def load_volume_from_h5(self,h5filename):
"""
Load the volume from an HDF5 file
"""
hf5 = tables.openFile(h5filename, 'r')
X0,Y0,Z0 = self.info['X0'], self.info['Y0'], self.info['Z0']
H,W,D = self.info['Height'], self.info['Width'], self.info['Depth']
np_tensor_3d = hf5.root.full_image[Z0:Z0+D, Y0:Y0+H, X0:X0+W]
hf5.close()
self.imgs = []
self.pixels = []
for z in range(D):
img_z = Image.fromarray(np_tensor_3d[z,:,:])
self.imgs.append(img_z)
self.pixels.append(img_z.load())
tee.log(z+1, 'images read into stack (from h5 file)')
def compute_hues(self, C, n_neighbors=6):
# colorize randomly
for i, c in enumerate(C):
c.index = i
colors = {c.index: int(random.uniform(0, 255)) for c in C}
if which('minizinc') is None:
for c in C:
c.hue = colors[c.index] / 255.0
return
# If we have minizinc then create a knn graph and run a constraint program to colorize nicely
edges = []
X = np.array([[c.x, c.y, c.z] for c in C])
kdtree = cKDTree(X)
purkinje_radius = 16
for c in C:
distances, neighbors = kdtree.query([c.x, c.y, c.z], n_neighbors)
for d, n in zip(distances, neighbors):
if d < 3 * purkinje_radius and c.index < C[n].index:
edges.append([c.index, C[n].index])
if len(
edges
) < 1000: # otherwise solving the constraint program might be too costly
with open(self.savedir + '/' + '/edges.dzn', 'w') as ostream:
print('n=%d;' % len(C), file=ostream)
print('num_edges=%d;' % len(edges), file=ostream)
print('E = array2d(1..num_edges, 1..2, [', file=ostream)
print(','.join(
map(str, [e[i] for e in edges for i in range(2)])),
file=ostream)
print(']);', file=ostream)
tee.log('Running minizinc on', len(edges), 'edges')
curdir = os.getcwd()
os.chdir(SHARE_DIR)
mzn_cmd = ('minizinc color.mzn -d %s/edges.dzn -o %s/mnz_sol.txt' %
(self.savedir, self.savedir))
tee.log(mzn_cmd)
os.system(mzn_cmd)
os.chdir(curdir)
istream = open(self.savedir + '/mnz_sol.txt')
colors = {}
for line in istream.readlines():
items = line.strip().split()
if items[0] == 'c':
colors[int(items[1])] = int(items[2])
for c in C:
c.hue = colors[c.index] / 255.0
def compute_hues(self, C, n_neighbors=6):
# colorize randomly
for i, c in enumerate(C):
c.index = i
colors = {c.index: int(random.uniform(0, 255)) for c in C}
if which('minizinc') is None:
for c in C:
c.hue = colors[c.index]/255.0
return
# If we have minizinc then create a knn graph and run a constraint program to colorize nicely
edges = []
X = np.array([[c.x, c.y, c.z] for c in C])
kdtree = cKDTree(X)
purkinje_radius = 16
for c in C:
distances, neighbors = kdtree.query([c.x, c.y, c.z], n_neighbors)
for d,n in zip(distances,neighbors):
if d < 3*purkinje_radius and c.index < C[n].index:
edges.append([c.index, C[n].index])
if len(edges) < 1000: # otherwise solving the constraint program might be too costly
with open(self.savedir+'/'+'/edges.dzn', 'w') as ostream:
print('n=%d;' % len(C), file=ostream)
print('num_edges=%d;' % len(edges), file=ostream)
print('E = array2d(1..num_edges, 1..2, [', file=ostream)
print(','.join(map(str, [e[i] for e in edges for i in range(2)])), file=ostream)
print(']);', file=ostream)
tee.log('Running minizinc on', len(edges), 'edges')
curdir = os.getcwd()
os.chdir(SHARE_DIR)
mzn_cmd = ('minizinc color.mzn -d %s/edges.dzn -o %s/mnz_sol.txt' % (self.savedir, self.savedir))
tee.log(mzn_cmd)
os.system(mzn_cmd)
os.chdir(curdir)
istream = open(self.savedir+'/mnz_sol.txt')
colors = {}
for line in istream.readlines():
items = line.strip().split()
if items[0] == 'c':
colors[int(items[1])] = int(items[2])
for c in C:
c.hue = colors[c.index]/255.0
def main(args):
st = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')
tee.log('find_cells.py running on',platform.node(),st)
mkdir_p(args.outdir+'/'+args.substack_id)
if args.pair_id is None:
tee.logto('%s/%s/log.txt' % (args.outdir, args.substack_id))
args.outdir=args.outdir+'/'+args.substack_id
else:
tee.logto('%s/%s/log_%s.txt' % (args.outdir, args.substack_id, args.pair_id))
args.outdir=args.outdir+'/'+args.substack_id+'/'+args.pair_id
mkdir_p(args.outdir)
timers = [mscd.pca_analysis_timer, mscd.mean_shift_timer, mscd.ms_timer, mscd.patch_ms_timer]
timers.extend([volume.save_vaa3d_timer, volume.save_markers_timer])
timers.extend([threshold.multi_kapur_timer])
for t in timers:
t.reset()
substack = volume.SubStack(args.indir, args.substack_id)
substack.load_volume(pair_id=args.pair_id)
if args.local:
mscd.pms(substack, args)
else:
mscd.ms(substack, args)
for t in timers:
if t.n_calls > 0:
tee.log(t)
st = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')
tee.log('find_cells.py finished on',platform.node(),st)
def load_volume_from_3D(self,
batch,
convert_to_gray=True,
flip=False,
ignore_info_files=False,
h5filename=None,
pair_id=None):
"""Loads a sequence of images into a stack
Parameters
----------
batch : np.array
Tensor of shape W,H,D with values between 0 and 1
convert_to_gray : bool
Should be set to true if reading from RGB tiff files
flip : bool
Flip along vertical (Y) axis
ignore_info_files : bool
If true, don't trust filenames in the info.json file
h5filename : str
If not none, read from this HDF5 file rather than TIFF files
"""
self.imgs = []
self.pixels = []
for z in range(batch.shape[0]):
data = (batch[z, :, :, ] * 255).astype('uint8')
#data = data.squeeze(0)
#tee.log(data.shape)
img_z = Image.fromarray(data, mode='L')
if flip: # when reading a stack saved in vaa3d format Y coordinates are reversed (used by save_substack only)
img_z = img_z.transpose(Image.FLIP_TOP_BOTTOM)
if convert_to_gray:
img_z = img_z.convert('L')
self.imgs.append(img_z)
self.pixels.append(img_z.load())
tee.log(z + 1, 'images read into stack')
pass
def load_volume(self,
convert_to_gray=True,
flip=False,
ignore_info_files=False,
h5filename=None,
pair_id=None):
"""Loads a sequence of images into a stack
Parameters
----------
convert_to_gray : bool
Should be set to true if reading from RGB tiff files
flip : bool
Flip along vertical (Y) axis
ignore_info_files : bool
If true, don't trust filenames in the info.json file
h5filename : str
If not none, read from this HDF5 file rather than TIFF files
"""
if h5filename is not None:
self.load_volume_from_h5(h5filename)
return
self.imgs = []
self.pixels = []
if pair_id is not None:
idir = self.indir + '/' + self.substack_id + '/' + pair_id
files = sorted([
idir + '/' + f for f in os.listdir(idir)
if f[0] != '.' and valid_suffix(f)
])
elif ignore_info_files:
idir = self.indir + '/' + self.substack_id
files = sorted([
idir + '/' + f for f in os.listdir(idir)
if f[0] != '.' and valid_suffix(f)
])
else:
files = [self.indir + '/' + fname for fname in self.info['Files']]
if len(files) == 0:
raise Exception('No valid files found')
for z, image_file in enumerate(files):
img_z = Image.open(image_file)
if flip: # when reading a stack saved in vaa3d format Y coordinates are reversed (used by save_substack only)
img_z = img_z.transpose(Image.FLIP_TOP_BOTTOM)
if convert_to_gray:
img_z = img_z.convert('L')
self.imgs.append(img_z)
self.pixels.append(img_z.load())
if z % 25 == 0:
tee.log(image_file, end='')
else:
tee.log('.', end='')
tee.log(z, 'images read into stack')
def load_volume(self, convert_to_gray=True, flip=False, ignore_info_files=False, h5filename=None, pair_id=None):
"""Loads a sequence of images into a stack
Parameters
----------
convert_to_gray : bool
Should be set to true if reading from RGB tiff files
flip : bool
Flip along vertical (Y) axis
ignore_info_files : bool
If true, don't trust filenames in the info.json file
h5filename : str
If not none, read from this HDF5 file rather than TIFF files
"""
if h5filename is not None:
self.load_volume_from_h5(h5filename)
return
self.imgs = []
self.pixels = []
if pair_id is not None:
idir = self.indir+'/'+self.substack_id+'/'+pair_id
