def merge_cells(self, label_c1, label_c2, params, segments_manager, image_manager):
"""merges two cells"""
label_c1 = int(label_c1)
label_c2 = int(label_c2)
print len(self.cells[str(label_c2)].outline)
print len(self.cells[str(label_c2)].lines)
self.cells[str(label_c2)].stats["Area"] = (
self.cells[str(label_c2)].stats["Area"] + self.cells[str(label_c1)].stats["Area"]
)
self.cells[str(label_c2)].lines.extend(self.cells[str(label_c1)].lines)
self.cells[str(label_c2)].merged_list.append(label_c1)
self.cells[str(label_c2)].outline.extend(self.cells[str(label_c1)].outline)
self.cells[str(label_c2)].stats["Neighbours"] = (
self.cells[str(label_c2)].stats["Neighbours"] + self.cells[str(label_c1)].stats["Neighbours"] - 2
)
del self.cells[str(label_c1)]
rotations = cp.rotation_matrices(params.axial_step)
self.cells[str(label_c2)].compute_axes(rotations, image_manager.mask.shape)
self.cells[str(label_c2)].recompute_outline(segments_manager.labels)
if len(self.cells[str(label_c2)].merged_list) > 0:
self.cells[str(label_c2)].merged_with = "Yes"
print len(self.cells[str(label_c2)].outline)
print len(self.cells[str(label_c2)].lines)
print len(self.original_cells[str(label_c2)].outline)
print len(self.original_cells[str(label_c2)].lines)
def merge_cells(self, label_c1, label_c2, params, segments_manager,
image_manager):
"""merges two cells"""
label_c1 = int(label_c1)
label_c2 = int(label_c2)
self.cells[str(label_c2)].stats["Area"] = self.cells[str(
label_c2)].stats["Area"] + self.cells[str(label_c1)].stats["Area"]
self.cells[str(label_c2)].lines.extend(self.cells[str(label_c1)].lines)
self.cells[str(label_c2)].merged_list.append(label_c1)
self.cells[str(label_c2)].outline.extend(
self.cells[str(label_c1)].outline)
self.cells[str(label_c2)].stats["Neighbours"] = self.cells[str(
label_c2)].stats["Neighbours"] + self.cells[str(
label_c1)].stats["Neighbours"] - 2
del self.cells[str(label_c1)]
rotations = cp.rotation_matrices(params.axial_step)
self.cells[str(label_c2)].compute_axes(rotations,
image_manager.mask.shape)
self.cells[str(label_c2)].recompute_outline(segments_manager.labels)
if len(self.cells[str(label_c2)].merged_list) > 0:
self.cells[str(label_c2)].merged_with = "Yes"
def compute_cells(self, params, image_manager, segments_manager):
"""Creates a cell list that is stored on self.cells as a dict, where
each cell id is a key of the dict.
Also creates an overlay of the cells edges over both the base and
fluor image.
Requires the loading of the images and the computation of the
segments"""
self.cell_regions_from_labels(segments_manager.labels)
rotations = cp.rotation_matrices(params.axial_step)
self.compute_box_axes(rotations, image_manager.mask.shape)
self.original_cells = deepcopy(self.cells)
for k in self.cells.keys():
try:
c = self.cells[k]
if len(c.neighbours) > 0:
bestneigh = max(c.neighbours.iterkeys(), key=(lambda key: c.neighbours[key]))
bestinterface = c.neighbours[bestneigh]
cn = self.cells[str(int(bestneigh))]
if cp.check_merge(c, cn, rotations, bestinterface, image_manager.mask, params):
self.merge_cells(c.label, cn.label, params, segments_manager, image_manager)
except KeyError:
print "Cell was already merged and deleted"
for k in self.cells.keys():
cp.assign_cell_color(self.cells[k], self.cells, self.cell_colors)
self.overlay_cells(image_manager)
def split_cells(self, label_c1, params, segments_manager, image_manager):
"""Splits a previously merged cell."""
merged_cells = self.cells[str(label_c1)].merged_list
merged_cells.append(label_c1)
del self.cells[str(label_c1)]
rotations = cp.rotation_matrices(params.axial_step)
for id in merged_cells:
id = int(id)
self.cells[str(id)] = deepcopy(self.original_cells[str(id)])
self.cells[str(id)].compute_axes(rotations, image_manager.mask.shape)
self.cells[str(id)].recompute_outline(segments_manager.labels)
if len(self.cells[str(id)].merged_list) == 0:
self.cells[str(id)].merged_with = "No"
for k in self.cells.keys():
cp.assign_cell_color(self.cells[k], self.cells, self.cell_colors)
def split_cells(self, label_c1, params, segments_manager, image_manager):
"""Splits a previously merged cell."""
merged_cells = self.cells[str(label_c1)].merged_list
merged_cells.append(label_c1)
del self.cells[str(label_c1)]
rotations = cp.rotation_matrices(params.axial_step)
for id in merged_cells:
id = int(id)
self.cells[str(id)] = deepcopy(self.original_cells[str(id)])
self.cells[str(id)].compute_axes(rotations,
image_manager.mask.shape)
self.cells[str(id)].recompute_outline(segments_manager.labels)
if len(self.cells[str(id)].merged_list) == 0:
self.cells[str(id)].merged_with = "No"
for k in self.cells.keys():
cp.assign_cell_color(self.cells[k], self.cells, self.cell_colors)
def compute_cells(self, params, image_manager, segments_manager):
"""Creates a cell list that is stored on self.cells as a dict, where
each cell id is a key of the dict.
