**2 + (y / b)**2 < 1) * 1
mode = params['mode']
cval = params['cval']
origin = params['origin']
return ndimage.grey_opening(img,
size=None,
footprint=footprint,
structure=None,
mode=mode,
cval=cval,
origin=origin)
def operationTask(input): D=operationArgumentDic #self.M.add_mem()#..................................................................................................... if operation=="binary_closing": return ndimage.binary_closing(input, structure=D["structure"], iterations=D["iterations"], output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"], brute_force=D["brute_force"]) elif operation=="binary_dilation": return ndimage.binary_dilation(input, structure=D["structure"], iterations=D["iterations"], output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"], brute_force=D["brute_force"]) elif operation=="binary_erosion": return ndimage.binary_erosion(input, structure=D["structure"], iterations=D["iterations"], output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"], brute_force=D["brute_force"]) elif operation=="binary_fill_holes": return ndimage.binary_fill_holes(input, structure=D["structure"],output=D["output"], origin=D["origin"]) elif operation=="binary_hit_or_miss": return ndimage.binary_hit_or_miss(input, structure1=D["structure1"],structure2=D["structure2"],output=D["output"], origin1=D["origin1"], origin2=D["origin2"]) elif operation=="binary_opening": return ndimage.binary_opening(input, structure=D["structure"], iterations=D["iterations"], output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"], brute_force=D["brute_force"]) elif operation=="binary_propagation": return ndimage.binary_propagation(input, structure=D["structure"],output=D["output"], origin=D["origin"], mask=D["mask"], border_value=D["border_value"]) elif operation=="black_tophat": return ndimage.black_tophat(input, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"]) elif operation=="grey_dilation": return ndimage.grey_dilation(input, structure=D["structure"],size=D["size"], footprint=D["footprint"],output=D["output"], mode=D["mode"], cval=D["cval"], origin=D["origin"]) elif operation=="grey_closing": return ndimage.grey_closing(input, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"]) elif operation=="grey_erosion": return ndimage.grey_erosion(input, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"]) elif operation=="grey_opening": return ndimage.grey_opening(input, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"]) elif operation=="morphological_gradient": return ndimage.morphological_gradient(input, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"]) elif operation=="morphological_laplace": return ndimage.morphological_laplace(input, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"]) elif operation=="white_tophat": return ndimage.white_tophat(input, structure=D["structure"], size=D["size"], footprint=D["footprint"], output=D["output"], origin=D["origin"],mode=D["mode"], cval=D["cval"]) elif operation=="intMultiply": return input*D["scalar"] else: return input
def readImage(thigh, img):
imgOr = cv2.imread(img, 0)
imgTh = cv2.imread(img, 0)
opening = ndimage.grey_opening(imgTh, size=(3, 4))
gauss = cv2.GaussianBlur(opening, (11, 11), 0)
imgTh[gauss < thigh] = 0
imgTh[gauss >= thigh] = 1
kernel = np.ones((1, 5))
erode = cv2.erode(imgTh, kernel, iterations=1)
kernel = np.ones((2, 15))
dilate = cv2.dilate(erode, kernel, iterations=1)
m.showImage6(imgOr, opening, gauss, imgTh, erode, dilate, "Original",
"Apertura gris", "Suav Gauss", "Th", "Erode", "Dilate")
return imgOr, dilate
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 = morphology.disk(radius)
elif im.ndim == 3:
selem = morphology.ball(radius)
else:
raise ValueError("Image input to 'morphop' should be 2D or 3D"
", got %iD" % im.ndim)
if operation.startswith('open'):
imout = ndi.grey_opening(im, footprint=selem)
elif operation.startswith('clos'):
imout = ndi.grey_closing(im, footprint=selem)
elif operation.startswith('dila'):
imout = ndi.grey_dilation(im, footprint=selem)
elif operation.startswith('ero'):
imout = ndi.grey_erosion(im, footprint=selem)
return imout
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 run(self, image):
"""
apply local otsu threshold to every z-stack and re-combine them
image.shape = (x, y, z)
"""
image = image.copy()
image = np.moveaxis(image, -1, 0)
result = []
image *= 255
image = image.astype(np.uint8)
for i, stack in enumerate(image):
local_otsu = filters.rank.otsu(stack, disk(self.radius))
mask = stack > local_otsu
stack = stack * mask
if self.parameters['open_radius']:
stack = ndimage.grey_opening(stack,
self.parameters['open_radius'])
result.append(stack)
result = np.stack(result, axis=0)
result = np.moveaxis(result, 0, -1)
self.mask = result > 0
self.image = self.apply_mask(result, self.mask)
return self.image
def closingFilter(image, mask):
""" Aplica o filtro de abertura em uma imagem,
de acordo com o tamanho da máscara passada por
parâmetro.