files = sorted([idir+'/'+f for f in os.listdir(idir) if f[0] != '.' and valid_suffix(f)])
elif ignore_info_files:
idir = self.indir+'/'+self.substack_id
files = sorted([idir+'/'+f for f in os.listdir(idir) if f[0] != '.' and valid_suffix(f)])
else:
files = [self.indir+'/'+fname for fname in self.info['Files']]
if len(files) == 0:
raise Exception('No valid files found')
for z, image_file in enumerate(files):
img_z = Image.open(image_file)
if flip: # when reading a stack saved in vaa3d format Y coordinates are reversed (used by save_substack only)
img_z = img_z.transpose(Image.FLIP_TOP_BOTTOM)
if convert_to_gray:
img_z = img_z.convert('L')
self.imgs.append(img_z)
self.pixels.append(img_z.load())
if z % 25 == 0:
tee.log(image_file, end='')
else:
tee.log('.', end='')
tee.log(z, 'images read into stack')
def main(args):
st = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d %H:%M:%S')
tee.log('find_cells.py running on', platform.node(), st)
mkdir_p(args.outdir + '/' + args.substack_id)
if args.pair_id is None:
tee.logto('%s/%s/log.txt' % (args.outdir, args.substack_id))
args.outdir = args.outdir + '/' + args.substack_id
else:
tee.logto('%s/%s/log_%s.txt' %
(args.outdir, args.substack_id, args.pair_id))
args.outdir = args.outdir + '/' + args.substack_id + '/' + args.pair_id
mkdir_p(args.outdir)
timers = [
mscd.pca_analysis_timer, mscd.mean_shift_timer, mscd.ms_timer,
mscd.patch_ms_timer
]
timers.extend([volume.save_vaa3d_timer, volume.save_markers_timer])
timers.extend([threshold.multi_kapur_timer])
for t in timers:
t.reset()
# dovremmo passare le informazinoi su w,h,d tramite pslist
substack = volume.SubStack(args.indir, args.substack_id)
# ignore_info_files setted to true to avoid errors
substack.load_volume(pair_id=args.pair_id, ignore_info_files=True)
#todo la nostra versione è questa
#substack.load_volume_from_3D()
#todo lo script va lanciato per ogni volume
if args.local:
mscd.pms(substack, args)
else:
mscd.ms(substack, args)
for t in timers:
if t.n_calls > 0:
tee.log(t)
st = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d %H:%M:%S')
tee.log('find_cells.py finished on', platform.node(), st)
def main(args):
st = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d %H:%M:%S')
tee.log('find_cells.py running on', platform.node(), st)
mkdir_p(args.outdir + '/' + args.substack_id)
if args.pair_id is None:
tee.logto('%s/%s/log.txt' % (args.outdir, args.substack_id))
args.outdir = args.outdir + '/' + args.substack_id
else:
tee.logto('%s/%s/log_%s.txt' %
(args.outdir, args.substack_id, args.pair_id))
args.outdir = args.outdir + '/' + args.substack_id + '/' + args.pair_id
mkdir_p(args.outdir)
timers = [
mscd.pca_analysis_timer, mscd.mean_shift_timer, mscd.ms_timer,
mscd.patch_ms_timer
]
timers.extend([volume.save_vaa3d_timer, volume.save_markers_timer])
timers.extend([threshold.multi_kapur_timer])
for t in timers:
t.reset()
substack = volume.SubStack(args.indir, args.substack_id)
substack.load_volume(pair_id=args.pair_id)
if args.local:
mscd.pms(substack, args)
else:
mscd.ms(substack, args)
for t in timers:
if t.n_calls > 0:
tee.log(t)
st = datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d %H:%M:%S')
tee.log('find_cells.py finished on', platform.node(), st)
mean_precision = np.mean(np.array(precisions))
precisions.append(mean_precision)
mean_recall = np.mean(np.array(recalls))
recalls.append(mean_recall)
mean_F1 = np.mean(np.array(F1s))
F1s.append(mean_F1)
var_precision = np.var(np.array(precisions))
precisions.append(var_precision)
var_recall = np.var(np.array(recalls))
recalls.append(var_recall)
var_F1 = np.var(np.array(F1s))
F1s.append(var_F1)
dict = {
'img_name': img_names,
'precision': precisions,
'recall': recalls,
'F1': F1s
}
df = pd.DataFrame(dict, columns=['img_name', 'precision', 'recall', 'F1'])
df.to_csv(save_path)
for t in timers:
tee.log(t)
