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)
# load image as float using ITK
logger.info('Loading image {} as float using ITK...'.format(args.image))
image_type = itk.Image[itk.F, 4] # causes PyDev to complain -> ignore error warning
reader = itk.ImageFileReader[image_type].New()
reader.SetFileName(args.image)
reader.Update()
logger.debug(itku.getInformation(reader.GetOutput()))
# process/smooth with anisotropic diffusion image filters (which preserves edges)
# for watershed one could use 20 iterations with conductance = 1.0
logger.info('Smoothing...')
for iteration in args.iterations:
for conductance in args.conductances:
for timestep in args.timesteps:
# build output image name
image_smoothed_name = '.'.join(args.output.split('.')[:-1]) # remove file ending
image_smoothed_name += '_i{}_c{}_t{}.'.format(iteration, conductance, timestep) # add parameter suffix
image_smoothed_name += args.output.split('.')[-1]
# check if output image exists
if not args.force:
if os.path.exists(image_smoothed_name):
logger.warning('The output image {} already exists. Skipping this step.'.format(image_smoothed_name))
continue
# execute the smoothing
logger.info('Smooth with settings: iter={} / cond={} / tstep={}...'.format(iteration, conductance, timestep))
image_smoothed = itk.GradientAnisotropicDiffusionImageFilter[image_type, image_type].New()
image_smoothed.SetNumberOfIterations(iteration)
image_smoothed.SetConductanceParameter(conductance)
image_smoothed.SetTimeStep(timestep)
image_smoothed.SetInput(reader.GetOutput())
image_smoothed.Update()
logger.debug(itku.getInformation(image_smoothed.GetOutput()))
# save file
logger.info('Saving smoothed image as {}...'.format(image_smoothed_name))
writer = itk.ImageFileWriter[image_type].New()
writer.SetFileName(image_smoothed_name)
writer.SetInput(image_smoothed.GetOutput())
writer.Update()
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)
# load image as float using ITK
logger.info('Loading image {} as float using ITK...'.format(args.image))
image_type = itk.Image[itk.F, 4] # causes PyDev to complain -> ignore error warning
reader = itk.ImageFileReader[image_type].New()
reader.SetFileName(args.image)
reader.Update()
logger.debug(itku.getInformation(reader.GetOutput()))
# process/smooth with anisotropic diffusion image filters (which preserves edges)
# for watershed one could use 20 iterations with conductance = 1.0
logger.info('Smoothing...')
for iteration in args.iterations:
for conductance in args.conductances:
for timestep in args.timesteps:
# build output image name
image_smoothed_name = '.'.join(args.output.split('.')[:-1]) # remove file ending
image_smoothed_name += '_i{}_c{}_t{}.'.format(iteration, conductance, timestep) # add parameter suffix
image_smoothed_name += args.output.split('.')[-1]
# check if output image exists
if not args.force:
if os.path.exists(image_smoothed_name):
logger.warning('The output image {} already exists. Skipping this step.'.format(image_smoothed_name))
continue
# execute the smoothing
logger.info('Smooth with settings: iter={} / cond={} / tstep={}...'.format(iteration, conductance, timestep))
image_smoothed = itk.GradientAnisotropicDiffusionImageFilter[image_type, image_type].New()
image_smoothed.SetNumberOfIterations(iteration)
image_smoothed.SetConductanceParameter(conductance)
image_smoothed.SetTimeStep(timestep)
image_smoothed.SetInput(reader.GetOutput())
image_smoothed.Update()
logger.debug(itku.getInformation(image_smoothed.GetOutput()))
# save file
logger.info('Saving smoothed image as {}...'.format(image_smoothed_name))
writer = itk.ImageFileWriter[image_type].New()
writer.SetFileName(image_smoothed_name)
writer.SetInput(image_smoothed.GetOutput())
writer.Update()
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)
# iterate over input images
for image in args.images:
# load image as float using ITK
logger.info('Loading image {} as float using ITK...'.format(image))
image_type = itk.Image[itk.F, 3]
reader = itk.ImageFileReader[image_type].New()
reader.SetFileName(image)
reader.Update()
logger.debug(itku.getInformation(reader.GetOutput()))
# compute ideal parameters
rs = reader.GetOutput().GetLargestPossibleRegion().GetSize()
threshold = underfit(rs.GetElement(0) * rs.GetElement(1) * rs.GetElement(2))
level = 0
logger.debug('{}, {}, {}'.format(rs.GetElement(0), rs.GetElement(1), rs.GetElement(2)))
logger.debug('The image contains {} voxels, the resulting threshold is {}.'.format(rs.GetElement(0) * rs.GetElement(1) * rs.GetElement(2), threshold))
# build output image name
image_watershed_name = args.folder + '/' + image.split('/')[-1][:-4] + '_watershed'
image_watershed_name += '_thr{}_lvl{}'.format(threshold, level)