Also creates an overlay of the cells edges over both the base and
fluor image.
Requires the loading of the images and the computation of the
segments"""
self.cell_regions_from_labels(segments_manager.labels,
params.imageloaderparams.pixel_size)
rotations = cp.rotation_matrices(
params.cellprocessingparams.axial_step)
self.compute_box_axes(rotations, image_manager.mask.shape,
params.imageloaderparams.pixel_size)
self.original_cells = deepcopy(self.cells)
for k in list(self.cells.keys()):
try:
c = self.cells[k]
if len(c.neighbours) > 0:
bestneigh = max(list(iter(c.neighbours.keys())),
key=(lambda key: c.neighbours[key]))
bestinterface = c.neighbours[bestneigh]
cn = self.cells[str(int(bestneigh))]
if cp.check_merge(c, cn, rotations, bestinterface,
image_manager.mask,
params.cellprocessingparams):
self.merge_cells(c.label, cn.label, params,
segments_manager, image_manager)
except KeyError:
print("Cell was already merged and deleted")
for k in self.cells.keys():
cp.assign_cell_color(self.cells[k], self.cells, self.cell_colors,
params.imageloaderparams.pixel_size)
self.overlay_cells(image_manager)
def remove_sept_from_membrane(self, maskshape):
"""Method used to remove the pixels of the septum that were still in
the membrane"""
# get outline points of septum mask
septum_outline = []
septum_mask = self.sept_mask
septum_outline = self.get_outline_points(septum_mask)
# compute box of the septum
septum_box = self.compute_sept_box_fix(septum_outline, maskshape)
# compute axis of the septum
rotations = cp.rotation_matrices(5)
points = np.asarray(septum_outline) # in two columns, x, y
width = len(points) + 1
# no need to do more rotations, due to symmetry
for rix in range(len(rotations) / 2 + 1):
r = rotations[rix]
nx0, ny0, nx1, ny1, nwidth = cp.bound_rectangle(
np.asarray(np.dot(points, r)))
if nwidth < width:
width = nwidth
x0 = nx0
x1 = nx1
y0 = ny0
y1 = ny1
angle = rix
rotation = rotations[angle]
# midpoints
mx = (x1 + x0) / 2
my = (y1 + y0) / 2
# assumes long is X. This duplicates rotations but simplifies
# using different algorithms such as brightness
long = [[x0, my], [x1, my]]
short = [[mx, y0], [mx, y1]]
short = np.asarray(np.dot(short, rotation.T), dtype=np.int32)
long = np.asarray(np.dot(long, rotation.T), dtype=np.int32)
# check if axis fall outside area due to rounding errors
bx0, by0, bx1, by1 = septum_box
short[0] = cp.bounded_point(bx0, bx1, by0, by1, short[0])
short[1] = cp.bounded_point(bx0, bx1, by0, by1, short[1])
long[0] = cp.bounded_point(bx0, bx1, by0, by1, long[0])
long[1] = cp.bounded_point(bx0, bx1, by0, by1, long[1])
length = np.linalg.norm(long[1] - long[0])
width = np.linalg.norm(short[1] - short[0])
if length < width:
dum = length
length = width
width = dum
dum = short
short = long
long = dum
# expand long axis to create a linmask
bx0, by0 = long[0]
bx1, by1 = long[1]
h, w = self.sept_mask.shape
linmask = np.zeros((h, w))
h, w = self.sept_mask.shape[0] - 2, self.sept_mask.shape[1] - 2
bin_factor = int(width)
if bx1 - bx0 == 0:
x, y = line(bx0, 0, bx0, w)
linmask[x, y] = 1
try:
linmask = morphology.binary_dilation(
linmask, np.ones((bin_factor, bin_factor))).astype(float)
except RuntimeError:
bin_factor = 4
linmask = morphology.binary_dilation(
linmask, np.ones((bin_factor, bin_factor))).astype(float)
else:
m = ((by1 - by0) / (bx1 - bx0))
b = by0 - m * bx0
if b < 0:
l_y0 = 0
l_x0 = int(-b / m)
if h * m + b > w:
l_y1 = w
l_x1 = int((w - b) / m)
else:
l_x1 = h
l_y1 = int(h * m + b)
elif b > w:
l_y0 = w
l_x0 = int((w - b) / m)
if h * m + b < 0:
l_y1 = 0
l_x1 = int(-b / m)
else:
l_x1 = h
l_y1 = int((h - b) / m)
else:
