def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# check if output file exists
if not args.force:
if os.path.exists(args.image):
logger.warning('The output file {} already exists. Exiting.'.format(args.image))
sys.exit(0)
logger.info('Unpickle testbench and loading label image...')
label, label_img, bounding_boxes, model_fg_ids, model_bg_ids, truth_fg, truth_bg = __load(args.testbench, args.label)
logger.info('Composing image image...')
image = scipy.zeros(label.shape, dtype=scipy.int8)
# set foreground ids
for rid in truth_fg:
image[bounding_boxes[rid - 1]][label[bounding_boxes[rid - 1]] == rid] = 1
# set background ids
for rid in truth_bg:
image[bounding_boxes[rid - 1]][label[bounding_boxes[rid - 1]] == rid] = 2
# set foreground model ids
for rid in model_fg_ids:
image[bounding_boxes[rid - 1]][label[bounding_boxes[rid - 1]] == rid] = 3
# set background model ids
for rid in model_bg_ids:
image[bounding_boxes[rid - 1]][label[bounding_boxes[rid - 1]] == rid] = 4
logger.info('Saving image as {} with data-type int8...'.format(args.image))
image_img = image_like(image, label_img)
image_img.get_header().set_data_dtype(scipy.int8)
save(image_img, args.image)
logger.info('Successfully terminated.')
def main():
# prepare logger
logger = Logger.getInstance()
logger.setLevel(logging.DEBUG)
# input image locations
#i = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/00originalvolumes/o09.nii' # original image
#i = '/home/omaier/Temp/test.nii' # original image
#i = '/home/omaier/Temp/o09_smoothed_i4.0_c0.1_t0.0625.nii'
i = '/home/omaier/Experiments/GraphCut/BoundaryTerm/Stawiaski/01gradient/o09_gradient.nii'
# output image locations
r = '/home/omaier/Temp/result_gradient.nii' # result mask
# load images
i_i = load(i)
# extract and prepare image data
i_d = scipy.squeeze(i_i.get_data())
# crop input images to achieve faster execution
crop = [slice(50, -200),
slice(50, -150),
slice(50, -100)]
#i_d = i_d[crop]
i_d = scipy.copy(i_d)
# !TODO: Test if input image is of size 0
logger.debug('input image shape={},ndims={},dtype={}'.format(i_d.shape, i_d.ndim, i_d.dtype))
result = watershed8(i_d, logger)
logger.info('Saving resulting region map...')
result_i = image_like(result, i_i)
result_i.get_header().set_data_dtype(scipy.int32)
save(result_i, r)
logger.info('Done!')
def main():
# prepare logger
logger = Logger.getInstance()
logger.setLevel(logging.DEBUG)
# input image locations
#i = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/00originalvolumes/o09.nii' # original image
#i = '/home/omaier/Temp/test.nii' # original image
#i = '/home/omaier/Temp/o09_smoothed_i4.0_c0.1_t0.0625.nii'
i = '/home/omaier/Experiments/GraphCut/BoundaryTerm/Stawiaski/01gradient/o09_gradient.nii'
# output image locations
r = '/home/omaier/Temp/result_gradient.nii' # result mask
# load images
i_i = load(i)
# extract and prepare image data
i_d = scipy.squeeze(i_i.get_data())
# crop input images to achieve faster execution
crop = [slice(50, -200), slice(50, -150), slice(50, -100)]
#i_d = i_d[crop]
i_d = scipy.copy(i_d)
# !TODO: Test if input image is of size 0
logger.debug('input image shape={},ndims={},dtype={}'.format(
i_d.shape, i_d.ndim, i_d.dtype))
result = watershed8(i_d, logger)
logger.info('Saving resulting region map...')
result_i = image_like(result, i_i)
result_i.get_header().set_data_dtype(scipy.int32)
save(result_i, r)
logger.info('Done!')
