def main(args):
total_start = timeit.default_timer()
print('Starting Preibisch fusion', args.substack_id)
ss = SubStack(args.first_view_dir, args.substack_id)
minz = int(ss.info['Files'][0].split("/")[-1].split('_')[-1].split('.tif')[0])
prefix = '_'.join(ss.info['Files'][0].split("/")[-1].split('_')[0:-1])+'_'
np_tensor_3d_first_view,_ = imtensor.load_nearby(args.tensorimage_first_view, ss, args.size_patch)
sc_in=np_tensor_3d_first_view.shape
if args.transformation_file is not None:
R, t = parse_transformation_file(args.transformation_file)
np_tensor_3d_second_view = transform_substack(args.second_view_dir, args.tensorimage_second_view, args.substack_id, R, t, args.size_patch, invert=True)
else:
np_tensor_3d_second_view,_ = imtensor.load_nearby(args.tensorimage_second_view, ss, args.size_patch)
fused_image,entropy_mask__view,entropy_mask_second_view = do_content_based_fusion(np_tensor_3d_first_view,np_tensor_3d_second_view,args.size_patch, args.size_patch, speedup=1,fast_computation=True)
if args.extramargin>args.size_patch:
args.extramargin=args.size_patch
offset_margin=args.size_patch - args.extramargin
fused_image_output=fused_image[offset_margin:sc_in[0]-offset_margin,offset_margin:sc_in[1]-offset_margin,offset_margin:sc_in[2]-offset_margin]
atom = tables.UInt8Atom()
mkdir_p(args.outdir)
h5f = tables.openFile(args.outdir + '/' + args.substack_id + '.h5', 'w')
sc_out=fused_image_output.shape
ca = h5f.createCArray(h5f.root, 'full_image', atom, sc_out)
for z in xrange(0, sc_out[0], 1):
ca[z, :, :] = fused_image_output[z,:,:]
h5f.close()
imtensor.save_tensor_as_tif(fused_image_output, args.outdir+'/'+args.substack_id, minz,prefix=prefix)
print ("total time Preibisch fusion: %s" %(str(timeit.default_timer() - total_start)))
def fuse_tensors(outdir,pixels_redChannel,pixels_greenChannel,pixels_blueChannel):
"""
Method that fuses multiple tensors in a RGB tensor
Parameters
----------
pixels_redChannel: numpy tensor
numpy tensor of the first view (R channel)
pixels_greeChannel: numpy tensor
numpy tensor of the second view (G channel)
pixels_blueChannel: numpy tensor
numpy tensor of the third view (B channel)
outdir: str
directory where the output tensor will be saved
"""
mkdir_p(outdir)
num_slices=pixels_redChannel.shape[0]
for z in xrange(0, num_slices, 1):
pixels_merged = Image.merge("RGB", (pixels_redChannel[z], pixels_greenChannel[z], pixels_blueChannel[z]))
im = Image.fromarray(pixels_merged)
tempname = '/tmp/'+str(uuid.uuid4())+'.tif'
im.save(tempname)
destname = outdir+'/slice_'+str(z).zfill(4)+'.tif'
os.system('tiffcp -clzw:2 ' + tempname + ' ' + destname)
os.remove(tempname)
print('...fusion computed (%s slices) ' %z)
print('fused stack saved in ', outdir)
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 main(args):
total_start = timeit.default_timer()
print('Starting Preibisch fusion', args.substack_id)
ss = SubStack(args.first_view_dir, args.substack_id)
minz = int(
ss.info['Files'][0].split("/")[-1].split('_')[-1].split('.tif')[0])
prefix = '_'.join(
ss.info['Files'][0].split("/")[-1].split('_')[0:-1]) + '_'
np_tensor_3d_first_view, _ = imtensor.load_nearby(
args.tensorimage_first_view, ss, args.size_patch)
sc_in = np_tensor_3d_first_view.shape
if args.transformation_file is not None:
R, t = parse_transformation_file(args.transformation_file)
np_tensor_3d_second_view = transform_substack(
args.second_view_dir,
args.tensorimage_second_view,
args.substack_id,
R,
t,
args.size_patch,
invert=True)
else:
np_tensor_3d_second_view, _ = imtensor.load_nearby(
args.tensorimage_second_view, ss, args.size_patch)
fused_image, entropy_mask__view, entropy_mask_second_view = do_content_based_fusion(
np_tensor_3d_first_view,
np_tensor_3d_second_view,
args.size_patch,
args.size_patch,
speedup=1,
fast_computation=True)
if args.extramargin > args.size_patch:
args.extramargin = args.size_patch
offset_margin = args.size_patch - args.extramargin
fused_image_output = fused_image[offset_margin:sc_in[0] - offset_margin,
offset_margin:sc_in[1] - offset_margin,
offset_margin:sc_in[2] - offset_margin]
atom = tables.UInt8Atom()
mkdir_p(args.outdir)
h5f = tables.openFile(args.outdir + '/' + args.substack_id + '.h5', 'w')
sc_out = fused_image_output.shape
ca = h5f.createCArray(h5f.root, 'full_image', atom, sc_out)
for z in xrange(0, sc_out[0], 1):
ca[z, :, :] = fused_image_output[z, :, :]
h5f.close()
imtensor.save_tensor_as_tif(fused_image_output,
args.outdir + '/' + args.substack_id,
minz,
prefix=prefix)
print("total time Preibisch fusion: %s" %
(str(timeit.default_timer() - total_start)))
def main(args):
substack = SubStack(args.indir, args.substack_id)
patch = substack.get_volume()
histogram = np.histogram(patch, bins=256, range=(0, 256))[0]
thresholds = threshold.multi_kapur(histogram, 2)
outfile = args.outdir + '/' + args.substack_id + '/' + basename(args.indir)
mkdir_p(args.outdir + '/' + args.substack_id)
f = open(outfile, 'w')
f.write(str(thresholds[0]) + ',' + str(thresholds[1]) + '\n')
f.close()
def main(args):
substack = SubStack(args.indir,args.substack_id)
patch = substack.get_volume()
histogram = np.histogram(patch, bins=256,range=(0,256))[0]
thresholds = threshold.multi_kapur(histogram, 2)
outfile=args.outdir+'/'+args.substack_id+'/'+basename(args.indir)
mkdir_p(args.outdir+'/'+args.substack_id)
f=open(outfile,'w')
f.write(str(thresholds[0])+','+str(thresholds[1])+'\n')
f.close()
def save_tensor_as_tif(np_tensor_3d,path, minz, prefix='full_'):
"""Export a 3D numpy tensor as a sequence of tiff files (one for each
z coordinate). Each file is named as prefix followed by an integer id,
which ranges in minz:minz+np_tensor_3d.shape[0]. The tensor is
expected to be stored in the order Z,Y,X
"""
import uuid
mkdir_p(path)
pbar = ProgressBar(widgets=['Saving %d tiff files: ' % np_tensor_3d.shape[0], Percentage(), ' ', AdaptiveETA()])
for z in pbar(range(np_tensor_3d.shape[0])):
out_img = Image.fromarray(np_tensor_3d[z,:,:])
tempname = '/tmp/'+str(uuid.uuid4())+'.tif'
out_img.save(tempname)
destname=path+'/'+prefix+'%06d.tif'%(minz+z)
os.system('tiffcp -clzw:2 ' + tempname + ' ' + destname)
os.remove(tempname)
print('Saved substack in',path)
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):
try:
C_firstview = m_load_markers(args.first_view,from_vaa3d=True)
except IOError:
print('Warning: first view marker file',args.first_view,'not found.')