@param image deve ser um PIL.Image.
@param mask string "row x cols"
@return matriz com novos valores após aplicação do filtro
"""
threshold = 0.8
sumColors = numpy.float(numpy.sum(image))
isbinary = sumColors / image.size <= 1 - threshold
(row, col) = [int(dim) for dim in mask.split('x')]
structure = [[1 for i in range(col)] for j in range(row)]
if isbinary:
return ndimage.binary_opening(image, structure=structure)
else:
return ndimage.grey_opening(image, structure=structure)
def tophat_baseline(intensities):
'''Perform "tophat" baseline removal, from the paper: Morphology-Based
Automated Baseline Removal for Raman Spectra of Artistic Pigments.
Perez-Pueyo et al., Appl. Spec. 2010'''
# find the optimal window length
old_b, num_equal = 0, 1
for window_length in count(start=3, step=2):
b1 = grey_opening(intensities, window_length)
if np.allclose(b1, old_b):
if num_equal == 2:
break
num_equal += 1
else:
num_equal = 1
old_b = b1
# use the smallest of the three equivalent window lengths
window_length -= 4
# compute another estimate of the baseline
b2 = 0.5 * (grey_dilation(intensities, window_length) +
grey_erosion(intensities, window_length))
# combine the two estimates
return np.minimum(b1, b2)
def opening(self, tissue, size=def_size):
nd.grey_opening(self.P[tissue], size=[size,size,size], output=self.P[tissue])
def opening(P, size=def_size):
return nd.grey_opening(P, size=[size,size,size])
def opening(src, r=2):
se = ball(r)
result = nd.grey_opening(src, footprint=se)
return result
[1, 1, 1, 1, 1], [0, 1, 1, 1, 0]] print xorg, yorg, pres, xmax, ymax red = get_band_data( ds, 1 ) green = get_band_data( ds, 2 ) blue = get_band_data( ds, 3 ) epsilon = 0.0001 norm_diff_ratio = (red - blue) / (epsilon+blue+red) data1 = norm_diff_ratio data1 = speckle_filter(data1,'median', 11) data1 = linear_stretch(data1, max_percentile=50.0) data1 = ndimage.grey_opening(data1, size=(5,5), structure=octagon_2) thresh = threshold_otsu(data1,nbins=7) print "otsu1", thresh data1[data1>=thresh] = 0 data1[red==0] = 0 #data1[data1>0] = 255 #thresh2 = threshold_otsu(data1,nbins=7) #print "otsu2", thresh2 #data1[data1>thresh2] = 0 write_data(data1, outfileName, ds) #norm_diff_ratio = (red - green) / (epsilon+green+red) #data2 = linear_stretch(norm_diff_ratio) #write_data(data2, outfileName2, ds)
def opening(src, r=2):
"""Using the opening image algrithm to process the src image."""
