def main(args):
total_start_time = time.time()
print('\n>> CLI Parameters ...\n')
print(args)
check_args(args)
feature_file_format = os.path.splitext(args.outputNucleiFeatureFile)[1]
if np.all(np.array(args.analysis_roi) == -1):
process_whole_image = True
else:
process_whole_image = False
#
# Initiate Dask client
#
print('\n>> Creating Dask client ...\n')
start_time = time.time()
c = cli_utils.create_dask_client(args)
print(c)
dask_setup_time = time.time() - start_time
print('Dask setup time = {} seconds'.format(dask_setup_time))
#
# Read Input Image
#
print('\n>> Reading input image ... \n')
ts = large_image.getTileSource(args.inputImageFile)
ts_metadata = ts.getMetadata()
print(json.dumps(ts_metadata, indent=2))
is_wsi = ts_metadata['magnification'] is not None
#
# Compute tissue/foreground mask at low-res for whole slide images
#
if is_wsi and process_whole_image:
print('\n>> Computing tissue/foreground mask at low-res ...\n')
start_time = time.time()
im_fgnd_mask_lres, fgnd_seg_scale = \
cli_utils.segment_wsi_foreground_at_low_res(ts)
fgnd_time = time.time() - start_time
print('low-res foreground mask computation time = {}'.format(
cli_utils.disp_time_hms(fgnd_time)))
#
# Compute foreground fraction of tiles in parallel using Dask
#
tile_fgnd_frac_list = [1.0]
it_kwargs = {
'tile_size': {
'width': args.analysis_tile_size
},
'scale': {
'magnification': args.analysis_mag
},
}
if not process_whole_image:
it_kwargs['region'] = {
'left': args.analysis_roi[0],
'top': args.analysis_roi[1],
'width': args.analysis_roi[2],
'height': args.analysis_roi[3],
'units': 'base_pixels'
}
if is_wsi:
print('\n>> Computing foreground fraction of all tiles ...\n')
start_time = time.time()
num_tiles = ts.getSingleTile(**it_kwargs)['iterator_range']['position']
print('Number of tiles = {}'.format(num_tiles))
if process_whole_image:
tile_fgnd_frac_list = htk_utils.compute_tile_foreground_fraction(
args.inputImageFile, im_fgnd_mask_lres, fgnd_seg_scale,
it_kwargs)
else:
tile_fgnd_frac_list = [1.0] * num_tiles
num_fgnd_tiles = np.count_nonzero(
tile_fgnd_frac_list >= args.min_fgnd_frac)
percent_fgnd_tiles = 100.0 * num_fgnd_tiles / num_tiles
fgnd_frac_comp_time = time.time() - start_time
print('Number of foreground tiles = {0:d} ((1:2f)%%)'.format(
num_fgnd_tiles, percent_fgnd_tiles))
print('Tile foreground fraction computation time = {}'.format(
cli_utils.disp_time_hms(fgnd_frac_comp_time)))
#
# Compute reinhard stats for color normalization
#
src_mu_lab = None
src_sigma_lab = None
if is_wsi and process_whole_image:
print('\n>> Computing reinhard color normalization stats ...\n')
start_time = time.time()
src_mu_lab, src_sigma_lab = htk_cnorm.reinhard_stats(
args.inputImageFile, 0.01, magnification=args.analysis_mag)
rstats_time = time.time() - start_time
print('Reinhard stats computation time = {}'.format(
cli_utils.disp_time_hms(rstats_time)))
#
# Detect and compute nuclei features in parallel using Dask
#
print('\n>> Detecting nuclei and computing features ...\n')
start_time = time.time()
tile_result_list = []
for tile in ts.tileIterator(**it_kwargs):
tile_position = tile['tile_position']['position']
if is_wsi and tile_fgnd_frac_list[tile_position] <= args.min_fgnd_frac:
continue
# detect nuclei
cur_result = dask.delayed(compute_tile_nuclei_features)(
args.inputImageFile, tile_position, args, it_kwargs, src_mu_lab,
src_sigma_lab)
# append result to list
tile_result_list.append(cur_result)
tile_result_list = dask.delayed(tile_result_list).compute()
nuclei_annot_list = [
annot for annot_list, fdata in tile_result_list for annot in annot_list
]
nuclei_fdata = pd.concat([fdata for annot_list, fdata in tile_result_list],
ignore_index=True)
nuclei_detection_time = time.time() - start_time
print('Number of nuclei = {}'.format(len(nuclei_annot_list)))
print('Nuclei detection time = {}'.format(
cli_utils.disp_time_hms(nuclei_detection_time)))
#
# Write annotation file
#
print('\n>> Writing annotation file ...\n')
annot_fname = os.path.splitext(
os.path.basename(args.outputNucleiAnnotationFile))[0]
annotation = {
"name": annot_fname + '-nuclei-' + args.nuclei_annotation_format,
"elements": nuclei_annot_list
}
with open(args.outputNucleiAnnotationFile, 'w') as annotation_file:
json.dump(annotation, annotation_file, indent=2, sort_keys=False)
#
# Create CSV Feature file
#
print('>> Writing CSV feature file')
if feature_file_format == '.csv':
nuclei_fdata.to_csv(args.outputNucleiFeatureFile, index=False)
elif feature_file_format == '.h5':
nuclei_fdata.to_hdf(args.outputNucleiFeatureFile,
'Features',
format='table',
mode='w')
else:
raise ValueError(
'Extension of output feature file must be .csv or .h5')
total_time_taken = time.time() - total_start_time
print('Total analysis time = {}'.format(
cli_utils.disp_time_hms(total_time_taken)))
def main(args):
total_start_time = time.time()
print('\n>> CLI Parameters ...\n')
print(args)
if not os.path.isfile(args.inputImageFile):
raise IOError('Input image file does not exist.')
