def optic_disk_detect_3(img):
'''
Method that seems to work well with DIARETDB1 database.
'''
hsi = rgb_to_hsi(img)
intensity = hsi[:, :, 2].copy()
#plt.axis('off'); show_image(intensity)
#i_sp = add_salt_and_pepper(intensity, 0.005)
i_med = mh.median_filter(intensity) # use Wiener filter instead?
i_clahe = skimage.exposure.equalize_adapthist(i_med)
#plt.axis('off'); show_image(i_clahe)
seeds = (i_clahe > 0.85)
seeds = skimage.morphology.remove_small_objects(seeds, min_size=300, connectivity=2)
#plt.axis('off'); show_image(seeds)
optic_disk_map = region_growing_1(i_clahe, radius=3, tol1=0.1, tol2=0.2, tol3=0.2, seeds=seeds)
optic_disk_map += 1
#plt.axis('off'); show_image(optic_disk_map)
_cl_areas = cl_areas(optic_disk_map)
print _cl_areas
optic_disk_map = leave_segments_by_mask(optic_disk_map,
(8000 < _cl_areas) & (_cl_areas < 30000))
#optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=500, connectivity=2)
optic_disk_map = mh.close_holes(mh.close(optic_disk_map, Bc=np.ones((10, 10))))
#optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=5000, connectivity=2)
if np.all(optic_disk_map == 0):
print 'Disk not found'
return optic_disk_map
def predict(self, filenames_list, scale=0.4, n_clusters=4):
if not isinstance(filenames_list, list):
raise Exception('Input list of files is not a list actually')
rectangles = []
for filename in filenames_list:
img_orig = gaussian(rgb2lab(imread(filename))[:, :, 1], 3)
h_orig, w_orig = img_orig.shape
h_small, w_small = int(h_orig * scale), int(w_orig * scale)
thumb = resize(img_orig, (h_small, w_small))
model = KMeans(n_clusters)
segments_flatten = model.fit_predict(thumb.reshape(-1, 1))
segments = segments_flatten.reshape(h_small, w_small)
seg_means = [
np.mean(thumb[segments == n])
for n in range(np.max(segments) + 1)
]
brightest_seg = np.argmax(seg_means)
mask = segments == brightest_seg
thumb_closed = mahotas.close(
mask, np.ones((int(30 * scale), int(30 * scale))))
label_objects, nb_labels = ndi.label(thumb_closed)
sizes = np.bincount(label_objects.ravel())
mask_sizes = sizes == np.sort(sizes)[-2]
img_cleaned = mask_sizes[label_objects]
img_final = resize(img_cleaned, (h_orig, w_orig)).astype(int)
rectangles.append(blob.bound_rect(img_final))
return rectangles
def segment_layer(filename, params): ''' Segment one layer in a stack ''' #extract pixel size in xy and z xsize, zsize = extract_zoom(params.folder) #load image img = tifffile.imread(params.inputfolder + params.folder + filename) #normalize image img = ndimage.median_filter(img, 3) per_low = np.percentile(img, 5) img[img < per_low] = per_low img = img - img.min() per_high = np.percentile(img, 99) img[img > per_high] = per_high img = img*255./img.max() imgf = ndimage.gaussian_filter(img*1., 30./xsize).astype(np.uint8) kmask = (imgf > mahotas.otsu(imgf.astype(np.uint8)))*255. sizefactor = 10 small = ndimage.interpolation.zoom(kmask, 1./sizefactor) #scale the image to a smaller size rad = int(300./xsize) small_ext = np.zeros([small.shape[0] + 4*rad, small.shape[1] + 4*rad]) small_ext[2*rad : 2*rad + small.shape[0], 2*rad : 2*rad + small.shape[1]] = small small_ext = mahotas.close(small_ext.astype(np.uint8), mahotas.disk(rad)) small = small_ext[2*rad : 2*rad + small.shape[0], 2*rad : 2*rad + small.shape[1]] small = mahotas.close_holes(small)*1. small = small*255./small.max() kmask = ndimage.interpolation.zoom(small, sizefactor) #scale back to normal size kmask = normalize(kmask) kmask = (kmask > mahotas.otsu(kmask.astype(np.uint8)))*255. #remove artifacts of interpolation if np.median(imgf[np.where(kmask > 0)]) < (np.median(imgf[np.where(kmask == 0)]) + 1)*3: kmask = np.zeros_like(kmask) #save indices of the kidney mask # ind = np.where(kmask > 0) # ind = np.array(ind) # np.save(params.inputfolder + '../segmented/masks/' + params.folder + filename[:-4] + '.npy', ind) #save outlines im = np.zeros([img.shape[0], img.shape[1], 3]) img = tifffile.imread(params.inputfolder + params.folder + filename) im[:,:,0] = im[:,:,1] = im[:,:,2] = np.array(img) output = overlay(kmask, im, (255,0,0), borders = True) tifffile.imsave(params.inputfolder + '../segmented/outlines/' + params.folder + filename[:-4] + '.tif', (output).astype(np.uint8))
