def main(args):
# Build mask parameters DataFrame
df_params = pd.DataFrame({"mask_name" : args.mask_name,\
"slice_axis" : args.slice_axis,\
"n_patches" : args.n_patches,\
"overlap" : args.overlap, \
"rotation" : args.rotation})
# print(df_params)
mpl.use(args.mpl_agg)
data_io.show_header()
if not os.path.exists(args.seg_path): os.makedirs(args.seg_path)
if args.run_seg:
# Understand input data format
if os.path.isdir(args.ct_fpath):
tiff_input = True
elif args.ct_fpath.split('.')[-1] in ("hdf5", "h5"):
tiff_input = False
if args.ct_data_tag == "":
raise ArgumentTypeError("dataset-name required for hdf5")
else:
raise ArgumentTypeError(
"input file type not recognized. must be tiff folder or hdf5 file"
)
ct_dfile = data_io.DataFile(args.ct_fpath, \
tiff = tiff_input,\
data_tag = args.ct_data_tag, \
VERBOSITY = args.rw_verbosity)
ct_dfile.show_stats()
chunk_shape = ct_dfile.chunk_shape
if args.stats_only:
print("\nSet stats_only = False and start over to run program.")
sys.exit()
# Load model from model repo
model_filename = os.path.join(args.model_path,
args.model_name + '.hdf5')
print("\nStarting segmentation mode ...")
segmenter = Segmenter(model_filename=model_filename)
print("Reading CT volume into memory...")
dd = ct_dfile.read_full()
if args.preprocess:
print("\tPreprocessing volume...")
if not os.path.exists("preprocessor.py"):
input(
"Looked for preprocessor.py, but not found! Please create one and press enter. Or press CTRL+C to exit"
)
from preprocessor import preprocessor
dd = preprocessor(dd)
for idx, row in df_params.iterrows(): # iterate over masks
# assign arguments from df_params for this mask
slice_axis = row["slice_axis"]
max_patches = row["n_patches"]
segfile_tag = row["mask_name"]
overlap = row["overlap"]
rotation = row["rotation"]
# define DataFile object for mask
seg_fname = os.path.join(args.seg_path, segfile_tag)
if not args.tiff_output: seg_fname = seg_fname + ".hdf5"
seg_dfile = data_io.DataFile(seg_fname, \
data_tag = "SEG",\
tiff = args.tiff_output, \
d_shape = ct_dfile.d_shape, \
d_type = np.uint8, \
chunk_shape = chunk_shape,\
VERBOSITY = args.rw_verbosity)
seg_dfile.create_new(overwrite=args.overwrite_OK)
t0 = time.time()
print("\nWorking on %s\n" % segfile_tag)
ch = process_data(dd, segmenter, \
slice_axis = slice_axis, \
rot_angle = rotation, \
max_patches = max_patches, \
overlap = overlap, \
nprocs = args.nprocs, \
arr_split = args.arr_split,\
arr_split_infer = args.arr_split_infer,\
crops = args.crops)
seg_dfile.write_full(ch)
t1 = time.time()
total_time = (t1 - t0) / 60.0
print(
"\nDONE on %s\nTotal time for generating %s mask: %.2f minutes"
% (time.ctime(), segfile_tag, total_time))
del slice_axis
del max_patches
del segfile_tag
del rotation
del ch
if args.run_ensemble:
print("\nStarting ensemble mode ...\n")
t0 = time.time()
# get the d_shape of one of the masks
temp_fname = os.path.join(args.seg_path, df_params.loc[0, "mask_name"])
if not args.tiff_output: temp_fname = temp_fname + ".hdf5"
temp_ds = data_io.DataFile(temp_fname,
data_tag="SEG",
tiff=args.tiff_output,
VERBOSITY=0)
mask_shape = temp_ds.d_shape
chunk_shape = temp_ds.chunk_shape
if not args.run_seg: temp_ds.show_stats()
del temp_ds
del temp_fname
if args.stats_only:
print("\nSet stats_only = False and start over to run program.")