se = ball(r)
result = nd.grey_opening(src, footprint=se)
return result
def hand(self): base_img = self.output_4326 in_img = os.path.join(self.hand_dir, HAND_FILE) out_img = self.hand_4326 # Get a subset of HAND for particular tile #if self.force or not os.path.isfile(out_img): #print "generate hand subset:"+out_img +" from:"+in_img self.generate_hand_subset(base_img, in_img, out_img) #if not os.path.isfile(self.hand_4326) and not self.force: # cmd = "gdalwarp -of GTIFF "+ out_img + " " + self.hand_4326 # print cmd # err = os.system(cmd) # print "Generating HAND Tif error:", err # #sys.exit(0) if os.path.isfile(self.output_4326_hand) and not self.force: return if verbose: print "Generating ", self.output_4326_hand src_ds = gdal.Open( self.output_4326_rgb ) driver = gdal.GetDriverByName( "GTiff" ) input_dataset = driver.CreateCopy( self.output_4326_hand, src_ds, 0, [ 'COMPRESS=DEFLATE' ] ) input_band = input_dataset.GetRasterBand(1) input_data = input_band.ReadAsArray(0, 0, input_dataset.RasterXSize, input_dataset.RasterYSize ) alpha_band = input_dataset.GetRasterBand(4) alpha_data = alpha_band.ReadAsArray(0, 0, input_dataset.RasterXSize, input_dataset.RasterYSize ) hand_ds = gdal.Open(out_img) hand_band = hand_ds.GetRasterBand(1) hand_data = hand_band.ReadAsArray(0, 0, hand_ds.RasterXSize, hand_ds.RasterYSize ) coastlines_ds = gdal.Open(self.coastlines) coastal_band = coastlines_ds.GetRasterBand(1) coastal_data = coastal_band.ReadAsArray(0, 0, coastlines_ds.RasterXSize, coastlines_ds.RasterYSize ) if app.verbose: print "hand_data:", hand_data.min(), hand_data.max() # HAND Masking mask = hand_data==0 input_data[mask]= 0 mask = hand_data==255 input_data[mask]= 0 mask = coastal_data>0 input_data[mask]= 0 # # Morphing to smooth and filter the data # octagon_2 =[[0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0]] morphed = ndimage.grey_opening(input_data, size=(5,5), structure=octagon_2) input_band.WriteArray(morphed, 0, 0) input_band.SetNoDataValue(0) # set transparency alpha_data[morphed<255]=0 alpha_data[morphed>=255]=255 alpha_band.WriteArray(alpha_data, 0, 0) input_data = None morphed = None input_dataset = None hand_band = None hand_ds = None src_ds = None coastlines_ds = None if app.verbose: print "Hand Morphed Done ", self.output_4326_hand
def opening(f, b=bm.create_structure_element_cross()):
return mm.grey_opening(f, structure=b)
unI = sorted(unique(im.ravel()))
nbin = len(unI)
h = histogram(im, unI)
P = h[0].astype(float)/sum(h[0])
w = cumsum(P)
nbin = len(P)
mu = cumsum(arange(1, nbin+1) * P)
sigma2B = (mu[-1] * w[1:-1] - mu[1:-1])** 2 / w[1:-1]/(1-w[1:-1])
idx = where(sigma2B == max(sigma2B))[0][0]
return h[1][idx]
# ------------------------------------------------------------------------------
im = array(Image.open('textures/structure/015.jpg').convert("L")) / 255.
bg = ndimage.grey_opening(im, footprint = circle(5))
pb.set_cmap(pb.cm.gray)
pb.figure()
pb.title("Original")
pb.imshow(im,vmin=0, vmax=1)
pb.savefig('result/structure/001.png', dpi=150)
imbgadj = imadjust(im)
pb.figure()
pb.title("Normalized")
pb.imshow(imbgadj, vmin = 0, vmax=1)
pb.savefig('result/structure/002.png', dpi=150)
t = otsu(imbgadj)
print t
def opening(f, b):
return mm.grey_opening(f, struture=b)
def watershed_cube(self):
writeVerbose = False;
#writeVerbose = self.dpWatershedTypes_verbose
readVerbose = False;
#readVerbose = self.dpWatershedTypes_verbose
# load the probability data, allocate as array of volumes instead of 4D ndarray to maintain C-order volumes
probs = [None]*self.ntypes; bwseeds = [None]*self.nfg_types
if self.srclabels:
# this code path is typically not used in favor of the label checker for fully labeled 3d gt components.
# but, some ground truth (for example, 2d ECS cases) was only labeled with voxel type,
# so this is used to create ground truth components from the voxel types.
loadh5 = emLabels.readLabels(srcfile=self.srclabels, chunk=self.chunk.tolist(), offset=self.offset.tolist(),
size=self.size.tolist(), data_type='uint16', verbose=writeVerbose)
self.datasize = loadh5.datasize; self.chunksize = loadh5.chunksize; self.attrs = loadh5.data_attrs
# pre-allocate for srclabels method, labeled areas are set to prob of 1 below
for i in range(self.ntypes): probs[i] = np.zeros(self.size, dtype=emProbabilities.PROBS_DTYPE, order='C')
if self.TminSrc < 2:
# simple method with no "cleaning"
for i in range(self.ntypes): probs[i][loadh5.data_cube==i] = 1
else:
# optionally "clean" labels by removing small bg and fg components for each foreground type
fgbwlabels = np.zeros(self.size, dtype=np.bool)
for i in range(self.nfg_types):
# background connected components and threshold
comps, nlbls = nd.measurements.label(loadh5.data_cube!=i+1)
comps, sizes = emLabels.thresholdSizes(comps, minSize=self.TminSrc)
# foreground connected components and threshold
comps, nlbls = nd.measurements.label(comps==0)
comps, sizes = emLabels.thresholdSizes(comps, minSize=self.TminSrc)
# keep track of mask for all foreground types
bwlabels = (comps > 0); fgbwlabels = np.logical_or(fgbwlabels, bwlabels)
probs[i+1][bwlabels] = 1
# set background type as all areas that are not in foreground types after "cleaning"
probs[0][np.logical_not(fgbwlabels)] = 1
else:
# check if background is in the prob file
hdf = h5py.File(self.probfile,'r'); has_bg = self.bg_type in hdf; hdf.close()
for i in range(0 if has_bg else 1, self.ntypes):
loadh5 = dpLoadh5.readData(srcfile=self.probfile, dataset=self.types[i], chunk=self.chunk.tolist(),
offset=self.offset.tolist(), size=self.size.tolist(), data_type=emProbabilities.PROBS_STR_DTYPE,
verbose=readVerbose)
self.datasize = loadh5.datasize; self.chunksize = loadh5.chunksize; self.attrs = loadh5.data_attrs
probs[i] = loadh5.data_cube; del loadh5
# if background was not in hdf5 then create it as 1-sum(fg type probs)
if not has_bg:
probs[0] = np.ones_like(probs[1])
for i in range(1,self.ntypes): probs[0] -= probs[i]
#assert( (probs[0] >= 0).all() ) # comment for speed
probs[0][probs[0] < 0] = 0 # rectify
# save some of the parameters as attributes
self.attrs['types'] = self.types; self.attrs['fg_types'] = self.fg_types
self.attrs['fg_types_labels'] = self.fg_types_labels
# save connnetivity structure and warping LUT because used on each iteration (for speed)
self.bwconn = nd.morphology.generate_binary_structure(dpLoadh5.ND, self.connectivity)
self.bwconn2d = self.bwconn[:,:,1]; self.simpleLUT = None
# load the warpings if warping mode is enabled
warps = None
if self.warpfile:
warps = [None]*self.nwarps
for i in range(self.nwarps):
loadh5 = dpLoadh5.readData(srcfile=self.warpfile, dataset=self.warp_datasets[i],
chunk=self.chunk.tolist(), offset=self.offset.tolist(), size=self.size.tolist(),
verbose=readVerbose)
warps[i] = loadh5.data_cube; del loadh5
# xxx - may need to revisit cropping, only intended to be used with warping method.
if self.docrop: c = self.cropborder; s = self.size # DO NOT use variables c or s below
# optionally apply filters in attempt to fill small background (membrane) probability gaps.
if self.close_bg > 0:
# create structuring element
n = 2*self.close_bg + 1; h = self.close_bg; strel = np.zeros((n,n,n),dtype=np.bool); strel[h,h,h]=1;
strel = nd.binary_dilation(strel,iterations=self.close_bg)
# xxx - this was the only thing tried here that helped some but didn't work well against the skeletons
probs[0] = nd.grey_closing( probs[0], structure=strel )
for i in range(self.nfg_types): probs[i+1] = nd.grey_opening( probs[i+1], structure=strel )
# xxx - this gave worse results
#probs[0] = nd.maximum_filter( probs[0], footprint=strel )
# xxx - this had almost no effect
#probs[0] = nd.grey_closing( probs[0], structure=strel )