l_x0 = 0
l_y0 = int(b)
if m > 0:
if h * m + b > w:
l_y1 = w
l_x1 = int((w - b) / m)
else:
l_x1 = h
l_y1 = int(h * m + b)
elif m < 0:
if h * m + b < 0:
l_y1 = 0
l_x1 = int(-b / m)
else:
l_x1 = h
l_y1 = int(h * m + b)
else:
l_x1 = h
l_y1 = int(b)
x, y = line(l_x0, l_y0, l_x1, l_y1)
linmask[x, y] = 1
try:
linmask = morphology.binary_dilation(
linmask, np.ones((bin_factor, bin_factor))).astype(float)
except RuntimeError:
bin_factor = 4
linmask = morphology.binary_dilation(
linmask, np.ones((bin_factor, bin_factor))).astype(float)
return img_as_float(linmask)
def remove_sept_from_membrane(self, maskshape):
"""Method used to remove the pixels of the septum that were still in
the membrane"""
# get outline points of septum mask
septum_outline = []
septum_mask = self.sept_mask
septum_outline = self.get_outline_points(septum_mask)
# compute box of the septum
septum_box = self.compute_sept_box_fix(septum_outline, maskshape)
# compute axis of the septum
rotations = cp.rotation_matrices(5)
points = np.asarray(septum_outline) # in two columns, x, y
width = len(points) + 1
# no need to do more rotations, due to symmetry
for rix in range(len(rotations) / 2 + 1):
r = rotations[rix]
nx0, ny0, nx1, ny1, nwidth = cp.bound_rectangle(np.asarray(np.dot(points, r)))
if nwidth < width:
width = nwidth
x0 = nx0
x1 = nx1
y0 = ny0
y1 = ny1
angle = rix
rotation = rotations[angle]
# midpoints
mx = (x1 + x0) / 2
my = (y1 + y0) / 2
# assumes long is X. This duplicates rotations but simplifies
# using different algorithms such as brightness
long = [[x0, my], [x1, my]]
short = [[mx, y0], [mx, y1]]
short = np.asarray(np.dot(short, rotation.T), dtype=np.int32)
long = np.asarray(np.dot(long, rotation.T), dtype=np.int32)
# check if axis fall outside area due to rounding errors
bx0, by0, bx1, by1 = septum_box
short[0] = cp.bounded_point(bx0, bx1, by0, by1, short[0])
short[1] = cp.bounded_point(bx0, bx1, by0, by1, short[1])
long[0] = cp.bounded_point(bx0, bx1, by0, by1, long[0])
long[1] = cp.bounded_point(bx0, bx1, by0, by1, long[1])
length = np.linalg.norm(long[1] - long[0])
width = np.linalg.norm(short[1] - short[0])
if length < width:
dum = length
length = width
width = dum
dum = short
short = long
long = dum
# expand long axis to create a linmask
bx0, by0 = long[0]
bx1, by1 = long[1]
h, w = self.sept_mask.shape
linmask = np.zeros((h, w))
h, w = self.sept_mask.shape[0] - 2, self.sept_mask.shape[1] - 2
bin_factor = int(width)
if bx1 - bx0 == 0:
x, y = line(bx0, 0, bx0, w)
linmask[x, y] = 1
try:
linmask = morphology.binary_dilation(linmask, np.ones((bin_factor, bin_factor))).astype(float)
except RuntimeError:
bin_factor = 4
linmask = morphology.binary_dilation(linmask, np.ones((bin_factor, bin_factor))).astype(float)
else:
m = (by1 - by0) / (bx1 - bx0)
b = by0 - m * bx0
if b < 0:
l_y0 = 0
l_x0 = int(-b / m)
if h * m + b > w:
l_y1 = w
l_x1 = int((w - b) / m)
else:
l_x1 = h
l_y1 = int(h * m + b)
elif b > w:
l_y0 = w
l_x0 = int((w - b) / m)
if h * m + b < 0:
l_y1 = 0
l_x1 = int(-b / m)
else:
l_x1 = h
l_y1 = int((h - b) / m)
else:
l_x0 = 0
l_y0 = int(b)
if m > 0:
if h * m + b > w:
l_y1 = w
l_x1 = int((w - b) / m)
else:
l_x1 = h
l_y1 = int(h * m + b)
elif m < 0:
if h * m + b < 0:
l_y1 = 0
l_x1 = int(-b / m)
else:
l_x1 = h
l_y1 = int(h * m + b)
else:
l_x1 = h
l_y1 = int(b)
x, y = line(l_x0, l_y0, l_x1, l_y1)
linmask[x, y] = 1
try:
linmask = morphology.binary_dilation(linmask, np.ones((bin_factor, bin_factor))).astype(float)
except RuntimeError:
bin_factor = 4
linmask = morphology.binary_dilation(linmask, np.ones((bin_factor, bin_factor))).astype(float)
return img_as_float(linmask)