def optic_disk_detect_2(img):
hsi = rgb_to_hsi(img)
intensity = hsi[:, :, 2].copy()
i_sp = add_salt_and_pepper(intensity, 0.005)
i_med = mh.median_filter(i_sp)
i_clahe = skimage.exposure.equalize_adapthist(i_med)
optic_disk_map = (i_clahe > 0.6) & (hsi[:, :, 1] < 0.3)
#show_image(optic_disk_map)
optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=500, connectivity=2)
optic_disk_map = mh.close_holes(mh.close(optic_disk_map, Bc=np.ones((30, 30))))
optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=10000, connectivity=2)
if np.all(optic_disk_map == 0):
print 'Disk not found'
return optic_disk_map
def predict(self, filenames_list, threshold=0.75):
if not isinstance(filenames_list, list):
raise Exception('Input list of files is not a list actually')
rectangles = []
for filename in filenames_list:
img = imread(filename)
img_a = color.scaler(gaussian(rgb2lab(img)[:,:,1], 3))
h,w = img_a.shape
mask = img_a > threshold
img_closed = mahotas.close(mask, disk(h/15))
label_objects, nb_labels = ndi.label(img_closed)
sizes = np.bincount(label_objects.ravel())
mask_sizes = sizes == np.sort(sizes)[-2]
img_cleaned = mask_sizes[label_objects]
rectangles.append(blob.bound_rect(img_cleaned))
return rectangles
if len(img.shape) == 3: #color image
#convert to grayscale
img = mh.colors.rgb2gray(img, dtype=np.uint8)
# thresholding
T_otsu = mh.otsu(img) # finds a numeric threshold
img = (img > T_otsu) # make image binary
# invert the image (just because the test image is stored the other way)
img = ~img
# close single-pixel holes. Also makes the skeletonization much more well-behaved,
# with less tiny branches close to terminals.
#
# This can create loops if two branches are separated by < 3 px of background
img = mh.close(img)
print('Thinning...')
# skeletonization from scikit image.
# Zhang-Suen algorithm (apparently with staircase removal)
skel = morphology.skeletonize(img)
# Try mahotas skeletonization. Makes many spikes.
# works better after mh.close(), but still splits many tips in two.
# Also gives staircases in the skeleton.
#skel = mh.thin(img)
#skel = morphology.skeletonize(skel) # one pass of the other skeletonization to remove staircases
################
import numpy as np
import mahotas as mh
image = mh.imread('../1400OS_10_01.jpeg')
image = mh.colors.rgb2gray(image, dtype=np.uint8)
thresh = mh.thresholding.otsu(image)
print(thresh)
otsubin = (image > thresh)
mh.imsave('otsu-threshold.jpeg', otsubin.astype(np.uint8) * 255)
otsubin = ~mh.close(~otsubin, np.ones((15, 15)))
mh.imsave('otsu-closed.jpeg', otsubin.astype(np.uint8) * 255)
thresh = mh.thresholding.rc(image)
print(thresh)
mh.imsave('rc-threshold.jpeg', (image > thresh).astype(np.uint8) * 255)
if len(img.shape) == 3: #color image
#convert to grayscale
img = mh.colors.rgb2gray(img, dtype=np.uint8)
# thresholding
T_otsu = mh.otsu(img) # finds a numeric threshold
img = (img > T_otsu) # make image binary
# invert the image (just because the test image is stored the other way)
img = ~img
# close single-pixel holes. Also makes the skeletonization much more well-behaved,
# with less tiny branches close to terminals.
#
# This can create loops if two branches are separated by < 3 px of background
img = mh.close(img)
print('Thinning...')
