def __init__(self, settings_filename=None, settings=None, prefix=''):
if settings is None and settings_filename is None:
raise ValueError("Either a settings object or a settings filename has to be given.")
if not settings is None:
self.settings = settings
elif not settings_filename is None:
self.settings = Settings(os.path.abspath(settings_filename), dctGlobals=globals())
for folder in self.settings.make_folder:
if not os.path.isdir(folder):
print 'made folder %s' % folder
os.makedirs(folder)
self.ov = Overlay()
self.sw = SimpleWorkflow(settings=self.settings)
self.prefix=prefix
class Select(object):
def __init__(self, settings_filename=None, settings=None, prefix=''):
if settings is None and settings_filename is None:
raise ValueError("Either a settings object or a settings filename has to be given.")
if not settings is None:
self.settings = settings
elif not settings_filename is None:
self.settings = Settings(os.path.abspath(settings_filename), dctGlobals=globals())
for folder in self.settings.make_folder:
if not os.path.isdir(folder):
print 'made folder %s' % folder
os.makedirs(folder)
self.ov = Overlay()
self.sw = SimpleWorkflow(settings=self.settings)
self.prefix=prefix
def overlay_graph(self, img, labels=None):
thickness = 3
radius_offset=2
if labels is None:
labels = []
width = img.shape[1]
height = img.shape[0]
#img = self.sw.reduce_range(img_16bit)
colim = color.gray2rgb(img)
if self.nodes is None:
print 'graph is not yet built.'
return
# max_color = max(colorvalue)
# if max_color > 1.0:
# colorvalue = [a / np.float(max_color) for a in colorvalue]
# first we draw the edges
for node_id, node in self.nodes.iteritems():
for nb_id in node['neighbors']:
nb_center = self.nodes[nb_id]['center']
rr, cc = skimage.draw.line(np.int(np.round(node['center'][0])),
np.int(np.round(node['center'][1])),
np.int(np.round(self.nodes[nb_id]['center'][0])),
np.int(np.round(self.nodes[nb_id]['center'][1])) )
colorvalue = (255,255,255)
indices = filter(lambda i: rr[i] > 0 and rr[i] < height and cc[i]>0 and cc[i]<width,
range(len(rr)) )
rr = rr[indices]
cc = cc[indices]
for i, col in enumerate(colorvalue):
colim[rr,cc,i] = col
# second, we draw the graph labels
for nodelabel, node in self.nodes.iteritems():
rr, cc = skimage.draw.circle(np.round(node['center'][0]),
np.round(node['center'][1]),
self.settings.graph_radius)
indices = filter(lambda i: rr[i] > 0 and rr[i] < height and cc[i]>0 and cc[i]<width,
range(len(rr)) )
rr = rr[indices]
cc = cc[indices]
if node['chosen']:
colorvalue = self.settings.graph_color_code[0]
elif node['allowed']:
colorvalue = self.settings.graph_color_code[1]
else:
colorvalue = self.settings.graph_color_code[2]
for i, col in enumerate(colorvalue):
colim[rr,cc,i] = col
if nodelabel in labels:
colorvalue = labels[nodelabel]
for cocentric in range(thickness):
rr, cc = skimage.draw.circle_perimeter(np.int(np.round(node['center'][0])),
np.int(np.round(node['center'][1])),
np.int(self.settings.graph_radius+radius_offset+cocentric),
method='andres')
indices = filter(lambda i: rr[i] > 0 and rr[i] < height and cc[i]>0 and cc[i]<width,
range(len(rr)) )
rr = rr[indices]
cc = cc[indices]
for i, col in enumerate(colorvalue):
colim[rr,cc,i] = col
return colim
def find_neighbors(self, imbin, max_extension):
background = np.zeros(imbin.shape)
background[imbin==0] = 255
distance = ndi.distance_transform_edt(background)
cell_labels = label(imbin, neighbors=4, background=0)
# this is a hack, as background pixels obtain label -1
# and we do not want to have negative values.
# from version 0.12 this can be removed, probably.
cell_labels = cell_labels - cell_labels.min()
# the mask is an extension of the initial shape by max_extension.
# it can be derived from the distance map (straight forward)
mask = np.zeros(imbin.shape)