sys.exit()
vote_fname = os.path.join(args.seg_path, args.vote_maskname)
if not args.tiff_output: vote_fname = vote_fname + ".hdf5"
vote_dfile = data_io.DataFile(vote_fname, \
tiff = args.tiff_output,\
data_tag = "SEG",\
d_shape = mask_shape, \
d_type = np.uint8, \
chunk_shape = chunk_shape,\
VERBOSITY = args.rw_verbosity)
vote_dfile.create_new(overwrite=args.overwrite_OK)
slice_start = 0
n_masks = len(df_params)
pbar = tqdm(total=mask_shape[0])
while slice_start < mask_shape[0]:
ch = [0] * len(df_params)
for idx, row in df_params.iterrows():
seg_fname = os.path.join(args.seg_path, row["mask_name"])
if not args.tiff_output: seg_fname = seg_fname + ".hdf5"
seg_dfile = data_io.DataFile(seg_fname, \
tiff = args.tiff_output, \
data_tag = "SEG", \
VERBOSITY = args.rw_verbosity)
if mask_shape != seg_dfile.d_shape:
raise ValueError("Shape of all masks must be same")
ch[idx], s = seg_dfile.read_chunk(axis = 0, \
slice_start = slice_start, \
max_GB = args.mem_thres/(n_masks))
ch = np.asarray(ch)
ch = np.median(ch, axis=0).astype(np.uint8)
vote_dfile.write_chunk(ch, axis=0, s=s)
del ch
slice_start = s.stop
pbar.update(s.stop - s.start)
pbar.close()
t1 = time.time()
total_time = (t1 - t0) / 60.0
print("\nDONE on %s\nTotal time for ensemble mask %s : %.2f minutes" %
(time.ctime(), args.vote_maskname, total_time))
if args.remove_masks:
print("Intermediate masks will be removed.")
for idx, row in df_params.iterrows(): # iterate over masks
seg_fname = os.path.join(args.seg_path, row["mask_name"])
if not args.tiff_output:
seg_fname = seg_fname + ".hdf5"
os.remove(seg_fname)
else:
rmtree(seg_fname)
if args.morpho_filt:
print("\nApplying morphological operations on ensemble vote...")
vote_fname = os.path.join(args.seg_path, args.vote_maskname)
if not args.tiff_output: vote_fname = vote_fname + ".hdf5"
vote_dfile = data_io.DataFile(vote_fname, \
tiff = args.tiff_output,\
data_tag = "SEG",\
VERBOSITY = args.rw_verbosity)
from ct_segnet.morpho import morpho_filter
vol = vote_dfile.read_full()
vol = morpho_filter(vol, radius = args.radius, \
ops = args.ops, \
crops = args.crops, \
invert_mask = args.invert_mask)
vote_dfile.write_full(vol)
return
def main(args_summary, **kwargs):
mpl.use('Agg')
# Inputs from argparser
args.kern_size = _make_tuples(args.kern_size)
args.kern_size_upconv = _make_tuples(args.kern_size_upconv)
args.pool_size = _make_tuples(args.pool_size)
data_io.show_header()
############### LOAD OR REBUILD fCNN MODEL ####################
model_file = os.path.join(args.model_path, args.model_name + ".hdf5")
model_history = os.path.join(args.model_path, 'history', args.model_name)
if not os.path.exists(model_history): os.makedirs(model_history)
if args.rebuild:
if args.config_model is None:
raise Exception("Model config file must be provided if rebuilding")
img_shape = (args.model_size, args.model_size, 1)
segmenter = build_Unet(img_shape, \
n_depth = args.n_depth,\
n_pools = args.n_pools,\
activation = args.activation,\
batch_norm = args.is_batch_norm,\
kern_size = args.kern_size,\
kern_size_upconv = args.kern_size_upconv,\
pool_size = args.pool_size,\
dropout_level = args.dropout_level,\
loss = args.loss_def,\
stdinput = args.stdinput)
if args.initialize_from is not None:
print("\nInitializing weights from file:\n%s" %
args.initialize_from)
segmenter.load_weights(args.initialize_from)
segmenter.save(model_file)
df_prev = None
elif os.path.exists(os.path.join(model_history, args.model_name + ".csv")):
segmenter = load_model(model_file, custom_objects=custom_objects_dict)
df_prev = pd.read_csv(
os.path.join(model_history, args.model_name + ".csv"))
else:
raise ValueError(
"Model history not available to retrain. Rebuild required.")