C_firstview = []
try:
C_secondview = m_load_markers(args.second_view,from_vaa3d=True)
except IOError:
print('Warning: second view marker file',args.second_view,'not found.')
C_secondview = []
mkdir_p(os.path.dirname(args.output_marker_file))
substack = SubStack(args.indir,args.substack_id)
if args.do_icp:
C_merged, C_onlyfirstview, C_onlyfirstview, _ = do_fuse_with_icp(substack,C_firstview,C_secondview,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
else:
C_merged, C_onlyfirstview, C_onlyfirstview, _ = do_fuse(substack,C_firstview,C_secondview,args.max_distance, args.verbose)
save_fused_markers(substack,C_merged,C_onlyfirstview,C_onlysecondview,output_marker_file,first_view_id,second_view_id,verbose)
def save_above_threshold(self, Lx, Ly, Lz, thresholds=None):
out_imgs = []
opixels = []
pixels = []
for z, img in enumerate(self.substack.imgs):
out_img = img.convert('RGB')
out_imgs.append(out_img)
opix = out_img.load()
w, h = out_img.size
for i in xrange(w):
for j in xrange(h):
if thresholds:
opix[i, j] = (self.substack.pixels[z][i,
j], 0, 0) # Red
else:
opix[i, j] = 0
opixels.append(opix)
pixels.append(img.load())
if thresholds:
for i in xrange(len(Lx)):
val = pixels[Lz[i]][Lx[i], Ly[i]]
if val < thresholds[
1]: # Note: since it's on the lists Lx,Ly,Lz, it's already above thresholds[0]!
opixels[Lz[i]][Lx[i], Ly[i]] = (0, val, 0) # Green
else:
opixels[Lz[i]][Lx[i], Ly[i]] = (val, val, 0) # Yellow
else:
for i in xrange(len(Lx)):
opixels[Lz[i]][Lx[i], Ly[i]] = (pixels[Lz[i]][Lx[i], Ly[i]],
pixels[Lz[i]][Lx[i], Ly[i]],
pixels[Lz[i]][Lx[i], Ly[i]])
save_name = 'above_threshold'
iter_dir = '%s/%s' % (self.savedir, save_name)
mkdir_p(iter_dir)
for z, out_img in enumerate(out_imgs):
out_img.save(iter_dir + '/' +
self.substack.info['Files'][z].split('/')[-1])
w, h = out_img.size
def save_tensor_as_tif(np_tensor_3d, path, minz, prefix='full_'):
"""Export a 3D numpy tensor as a sequence of tiff files (one for each
z coordinate). Each file is named as prefix followed by an integer id,
which ranges in minz:minz+np_tensor_3d.shape[0]. The tensor is
expected to be stored in the order Z,Y,X
"""
import uuid
mkdir_p(path)
pbar = ProgressBar(widgets=[
'Saving %d tiff files: ' % np_tensor_3d.shape[0],
Percentage(), ' ',
ETA()
])
for z in pbar(range(np_tensor_3d.shape[0])):
out_img = Image.fromarray(np_tensor_3d[z, :, :])
tempname = '/tmp/' + str(uuid.uuid4()) + '.tif'
out_img.save(tempname)
destname = path + '/' + prefix + '%04d.tif' % (minz + z)
os.system('tiffcp -clzw:2 ' + tempname + ' ' + destname)
os.remove(tempname)
print('Saved substack in', path)
def save_above_threshold(self, Lx, Ly, Lz, thresholds=None):
out_imgs = []
opixels = []
pixels = []
for z, img in enumerate(self.substack.imgs):
out_img = img.convert('RGB')
out_imgs.append(out_img)
opix = out_img.load()
w, h = out_img.size
for i in xrange(w):
for j in xrange(h):
if thresholds:
opix[i, j] = (self.substack.pixels[z][i,j], 0, 0) # Red
else:
opix[i, j] = 0
opixels.append(opix)
pixels.append(img.load())
if thresholds:
for i in xrange(len(Lx)):
val = pixels[Lz[i]][Lx[i], Ly[i]]
if val < thresholds[1]: # Note: since it's on the lists Lx,Ly,Lz, it's already above thresholds[0]!
opixels[Lz[i]][Lx[i], Ly[i]] = (0,val,0) # Green
else:
opixels[Lz[i]][Lx[i], Ly[i]] = (val,val,0) # Yellow
else:
for i in xrange(len(Lx)):
opixels[Lz[i]][Lx[i], Ly[i]] = (pixels[Lz[i]][Lx[i], Ly[i]],
pixels[Lz[i]][Lx[i], Ly[i]],
pixels[Lz[i]][Lx[i], Ly[i]])
save_name = 'above_threshold'
iter_dir = '%s/%s' % (self.savedir, save_name)
mkdir_p(iter_dir)
for z, out_img in enumerate(out_imgs):
out_img.save(iter_dir+'/'+self.substack.info['Files'][z].split('/')[-1])
w, h = out_img.size
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 make_pos_neg_dataset(tensor_first_view, tensor_second_view, ss, C, view1_id, view2_id, default_sigma=0.8,size=5, save_tiff_files=False, find_negative=True):
tensor_first_view = tensor_first_view.astype(np.float32)
tensor_second_view = tensor_second_view.astype(np.float32)
H, W, D = ss.info['Height'], ss.info['Width'], ss.info['Depth']
patchlen = (1 + 2 * size) ** 3
margin = ss.plist['Margin'] / 2
c_array = np.array([[c.x, c.y, c.z] for c in C])
trunc=1.5
fixed_radiiassigned={c:default_sigma for c in C if inside_margin(c,ss) > 0 }
cccc=fixed_radiiassigned.copy()
kdt = cKDTree(c_array)
dist,ind = kdt.query(c_array,k=c_array.shape[0],distance_upper_bound=(2*default_sigma*trunc)+2.)