# argmax produces the winner-take-all assignment for each supervoxel.
# background type was put first, so voxType of zero is background (membrane).
voxType = np.concatenate([x.reshape(x.shape + (1,)) for x in probs], axis=3).argmax(axis=3)
# write out the winning type for each voxel
# save some params from this watershed run in the attributes
d = self.attrs.copy(); d['thresholds'] = self.Ts; d['Tmins'] = self.Tmins
data = voxType.astype(emVoxelType.VOXTYPE_DTYPE)
if self.docrop: data = data[c[0]:s[0]-c[0],c[1]:s[1]-c[1],c[2]:s[2]-c[2]]
emVoxelType.writeVoxType(outfile=self.outlabels, chunk=self.chunk.tolist(),
offset=self.offset_crop.tolist(), size=self.size_crop.tolist(), datasize=self.datasize.tolist(),
chunksize=self.chunksize.tolist(), verbose=writeVerbose, attrs=d,
data=data)
# only allow a voxel to be included in the type of component that had max prob for that voxel.
# do this by setting the non-winning probabilities to zero.
for i in range(self.ntypes): probs[i][voxType != i] = 0;
# create a type mask for each foreground type to select only current voxel type (winner-take-all from network)
voxTypeSel = [None] * self.nfg_types; voxTypeNotSel = [None] * self.nfg_types
for i in range(self.nfg_types):
voxTypeSel[i] = (voxType == i+1)
# create an inverted version, only used for complete fill not for warping (which requires C-contiguous),
# so apply crop here if cropping enabled
voxTypeNotSel[i] = np.logical_not(voxTypeSel[i])
if self.docrop: voxTypeNotSel[i] = voxTypeNotSel[i][c[0]:s[0]-c[0],c[1]:s[1]-c[1],c[2]:s[2]-c[2]]
# need C-contiguous probabilities for binary_warping.
for i in range(self.nfg_types):
if not probs[i+1].flags.contiguous or np.isfortran(probs[i+1]):
probs[i+1] = np.ascontiguousarray(probs[i+1])
# iteratively apply thresholds, each time only keeping components that have fallen under size Tmin.
# at last iteration keep all remaining components.
# do this separately for foreground types.
for k in range(self.nTmin):
for i in range(self.nfg_types): bwseeds[i] = np.zeros(self.size, dtype=np.bool, order='C')
for i in range(self.nthresh):
if self.dpWatershedTypes_verbose:
print('creating supervoxels at threshold = %.8f with Tmin = %d' % (self.Ts[i], self.Tmins[k]))
t = time.time()
types_labels = [None]*self.nfg_types; types_uclabels = [None]*self.nfg_types;
if self.skeletonize: types_sklabels = [None]*self.nfg_types
types_nlabels = np.zeros((self.nfg_types,),dtype=np.int64)
types_ucnlabels = np.zeros((self.nfg_types,),dtype=np.int64)
for j in range(self.nfg_types):
# run connected components at this threshold on labels
labels, nlabels = nd.measurements.label(probs[j+1] > self.Ts[i], self.bwconn)
# merge the current thresholded components with the previous seeds to get current bwlabels
bwlabels = np.logical_or(labels, bwseeds[j])
# take the current components under threshold and merge with the seeds for the next iteration
if i < self.nthresh-1:
labels, sizes = emLabels.thresholdSizes(labels, minSize=-self.Tmins[k])
bwseeds[j] = np.logical_or(labels, bwseeds[j])
# this if/elif switch determines the main method for creating the labels.
# xxx - make cropping to be done in more efficient way, particular to avoid filling cropped areas
if self.method == 'overlap':
# definite advantage to this method over other methods, but cost is about 2-3 times slower.
# labels are linked per zslice using precalculated slice to slice warpings based on the probs.
labels, nlabels = self.label_overlap(bwlabels, voxTypeSel[j], warps)
# xxx - add switches to only optionally export the unconnected labels
#uclabels = labels; ucnlabels = nlabels;