###### LOAD TRAINING AND TESTING DATA #############
train_path = os.path.join(args.data_path, args.train_fname)
X = data_io.DataFile(train_path, tiff=False, data_tag='X', VERBOSITY=0)
Y = data_io.DataFile(train_path, tiff=False, data_tag='Y', VERBOSITY=0)
# used to evaluate accuracy metrics during training
test_path = os.path.join(args.data_path, args.test_fname)
Xtest = data_io.DataFile(test_path, tiff=False, data_tag='X', VERBOSITY=0)
Ytest = data_io.DataFile(test_path, tiff=False, data_tag='Y', VERBOSITY=0)
print("\nTotal test data shape: " + str(Ytest.d_shape))
print("Total training data shape: " + str(Y.d_shape))
# save_datasnaps(data_generator(X, Y, args.n_save), model_history)
##### DO SOME LOGGING #####################
rw = "w+" if args.rebuild else "a+"
logfile = os.path.join(model_history, "README_" + args.model_name + ".txt")
with open(logfile, rw) as f:
if args.rebuild:
f.write("\n")
with redirect_stdout(f):
segmenter.summary()
f.write("\nNew Entry\n")
f.write("Training Started: " + time.ctime() + "\n")
if not args.rebuild: args_summary = args_summary.split("Defaults")[0]
f.write(args_summary)
f.write("\nTotal train data size: %s" % (str(Y.d_shape)))
f.write("\nTotal test data size: %s" % (str(Ytest.d_shape)))
f.write("\n")
######### START TRAINING ##################
model_paths = {'name' : args.model_name,\
'history' : model_history,\
'file' : model_file}
logger = Logger(Xtest,
Ytest,
model_paths,
args.autosave_freq,
df_prev=df_prev,
n_test=args.n_test,
check_norm=args.check_norm,
augmenter_todo=None)
n_train = min(args.nmax_train, X.d_shape[0])
t0 = time.time()
try:
if args.fit_generator:
steps_per_epoch = int(
(1 - args.validation_split) * n_train // args.batch_size)
validation_steps = int(args.validation_split * n_train //
args.batch_size)
# data generator will work in 'inplace' mode (faster) and generate data of size = batch_size
dg = data_generator(X, Y, args.batch_size, \
check_norm = args.check_norm, \
augmenter_todo = args.augmenter_todo, \
min_SNR = args.min_SNR, inplace = True, nprocs = 1)
hist = segmenter.fit_generator(dg,\
steps_per_epoch = steps_per_epoch,\
validation_data = data_generator(X, Y, args.batch_size),\
validation_steps = validation_steps,\
epochs = args.n_epochs,\
verbose = 2, \
callbacks = [logger])
else:
dg = data_generator(X, Y, n_train, \
check_norm = args.check_norm, \
augmenter_todo = args.augmenter_todo, \
min_SNR = args.min_SNR, inplace = False)
x_train, y_train = next(dg)
hist = segmenter.fit(x = x_train, y = y_train,\
verbose = 2, initial_epoch = 0, validation_split = args.validation_split,\
epochs = args.n_epochs, batch_size = args.batch_size, callbacks = [logger])
print("\nModel training complete")
with open(logfile, "a+") as f:
f.write("\nTraining Completed: " + time.ctime() + "\n")
hours = (time.time() - t0) / (60 * 60.0)
f.write("\nTotal time: %.4f hours\n" % (hours))
f.write("\nEnd of Entry")
print("\n Total time: %.4f hours" % (hours))
segmenter.save(model_file)
########### EVALUATED MODEL AND SAVE SOME TEST IMAGES ############
print("\nSaving some test images...")