for c,id_c in zip(C,xrange(c_array.shape[0])):
if inside_margin(c, ss) > 0:
for j in xrange(2,c_array.shape[0]):
if not np.isinf(dist[id_c][j]):
if inside_margin(C[ind[id_c][j]], ss) > 0:
if trunc*(fixed_radiiassigned[c]+fixed_radiiassigned[C[ind[id_c][j]]]) > dist[id_c][j] - 2.:
new_sigma=((dist[id_c][j]-1.)/trunc)*fixed_radiiassigned[c]/(fixed_radiiassigned[c]+fixed_radiiassigned[C[ind[id_c][j]]])
fixed_radiiassigned[C[ind[id_c][j]]]=(new_sigma*fixed_radiiassigned[C[ind[id_c][j]]])/fixed_radiiassigned[c]
fixed_radiiassigned[c]=new_sigma
target_tensor_3d = np.zeros(tensor_first_view.shape,dtype=np.uint8)
for c in C:
if inside_margin(c, ss) > 0:
target_tensor_3d_tmp = np.zeros(tensor_first_view.shape)
target_tensor_3d_tmp[c.z, c.y, c.x] = 1
sigma=fixed_radiiassigned[c]
target_tensor_3d_tmp = gfilter.gaussian_filter(target_tensor_3d_tmp, sigma,
mode='constant', cval=0.0,
truncate=trunc) #sigma=3.5,mode='constant',cval=0.0,truncate=1.5
target_tensor_3d_tmp = (target_tensor_3d_tmp.astype(np.float32) / np.max(target_tensor_3d_tmp))
target_tensor_3d = np.maximum(np.array(target_tensor_3d_tmp * 255.0, dtype=np.uint8), target_tensor_3d)
if save_tiff_files:
debug_path='/tmp/debug/sigma_'+str(default_sigma)
mkdir_p(debug_path)
minz = 0
imtensor.save_tensor_as_tif(target_tensor_3d, debug_path+'/'+ss.substack_id+'_'+view1_id+'_'+view2_id, minz)
print('num markers =',len(C))
target_tensor_3d = (target_tensor_3d.astype(np.float32) / np.max(target_tensor_3d))
tensor_first_view = tensor_first_view.astype(np.float32) / 255.0
tensor_second_view = tensor_second_view.astype(np.float32) / 255.0
nrej_intensity = 0
num_negative_targets = 0
num_positive_targets = 0
step_x = 1
step_y = 1
step_z = 1
num_iterations = (W - 2*margin +1) * (H- 2*margin) * (D - 2*margin) / (step_x * step_y * step_z)
pbar = ProgressBar(widgets=['Making positive and negative examples for %d points: ' % num_iterations, Percentage()],
maxval=num_iterations).start()
X_positive = np.zeros((num_iterations, patchlen*2), dtype=np.float32)
y_positive = np.zeros((num_iterations, patchlen), dtype=np.float32)
X_negative = []
y_negative = []
if find_negative == True:
X_negative = np.zeros((num_iterations, patchlen*2), dtype=np.float32)
y_negative = np.zeros((num_iterations, patchlen), dtype=np.float32)
pbi = 0
for x0 in range(margin, W - margin, step_x):
for y0 in range(margin, H - margin, step_y):
for z0 in range(margin, D - margin, step_z):
if inside_patch(c_array, x0, y0, z0, size, offset=2.0):
patch_left = tensor_first_view[z0 - size: z0 + size + 1, y0 - size: y0 + size + 1, x0 - size: x0 + size + 1]
patch_right = tensor_second_view[z0 - size: z0 + size + 1, y0 - size: y0 + size + 1, x0 - size: x0 + size + 1]
X_positive[num_positive_targets, 0:patchlen] = np.reshape(patch_left, (patchlen,))
X_positive[num_positive_targets, patchlen:2*patchlen] = np.reshape(patch_right, (patchlen,))
ypatch = target_tensor_3d[z0 - size:z0 + size + 1, y0 - size:y0 + size + 1,
x0 - size:x0 + size + 1]
y_positive[num_positive_targets, :] = np.reshape(ypatch, (patchlen,))
num_positive_targets += 1
elif find_negative == True:
patch_left = tensor_first_view[z0 - size: z0 + size + 1, y0 - size: y0 + size + 1, x0 - size: x0 + size + 1]
patch_right = tensor_second_view[z0 - size: z0 + size + 1, y0 - size: y0 + size + 1, x0 - size: x0 + size + 1]
if np.mean(patch_left * 255) > 5 or np.mean(patch_right * 255) > 5:
X_negative[num_negative_targets, 0:patchlen] = np.reshape(patch_left, (patchlen,))
X_negative[num_negative_targets, patchlen:2*patchlen] = np.reshape(patch_right, (patchlen,))
ypatch = target_tensor_3d[z0 - size: z0 + size + 1, y0 - size: y0 + size + 1, x0 - size: x0 + size + 1]
y_negative[num_negative_targets, :] = np.reshape(ypatch, (patchlen,))
num_negative_targets += 1
else:
nrej_intensity += 1
pbar.update(pbi+ 1)
pbi += 1
pbar.finish()
X_positive = X_positive[0: num_positive_targets]
y_positive = y_positive[0: num_positive_targets]
print('Total positive examples for substack (', ss.substack_id, '):', num_positive_targets)
if find_negative == True:
print('Total negative examples for substack (', ss.substack_id, '):', num_negative_targets)
print('Rejected by intensity ', nrej_intensity)
X_negative = X_negative[0: num_negative_targets]
y_negative = y_negative[0: num_negative_targets]
if (num_negative_targets > num_positive_targets):
print('Shuffling negative examples...')
perm = np.random.permutation(len(X_negative)).astype(int)
X_negative = X_negative[perm]
y_negative = y_negative[perm]
print('Shuffling done')
X_negative = X_negative[0: num_positive_targets]
y_negative = y_negative[0: num_positive_targets]
return X_positive, y_positive, X_negative, y_negative
def save_vaa3d(self, C, Lx, Ly, Lz, Lcluster,
draw_centers=True,
colorize_voxels=True,
trajectories=None,
floating_point=False):
"""
save_vaa3d(C, Lx, Ly, Lz, Lcluster,
draw_centers=True, colorize_voxels=True, trajectories=None)
Dump a colored substack so that Vaa3D can be used to inspect clusters
Parameters
----------
C : list
List of centers
Lx, Ly, Lz : array-like
Coordinates of foreground voxels
Lcluster : array-like
Cluster index of every foreground voxel
draw_centers : bool, optional
Should centers be drawn as small circles?