def main():
# prepare logger
logger = Logger.getInstance()
logger.setLevel(logging.DEBUG)
# input image locations
#i = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/00originalvolumes/o09.nii' # original image
g = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/01gradient/o09_gradient.nii' # gradient magnitude image
l = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/label_full.nii' # watershed label image
fg = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/fg_markers.nii'
bg = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/bg_markers.nii'
# output image locations
r = '/home/omaier/Experiments/GraphCut/BoundaryTerm/graphcut_full.nii' # liver mask
# load images
#i_i = load(i)
g_i = load(g)
l_i = load(l)
fg_i = load(fg)
bg_i = load(bg)
# extract and prepare image data
#i_d = scipy.squeeze(i_i.get_data())
g_d = scipy.squeeze(g_i.get_data())
l_d = scipy.squeeze(l_i.get_data())
fg_d = scipy.squeeze(fg_i.get_data())
bg_d = scipy.squeeze(bg_i.get_data())
# crop input images to achieve faster execution
#crop = [slice(50, -100),
# slice(50, -100),
# slice(50, -100)]
#g_d = g_d[crop]
#l_d = l_d[crop]
#fg_d = fg_d[crop]
#bg_d = bg_d[crop]
# recompute the label ids to start from id
logger.info('Relabel input image...')
l_d = filter.relabel(l_d)
# generate graph
logger.info('Preparing graph...')
gr = graphcut.graph_from_labels(l_d, fg_d, bg_d, boundary_term = graphcut.boundary_stawiaski, boundary_term_args = g_d)
#inconsistent = gr.inconsistent()
#if inconsistent:
# logger.error('The created graph contains inconsistencies: {}'.format('\n'.join(inconsistent)))
# build graph cut graph from graph
logger.info('Generating BK_MFMC C++ graph...')
gcgraph = graphcut.GraphDouble(len(gr.get_nodes()), len(gr.get_nweights()))
gcgraph.add_node(len(gr.get_nodes()))
for node, weight in gr.get_tweights().iteritems():
gcgraph.add_tweights(int(node - 1), weight[0], weight[1])
for edge, weight in gr.get_nweights().iteritems():
gcgraph.add_edge(int(edge[0] - 1), int(edge[1] - 1), weight[0], weight[1])
# execute min-cut
logger.info('Executing min-cut...')
maxflow = gcgraph.maxflow()
logger.debug('Maxflow is {}'.format(maxflow))
# collect
logger.info('Applying results...')
l_d = filter.relabel_map(l_d, gcgraph.what_segment, lambda fun, rid: 0 if gcgraph.termtype.SINK == fun(int(rid) - 1) else 1)
logger.info('Saving images resulting mask...')
# save resulting mask
l_d = l_d.astype(scipy.bool_)
save(image_like(l_d, fg_i), r)
logger.info('Done!')
def main():
# prepare logger
logger = Logger.getInstance()
logger.setLevel(logging.DEBUG)
# input image locations
#i = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/00originalvolumes/o09.nii' # original image
g = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/01gradient/o09_gradient.nii' # gradient magnitude image
l = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/label_full.nii' # watershed label image
fg = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/fg_markers.nii'
bg = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/bg_markers.nii'
# output image locations
r = '/home/omaier/Experiments/GraphCut/BoundaryTerm/graphcut_full.nii' # liver mask
# load images
#i_i = load(i)
g_i = load(g)
l_i = load(l)
fg_i = load(fg)
bg_i = load(bg)
# extract and prepare image data
#i_d = scipy.squeeze(i_i.get_data())
g_d = scipy.squeeze(g_i.get_data())
l_d = scipy.squeeze(l_i.get_data())
fg_d = scipy.squeeze(fg_i.get_data())
bg_d = scipy.squeeze(bg_i.get_data())
# crop input images to achieve faster execution
#crop = [slice(50, -100),
# slice(50, -100),
# slice(50, -100)]
#g_d = g_d[crop]
#l_d = l_d[crop]
#fg_d = fg_d[crop]
#bg_d = bg_d[crop]
# recompute the label ids to start from id
logger.info('Relabel input image...')