mask[distance < max_extension] = 255
# The watershed of the distance transform of the background.
# this corresponds an approximation of the "cells"
labels = watershed(distance, cell_labels, mask=mask)
if self.settings.debug_screen_output:
out_filename = os.path.join(self.settings.img_debug_folder, '%s16_distance.png' % self.prefix)
temp = distance / distance.max()
skimage.io.imsave(out_filename, temp)
out_filename = os.path.join(self.settings.img_debug_folder, '%s16_labels_from_ws.png' % self.prefix)
skimage.io.imsave(out_filename, labels)
return cell_labels, labels
def read_header(self, filename):
impil = Image.open(filename)
tagged_string = impil.tag.tagdata[270]
root = ET.fromstring(tagged_string.strip('\x00'))
subtree = root.find('PlaneInfo')
properties = subtree.findall('prop')
#<prop id="stage-position-x" type="float" value="18988"/>
#<prop id="stage-position-y" type="float" value="15355"/>
#<prop id="stage-label" type="string" value=""/>
#<prop id="z-position" type="float" value="-1392.99"/>
x = None
y = None
z = None
for pr in properties:
if 'id' in pr.attrib and pr.attrib['id'] == 'z-position':
z = float(pr.attrib['value'])
if 'id' in pr.attrib and pr.attrib['id'] == 'stage-position-x':
x = float(pr.attrib['value'])
if 'id' in pr.attrib and pr.attrib['id'] == 'stage-position-y':
y = float(pr.attrib['value'])
print 'x %f, y %f, z %f ' % (x, y, z)
return x, y, z
def get_properties(self, imbin, img):
props = {}
cell_labels = label(imbin, neighbors=4, background=0)
cell_labels = cell_labels - cell_labels.min()
properties = measure.regionprops(cell_labels, img)
areas = [0] + [pr.area for pr in properties]
convex_areas = [1] + [pr.convex_area for pr in properties]
mean_intensities = [0.0] + [pr.mean_intensity for pr in properties]
eccentricities = [0.0] + [pr.eccentricity for pr in properties]
centers = [(0.0, 0.0)] + [pr.centroid for pr in properties]
perimeters = [1.0] + [pr.perimeter for pr in properties]
a = np.array(areas)
b = np.array(perimeters)
b[b==0.0] = 1.0
circ = 2 * np.sqrt(np.pi) * a / b
c = np.array(convex_areas)
cc_ar = a.astype(np.dtype('float')) / c.astype(np.dtype('float'))
for i in range(1, cell_labels.max()+1):
props[i] = {
'area': areas[i],
'mean_intensity': mean_intensities[i],
'eccentricity': eccentricities[i],
'center': centers[i],
'circularity': circ[i],
'cc_ar': cc_ar[i],
}
return props
def distance_to_avg(self, props):
# calc average:
mean_area = np.mean([props[i]['area'] for i in props.keys()])
std_area = np.std([props[i]['area'] for i in props.keys()])
mean_intensity = np.mean([props[i]['mean_intensity'] for i in props.keys()])
std_intensity = np.std([props[i]['mean_intensity'] for i in props.keys()])
mean_ecc = np.mean([props[i]['eccentricity'] for i in props.keys()])
std_ecc = np.std([props[i]['eccentricity'] for i in props.keys()])
mean_circ = np.mean([props[i]['circularity'] for i in props.keys()])
std_circ = np.std([props[i]['circularity'] for i in props.keys()])
mean_cc_ar = np.mean([props[i]['cc_ar'] for i in props.keys()])
std_cc_ar = np.std([props[i]['cc_ar'] for i in props.keys()])
if std_area==0.0: std_area = 1.0
if std_intensity==0.0: std_intensity = 1.0
if std_ecc==0.0: std_ecc = 1.0
if std_circ==0.0: std_circ = 1.0
if std_cc_ar==0.0: std_cc_ar = 1.0
for i in props.keys():
# temp = (props[i]['area'] - mean_area)**2 + \
# (props[i]['mean_intensity'] - mean_intensity)**2 + \
# (props[i]['eccentricity'] - mean_ecc)**2
temp = ((props[i]['area'] - mean_area) / std_area)**2 + \
((props[i]['mean_intensity'] - mean_intensity) / std_intensity)**2 + \
((props[i]['eccentricity'] - mean_ecc) / std_ecc)**2 + \
((props[i]['cc_ar'] - mean_cc_ar) / std_cc_ar)**2 + \
((props[i]['circularity'] - mean_circ) / std_circ)**2
props[i]['distance'] = np.sqrt(temp)
return props