colorize_voxels : bool, optional
Should we paint voxels in colors?
trajectories : list or None, optional
If not None, draw mean shift trajectories for every seed
floating_point: bool, optional
If true, save coordinates in floating point, else round to int
"""
out_imgs = []
opixels = []
pixels = []
self.compute_hues(C)
for z, img in enumerate(self.substack.imgs):
out_img = img.convert('RGB')
out_imgs.append(out_img)
opixels.append(out_img.load())
pixels.append(img.load())
if colorize_voxels:
for i in xrange(len(Lx)):
if Lcluster[i] is not None:
# draw = ImageDraw.Draw(out_imgs[Lz[i]])
opixels[Lz[i]][Lx[i], Ly[i]] = hi2rgb[int(255*Lcluster[i].hue)][pixels[Lz[i]][Lx[i], Ly[i]]]
if draw_centers:
for c in C:
draw = ImageDraw.Draw(out_imgs[int(round(c.z))])
rgb = hi2rgb[int(255*c.hue)][156]
draw.ellipse((c.x-1, c.y-1, c.x+1, c.y+1), fill=rgb)
if trajectories:
for seed_no in trajectories:
rgb = hi2rgb[random.randint(0, 255)][156]
for mean in trajectories[seed_no]:
draw = ImageDraw.Draw(out_imgs[int(round(mean[2]))])
draw.point((mean[0], mean[1]), fill=rgb)
save_name = 'last'
iter_dir = '%s/%s' % (self.savedir, save_name)
mkdir_p(iter_dir)
for z, out_img in enumerate(out_imgs):
out_img.save(iter_dir+'/'+self.substack.info['Files'][z].split('/')[-1])
w, h = out_img.size
# ---------- marker file used by vaa3d
self.substack.save_markers('%s/%s.marker' % (self.savedir, save_name), C, floating_point)
def main(args):
"""
Files must be numbered consecutively.
"""
prefix = None
#convert_to_grey = True
savewd = os.getcwd()
os.chdir(args.indir)
full_path = os.getcwd()
os.chdir(savewd)
files = os.listdir(args.indir)
files = [f for f in files if f[0] != '.' and f.endswith(args.suffix)]
prefix = prefix or os.path.commonprefix(files)
if prefix == '':
raise Exception('Files in ' + args.indir +
' do not have a common prefix ' + str(files))
# trailing zero dropped -- WHY???
# zrange = sorted([int(str(f.split(prefix)[1].split(args.suffix)[0])[:-1]) for f in files])
zrange = sorted([int(f.split('_')[-1].split('.')[0]) for f in files])
# n_digits = len(files[0].split(prefix)[1].split(args.suffix)[0])
n_digits = len(f.split('_')[-1].split('.')[0])
if zrange != range(zrange[0], zrange[-1] + 1):
raise Exception('Files in ' + args.indir +
' are not a complete sequence: ' + str(zrange))
print('Checking image files')
prefix = '_'.join(prefix.split('_')[:-1])
for z in zrange:
image_file = ('%s_%0' + str(n_digits) + 'd' + args.suffix) % (prefix,
z)
img_z = Image.open(full_path + '/' + image_file)
if z == zrange[0]:
width, height = img_z.size
else:
if (width, height) != img_z.size:
raise Exception('OOps, file', image_file, 'has size',
img_z.size, 'instead of', (width, height))
depth = len(zrange)
print(full_path)
full_path_of_substacks = "/".join(
full_path.split('/')[:-2]) + '/substacks/' + "/".join(
full_path.split('/')[-2:])
print('Volume geometry:', width, height, depth)
print('Finding substacks')
substacks = dict()
# nx,ny,nz=3,8,3 # Good for V000_maggio_large_substack_blurred
w = int(round(float((width - args.margin)) / float(args.nx)))
h = int(round(float((height - args.margin)) / float(args.ny)))
d = int(round(float((depth - args.margin)) / float(args.nz)))
print(w, h, d)
for i in range(args.nx):
for j in range(args.ny):
for k in range(args.nz):
x0 = w * i
y0 = h * j
z0 = d * k
x1 = min(width, x0 + w + args.margin)
y1 = min(height, y0 + h + args.margin)
z1 = min(depth, z0 + d + args.margin)
identifier = "%02d%02d%02d" % (i, j, k)
substack = dict(Width=x1 - x0,
Height=y1 - y0,
Depth=z1 - z0,
X0=x0,
Y0=y0,
Z0=z0,
Files=[])
if args.verbose:
print('Substack', (i, j, k))
print('X:', x0, x1, w)
print('Y:', y0, y1, h)
print('Z:', z0, z1, d)
print('volume:',
((x1 - x0) * (y1 - y0) * (z1 - z0)) / 1000000.0,
'MVoxels')
substacks[identifier] = substack
mkdir_p(full_path_of_substacks + '/' + identifier)
num_slices = len(zrange)
print('Saving cropped image files into', full_path_of_substacks)
for z_from_zero, z in enumerate(zrange):
image_file = ('%s_%0' + str(n_digits) + 'd' + args.suffix) % (prefix,
z)
img_z = Image.open(full_path + '/' + image_file)
if args.verbose:
print(full_path + '/' + image_file, end='')
print('==========', z + 1, '/', num_slices, '=============')
for substack_id, substack in substacks.iteritems():
substack_dir = full_path_of_substacks + '/' + substack_id
#mkdir_p(substack_dir)
Z0 = substack['Z0']
Depth = substack['Depth']
# print(substack)
if z_from_zero in range(Z0, Z0 + Depth):
X0 = substack['X0']
Width = substack['Width']
Y0 = substack['Y0']
Height = substack['Height']
region = img_z.crop((X0 + 0, Y0 + 0, X0 + Width, Y0 + Height))
if args.convert_to_grey:
region = region.convert('L')
if args.verbose:
print(' ', substack_id, end='')
region.save(substack_dir + '/' + image_file)
tmpfile = '/tmp/' + str(uuid.uuid4()) + '.tif'
region.save(tmpfile)
subprocess.call([
'tiffcp', '-clzw:2', tmpfile,
substack_dir + '/' + image_file
])
os.unlink(tmpfile)
# substack['Files'].append(substack_dir+'/'+image_file)
# the path to files is now relative
substack['Files'].append(substack_id + '/' + image_file)
if args.verbose:
print()
print('Saving substack info into', full_path_of_substacks + '/info.json')
info = dict(ImageSequenceDirectory=full_path,
Width=width,
Height=height,
Depth=depth,
Margin=args.margin,
SubStacks=substacks,
Full_Path_Of_Substacks=full_path_of_substacks)
with open(full_path_of_substacks + '/info.json', 'w') as ostream:
print(json.dumps(info), file=ostream)
def transform_substack(indir, tensorimage, substack_id, R, t, extramargin, outdir=None, invert=False, save_tiff=False, save_hdf5=False):
"""
Method that applies a previously estimated rigid transformation to a specific substack.