# skeletonization from scikit image.
# Zhang-Suen algorithm (apparently with staircase removal)
skel = morphology.skeletonize(img)
# Try mahotas skeletonization. Makes many spikes.
# works better after mh.close(), but still splits many tips in two.
# Also gives staircases in the skeleton.
#skel = mh.thin(img)
#skel = morphology.skeletonize(skel) # one pass of the other skeletonization to remove staircases
################
# find terminals and junctions. t and j are in the format [(x1, y1), (x2, y2), ...]
# This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
import numpy as np
import mahotas as mh
image = mh.imread('../SimpleImageDataset/building05.jpg')
image = mh.colors.rgb2gray(image, dtype=np.uint8)
thresh = mh.thresholding.otsu(image)
print(thresh)
otsubin = (image > thresh)
mh.imsave('otsu-threshold.jpeg', otsubin.astype(np.uint8) * 255)
otsubin = ~ mh.close(~otsubin, np.ones((15, 15)))
mh.imsave('otsu-closed.jpeg', otsubin.astype(np.uint8) * 255)
thresh = mh.thresholding.rc(image)
print(thresh)
mh.imsave('rc-threshold.jpeg', (image > thresh).astype(np.uint8) * 255)
def findClosestEdge(G, p):
emin = None
dmin = 1e100
for e in G.edges():
d = distPointLine(e[0], e[1], p)
if d < dmin:
dmin = d
emin = e
return emin
#@profile
#def my_func():
###################################################################################
# Main code starts here
#
# take the file name from the command line, if given
"""
if len(sys.argv) > 1:
filename = sys.argv[1]
else:
filename = 'img/f.png'
"""
filename = '1-1.png'
# =============================================================================
# # read database of leaves
# leaves = {}
#
# fileHandle = None
# try:
# leavesFile = getScriptPath() + '/leaves.json'
# print('Database file: ' + leavesFile)
# fileHandle = open(leavesFile, 'r')
# except:
# print('Could not load the leaf data base')
#
# if fileHandle != None:
# # if the file exists, but the JSON is not correct, this will fail
# # This is on purpose, since if we continue and save the database at the end,
# # the data in it will be overwritten
# leaves = json.load(fileHandle)
#
#
# path_basename = os.path.splitext(filename)[0]
# print('path_basename', path_basename)
# basename = os.path.splitext(os.path.basename(filename))[0]
#
# ## Read from .json
# leafData = {}
# try:
# #find the current leaf, based on file name
# leafData = leaves[basename]
# except:
# print('%s was not found in the leaf data base'%basename)
# # add an entry for this leaf to the ditionary
# leaves[basename] = leafData
#
# print('Data for this leaf: ' + str(leafData))
#
# px_mm = None
# root = None
#
# try:
# px_mm = leafData['px_mm']
# except:
# print('No resolution found for leaf %s'%basename)
#
# try:
# root = tuple(leafData['root'])
# except:
# print('No root found for leaf %s'%basename)
#
# print('Resolution: ' + str(px_mm) + ' px/mm')
# print('Root: ' + str(root))
# print()
#
# print('Reading image %s'%filename)
# =============================================================================
root = None
img = mh.imread(filename)
# color image handling - works if the background is brighter than the object
if len(img.shape) == 3: #color image
#convert to grayscale
img = mh.colors.rgb2gray(img, dtype=np.uint8)
# thresholding
T_otsu = mh.otsu(img) # finds a numeric threshold
img = (img > T_otsu) # make image binary
# invert the image (just because the test image is stored the other way)
img = ~img
# close single-pixel holes. Also makes the skeletonization much more well-behaved,
# with less tiny branches close to terminals.
#
# This can create loops if two branches are separated by < 3 px of background
img = mh.close(img)
# print('Thinning...')
# skeletonization from scikit image.
# Zhang-Suen algorithm (apparently with staircase removal)
skel = morphology.skeletonize(img)
# Try mahotas skeletonization. Makes many spikes.
# works better after mh.close(), but still splits many tips in two.
# Also gives staircases in the skeleton.
#skel = mh.thin(img)
#skel = morphology.skeletonize(skel) # one pass of the other skeletonization to remove staircases