def old_select_label(self, labels=None):
if labels is None:
keys = sorted(filter(lambda x: self.nodes[x]['allowed'], self.nodes.keys()))
elif len(labels) > 0:
keys = sorted(filter(lambda x: self.nodes[x]['allowed'], labels))
else:
keys = []
if len(keys) == 0:
return None
distances = [self.nodes[i]['distance'] for i in keys]
min_distance = np.min(distances)
possible_labels = filter(lambda i: self.nodes[i]['distance'] == min_distance, keys)
if len(possible_labels) > 0:
lb = possible_labels[0]
self.nodes[lb]['chosen'] = True
self.nodes[lb]['allowed'] = False
else:
lb = None
return lb
def select_label(self, labels=None):
if labels is None:
keys = sorted(filter(lambda x: self.nodes[x]['allowed'], self.nodes.keys()))
elif len(labels) > 0:
keys = sorted(filter(lambda x: self.nodes[x]['allowed'], labels))
else:
keys = []
if len(keys) == 0:
return None
distances = np.array([self.nodes[i]['distance'] for i in keys])
min_distance = np.min(distances)
possible_labels = filter(lambda i: self.nodes[i]['distance'] == min_distance, keys)
if len(possible_labels) > 0:
lb = possible_labels[0]
else:
lb = None
return lb
def eudist(self, a, b):
res = np.sqrt(np.sum((np.array(a) - np.array(b))**2))
return res
def normalize_vec_eu(self, a):
mean_a = np.mean(a)
std_a = np.std(a)
if std_a > 0:
norm_vec = (a - mean_a) / std_a
else:
norm_vec = a - mean_a
return norm_vec
def normalize_vec(self, a):
min_a = np.min(a)
max_a = np.max(a)
if max_a > min_a:
norm_vec = (a.astype('float') - min_a) / (max_a - min_a)
else:
norm_vec = a.astype('float') - min_a
return norm_vec
def select_cluster_nodes(self, labels=None):
if labels is None:
# in this case we take all the allowed nodes
keys = sorted(filter(lambda x: self.nodes[x]['allowed'], self.nodes.keys()))
elif len(labels) > 0:
# in this case we take only the given set of labels (but only the subset which is allowed).
keys = sorted(filter(lambda x: self.nodes[x]['allowed'], labels))
else:
return None
if len(keys) == 0:
return None
# among these labels, we need to find a seed for a cluster of cluster_size.
# this seed is found by minimizing the feature distance. As no seed is guaranteed
# to result in a valid cluster, we take all valid nodes and rank them according to their distance to the mean cell.
if self.settings.ordered_cell_selection:
dist_to_avg = self.normalize_vec(np.array([self.nodes[i]['distance'] for i in keys]))
np_connections = self.normalize_vec(np.array([len(self.nodes[i]['neighbors']) for i in keys]))
x = self.normalize_vec(np.array([self.nodes[i]['center'][1] for i in keys]))
y = self.normalize_vec(np.array([self.nodes[i]['center'][0] for i in keys]))
scores = np.array(1000 * np_connections + 100 * x + 10 * y + dist_to_avg)
indices = scores.argsort()
ranked_keys = np.array(keys)[indices]
else:
dist_to_avg = self.normalize_vec(np.array([self.nodes[i]['distance'] for i in keys]))
#x = self.normalize_vec(np.array([self.nodes[i]['center'][1] for i in keys]))
#y = self.normalize_vec(np.array([self.nodes[i]['center'][0] for i in keys]))
np_connections = self.normalize_vec(np.array([len(self.nodes[i]['neighbors']) for i in keys]))
scores = np.array(10 * (np.max(np_connections) - np_connections) + dist_to_avg)
indices = scores.argsort()
ranked_keys = np.array(keys)[indices]
#print 'node selection : ', labels, ' --> ', ranked_keys
#print '\t', [len(self.nodes[i]['neighbors']) for i in keys]
#print '\t', [len(self.nodes[i]['neighbors']) for i in ranked_keys]
cluster_size = self.settings.cluster_size
cluster_defined = False
# loop over all potential cluster seeds
while not cluster_defined and len(ranked_keys) > 0:
# get the cluster seed (the best one remaining)
v = ranked_keys[0]
ranked_keys = ranked_keys[1:]
#cluster_nodes = [ranked_keys[0]]
cluster_nodes = []
waiting_nodes = np.array([v])
nb_chosen = len(cluster_nodes)