model_results = os.path.join(args.model_path, 'history',
args.model_name, 'testing')
dg = data_generator(Xtest, Ytest, args.n_save, \
augmenter_todo = args.augmenter_todo, \
min_SNR = args.min_SNR, \
inplace = False, nprocs = 1)
save_results(dg, model_results, segmenter)
# Log keyboard interrupt exception
except KeyboardInterrupt:
print("\nModel training interrupted")
with open(logfile, "a+") as f:
f.write("\nTraining Interrupted after %i epochs: " % logger.i +
time.ctime() + "\n")
f.write("\nEnd of Entry")
return
def main(args):
data_io.show_header()
print("\nLet's make some training data.\n")
# Understand input data format
ct_istiff = data_io._istiff(args.ct_fpath, args.ct_data_tag)
seg_istiff = data_io._istiff(args.seg_path, args.seg_data_tag)
dfile_recon = data_io.DataFile(args.ct_fpath,
data_tag=args.ct_data_tag,
tiff=ct_istiff,
VERBOSITY=0)
dfile_seg = data_io.DataFile(args.seg_path,
data_tag=args.seg_data_tag,
tiff=seg_istiff,
VERBOSITY=0)
if args.output_fpath == "":
args.output_fpath = os.path.split(args.seg_path)[0]
args.output_fname = args.output_fname.split('.')[0] + ".hdf5"
output_fname = os.path.join(args.output_fpath, args.output_fname)
print("Grayscale CT data:\n%s\n" % dfile_recon.fname)
dfile_recon.show_stats()
print("Manually segmented mask:\n%s\n" % dfile_seg.fname)
dfile_seg.show_stats()
if np.prod(dfile_seg.d_shape) != np.prod(dfile_recon.d_shape):
raise ValueError(
"CT data and segmentation mask must be exactly same shape / size")
# Decide domain extents
crop_shape = data_io.get_cropped_shape(args.crops, dfile_seg.d_shape)
# Decide patching / slicing strategy
df_params = pd.DataFrame({
"slice axis": args.slice_axis,
"max patches": args.n_patches
})
skip_fac = args.skip_fac
# Estimate the shapes of hdf5 data files to be written
n_images = 0
for idx, row in df_params.iterrows():
_len = np.ceil(crop_shape[row["slice axis"]] / skip_fac)
_len = _len * np.prod(row["max patches"])
print("Calculated length of set %i: %i" % (idx + 1, _len))
n_images = _len + n_images
write_dshape = (int(n_images), args.model_size, args.model_size)
# Create datafile objects for writing stuff, and get file paths for it also
w_dfile_seg = data_io.DataFile(output_fname,
data_tag='Y',
tiff=False,
chunked_slice_size=None,
d_shape=write_dshape,
d_type=np.uint8)
w_dfile_recon = data_io.DataFile(output_fname,
data_tag='X',
tiff=False,
chunked_slice_size=None,
d_shape=write_dshape,
d_type=np.float32)
w_dfile_seg.create_new(overwrite=False)
w_dfile_recon.create_new(overwrite=False)
# Work on seg data
print("Working on the segmentation map...")
d_seg = dfile_seg.read_full()
d_seg = d_seg[slice(*args.crops[0]),
slice(*args.crops[1]),
slice(*args.crops[2])]
gc.collect()
slice_start = 0
pbar = tqdm(total=n_images)
for idx, row in df_params.iterrows():
p = process_data(d_seg,
skip_fac=skip_fac,
nprocs=args.nprocs,
patch_size=args.model_size,
n_patches=row['max patches'],
axis=row['slice axis'])
slice_end = p.shape[0] + slice_start
s = slice(slice_start, slice_end)
w_dfile_seg.write_chunk(p, axis=0, s=s)
slice_start = slice_end
del p
pbar.update(s.stop - s.start)
gc.collect()
pbar.close()
del d_seg
gc.collect()
# Work on recon data
print("Working on the grayscale CT volume...")