################
# find terminals and junctions. t and j are in the format [(x1, y1), (x2, y2), ...]
# print('Features...')
t, j = terminals(skel)
if root == None:
# unpack the tuples to separate lists of x:s and y:s, for plotting and root selection
#tx = [x[0] for x in t]
#ty = [x[1] for x in t]
dist_terminal = []
for x in t:
_dist = distPointLine([0, 316], [img.shape[1], 316], list(x))
dist_terminal.append(_dist)
# find the index of the lowest terminal. Use that as the root, for now.
iroot = dist_terminal.index(min(dist_terminal))
# find the lowest node, to use as root
root = t[iroot]
else:
if root not in t + j:
newroot = findClosest(t + j, root)
print('Moving the root to a node in the tree. New root is',
str(newroot), 'old root was', str(root), 'distance',
dist(newroot, root))
root = newroot
# print('Plotting')
# make the skeleton image's background transparent
skel = np.ma.masked_where(skel == 0, skel)
#visited = np.ma.masked_where (visited==0, visited)
# fig = plt.figure()
# Plot images. Inversion here is just for nicer coloring
# interpolation=nearest is to turn smoothing off.
width = skel.shape[1]
height = skel.shape[0]
# plt.axis((0,width,height,0))
# plt.imshow(~img, cmap=leaf_colors, interpolation="nearest")
#plt.imshow(np.sqrt(dmap), cmap=mpl.cm.jet_r, interpolation="nearest")
# plt.imshow(skel, cmap=skel_colors, interpolation="nearest")
#plt.imshow(visited, cmap=mpl.cm.cool, interpolation="nearest")
# update measures that depend on graph structure or root placement
def updateMeasures(G, root):
#print(' Strahler order...')
StrahlerOrder(G, root)
#print(' apical distances...')
measureApicalDist(G)
#print(' branch angles...')
measureAngles(G)
def buildGraph(img, skel):
#print('Building tree...')
G = nx.Graph()
visited = np.zeros_like(
skel) # a new array of zeros, same dimensions as skel
#print(' distance transform...')
dmap = mh.distance(img)
#print(' constructing tree...')
buildTree(skel, visited, dmap, root, j, t, G)
# measure node diameters
measureDia(G, dmap)
# automatically remove bad nodes and branches, e.g. too small ones
removed = cleanup(G, root)
# show (automatically) removed nodes
for x, y in removed:
plt.gca().add_patch(plt.Circle((x, y), radius=4, alpha=.4))
updateMeasures(G, root)
#print('Done.')
return G
# read in the graph from a previous run, if it exists
path_basename = os.path.splitext(filename)[0]
try:
G = nx.read_gpickle(path_basename + '_graph.pkl')
print('Loaded graph from ' + path_basename + '_graph.pkl')
except:
# could not read the graph. Constructing it now
G = buildGraph(img, skel)
# handles to plot elements
nodes = None
edges = None
node_labels = None
edge_labels = None
rad = []
# plot_graph(G)
# semi-transparent circles on the nodes
# for p,r in zip(G.nodes(), rad):
# plt.gca().add_patch(plt.Circle(p, radius=r, alpha=terminal_disk_alpha, color=terminal_disk_color))
# root_patch = plt.Circle(root, radius=40, alpha=root_disk_alpha, color=root_disk_color)
# plt.gca().add_patch(root_patch)
# =============================================================================
# def setRoot(root):
# root_patch.center = root;
#
# # save the new root in database
# # convert to int from numpy type, for JSON to work later
# leafData['root'] = (int(root[0]), int(root[1]))
#
#
# # a function called when the user clicks a node
# =============================================================================
def onpick(event):
global root
# make the clicked node the new root
# for some reason it's difficult to get the coordinates of the clicked node
# so we use mouse coordinates and search for the closest node.
root = findClosest(G.nodes(),
(event.mouseevent.xdata, event.mouseevent.ydata))
print('New root: ' + str(root))
#setRoot(root)
updateMeasures(G, root)
##report(G)
# plot_graph(G)
# fig.canvas.draw()
undo_stack = []
# a function called on keypress events
def keypress(event):
global nodes, edges, node_labels, G, root
#print('press', event.key)
sys.stdout.flush()
if event.key == 'd': # delete closest node
p = findClosest(G.nodes(), (event.xdata, event.ydata))
print('closest node is (%5.1f, %5.1f)' % p)
if p == root:
print('Cannot remove the root.')