# loop to expand the cluster seed to reach cluster_size if possible.
while nb_chosen < cluster_size and len(waiting_nodes) > 0:
# for all waiting_nodes we calculate the maximal distance to the already selected nodes (cluster_nodes)
# minimizing the maximal distance allows to have compact classes.
if len(waiting_nodes) > 1 and len(cluster_nodes) > 0:
eu_dist = [np.max(np.array([self.eudist(self.nodes[wo]['center'],
self.nodes[cn]['center'])
for cn in cluster_nodes]))
for wo in waiting_nodes]
indices = np.array(eu_dist).argsort()
waiting_nodes = waiting_nodes[indices]
node_added = False
while not node_added and len(waiting_nodes) > 0:
o = waiting_nodes[0]
waiting_nodes = waiting_nodes[1:]
#if self.nodes[o]['allowed'] and not o in cluster_nodes:
if not o in cluster_nodes:
cluster_nodes.append(o)
node_added = True
# add the neighbors to the waiting_nodes
for nb in self.nodes[o]['neighbors']:
# neighbors are added
if self.nodes[nb]['allowed'] and not nb in waiting_nodes and not nb in cluster_nodes:
waiting_nodes = np.append(waiting_nodes, nb)
nb_chosen = len(cluster_nodes)
if len(cluster_nodes) < cluster_size:
# in this case, it was not possible to expand the cluster sufficiently. We can therefore set the corresponding
# nodes to "not allowed"
for node_id in cluster_nodes:
self.nodes[node_id]['allowed'] = False
else:
cluster_defined = True
if not cluster_defined:
cluster_nodes = None
return cluster_nodes
def build_graph(self, adjmat, props):
self.nodes = {}
for i in range(1, adjmat.shape[0]):
lab_vec = np.where(adjmat[i,:])[0]
self.nodes[i] = {'allowed': True,
'neighbors': lab_vec[lab_vec>0],
'distance': props[i]['distance'],
'center': props[i]['center'],
'chosen': False,
'min_dist': adjmat.shape[0] + 1}
return
# recursive expansion
def rec_expansion(self, lb, k):
if k==0:
return
neighbors = self.nodes[lb]['neighbors']
for nb in neighbors:
self.nodes[nb]['allowed'] = False
self.expansion(nb, k-1)
return
def expansion_one_step(self, lb):
changed=[]
neighbors = self.nodes[lb]['neighbors']
for nb in neighbors:
if self.nodes[nb]['allowed']:
changed.append(nb)
self.nodes[nb]['allowed'] = False
return changed
def expansion(self, lb, K):
labels_todo = [lb]
next_step = []
for i in range(K):
for lb in labels_todo:
changed = self.expansion_one_step(lb)
next_step.extend(changed)
labels_todo = next_step
next_step = []
candidates = []
for lb in labels_todo:
candidates.extend(self.nodes[lb]['neighbors'].tolist())
candidates = list(set(candidates))
candidates = filter(lambda x: self.nodes[x]['allowed'], candidates)
return candidates
def get_chosen_labels(self):
chosen = filter(lambda x: self.nodes[x]['chosen'], self.nodes.keys())
return chosen
def choose_labels_backup(self, K, imin_param=None):
if self.settings.debug_screen_output:
for node_id, node in self.nodes.iteritems():
print '%i: %s' % (node_id, self.nodes[node_id]['neighbors'])
imin = imin_param
if not imin_param is None:
if not imin_param.dtype == 'uint8':
imin = self.sw.reduce_range(imin_param, minmax=True)
# initialization
Lres = []
lb = self.select_label()
#self.nodes[lb]['chosen'] = True
#self.nodes[lb]['min_dist'] = 0
#self.nodes[lb]['allowed'] = False
#Lcand = self.nodes[lb]['neighbors'].tolist()
Lcand = [lb]
#for nb in self.nodes[lb]['neighbors']:
# Lcand.append(nb)
r=0
while len(Lcand) > 0:
# gets the best label among the candidates
# the best means the closest to the average.
v = self.select_label(Lcand)
if self.settings.debug_screen_output:
if not v is None:
print 'Candidate: %i\tlabel: %i from %s' % (r, v, str(Lcand))
else:
print 'no suitable candidate from candidate list'
if v is None:
# if none of the candidates is suitable,
# we enlarge the search to all nodes.
v = self.select_label()
if v is None:
# this means that there is no suitable candidate.
Lcand = []
break
# v has been selected.
if self.settings.graph_overlay and not imin is None:
colim = self.overlay_graph(imin, labels=dict(zip(Lcand, [(230, 200, 0) for ttt in Lcand])))
skimage.io.imsave(os.path.join(self.settings.img_graph_overlay_folder, '%sgraph_overlay_candidate%03i_a.png' % (self.prefix, r)), colim)
self.nodes[v]['chosen'] = True
if self.settings.graph_overlay and not imin is None:
colim = self.overlay_graph(imin, labels=dict(zip(Lcand, [(230, 200, 0) for ttt in Lcand])))
skimage.io.imsave(os.path.join(self.settings.img_graph_overlay_folder, '%sgraph_overlay_candidate%03i_b.png' % (self.prefix, r)), colim)
Lres.append(v)
Q1 = Queue()
Q2 = Queue()
Q1.put(v)
# expansion step
for k in range(K+1):
if self.settings.debug_screen_output and k>0:
print '\texpansion: node %i\texpansion step %i\tcandidate: %i' % (v, k, r)
while not Q1.empty():
n = Q1.get()
self.nodes[n]['min_dist'] = min(self.nodes[n]['min_dist'], k)
self.nodes[n]['allowed'] = False
if self.settings.debug_screen_output:
print '\t\t--> from Q1: %i' % n
for nb in self.nodes[n]['neighbors']:
if self.nodes[nb]['min_dist'] > k:
Q2.put(nb)
if self.settings.graph_overlay and not imin is None and k>0:
lc_filtered = filter(lambda x: self.nodes[x]['allowed'], Lcand)
colim = self.overlay_graph(imin, labels=dict(zip(lc_filtered, [(230, 200, 0) for ttt in lc_filtered])))
skimage.io.imsave(os.path.join(self.settings.img_graph_overlay_folder, '%sgraph_overlay_candidate%03i_step%03i.png' % (self.prefix, r, k)), colim)
Q1 = Q2
Q2 = Queue()
# add new candidates
while not Q1.empty():
n = Q1.get()
if self.nodes[n]['allowed']:
Lcand.append(n)
if len(Lcand) == 0:
v = self.select_label()
if not v is None:
Lcand = [v]
Lcand = list(set(filter(lambda x: x != v and self.nodes[x]['allowed'], Lcand)))
r += 1
return Lres
def choose_labels(self, K, imin_param=None):
if self.settings.debug_screen_output:
for node_id, node in self.nodes.iteritems():
print '%i: %s' % (node_id, self.nodes[node_id]['neighbors'])
imin = imin_param
if not imin_param is None:
if not imin_param.dtype == 'uint8':
imin = self.sw.reduce_range(imin_param, minmax=True)
cluster_size = self.settings.cluster_size
cluster_dist = self.settings.cluster_dist
# initialization
Lres = []
if self.settings.ordered_cell_selection:
#np_connections = self.normalize_vec(np.array([len(self.nodes[i]['neighbors']) for i in keys]))
keys = sorted(self.nodes.keys())
x = self.normalize_vec(np.array([self.nodes[i]['center'][1] for i in keys]))
y = self.normalize_vec(np.array([self.nodes[i]['center'][0] for i in keys]))
scores = np.array(x**2 + y**2)
indices = scores.argsort()
lb = keys[indices[0]]
else:
lb = self.select_label()
Lcand = [lb]
#Lcand = self.select_cluster_nodes()
r=0
while len(Lcand) > 0:
# gets the best label among the candidates
# the best means the closest to the average.
#v = self.select_label(Lcand)
v = self.select_cluster_nodes(Lcand)
# if self.settings.debug_screen_output:
# if not v is None:
# print 'Candidate: %i\tlabel: %i from %s' % (r, v, str(Lcand))
# else:
# print 'no suitable candidate from candidate list'
if v is None:
# if none of the candidates is suitable,
# we enlarge the search to all nodes.
v = self.select_cluster_nodes()
if v is None:
# this means that there is no suitable candidate.
# the algorithm has to stop
Lcand = []
break
# v has been selected.
if self.settings.graph_overlay and not imin is None:
colim = self.overlay_graph(imin, labels=dict(zip(Lcand, [(230, 200, 0) for ttt in Lcand])))
skimage.io.imsave(os.path.join(self.settings.img_graph_overlay_folder, '%sgraph_overlay_candidate%03i_a.png' % (self.prefix, r)), colim)
# all nodes in the cluster are set to "chosen"
for s in v:
self.nodes[s]['chosen'] = True
if self.settings.graph_overlay and not imin is None:
colim = self.overlay_graph(imin, labels=dict(zip(Lcand, [(230, 200, 0) for ttt in Lcand])))
skimage.io.imsave(os.path.join(self.settings.img_graph_overlay_folder, '%sgraph_overlay_candidate%03i_b.png' % (self.prefix, r)), colim)
Lres.extend(v)
Q1 = Queue()
Q2 = Queue()
for node_id in v:
Q1.put(node_id)
# expansion step
for k in range(K+1):
#if self.settings.debug_screen_output and k>0:
# print '\texpansion: node %i\texpansion step %i\tcandidate: %i' % (v, k, r)
while not Q1.empty():
n = Q1.get()
self.nodes[n]['min_dist'] = min(self.nodes[n]['min_dist'], k)
self.nodes[n]['allowed'] = False
# if self.settings.debug_screen_output:
# print '\t\t--> from Q1: %i' % n
for nb in self.nodes[n]['neighbors']:
if self.nodes[nb]['min_dist'] > k:
Q2.put(nb)
if self.settings.graph_overlay and not imin is None and k>0:
lc_filtered = filter(lambda x: self.nodes[x]['allowed'], Lcand)
colim = self.overlay_graph(imin, labels=dict(zip(lc_filtered, [(230, 200, 0) for ttt in lc_filtered])))
skimage.io.imsave(os.path.join(self.settings.img_graph_overlay_folder, '%sgraph_overlay_candidate%03i_step%03i.png' % (self.prefix, r, k)), colim)
Q1 = Q2
Q2 = Queue()
# add new candidates
while not Q1.empty():
n = Q1.get()
if self.nodes[n]['allowed']:
Lcand.append(n)
if len(Lcand) == 0:
v = self.select_cluster_nodes()
if not v is None:
Lcand = v
Lcand = list(set(filter(lambda x: not self.nodes[x]['chosen'] and self.nodes[x]['allowed'], Lcand)))
r += 1
return Lres
def centers_to_text_file(self, imout, filename):
# get the centers
labels = label(imout, neighbors=4, background=0)
properties = measure.regionprops(labels, imout)
#pdb.set_trace()
centers = [(0.0, 0.0)] + [ (pr.centroid[0] * self.settings.param_pixel_size,
pr.centroid[1] * self.settings.param_pixel_size)
for pr in properties]
fp = open(filename, 'w')
for x,y in centers[1:]:
fp.write('%f\t%f\n' % (x,y))
fp.close()
return
def centers_to_px_text_file(self, imout, filename):
# get the centers
labels = label(imout, neighbors=4, background=0)
properties = measure.regionprops(labels, imout)
centers = [(0.0, 0.0)] + [pr.centroid for pr in properties]
fp = open(filename, 'w')
for x,y in centers[1:]:
fp.write('%i\t%i\n' % (x,y))
fp.close()
return
def export_metamorph(self, imout, filename, stage_coord=None):
if stage_coord is None:
stage_coord = (0, 0, 0)
# get the centers
labels = label(imout, neighbors=4, background=0)
properties = measure.regionprops(labels, imout)
# check centers
#for pr in properties:
# print 'image coordinates (pixel) : %i, %i' % (pr.centroid[0], pr.centroid[1])
centers = [(0.0, 0.0)] + [ (pr.centroid[0] * self.settings.param_pixel_size,
pr.centroid[1] * self.settings.param_pixel_size)
for pr in properties]