slice_start = 0
d_recon = dfile_recon.read_full()
d_recon = d_recon[slice(*args.crops[0]),
slice(*args.crops[1]),
slice(*args.crops[2])]
pbar = tqdm(total=n_images)
for idx, row in df_params.iterrows():
p = process_data(d_recon,
skip_fac=skip_fac,
nprocs=args.nprocs,
patch_size=args.model_size,
n_patches=row['max patches'],
axis=row['slice axis'])
slice_end = p.shape[0] + slice_start
s = slice(slice_start, slice_end)
w_dfile_recon.write_chunk(p, axis=0, s=s)
slice_start = slice_end
del p
pbar.update(s.stop - s.start)
gc.collect()
pbar.close()
def main(args):
data_io.show_header()
# Understand input data format
if os.path.isdir(args.input_fname):
tiff_input = True
if args.dataset_name == "": args.dataset_name = "data"
elif args.input_fname.split('.')[-1] in ("hdf5", "h5"):
tiff_input = False
if args.dataset_name == "": raise ArgumentTypeError("dataset-name required for hdf5")
else:
raise ArgumentTypeError("input file type not recognized. must be tiff folder or hdf5 file")
input_fname = args.input_fname
# set up output file name / path / chunks parameter
if args.output_fpath == "":
args.output_fpath = os.path.split(args.input_fname)[0]
args.output_fname = args.output_fname.split('.')[0] + ".hdf5"
output_fname = os.path.join(args.output_fpath, args.output_fname)
if type(args.chunk_param) in (int, float):
chunk_size = args.chunk_param/1e3 # convert to GB
chunk_shape = None
elif type(args.chunk_param) == tuple:
chunk_shape = args.chunk_param
chunk_size = None
chunked_slice_size = args.chunked_slice_size
# print("Type chunk_param" + str(type(args.chunk_param)))
# print("Type chunked_slice_size" + str(type(args.chunked_slice_size)))
# print("Overwrite OK is %s"%args.overwrite_OK)
# print("Stats only is %s"%args.stats_only)
# print("Delete is %s"%args.delete)
# sys.exit()
# Define DataFile instances - quit here if stats_only requested
r_dfile = data_io.DataFile(input_fname, tiff = tiff_input, \
data_tag = args.dataset_name, \
VERBOSITY = args.verbosity)
print("Input data stats:")
r_dfile.show_stats()
if args.stats_only:
sys.exit()
w_shape = r_dfile.d_shape # future implementation must allow resampling dataset
w_dtype = r_dfile.d_type # future implementation must allow changing dtype (with renormalization)
w_dfile = data_io.DataFile(output_fname, tiff = False, \
data_tag = args.dataset_name, \
VERBOSITY = args.verbosity, \
d_shape = w_shape, d_type = w_dtype, \
chunk_shape = chunk_shape, \
chunk_size = chunk_size, \
chunked_slice_size = chunked_slice_size)
print("\nChunking scheme estimated as: %s"%str(w_dfile.chunk_shape))
input("\nHDF5 file will be saved to the following location.\n%s\nPress any key to continue."%output_fname)
w_dfile.create_new(overwrite = args.overwrite_OK)
t0 = time.time()
slice_start = 0
print("\n")
pbar = tqdm(total = r_dfile.d_shape[0])
while slice_start < r_dfile.d_shape[0]:
dd, s = r_dfile.read_chunk(axis = 0, slice_start = slice_start, \
max_GB = mem_thres, \
chunk_shape = w_dfile.chunk_shape)
w_dfile.write_chunk(dd, axis = 0, s = s)
slice_start = s.stop
pbar.update(s.stop - s.start)
pbar.close()
total_time = (time.time() - t0)/60.0 # minutes
print("\nTotal time: %.2f minutes"%(total_time))
if args.delete:
input("Delete old file? Press any key")
if tiff_input:
rmtree(input_fname)
else:
os.remove(input_fname)
def main(args):
df_params = pd.DataFrame({"mask_name" : args.mask_name,\
"slice_axis" : args.slice_axis,\
"n_patches" : args.n_patches,\
"overlap" : args.overlap})
mpl.use(args.mpl_agg)
# print(df_params)
data_io.show_header()
if not os.path.exists(args.seg_path): os.makedirs(args.seg_path)
if args.run_seg:
# Load model from model repo
ct_dfile = data_io.DataFile(args.ct_fpath, \
tiff = False,\
data_tag = args.ct_data_tag, \
VERBOSITY = args.rw_verbosity)
ct_dfile.show_stats()
if args.stats_only:
print("\nSet stats_only = False and start over to run program.")
sys.exit()
chunk_shape = ct_dfile.chunk_shape
model_filename = os.path.join(args.model_path, args.model_name + '.hdf5')
print("\nStarting segmentation mode ...")
