def main(n_instances, curr_instance):
images_with_no_labels = []
skip_count = 0
images = recursive_glob(img_dir, in_image_search)
N = len(images)
Nstep = np.ceil(float(N) / n_instances)
for_start = int((curr_instance - 1) * Nstep)
for_end = int(for_start + Nstep)
if for_end > N:
for_end = N
print('for_start:for_end is {}:{}'.format(for_start, for_end))
for idx in range(for_start, for_end):
image = images[idx]
if ('.d' in image) or ('.H' in image):
print('SKIP ', image)
skip_count += 1
continue
basename = os.path.splitext(
os.path.basename(image.replace(in_image_search, '')))[0]
basename = basename.replace('PANCREAS_', '')
#label = recursive_glob2(label_dir,basename,in_label_search)
label = [os.path.join(label_dir, basename + in_label_search)]
if len(label) != 1:
raise ValueError('No unique label found for {} ({})'.format(
image, len(label)))
print('[WARNING] No unique label found for {} ({})'.format(
image, len(label)))
images_with_no_labels.append(image)
label = None
else:
label = label[0]
print('image: {}'.format(image))
print('label: {}'.format(label))
prepare(image, label, output_root)
####prepare2(image,label,output_root)
print('{} images_with_no_labels:'.format(len(images_with_no_labels)))
for i in images_with_no_labels:
print(i)
print('Skipped {} of {} images'.format(skip_count, N))
## Input output
img_dir = '/mnt/dataset/m-organs/averaged-training-images'
img_search = '.nii.gz'
label_dir = '/mnt/dataset/m-organs/averaged-training-labels'
label_search = '.nii.gz'
output_root = '/root/output'
######################## FUNCTIONS ###############################
import os
import sys
from deploy_cascade import deploy_cascade
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
from data import recursive_glob, recursive_glob2
images_with_no_labels = []
images = recursive_glob(img_dir, img_search)
#DEVICE=None # CPU only mode
DEVICE = 0
for image in images:
basename = os.path.splitext(os.path.basename(image))[0]
basename = basename.replace('series', '')
if label_dir is not None:
label = recursive_glob2(label_dir, basename, '.raw')
else:
label = []
if len(label) != 1:
print('[WARNING] No unique label found for {}'.format(image))
images_with_no_labels.append(image)
label = None
else:
def main(n_instances, curr_instance):
label_files = recursive_glob(in_label_dir, in_label_search)
N = len(label_files)
if N > 0:
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
#flist = open(out_list, 'w', 0)
flist = open(out_list, 'a', 0) #append to existing
else:
raise ValueError('No files found!')
Nstep = np.ceil(float(N) / n_instances)
for_start = int((curr_instance - 1) * Nstep)
for_end = int(for_start + Nstep)
if for_end > N:
for_end = N
print('for_start:for_end is {}:{}'.format(for_start, for_end))
converted = 0
already_computed = 0
img_pair_not_found = 0
skip_invalid = 0
failed_count = 0
for i, label_file in enumerate(label_files[for_start:for_end]):
print('{} of {}: {}'.format(i + 1, N, label_file))
basename = os.path.split(label_file.replace(in_label_search, ''))[1]
basename = basename[0:basename.find('_')].lower()
img_file = recursive_glob2(in_image_dir, basename, in_image_search)[0]
curr_dir = img_file.replace(in_image_dir,
out_dir).replace(in_image_search, '')
FOUND = False
mask_file = None
if LOAD_MASK:
mask_file = recursive_glob2(in_mask_dir, in_mask_search, basename)
if len(mask_file) != 1:
FOUND = False
img_pair_not_found += 1
print(' WARNING: no mask found for {}\n in {} with {} search'.
format(label_file, in_mask_dir, basename))
mask_file = None
continue
curr_dir = curr_dir.replace('.nii.gz', '')
curr_dir = curr_dir.replace('.nii', '')
out_file = curr_dir + '/' + os.path.split(curr_dir)[1] + out_suffix
if not os.path.isfile(out_file):
print(100 * '=')
try:
if LOAD_MASK:
convert_image_and_label_to_h5(img_file, label_file,
out_file, mask_file[0])
else:
convert_image_and_label_to_h5(img_file, label_file,
out_file)
converted += 1
except Exception as inst:
print type(inst) # the exception instance
print inst.args # arguments stored in .args
print inst # __str__ allows args to be printed directly
failed_count += 1
sys.exit()
else:
already_computed += 1
print(' Already computed ({}): {}'.format(already_computed,
out_file))
flist.write('{}\n'.format(out_file))
FOUND = True
if not FOUND:
img_pair_not_found += 1
print(' WARNING: no image found for {}'.format(label_file))
print('Converted {} of {} file pairs.'.format(converted, N))
print('Already converted {} of {} file pairs.'.format(already_computed, N))
print('No suitable image found {} of {} file pairs.'.format(
img_pair_not_found, N))
print('Skip/invalid {} of {} file pairs.'.format(skip_invalid, N))
print('Convert failed {} of {} file pairs.'.format(failed_count, N))
flist.close()