nb_cells = len(centers) -1
# write the information in a metamorph .stg format.
fp = open(filename, 'w')
fp.write('"Stage Memory List", Version 6.0\n')
fp.write('0, 0, 0, 0, 0, 0, 0, "um", "um"\n')
fp.write('0\n')
fp.write('%i\n' % nb_cells)
# image dimension in micrometer
w = imout.shape[1] * self.settings.param_pixel_size
h = imout.shape[0] * self.settings.param_pixel_size
print 'width and height : ', w, h
i = 1
for y, x in centers[1:]:
x_o = stage_coord[0] + y - h / 2.0
y_o = stage_coord[1] + w / 2.0 - x
z_o = stage_coord[2]
#print x, y, ' ---> ', x_o, y_o, z_o
tempStr = '"Cell%i", %f, %f, %f, 0, 0, FALSE, -9999, TRUE, TRUE, 0, -1, ""\n' % (i, x_o, y_o, z_o)
fp.write(tempStr)
i += 1
fp.close()
# write the original point (in order to get back to the origin)
extension = os.path.splitext(filename)[-1]
basefilename = os.path.splitext(filename)[0]
origin_filename = basefilename + '_origin' + extension
fp = open(origin_filename, 'w')
fp.write('"Stage Memory List", Version 6.0\n')
fp.write('0, 0, 0, 0, 0, 0, 0, "um", "um"\n')
fp.write('0\n')
fp.write('1\n')
tempStr = '"Cell1", %f, %f, %f, 0, 0, FALSE, -9999, TRUE, TRUE, 0, -1, ""\n' % (stage_coord[0], stage_coord[1], stage_coord[2])
fp.write(tempStr)
fp.close()
return
def circles_to_xml_file(self, imout, filename):
# get the centers
labels = label(imout, neighbors=4, background=0)
properties = measure.regionprops(labels, imout)
centers = [(0.0, 0.0)] + [pr.centroid for pr in properties]
fp = open(filename, 'w')
for x,y in centers[1:]:
fp.write('%i\t%i\n' % (x,y))
fp.close()
return
def __call__(self, imin, imbin, K, max_extension=100):
# First we label cellular regions.
# this can be done by a simple voronoi approach or by some other method.
# here it is done with voronoi (with max extension of 100).
cell_labels, labels = self.find_neighbors(imbin, max_extension)
if self.settings.debug:
skimage.io.imsave(os.path.join(self.settings.img_debug_folder,
'%s16_basis_for_graph.png' % self.prefix), labels)
# find the co-occurence matrix.
# The co-occurence matrix informs us about the neighboring relationships.
# From there, we can build the graph.
# attention : this does only work for < 256 objects.
# in order to fix this, I need to patch skimage.
cooc = skimage.feature.greycomatrix(labels, [1],
[0, np.pi/4, np.pi/2, 3*np.pi/4,
np.pi, 5*np.pi/4, 3*np.pi/2, 7*np.pi/4],
levels=labels.max() + 1)
# we sum over different directions
cooc = np.sum(cooc[:,:,0,:], axis=2)
# and we remove the diagonal
cooc = cooc - np.diag(np.diag(cooc))
# adjacency matrix corresponds to the entries > 0.
adjmat = cooc>0
# calculate properties to find the most "representative cell"
node_properties = self.get_properties(imbin, imin)
self.distance_to_avg(node_properties)
# build graph
self.build_graph(adjmat, node_properties)
# select the cells (main algorithm)
labres = self.choose_labels(K, imin)
# get an output image
label_values = np.zeros((labels.max() + 1)).astype(np.uint8)
label_values[np.array(labres)] = 255
imout = label_values[cell_labels]
if self.settings.single_mask:
skimage.io.imsave(os.path.join(self.settings.img_single_output_folder,
'%s_selected_cells.png' % self.prefix), imout)
label_values[label_values==0] = 100
label_values[0] = 0
temp = label_values[cell_labels]
skimage.io.imsave(os.path.join(self.settings.img_single_output_folder,
'%s_selected_and_other_cells.png' % self.prefix), temp)
return imout