# crop right after the labels are created and stay uncropped from here.
# xxx - labels will be wrong unless method implicitly handled the cropping during the labeling.
# currently only the warping method is doing, don't need cropping for other methods anyways.
if self.docrop: labels = labels[c[0]:s[0]-c[0],c[1]:s[1]-c[1],c[2]:s[2]-c[2]]
# this method can not create true unconnected 3d labels, but should be unconnected in 2d.
# NOTE: currently this only removes 6-connectivity, no matter what specified connecitity is
# xxx - some method of removing adjacencies with arbitrary connectivity?
uclabels, ucnlabels = emLabels.remove_adjacencies(labels)
elif self.method == 'skim-ws':
# xxx - still trying to evaluate if there is any advantage to this more traditional watershed.
# it does not leave a non-adjacency boundary and is about 1.5 times slower than bwmorph
# run connected components on the thresholded labels merged with previous seeds
labels, nlabels = nd.measurements.label(bwlabels, self.bwconn)
# run a true watershed based the current foreground probs using current components as markers
labels = morph.watershed(probs[j+1], labels, connectivity=self.bwconn, mask=voxTypeSel[j])
# remove any adjacencies created during the watershed
# NOTE: currently this only removes 6-connectivity, no matter what specified connecitity is
# xxx - some method of removing adjacencies with arbitrary connectivity?
uclabels, ucnlabels = emLabels.remove_adjacencies(labels)
else:
if self.method == 'comps-ws' and i>1:
# this is an alternative to the traditional watershed that warps out only based on stepping
# back through the thresholds in reverse order. has advantages of non-connectivity.
# may help slightly for small supervoxels but did not show much improved metrics in
# terms of large-scale connectivity (against skeletons)
# about 4-5 times slower than regular warping method.
# make an unconnected version of bwlabels by warping out but with mask only for this type
# everything above current threshold is already labeled, so only need to use gray thresholds
# starting below the current threshold level.
bwlabels, diff, self.simpleLUT = binary_warping(bwlabels, np.ones(self.size,dtype=np.bool),
mask=voxTypeSel[j], borderval=False, slow=True, simpleLUT=self.simpleLUT,
connectivity=self.connectivity, gray=probs[j+1],
grayThresholds=self.Ts[i-1::-1].astype(np.float32, order='C'))
else:
assert( self.method == 'comps' ) # bad method option
# make an unconnected version of bwlabels by warping out but with mask only for this type
bwlabels, diff, self.simpleLUT = binary_warping(bwlabels, np.ones(self.size,dtype=np.bool),
mask=voxTypeSel[j], borderval=False, slow=True, simpleLUT=self.simpleLUT,
connectivity=self.connectivity)
# run connected components on the thresholded labels merged with previous seeds (warped out)
uclabels, ucnlabels = nd.measurements.label(bwlabels, self.bwconn);
# in this case the normal labels are the same as the unconnected labels because of warping
labels = uclabels; nlabels = ucnlabels;
# optionally make a skeletonized version of the unconnected labels
# xxx - revisit this, currently not being used for anything, started as a method to skeletonize GT
if self.skeletonize:
# method to skeletonize using max range endpoints only
sklabels, sknlabels = emLabels.ucskeletonize(uclabels, mask=voxTypeSel[j],
sampling=self.attrs['scale'] if hasattr(self.attrs,'scale') else None)
assert( sknlabels == ucnlabels )