return
undo_stack.append((G.copy(), root))
G.remove_node(p)
updateMeasures(G, root)
# plot_graph(G)
# fig.canvas.draw()
if event.key == 'x': # delete closest branch
e = findClosestEdge(G, (event.xdata, event.ydata))
undo_stack.append((G.copy(), root))
G.remove_edge(*e)
updateMeasures(G, root)
# plot_graph(G)
# fig.canvas.draw()
if event.key == 'u': # undo
if len(undo_stack) > 0:
print('Undo')
G, root = undo_stack.pop()
#setRoot(root)
updateMeasures(G, root)
# plot_graph(G)
# fig.canvas.draw()
else:
print('No further undo')
if event.key == 'r': #re-build the graph from skeleton
print('Rebuilding tree')
undo_stack.append((G.copy(), root))
G = buildGraph(img, skel)
# plot_graph(G)
# fig.canvas.draw()
# register the event callback functions
# fig.canvas.mpl_connect('pick_event', onpick)
# fig.canvas.mpl_connect('key_press_event', keypress)
# plot disks for the apical distance, just for testing
#for n in G.nodes():
# if G.node[n]['level'] == 1:
# r = G.node[n]['apicaldist']
# plt.gca().add_patch(plt.Circle(n, radius=r, alpha=.4))
# print(G.node[n])
# report(G)
# plt.tight_layout()
# show the plot - program pauses here for as long as the window is open
# plt.show()
#
# saveTreeText(G, path_basename+'_branches.txt', path_basename+'_nodes.txt');
# nx.write_gpickle(G, path_basename+'_graph.pkl')
def saveReport(reportFile, report_text, header_text):
# if the file does not exist already, write the header
present = os.path.isfile(reportFile)
print('Saving leaf report in file ' + reportFile)
try:
f = open(reportFile, 'at')
if not present:
f.write(header_text + '\n')
f.write(report_text + '\n')
except:
print('Error when saving the report')
def main(image, mask, threshold=150, bead_size=2, superpixel_size=4,
close_surface=False, close_disc_size=8, plot=False):
'''Converts an image stack with labelled cell surface to a cell
`volume` image
Parameters
----------
image: numpy.ndarray[Union[numpy.uint8, numpy.uint16]]
grayscale image in which beads should be detected (3D)
mask: numpy.ndarray[Union[numpy.int32, numpy.bool]]
binary or labeled image of cell segmentation (2D)
threshold: int, optional
intensity of bead (default: ``150``)
bead_size: int, optional
minimal size of bead (default: ``2``)
superpixel_size: int, optional
size of superpixels for searching the 3D position of a bead
close_surface: bool, optional
whether the interpolated surface should be morphologically closed
close_disc_size: int, optional
size in pixels of the disc used to morphologically close the
interpolated surface
plot: bool, optional
whether a plot should be generated (default: ``False``)
Returns
-------
jtmodules.generate_volume_image.Output
'''
n_slices = image.shape[-1]
logger.debug('input image has size %d in last dimension', n_slices)
logger.debug('mask beads inside cell')
beads_outside_cell = np.copy(image)
for iz in range(n_slices):
beads_outside_cell[mask > 0, iz] = 0
logger.debug('search for 3D position of beads outside cell')
slide = np.argmax(beads_outside_cell, axis=2)
slide[slide > np.percentile(slide[mask == 0], 20)] = 0
logger.debug('determine surface of slide')
slide_coordinates = array_to_coordinate_list(slide)
bottom_surface = fit_plane(subsample_coordinate_list(
slide_coordinates, 2000)
)
logger.debug('detect_beads in 2D')
mip = np.max(image, axis=-1)
try:
# TODO: use LOG filter???
beads, beads_centroids = detect_blobs(
image=mip, mask=np.invert(mask > 0), threshold=threshold,
min_area=bead_size
)
except:
logger.warn('detect_blobs failed, returning empty volume image')
volume_image = np.zeros(shape=mask.shape, dtype=image.dtype)
figure = str()
return Output(volume_image, figure)
n_beads = np.count_nonzero(beads_centroids)
logger.info('found %d beads on cells', n_beads)
if n_beads == 0:
logger.warn('empty volume image')
volume_image = np.zeros(shape=mask.shape, dtype=image.dtype)
else:
logger.debug('locate beads in 3D')
beads_coords_3D = locate_in_3D(
image=image, mask=beads_centroids,
bin_size=superpixel_size
)
logger.info('interpolate cell surface')
volume_image = interpolate_surface(
coords=beads_coords_3D,
output_shape=np.shape(image[:, :, 1]),
method='linear'
)
volume_image = volume_image.astype(image.dtype)
if (close_surface is True):
import mahotas as mh
logger.info('morphological closing of cell surface')
volume_image = mh.close(volume_image,
Bc=mh.disk(close_disc_size))
volume_image[mask == 0] = 0
if plot:
logger.debug('convert bottom surface plane to image for plotting')
bottom_surface_image = np.zeros(slide.shape, dtype=np.uint8)
for ix in range(slide.shape[0]):
for iy in range(slide.shape[1]):
bottom_surface_image[ix, iy] = plane(
ix, iy, bottom_surface.x)
logger.info('create plot')
from jtlib import plotting
plots = [
plotting.create_intensity_image_plot(
mip, 'ul', clip=True
),
plotting.create_intensity_image_plot(
bottom_surface_image, 'll', clip=True
),
plotting.create_intensity_image_plot(
volume_image, 'ur', clip=True
)
]
figure = plotting.create_figure(
plots, title='Convert stack to volume image'
)
else:
figure = str()
return Output(volume_image, figure)
def main(image,
mask,
threshold=150,
bead_size=2,
superpixel_size=4,
close_surface=False,
close_disc_size=8,
plot=False):
'''Converts an image stack with labelled cell surface to a cell
`volume` image
Parameters
----------
image: numpy.ndarray[Union[numpy.uint8, numpy.uint16]]
grayscale image in which beads should be detected (3D)
mask: numpy.ndarray[Union[numpy.int32, numpy.bool]]
binary or labeled image of cell segmentation (2D)
threshold: int, optional
intensity of bead (default: ``150``)
bead_size: int, optional
minimal size of bead (default: ``2``)
superpixel_size: int, optional
size of superpixels for searching the 3D position of a bead
close_surface: bool, optional
whether the interpolated surface should be morphologically closed
close_disc_size: int, optional
size in pixels of the disc used to morphologically close the
interpolated surface
plot: bool, optional
whether a plot should be generated (default: ``False``)
Returns
-------
jtmodules.generate_volume_image.Output
'''
n_slices = image.shape[-1]
logger.debug('input image has size %d in last dimension', n_slices)
logger.debug('mask beads inside cell')
beads_outside_cell = np.copy(image)
for iz in range(n_slices):
beads_outside_cell[mask > 0, iz] = 0
logger.debug('search for 3D position of beads outside cell')
slide = np.argmax(beads_outside_cell, axis=2)
slide[slide > np.percentile(slide[mask == 0], 20)] = 0
logger.debug('determine surface of slide')
slide_coordinates = array_to_coordinate_list(slide)
bottom_surface = fit_plane(
subsample_coordinate_list(slide_coordinates, 2000))
logger.debug('detect_beads in 2D')
mip = np.max(image, axis=-1)
try:
# TODO: use LOG filter???
beads, beads_centroids = detect_blobs(image=mip,
mask=np.invert(mask > 0),
threshold=threshold,
min_area=bead_size)
except:
logger.warn('detect_blobs failed, returning empty volume image')
volume_image = np.zeros(shape=mask.shape, dtype=image.dtype)
figure = str()
return Output(volume_image, figure)
n_beads = np.count_nonzero(beads_centroids)
logger.info('found %d beads on cells', n_beads)
if n_beads == 0:
logger.warn('empty volume image')
volume_image = np.zeros(shape=mask.shape, dtype=image.dtype)
else:
logger.debug('locate beads in 3D')
beads_coords_3D = locate_in_3D(image=image,
mask=beads_centroids,
bin_size=superpixel_size)
logger.info('interpolate cell surface')
volume_image = interpolate_surface(coords=beads_coords_3D,
output_shape=np.shape(image[:, :,
1]),
method='linear')
volume_image = volume_image.astype(image.dtype)
if (close_surface is True):
import mahotas as mh
logger.info('morphological closing of cell surface')
volume_image = mh.close(volume_image, Bc=mh.disk(close_disc_size))
volume_image[mask == 0] = 0
if plot:
logger.debug('convert bottom surface plane to image for plotting')
bottom_surface_image = np.zeros(slide.shape, dtype=np.uint8)
for ix in range(slide.shape[0]):
for iy in range(slide.shape[1]):
bottom_surface_image[ix, iy] = plane(ix, iy, bottom_surface.x)
logger.info('create plot')
from jtlib import plotting
plots = [
plotting.create_intensity_image_plot(mip, 'ul', clip=True),
plotting.create_intensity_image_plot(bottom_surface_image,
'll',
clip=True),
plotting.create_intensity_image_plot(volume_image, 'ur', clip=True)
]
figure = plotting.create_figure(plots,
title='Convert stack to volume image')
else:
figure = str()
return Output(volume_image, figure)