segmenter = Segmenter(model_filename = model_filename)
for idx, row in df_params.iterrows(): # iteratve over masks
# assign arguments from df_params for this mask
slice_axis = row["slice_axis"]
max_patches = row["n_patches"]
segfile_tag = row["mask_name"]
overlap = row["overlap"]
# define DataFile object for mask
seg_fname = os.path.join(args.seg_path, segfile_tag + ".hdf5")
seg_dfile = data_io.DataFile(seg_fname, \
data_tag = "SEG",\
tiff = False, \
d_shape = ct_dfile.d_shape, \
d_type = np.uint8, \
chunk_shape = chunk_shape,\
VERBOSITY = args.rw_verbosity)
seg_dfile.create_new(overwrite = args.overwrite_OK)
t0 = time.time()
slice_start = 0
print("\nWorking on %s\n"%segfile_tag)
pbar = tqdm(total = seg_dfile.d_shape[slice_axis])
while slice_start < seg_dfile.d_shape[slice_axis]:
ch, s = ct_dfile.read_chunk(axis = slice_axis, \
slice_start = slice_start, \
max_GB = args.mem_thres)
ch = segmenter.seg_chunk(ch, \
max_patches = max_patches, \
overlap = overlap,\
nprocs = args.nprocs, \
arr_split = args.arr_split)
seg_dfile.write_chunk(ch, axis = slice_axis, s = s)
del ch
slice_start = s.stop
pbar.update(s.stop - s.start)
pbar.close()
t1 = time.time()
total_time = (t1 - t0) / 60.0
print("\nDONE on %s\nTotal time for generating %s mask: %.2f minutes"%(time.ctime(), segfile_tag, total_time))
del slice_axis
del max_patches
del segfile_tag
if args.run_ensemble:
print("\nStarting ensemble mode ...\n")
t0 = time.time()
# get the d_shape of one of the masks
temp_fname = os.path.join(args.seg_path, df_params.loc[0,"mask_name"] + ".hdf5")
temp_ds = data_io.DataFile(temp_fname, data_tag = "SEG", tiff = False, VERBOSITY = 0)
mask_shape = temp_ds.d_shape
chunk_shape = temp_ds.chunk_shape
if not args.run_seg: temp_ds.show_stats()
del temp_ds
del temp_fname
if args.stats_only:
print("\nSet stats_only = False and start over to run program.")
sys.exit()
vote_fname = os.path.join(args.seg_path, args.vote_maskname)
if not args.tiff_output: vote_fname = vote_fname + ".hdf5"
vote_dfile = data_io.DataFile(vote_fname, \
tiff = args.tiff_output,\
data_tag = "SEG", \
d_shape = mask_shape, \
d_type = np.uint8, \
chunk_shape = chunk_shape,\
VERBOSITY = args.rw_verbosity)
vote_dfile.create_new(overwrite = args.overwrite_OK)
slice_start = 0
n_masks = len(df_params)
pbar = tqdm(total = mask_shape[0])
while slice_start < mask_shape[0]:
ch = [0]*len(df_params)
for idx, row in df_params.iterrows():
seg_fname = os.path.join(args.seg_path, row["mask_name"]) + ".hdf5"
seg_dfile = data_io.DataFile(seg_fname, \
tiff = False, \
data_tag = "SEG", \
VERBOSITY = args.rw_verbosity)
if mask_shape != seg_dfile.d_shape:
raise ValueError("Shape of all masks must be same")
ch[idx], s = seg_dfile.read_chunk(axis = 0, \
slice_start = slice_start, \
max_GB = args.mem_thres/(n_masks))
ch = np.asarray(ch)
ch = np.median(ch, axis = 0).astype(np.uint8)
vote_dfile.write_chunk(ch, axis = 0, s = s)
del ch
slice_start = s.stop
pbar.update(s.stop - s.start)
pbar.close()
t1 = time.time()
total_time = (t1 - t0) / 60.0
print("\nDONE on %s\nTotal time for ensemble mask %s : %.2f minutes"%(time.ctime(), args.vote_maskname, total_time))
if args.remove_masks:
print("Intermediate masks will be removed.")
for idx, row in df_params.iterrows(): # iterate over masks
seg_fname = os.path.join(args.seg_path, row["mask_name"]) + ".hdf5"
os.remove(seg_fname)
return