# fill out these labels out so that they fill in remaining voxels based on voxType.
# this uses bwdist method for finding nearest neighbors, so connectivity can be violoated.
# this is mitigated by first filling out background using the warping transformation
# (or watershed) above, then this step is only to fill in remaining voxels for the
# current foreground voxType.
labels = emLabels.nearest_neighbor_fill(labels, mask=voxTypeNotSel[j],
sampling=self.attrs['scale'] if hasattr(self.attrs,'scale') else None)
# save the components labels generated for this type
types_labels[j] = labels.astype(emLabels.LBLS_DTYPE, copy=False);
types_uclabels[j] = uclabels.astype(emLabels.LBLS_DTYPE, copy=False);
types_nlabels[j] = nlabels if self.fg_types_labels[j] < 0 else 1
types_ucnlabels[j] = ucnlabels if self.fg_types_labels[j] < 0 else 1
if self.skeletonize: types_sklabels[j] = sklabels.astype(emLabels.LBLS_DTYPE, copy=False)
# merge the fg components labels. they can not overlap because voxel type is winner-take-all.
nlabels = 0; ucnlabels = 0;
labels = np.zeros(self.size_crop, dtype=emLabels.LBLS_DTYPE);
uclabels = np.zeros(self.size_crop, dtype=emLabels.LBLS_DTYPE);
if self.skeletonize: sklabels = np.zeros(self.size, dtype=emLabels.LBLS_DTYPE);
for j in range(self.nfg_types):
sel = (types_labels[j] > 0); ucsel = (types_uclabels[j] > 0);
if self.skeletonize: sksel = (types_sklabels[j] > 0);
if self.fg_types_labels[j] < 0:
labels[sel] += (types_labels[j][sel] + nlabels);
uclabels[ucsel] += (types_uclabels[j][ucsel] + ucnlabels);
if self.skeletonize: sklabels[sksel] += (types_sklabels[j][sksel] + ucnlabels);
nlabels += types_nlabels[j]; ucnlabels += types_ucnlabels[j];
else:
labels[sel] = self.fg_types_labels[j];
uclabels[ucsel] = self.fg_types_labels[j];
if self.skeletonize: sklabels[sksel] = self.fg_types_labels[j]
nlabels += 1; ucnlabels += 1;
if self.dpWatershedTypes_verbose:
print('\tnlabels = %d' % (nlabels,))
#print('\tnlabels = %d %d' % (nlabels,labels.max())) # for debug only
#assert(nlabels == labels.max()) # sanity check for non-overlapping voxTypeSel, comment for speed
print('\tdone in %.4f s' % (time.time() - t,))
# make a fully-filled out version using bwdist nearest foreground neighbor
wlabels = emLabels.nearest_neighbor_fill(labels, mask=None,
sampling=self.attrs['scale'] if hasattr(self.attrs,'scale') else None)
# write out the results
if self.nTmin == 1: subgroups = ['%.8f' % (self.Ts[i],)]
else: subgroups = ['%d' % (self.Tmins[k],), '%.8f' % (self.Ts[i],)]
d = self.attrs.copy(); d['threshold'] = self.Ts[i];
d['types_nlabels'] = types_nlabels; d['Tmin'] = self.Tmins[k]
emLabels.writeLabels(outfile=self.outlabels, chunk=self.chunk.tolist(),
offset=self.offset_crop.tolist(), size=self.size_crop.tolist(), datasize=self.datasize.tolist(),
chunksize=self.chunksize.tolist(), data=labels, verbose=writeVerbose,
attrs=d, strbits=self.outlabelsbits, subgroups=['with_background']+subgroups )
emLabels.writeLabels(outfile=self.outlabels, chunk=self.chunk.tolist(),
offset=self.offset_crop.tolist(), size=self.size_crop.tolist(), datasize=self.datasize.tolist(),
chunksize=self.chunksize.tolist(), data=wlabels, verbose=writeVerbose,
attrs=d, strbits=self.outlabelsbits, subgroups=['zero_background']+subgroups )
d['type_nlabels'] = types_ucnlabels;
emLabels.writeLabels(outfile=self.outlabels, chunk=self.chunk.tolist(),
offset=self.offset_crop.tolist(), size=self.size_crop.tolist(), datasize=self.datasize.tolist(),
chunksize=self.chunksize.tolist(), data=uclabels, verbose=writeVerbose,
attrs=d, strbits=self.outlabelsbits, subgroups=['no_adjacencies']+subgroups )
if self.skeletonize:
emLabels.writeLabels(outfile=self.outlabels, chunk=self.chunk.tolist(),
offset=self.offset_crop.tolist(), size=self.size_crop.tolist(), datasize=self.datasize.tolist(),
chunksize=self.chunksize.tolist(), data=sklabels, verbose=writeVerbose,
attrs=d, strbits=self.outlabelsbits, subgroups=['skeletonized']+subgroups )