l_d = filter.relabel(l_d)
# generate graph
logger.info('Preparing graph...')
gr = graphcut.graph_from_labels(l_d,
fg_d,
bg_d,
boundary_term=graphcut.boundary_stawiaski,
boundary_term_args=g_d)
#inconsistent = gr.inconsistent()
#if inconsistent:
# logger.error('The created graph contains inconsistencies: {}'.format('\n'.join(inconsistent)))
# build graph cut graph from graph
logger.info('Generating BK_MFMC C++ graph...')
gcgraph = graphcut.GraphDouble(len(gr.get_nodes()), len(gr.get_nweights()))
gcgraph.add_node(len(gr.get_nodes()))
for node, weight in gr.get_tweights().iteritems():
gcgraph.add_tweights(int(node - 1), weight[0], weight[1])
for edge, weight in gr.get_nweights().iteritems():
gcgraph.add_edge(int(edge[0] - 1), int(edge[1] - 1), weight[0],
weight[1])
# execute min-cut
logger.info('Executing min-cut...')
maxflow = gcgraph.maxflow()
logger.debug('Maxflow is {}'.format(maxflow))
# collect
logger.info('Applying results...')
l_d = filter.relabel_map(
l_d, gcgraph.what_segment, lambda fun, rid: 0
if gcgraph.termtype.SINK == fun(int(rid) - 1) else 1)
logger.info('Saving images resulting mask...')
# save resulting mask
l_d = l_d.astype(scipy.bool_)
save(image_like(l_d, fg_i), r)
logger.info('Done!')
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# build output image name
output_hdr_name = args.output + '.hdr'
output_img_name = args.output + '.img'
output_msk_name = args.output + '.msk'
# check if output image exists
if not args.force:
if os.path.exists(output_hdr_name):
logger.warning(
'The output header {} already exists. Breaking.'.format(
output_hdr_name))
exit(1)
elif os.path.exists(output_img_name):
logger.warning(
'The output image {} already exists. Breaking.'.format(
output_img_name))
exit(1)
elif os.path.exists(output_msk_name):
logger.warning(
'The output infor file {} already exists. Breaking.'.format(
output_msk_name))
exit(1)
# decide on most suitable bit format
if len(args.masks) / 2 <= 8:
bit_format = scipy.uint8
elif len(args.masks) / 2 <= 16:
bit_format = scipy.uint16
elif len(args.masks) / 2 <= 32:
bit_format = scipy.uint32
elif len(args.masks) / 2 <= 64:
bit_format = scipy.uint64
else:
raise ArgumentError(
'It is not possible to combine more than 64 single masks.')
logger.info(
'Creating a Radiance® segmentation image in {} bit format...'.format(
bit_format))
# loading first mask image as reference and template for saving
logger.info('Loading mask {} ({} segmentation) using NiBabel...'.format(
args.masks[0], args.masks[1]))
image_mask = load(args.masks[0])
image_mask_data = scipy.squeeze(image_mask.get_data())
# prepare result image
image_radiance_data = scipy.zeros(image_mask_data.shape, dtype=bit_format)
logger.debug('Result image is of dimensions {} and type {}.'.format(
image_radiance_data.shape, image_radiance_data.dtype))
# preparing .msk file
f = open(output_msk_name, 'w')
# adding first mask to result image
image_radiance_data[image_mask_data > 0] = 1
# adding first mask segmentation identifier to the .msk file
f.write('{}\t1\t{}\t{}\t{}\n'.format(args.masks[1],
*__COLOURS[0 % len(__COLOURS)]))
for i in range(2, len(args.masks), 2):
# loading mask image
logger.info(
'Loading mask {} ({} segmentation) using NiBabel...'.format(
args.masks[i], args.masks[i + 1]))
image_mask_data = scipy.squeeze(load(args.masks[i]).get_data())
# check if the shape of the images is consistent
if image_mask_data.shape != image_radiance_data.shape:
raise ArgumentError(
'Mask {} is with {} of a different shape as the first mask image (which has {}).'