if len(args.reference_mu_lab) != 3:
raise ValueError('Reference Mean LAB should be a 3 element vector.')
if len(args.reference_std_lab) != 3:
raise ValueError('Reference Stddev LAB should be a 3 element vector.')
if len(args.analysis_roi) != 4:
raise ValueError('Analysis ROI must be a vector of 4 elements.')
if np.all(np.array(args.analysis_roi) == -1):
process_whole_image = True
else:
process_whole_image = False
#
# Initiate Dask client
#
print('\n>> Creating Dask client ...\n')
start_time = time.time()
c = cli_utils.create_dask_client(args)
print(c)
dask_setup_time = time.time() - start_time
print('Dask setup time = {}'.format(
cli_utils.disp_time_hms(dask_setup_time)))
#
# Read Input Image
#
print('\n>> Reading input image ... \n')
ts = large_image.getTileSource(args.inputImageFile)
ts_metadata = ts.getMetadata()
print(json.dumps(ts_metadata, indent=2))
is_wsi = ts_metadata['magnification'] is not None
#
# Compute tissue/foreground mask at low-res for whole slide images
#
if is_wsi and process_whole_image:
print('\n>> Computing tissue/foreground mask at low-res ...\n')
start_time = time.time()
im_fgnd_mask_lres, fgnd_seg_scale = \
cli_utils.segment_wsi_foreground_at_low_res(ts)
fgnd_time = time.time() - start_time
print('low-res foreground mask computation time = {}'.format(
cli_utils.disp_time_hms(fgnd_time)))
#
# Compute foreground fraction of tiles in parallel using Dask
#
tile_fgnd_frac_list = [1.0]
it_kwargs = {
'tile_size': {
'width': args.analysis_tile_size
},
'scale': {
'magnification': args.analysis_mag
},
}
if not process_whole_image:
it_kwargs['region'] = {
'left': args.analysis_roi[0],
'top': args.analysis_roi[1],
'width': args.analysis_roi[2],
'height': args.analysis_roi[3],
'units': 'base_pixels'
}
if is_wsi:
print('\n>> Computing foreground fraction of all tiles ...\n')
start_time = time.time()
num_tiles = ts.getSingleTile(**it_kwargs)['iterator_range']['position']
print('Number of tiles = {}'.format(num_tiles))
if process_whole_image:
tile_fgnd_frac_list = htk_utils.compute_tile_foreground_fraction(
args.inputImageFile, im_fgnd_mask_lres, fgnd_seg_scale,
**it_kwargs)
else:
tile_fgnd_frac_list = np.full(num_tiles, 1.0)
num_fgnd_tiles = np.count_nonzero(
tile_fgnd_frac_list >= args.min_fgnd_frac)
percent_fgnd_tiles = 100.0 * num_fgnd_tiles / num_tiles
fgnd_frac_comp_time = time.time() - start_time
print('Number of foreground tiles = {0:d} ({1:2f}%%)'.format(
num_fgnd_tiles, percent_fgnd_tiles))
print('Tile foreground fraction computation time = {}'.format(
cli_utils.disp_time_hms(fgnd_frac_comp_time)))
#
# Compute reinhard stats for color normalization
#
src_mu_lab = None
src_sigma_lab = None
if is_wsi and process_whole_image:
print('\n>> Computing reinhard color normalization stats ...\n')
start_time = time.time()
src_mu_lab, src_sigma_lab = htk_cnorm.reinhard_stats(
args.inputImageFile, 0.01, magnification=args.analysis_mag)
rstats_time = time.time() - start_time
print('Reinhard stats computation time = {}'.format(
cli_utils.disp_time_hms(rstats_time)))
#
# Detect nuclei in parallel using Dask
#
print('\n>> Detecting nuclei ...\n')
start_time = time.time()
tile_nuclei_list = []
for tile in ts.tileIterator(**it_kwargs):
tile_position = tile['tile_position']['position']
if is_wsi and tile_fgnd_frac_list[tile_position] <= args.min_fgnd_frac:
continue
# detect nuclei
cur_nuclei_list = dask.delayed(detect_tile_nuclei)(args.inputImageFile,
tile_position, args,
it_kwargs,
src_mu_lab,
src_sigma_lab)
# append result to list
tile_nuclei_list.append(cur_nuclei_list)
tile_nuclei_list = dask.delayed(tile_nuclei_list).compute()
nuclei_list = list(itertools.chain.from_iterable(tile_nuclei_list))
nuclei_detection_time = time.time() - start_time
print('Number of nuclei = {}'.format(len(nuclei_list)))
print('Nuclei detection time = {}'.format(
cli_utils.disp_time_hms(nuclei_detection_time)))
#
# Write annotation file
#
print('\n>> Writing annotation file ...\n')
annot_fname = os.path.splitext(
os.path.basename(args.outputNucleiAnnotationFile))[0]
annotation = {
"name": annot_fname + '-nuclei-' + args.nuclei_annotation_format,
"elements": nuclei_list
}
with open(args.outputNucleiAnnotationFile, 'w') as annotation_file:
json.dump(annotation, annotation_file, indent=2, sort_keys=False)
total_time_taken = time.time() - total_start_time
print('Total analysis time = {}'.format(
cli_utils.disp_time_hms(total_time_taken)))