Parameters
----------
indir : str
substack dir of the input view
tensorimage : str
the whole tensor in hdf5 format
substack_id : str
id of the substack that will be transformed
R : numpy array of shape (3, 3)
rotational component of the estimated rigid transformation
t : numpy array of shape (3)
translational component of the estimated rigid transformation
extramargin : int
extramargin used to extract the transformed substack from tensorimage
outdir : str
output directory where the transformed substack will be saved (Default: None)
invert : boolean
if True the tranformation is inverted (Default: False)
save_tiff : boolean
save the transformed substack in a stack of tiff slices (Default: False)
save_hdf5 : boolean
save the transformed substack as a hdf5 tensor (Default: False)
Returns
-------
pixels_transformed_input: numpy tensor
tensor of the transformed substack
"""
ss = SubStack(indir, substack_id)
input_stack_file = tensorimage
hf5 = tables.openFile(input_stack_file, 'r')
full_D, full_H, full_W = hf5.root.full_image.shape
X0,Y0,Z0 = ss.info['X0'], ss.info['Y0'], ss.info['Z0']
origin = (Z0, Y0, X0)
H,W,D = ss.info['Height'], ss.info['Width'], ss.info['Depth']
or_ss_shape = (D,H,W)
offset_W=int((3**(1/2.0)*W + W/2.0 - W)/2.0)
offset_H=int((3**(1/2.0)*H + H/2.0 - H)/2.0)
offset_D=int((3**(1/2.0)*D + D/2.0 - D)/2.0)
if offset_W < extramargin:
offset_W = extramargin
if offset_H < extramargin:
offset_H = extramargin
if offset_D < extramargin:
offset_D = extramargin
offset_D_left = offset_D if int(origin[0] - offset_D) > 0 else origin[0]
offset_H_left = offset_H if int(origin[1] - offset_H) > 0 else origin[1]
offset_W_left = offset_W if int(origin[2] - offset_W) > 0 else origin[2]
offset_D_right = offset_D if int(origin[0] + or_ss_shape[0] + offset_D) <= full_D else full_D - (origin[0] + or_ss_shape[0])
offset_H_right = offset_H if int(origin[1] + or_ss_shape[1] + offset_H) <= full_H else full_H - (origin[1] + or_ss_shape[1])
offset_W_right = offset_W if int(origin[2] + or_ss_shape[2] + offset_W) <= full_W else full_W - (origin[2] + or_ss_shape[2])
pixels_input = hf5.root.full_image[origin[0] - offset_D_left:origin[0] + or_ss_shape[0] + offset_D_right,
origin[1] - offset_H_left:origin[1] + or_ss_shape[1] + offset_H_right,
origin[2] - offset_W_left:origin[2] + or_ss_shape[2] + offset_W_right]
exmar_D_left = 0 if offset_D_left == origin[0] else extramargin
exmar_H_left = 0 if offset_H_left == origin[1] else extramargin
exmar_W_left = 0 if offset_W_left == origin[2] else extramargin
depth_target, height_target, width_target = or_ss_shape[0] + 2 * extramargin, or_ss_shape[1] + 2 * extramargin, or_ss_shape[2] + 2 * extramargin #new
depth_input, height_input, width_input = pixels_input.shape[0], pixels_input.shape[1], pixels_input.shape[2]
pixels_transformed_input = np.zeros((depth_target,height_target,width_target), dtype=np.uint8)
total_start = timeit.default_timer()
coords_2d_target = np.vstack(np.indices((width_target,height_target)).swapaxes(0,2).swapaxes(0,1))
invR = R.T
if invert:
t = -np.dot(invR, t)
invR = R
invR_2d_transpose = np.transpose(np.dot(invR[:, 0:2], np.transpose(coords_2d_target - t[0:2])))
offset_coords = np.array([[offset_W_left - exmar_W_left, offset_H_left - exmar_H_left, offset_D_left - exmar_D_left]]*invR_2d_transpose.shape[0])#new
for z in xrange(0, depth_target, 1):
R_t_3d = np.transpose(invR_2d_transpose + invR[:, 2] * (z - t[2]) + offset_coords)
good_indices = np.array(range(R_t_3d.shape[1]))
good_indices = good_indices[(R_t_3d[0, :] > 0) * (R_t_3d[1, :] > 0) * (R_t_3d[2, :] > 0) * (R_t_3d[0, :] < (width_input - 1)) * (R_t_3d[1, :] < (height_input - 1)) * (R_t_3d[2, :] < (depth_input - 1))]
R_t_3d = R_t_3d.take(good_indices,axis=1)
R_t_3d = np.round(R_t_3d).astype(int)
coords_2d_target_tmp = coords_2d_target.take(good_indices, axis=0)
coords_3d_target_tmp = np.hstack((coords_2d_target_tmp, np.ones((coords_2d_target_tmp.shape[0], 1)).astype(int)*z))
pixels_transformed_input[coords_3d_target_tmp[:, 2], coords_3d_target_tmp[:, 1], coords_3d_target_tmp[:, 0]] = pixels_input[R_t_3d[2, :], R_t_3d[1, :], R_t_3d[0, :]]
total_stop = timeit.default_timer()
print ("total time transformation stack:%s "%(str(total_stop - total_start)))
pixels_transformed_input = np.array(pixels_transformed_input, dtype=np.uint8)
if save_tiff or save_hdf5:
mkdir_p(outdir)
if save_tiff:
minz = int(ss.info['Files'][0].split("/")[-1].split('_')[-1].split('.tif')[0])
_prefix = '_'.join(ss.info['Files'][0].split("/")[-1].split('_')[0:-1])+'_'
substack_outdir = outdir + '/' + substack_id
imtensor.save_tensor_as_tif(pixels_transformed_input, substack_outdir, minz, prefix=_prefix)
if save_hdf5:
target_shape = (depth_target, height_target, width_target)
atom = tables.UInt8Atom()
h5f = tables.openFile(outdir + '/' + ss.substack_id + '.h5', 'w')
ca = h5f.createCArray(h5f.root, 'full_image', atom, target_shape)
for z in xrange(0, depth_target, 1):
ca[z, :, :] = pixels_transformed_input[z,:,:]
h5f.close()
return pixels_transformed_input
def merge_views(args):
view_ids=args.view_ids
max_len=0
hue=0.