.format(args.masks[i], image_mask_data.shape,
image_radiance_data.shape))
# adding mask to result image
image_radiance_data[image_mask_data > 0] += pow(2, i / 2)
# adding mask segmentation identifier to the .msk file
f.write('{}\t{}\t{}\t{}\t{}\n'.format(
args.masks[i + 1], pow(2, i / 2),
*__COLOURS[(i / 2) % len(__COLOURS)]))
logger.info(
'Saving Radiance® segmentation image as {}/.img/.msk...'.format(
output_hdr_name))
image_mask.get_header().set_data_dtype(bit_format)
save(image_like(image_radiance_data, image_mask), output_hdr_name)
logger.info('Successfully terminated.')
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# build output image name
output_hdr_name = args.output + '.hdr'
output_img_name = args.output + '.img'
output_msk_name = args.output + '.msk'
# check if output image exists
if not args.force:
if os.path.exists(output_hdr_name):
logger.warning('The output header {} already exists. Breaking.'.format(output_hdr_name))
exit(1)
elif os.path.exists(output_img_name):
logger.warning('The output image {} already exists. Breaking.'.format(output_img_name))
exit(1)
elif os.path.exists(output_msk_name):
logger.warning('The output infor file {} already exists. Breaking.'.format(output_msk_name))
exit(1)
# decide on most suitable bit format
if len(args.masks) / 2 <= 8:
bit_format = scipy.uint8
elif len(args.masks) / 2 <= 16:
bit_format = scipy.uint16
elif len(args.masks) / 2 <= 32:
bit_format = scipy.uint32
elif len(args.masks) / 2 <= 64:
bit_format = scipy.uint64
else:
raise ArgumentError('It is not possible to combine more than 64 single masks.')
logger.info('Creating a Radiance® segmentation image in {} bit format...'.format(bit_format))
# loading first mask image as reference and template for saving
logger.info('Loading mask {} ({} segmentation) using NiBabel...'.format(args.masks[0], args.masks[1]))
image_mask = load(args.masks[0])
image_mask_data = scipy.squeeze(image_mask.get_data())
# prepare result image
image_radiance_data = scipy.zeros(image_mask_data.shape, dtype=bit_format)
logger.debug('Result image is of dimensions {} and type {}.'.format(image_radiance_data.shape, image_radiance_data.dtype))
# preparing .msk file
f = open(output_msk_name, 'w')
# adding first mask to result image
image_radiance_data[image_mask_data > 0] = 1
# adding first mask segmentation identifier to the .msk file
f.write('{}\t1\t{}\t{}\t{}\n'.format(args.masks[1], *__COLOURS[0%len(__COLOURS)]))
for i in range(2, len(args.masks), 2):
# loading mask image
logger.info('Loading mask {} ({} segmentation) using NiBabel...'.format(args.masks[i], args.masks[i+1]))
image_mask_data = scipy.squeeze(load(args.masks[i]).get_data())
# check if the shape of the images is consistent
if image_mask_data.shape != image_radiance_data.shape:
raise ArgumentError('Mask {} is with {} of a different shape as the first mask image (which has {}).'.format(args.masks[i], image_mask_data.shape, image_radiance_data.shape))
# adding mask to result image
image_radiance_data[image_mask_data > 0] += pow(2, i/2)
# adding mask segmentation identifier to the .msk file
f.write('{}\t{}\t{}\t{}\t{}\n'.format(args.masks[i+1], pow(2, i/2), *__COLOURS[(i/2)%len(__COLOURS)]))
logger.info('Saving Radiance® segmentation image as {}/.img/.msk...'.format(output_hdr_name))
image_mask.get_header().set_data_dtype(bit_format)
save(image_like(image_radiance_data, image_mask), output_hdr_name)
logger.info('Successfully terminated.')