C_substack=[]
if args.thresholds_dir is None:
print(view_ids)
list_views = get_visible_pairs(args.base_indir,args.substack_id,view_ids)
else:
list_views = get_visible_pairs_from_dir(args.thresholds_dir, args.substack_id, view_ids)
substack = SubStack(args.base_indir+'/'+view_ids[0],args.substack_id)
valid_pairs = filter_valid_pairs(args,substack,list_views)
if len(valid_pairs.keys())==1:
C_substack+=valid_pairs.values()[0]
else:
keys_view_1=[]
keys_view_3=[]
for view_key in valid_pairs.keys():
if view_key[0] == view_ids[2]:
keys_view_3.append(view_key)
elif view_key[0] == view_ids[0]:
keys_view_1.append(view_key)
if len(valid_pairs)==2:
if len(keys_view_1) == 2 or len(keys_view_3) == 2:
total_list=compute_fusion(substack,valid_pairs.values()[0],valid_pairs.values()[1],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
elif len(keys_view_1) == 1 and len(keys_view_3) == 1:
C_view_3_t=transform_markers(args,substack,valid_pairs[keys_view_3[0]], view_ids)
total_list=compute_fusion(substack,valid_pairs[keys_view_1[0]],C_view_3_t,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
else:
raise Exception('not valid list of views %s'%(str(valid_pairs)))
elif len(valid_pairs)==3:
if len(keys_view_1) == 2:
C_view_1=compute_fusion(substack,valid_pairs[keys_view_1[0]],valid_pairs[keys_view_1[1]],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_view_3_t=transform_markers(args,substack,valid_pairs[keys_view_3[0]], view_ids)
total_list=compute_fusion(substack,C_view_1,C_view_3_t,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
elif len(keys_view_3) == 2:
C_view_3=compute_fusion(substack,valid_pairs[keys_view_3[0]],valid_pairs[keys_view_3[1]],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_view_3_t=transform_markers(args,substack,C_view_3, view_ids)
C_view_1=valid_pairs[keys_view_1[0]]
total_list=compute_fusion(substack,C_view_1,C_view_3_t,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
else:
raise Exception('not valid list of views %s'%(str(valid_pairs)))
elif len(valid_pairs)==4:
if len(keys_view_1) == 2 and len(keys_view_3) == 2:
C_view_1=compute_fusion(substack,valid_pairs[keys_view_1[0]],valid_pairs[keys_view_1[1]],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_view_3=compute_fusion(substack,valid_pairs[keys_view_3[0]],valid_pairs[keys_view_3[1]],args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_view_3_t=transform_markers(args,substack,C_view_3, view_ids)
total_list=compute_fusion(substack,C_view_1,C_view_3_t,args.max_distance,match_distance=args.match_distance,verbose=args.verbose)
C_substack+=total_list
else:
raise Exception('not valid list of views %s'%(str(valid_pairs)))
mkdir_p(args.outdir+'/'+args.substack_id)
if len(C_substack)>0:
C_substack = filter_outside_markers(C_substack,substack)
substack.save_markers(args.outdir+'/'+args.substack_id+'/ms.marker', C_substack, floating_point=True)
if len(sys.argv) != 4:
usage()
sys.exit(2)
indir = sys.argv[1]
infile = sys.argv[2]
outdir = sys.argv[3]
substack_ids = map(string.strip, os.popen('ls ' + indir).readlines())
substack_ids = [f for f in substack_ids if len(f) == 6]
C = load_markers(infile)
plist = None
substacks = {}
for substack_id in substack_ids:
substack = SubStack(indir, substack_id, plist)
plist = substack.plist
substacks[substack.substack_id] = substack
tx, ty, tz, idx = build_trees(substacks)
C_locals = get_locals(C, tx, ty, tz, idx, substacks)
pbar = ProgressBar(
widgets=['Saving %d files: ' % len(C_locals),
Percentage()],
maxval=len(C_locals)).start()
for pbi, (substack_id, C_local) in enumerate(C_locals.iteritems()):
mkdir_p(outdir + '/' + substack_id)
substack.save_markers(outdir + '/' + substack_id + '/ms.marker',
C_local)
pbar.update(pbi + 1)
pbar.finish()
def make_pos_neg_dataset(tensor_view, ss, C, size=5, save_tiff_files=False, default_sigma=0.8, find_negative=True):
tensor_view = tensor_view.astype(np.float32)
H, W, D = ss.info['Height'], ss.info['Width'], ss.info['Depth']
patchlen = (1 + 2 * size) ** 3
c_array = np.array([[c.x, c.y, c.z] for c in C])
target_tensor_3d = np.zeros(tensor_view.shape)
margin = ss.plist['Margin'] / 2
trunc=1.5
fixed_radiiassigned={c:default_sigma for c in C if inside_margin(c,ss) > 0 }
kdt = cKDTree(c_array)
dist,ind = kdt.query(c_array,k=c_array.shape[0],distance_upper_bound=(2*default_sigma*trunc)+2.)
for c,id_c in zip(C,xrange(c_array.shape[0])):
if inside_margin(c, ss) > 0:
for j in xrange(2,c_array.shape[0]):
if not np.isinf(dist[id_c][j]):
if inside_margin(C[ind[id_c][j]], ss) > 0:
if trunc*(fixed_radiiassigned[c]+fixed_radiiassigned[C[ind[id_c][j]]]) > dist[id_c][j] - 2.:
new_sigma=((dist[id_c][j]-1.)/trunc)*fixed_radiiassigned[c]/(fixed_radiiassigned[c]+fixed_radiiassigned[C[ind[id_c][j]]])
fixed_radiiassigned[C[ind[id_c][j]]]=(new_sigma*fixed_radiiassigned[C[ind[id_c][j]]])/fixed_radiiassigned[c]
fixed_radiiassigned[c]=new_sigma
for c in C:
if inside_margin(c, ss) > 0:
target_tensor_3d_tmp = np.zeros(tensor_view.shape)
target_tensor_3d_tmp[c.z, c.y, c.x] = 1
sigma=fixed_radiiassigned[c]
target_tensor_3d_tmp = gfilter.gaussian_filter(target_tensor_3d_tmp, sigma,
mode='constant', cval=0.0,
truncate=trunc)
target_tensor_3d_tmp = (target_tensor_3d_tmp.astype(np.float32) / np.max(target_tensor_3d_tmp))
target_tensor_3d = np.maximum(np.array(target_tensor_3d_tmp * 255.0, dtype=np.uint8), target_tensor_3d)
if save_tiff_files:
debug_path='/mnt/data/marco/experiments/markers/debug_single_view_on_fused_image'
mkdir_p(debug_path)
minz = 0
target_tensor_3d = np.array(target_tensor_3d, dtype=np.uint8)
imtensor.save_tensor_as_tif(target_tensor_3d, debug_path+'/'+ss.substack_id, minz)
target_tensor_3d = (target_tensor_3d.astype(np.float32) / np.max(target_tensor_3d))
tensor_view = tensor_view.astype(np.float32) / 255.0
nrej_intensity = 0
num_negative_targets = 0
num_positive_targets = 0
step_x = 1
step_y = 1
step_z = 1
num_iterations = (W - 2*margin) * (H- 2*margin) * (D - 2*margin) / (step_x * step_y * step_z)
pbar = ProgressBar(widgets=['Making positive and negative examples for %d points: ' % num_iterations, Percentage()],
maxval=num_iterations).start()
X_positive = np.zeros((num_iterations, patchlen), dtype=np.float32)
y_positive = np.zeros((num_iterations, patchlen), dtype=np.float32)
X_negative = []
y_negative = []
if find_negative == True:
X_negative = np.zeros((num_iterations, patchlen), dtype=np.float32)
y_negative = np.zeros((num_iterations, patchlen), dtype=np.float32)
pbi = 0
for x0 in range(margin, W - margin, step_x):
for y0 in range(margin, H - margin, step_y):
for z0 in range(margin, D - margin, step_z):
if inside_patch(c_array, x0, y0, z0, size,offset=2.0): #found a marker inside margin
patch = tensor_view[z0 - size: z0 + size + 1, y0 - size: y0 + size + 1, x0 - size: x0 + size + 1]
X_positive[num_positive_targets, :] = np.reshape(patch, (patchlen,))
ypatch = target_tensor_3d[z0 - size:z0 + size + 1, y0 - size:y0 + size + 1,
x0 - size:x0 + size + 1]
y_positive[num_positive_targets, :] = np.reshape(ypatch, (patchlen,))
num_positive_targets += 1 # Sample as many negatives as positives
elif find_negative == True: #min_distance > (size * (3**(1/2.0))) + 2.0
patch = tensor_view[z0 - size: z0 + size + 1, y0 - size: y0 + size + 1, x0 - size: x0 + size + 1]
if np.mean(patch * 255) > 5:
X_negative[num_negative_targets, :] = np.reshape(patch, (patchlen,))
ypatch = target_tensor_3d[z0 - size: z0 + size + 1, y0 - size: y0 + size + 1, x0 - size: x0 + size + 1]
y_negative[num_negative_targets, :] = np.reshape(ypatch, (patchlen,))
num_negative_targets += 1
else:
nrej_intensity += 1
pbar.update(pbi + 1)
pbi += 1
pbar.finish()
X_positive = X_positive[0: num_positive_targets]
y_positive = y_positive[0: num_positive_targets]
print('Total positive examples for substack (', ss.substack_id, '):', num_positive_targets)
if find_negative == True:
print('Total negative examples for substack (', ss.substack_id, '):', num_negative_targets)
print('Rejected by intensity ', nrej_intensity)
X_negative = X_negative[0: num_negative_targets]
y_negative = y_negative[0: num_negative_targets]
if (num_negative_targets > num_positive_targets):
print('Shuffling negative examples...')
perm = np.random.permutation(len(X_negative)).astype(int)
X_negative = X_negative[perm]
y_negative = y_negative[perm]
print('Shuffling done')
X_negative = X_negative[0: num_positive_targets]
y_negative = y_negative[0: num_positive_targets]
return X_positive, y_positive, X_negative, y_negative
def main(args):
"""
Files must be numbered consecutively.
"""
prefix = None
#convert_to_grey = True
savewd = os.getcwd()
os.chdir(args.indir)
full_path = os.getcwd()
os.chdir(savewd)
files = os.listdir(args.indir)
files = [f for f in files if f[0] != '.' and f.endswith(args.suffix)]
prefix = prefix or os.path.commonprefix(files)
if prefix == '':
raise Exception('Files in '+args.indir+' do not have a common prefix ' + str(files))
# trailing zero dropped -- WHY???
# zrange = sorted([int(str(f.split(prefix)[1].split(args.suffix)[0])[:-1]) for f in files])
zrange = sorted([int(f.split('_')[-1].split('.')[0]) for f in files])
# n_digits = len(files[0].split(prefix)[1].split(args.suffix)[0])
n_digits = len(f.split('_')[-1].split('.')[0])
if zrange != range(zrange[0], zrange[-1]+1):
raise Exception('Files in '+args.indir+' are not a complete sequence: '+str(zrange))
print('Checking image files')
prefix = '_'.join(prefix.split('_')[:-1])
for z in zrange:
image_file = ('%s_%0'+str(n_digits)+'d'+args.suffix) % (prefix, z)
img_z = Image.open(full_path+'/'+image_file)
if z == zrange[0]:
width, height = img_z.size
else:
if (width, height) != img_z.size:
raise Exception('OOps, file', image_file, 'has size', img_z.size, 'instead of', (width, height))
depth = len(zrange)
print(full_path)
full_path_of_substacks = "/".join(full_path.split('/')[:-2]) + '/substacks/' + "/".join(full_path.split('/')[-2:])
print('Volume geometry:', width, height, depth)
print('Finding substacks')
substacks = dict()
# nx,ny,nz=3,8,3 # Good for V000_maggio_large_substack_blurred
w = int(round(float((width-args.margin))/float(args.nx)))
h = int(round(float((height-args.margin))/float(args.ny)))
d = int(round(float((depth-args.margin))/float(args.nz)))
print(w,h,d)
for i in range(args.nx):
for j in range(args.ny):
for k in range(args.nz):
x0 = w*i
y0 = h*j
z0 = d*k
x1 = min(width, x0+w+args.margin)
y1 = min(height, y0+h+args.margin)
z1 = min(depth, z0+d+args.margin)
identifier = "%02d%02d%02d" % (i, j, k)
substack = dict(Width=x1-x0, Height=y1-y0, Depth=z1-z0, X0=x0, Y0=y0, Z0=z0, Files=[])
if args.verbose:
print('Substack', (i, j, k))
print('X:', x0, x1, w)
print('Y:', y0, y1, h)
print('Z:', z0, z1, d)
print('volume:', ((x1-x0)*(y1-y0)*(z1-z0))/1000000.0, 'MVoxels')
substacks[identifier] = substack
mkdir_p(full_path_of_substacks + '/' + identifier)
num_slices = len(zrange)
print('Saving cropped image files into', full_path_of_substacks)
for z_from_zero, z in enumerate(zrange):
image_file = ('%s_%0'+str(n_digits)+'d'+args.suffix) % (prefix, z)
img_z = Image.open(full_path+'/'+image_file)
if args.verbose:
print(full_path+'/'+image_file, end='')
print('==========',z+1,'/',num_slices,'=============')
for substack_id, substack in substacks.iteritems():
substack_dir = full_path_of_substacks+'/'+substack_id
#mkdir_p(substack_dir)
Z0 = substack['Z0']
Depth = substack['Depth']
# print(substack)
if z_from_zero in range(Z0, Z0+Depth):
X0 = substack['X0']
Width = substack['Width']
Y0 = substack['Y0']
Height = substack['Height']
region = img_z.crop((X0+0, Y0+0, X0+Width, Y0+Height))
if args.convert_to_grey:
region = region.convert('L')
if args.verbose:
print(' ', substack_id, end='')
region.save(substack_dir+'/'+image_file)
tmpfile = '/tmp/'+str(uuid.uuid4())+'.tif'
region.save(tmpfile)
subprocess.call(['tiffcp', '-clzw:2', tmpfile, substack_dir+'/'+image_file])
os.unlink(tmpfile)
# substack['Files'].append(substack_dir+'/'+image_file)
# the path to files is now relative
substack['Files'].append(substack_id+'/'+image_file)
if args.verbose:
print()
print('Saving substack info into', full_path_of_substacks+'/info.json')
info = dict(ImageSequenceDirectory=full_path,
Width=width,
Height=height,
Depth=depth,
Margin=args.margin,
SubStacks=substacks,
Full_Path_Of_Substacks=full_path_of_substacks)
with open(full_path_of_substacks+'/info.json', 'w') as ostream:
print(json.dumps(info), file=ostream)
def merge_views(args):
view_ids = args.view_ids
max_len = 0
hue = 0.
C_substack = []
if args.thresholds_dir is None:
print(view_ids)
list_views = get_visible_pairs(args.base_indir, args.substack_id,
view_ids)
else:
list_views = get_visible_pairs_from_dir(args.thresholds_dir,
args.substack_id, view_ids)
substack = SubStack(args.base_indir + '/' + view_ids[0], args.substack_id)
valid_pairs = filter_valid_pairs(args, substack, list_views)
if len(valid_pairs.keys()) == 1:
C_substack += valid_pairs.values()[0]
else:
keys_view_1 = []
keys_view_3 = []
for view_key in valid_pairs.keys():
if view_key[0] == view_ids[2]:
keys_view_3.append(view_key)
elif view_key[0] == view_ids[0]:
keys_view_1.append(view_key)
if len(valid_pairs) == 2:
if len(keys_view_1) == 2 or len(keys_view_3) == 2:
total_list = compute_fusion(substack,
valid_pairs.values()[0],
valid_pairs.values()[1],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
elif len(keys_view_1) == 1 and len(keys_view_3) == 1:
C_view_3_t = transform_markers(args, substack,
valid_pairs[keys_view_3[0]],
view_ids)
total_list = compute_fusion(substack,
valid_pairs[keys_view_1[0]],
C_view_3_t,
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
else:
raise Exception('not valid list of views %s' % (str(valid_pairs)))
elif len(valid_pairs) == 3:
if len(keys_view_1) == 2:
C_view_1 = compute_fusion(substack,
valid_pairs[keys_view_1[0]],
valid_pairs[keys_view_1[1]],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_view_3_t = transform_markers(args, substack,
valid_pairs[keys_view_3[0]],
view_ids)
total_list = compute_fusion(substack,
C_view_1,
C_view_3_t,
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
elif len(keys_view_3) == 2:
C_view_3 = compute_fusion(substack,
valid_pairs[keys_view_3[0]],
valid_pairs[keys_view_3[1]],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_view_3_t = transform_markers(args, substack, C_view_3, view_ids)
C_view_1 = valid_pairs[keys_view_1[0]]
total_list = compute_fusion(substack,
C_view_1,
C_view_3_t,
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
else:
raise Exception('not valid list of views %s' % (str(valid_pairs)))
elif len(valid_pairs) == 4:
if len(keys_view_1) == 2 and len(keys_view_3) == 2:
C_view_1 = compute_fusion(substack,
valid_pairs[keys_view_1[0]],
valid_pairs[keys_view_1[1]],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_view_3 = compute_fusion(substack,
valid_pairs[keys_view_3[0]],
valid_pairs[keys_view_3[1]],
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_view_3_t = transform_markers(args, substack, C_view_3, view_ids)
total_list = compute_fusion(substack,
C_view_1,
C_view_3_t,
args.max_distance,
match_distance=args.match_distance,
verbose=args.verbose)
C_substack += total_list
else:
raise Exception('not valid list of views %s' % (str(valid_pairs)))
mkdir_p(args.outdir + '/' + args.substack_id)
if len(C_substack) > 0:
C_substack = filter_outside_markers(C_substack, substack)
substack.save_markers(args.outdir + '/' + args.substack_id +
'/ms.marker',
C_substack,
floating_point=True)
if __name__ == '__main__':
if len(sys.argv) != 4:
usage()
sys.exit(2)
indir = sys.argv[1]
infile = sys.argv[2]
outdir = sys.argv[3]
substack_ids = map(string.strip,os.popen('ls '+indir).readlines())
substack_ids = [f for f in substack_ids if len(f)==6]
C = load_markers(infile)
plist = None
substacks = {}
for substack_id in substack_ids:
substack=SubStack(indir,substack_id, plist)
plist = substack.plist
substacks[substack.substack_id] = substack
tx, ty, tz, idx = build_trees(substacks)
C_locals = get_locals(C, tx, ty, tz, idx, substacks)
pbar = ProgressBar(widgets=['Saving %d files: ' % len(C_locals), Percentage()],
maxval=len(C_locals)).start()
for pbi, (substack_id, C_local) in enumerate(C_locals.iteritems()):
mkdir_p(outdir + '/' + substack_id)
substack.save_markers(outdir + '/' + substack_id + '/ms.marker',C_local)
pbar.update(pbi+1)
pbar.finish()