image_watershed_name += image.split('/')[-1][-4:]
# check if output image exists
if not args.force:
if os.path.exists(image_watershed_name):
logger.warning('The output image {} already exists. Skipping this step.'.format(image_watershed_name))
continue
# initialize the watershed filter object
image_watershed = itk.WatershedImageFilter[image_type].New()
image_watershed.SetInput(reader.GetOutput())
image_watershed.SetThreshold(threshold)
image_watershed.SetLevel(level)
logger.info('Watershedding with settings: thr={} / level={}...'.format(threshold, level))
image_watershed.Update()
logger.debug(itku.getInformation(image_watershed.GetOutput()))
# save file
logger.info('Saving watershed image as {}...'.format(image_watershed_name))
watershed_image_type = itku.getImageType(image_watershed.GetOutput())
writer = itk.ImageFileWriter[watershed_image_type].New()
writer.SetFileName(image_watershed_name)
writer.SetInput(image_watershed.GetOutput())
writer.Update()
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)
# iterate over input images
for image in args.images:
# load image as float using ITK
logger.info('Loading image {} as float using ITK...'.format(image))
image_type = itk.Image[itk.F, 3] # causes PyDev to complain -> ignore error warning
reader = itk.ImageFileReader[image_type].New()
reader.SetFileName(image)
reader.Update()
logger.debug(itku.getInformation(reader.GetOutput()))
# build output image name
image_watershed_name = args.folder + '/' + image.split('/')[-1][:-4] + '_watershed' + image.split('/')[-1][-4:]
# check if output image exists
if not args.force:
if os.path.exists(image_watershed_name):
logger.warning('The output image {} already exists. Skipping this input image.'.format(image_watershed_name))
continue
# prepare watershed
# assign to watershed_source the image to be passed to the watershed
watershed_source = reader.GetOutput()
# extract data from original image to scipy
itk_py_converter_orig = itk.PyBuffer[image_type] # causes PyDev to complain -> ignore error warning
original_image_array = itk_py_converter_orig.GetArrayFromImage(reader.GetOutput())
# recover input image dimensions
rs = original_image_array.shape
# create empty final label image array
image_label_final_array = scipy.zeros(rs[0] * rs[1] * rs[2], dtype=scipy.int32).reshape(rs) #!TODO: Is 32bit enough?
# create an empty watershed result array
watershed_result_array = scipy.zeros_like(image_label_final_array)
# create initial label mask covering the whole image space
label_mask = scipy.zeros(rs[0] * rs[1] * rs[2], dtype=scipy.bool_).reshape(rs)
label_mask.fill(True)
# set threhold to use
threshold = __INITIAL_THRESHOLD
# set the point to the largest region to process
region_pointer = -1
# apply the watershed
logger.info('Applying watershed...')
while True:
# initialize the watershed filter object
logger.info('Watershedding with settings: thr={} / level={}...'.format(threshold, 0.0))
image_watershed = itk.WatershedImageFilter[image_type].New()
image_watershed.SetInput(watershed_source)
image_watershed.SetThreshold(threshold)
image_watershed.SetLevel(0.0)
try:
image_watershed.Update()
logger.debug(itku.getInformation(image_watershed.GetOutput()))
logger.info('Adding resulting labels to final result image...')
# converting to scipy
itk_py_converter_ws = itk.PyBuffer[itku.getImageType(image_watershed.GetOutput())] # causes PyDev to complain -> ignore error warning
watershed_result_array = itk_py_converter_ws.GetArrayFromImage(image_watershed.GetOutput())
# recovering current offset for the new label ids
min_label_id = image_label_final_array.max()
# updating final label image
image_label_final_array[label_mask] = watershed_result_array[label_mask] + min_label_id
logger.info('Last watershed led to {} new labels, {} in total now...'.format(len(scipy.unique(watershed_result_array)) - 1, len(scipy.unique(image_label_final_array))))
# # lower the threshold if no new labels were created, or skip label if a threshold of 0 did not help
if 100 >= len(scipy.unique(watershed_result_array)) - 1:
if 0 == threshold:
region_pointer -= 1
logger.info('Skipping this region as threshold lowering did not lead to the production of more than 100 labels...')
else:
threshold = threshold / 2.
if threshold < 0.001: threshold = 0.0
logger.info('Lowering threshold for next region to {} ...'.format(threshold))
else:
threshold = __INITIAL_THRESHOLD
logger.info('Resetting threshold to original value of {} ...'.format(__INITIAL_THRESHOLD))
except RuntimeError as error:
if 0 == threshold:
region_pointer -= 1
logger.warning('Watershed processing terminated with error {}. Skipping this region as threshold lowering did not succeed.'.format(error))
else:
threshold = threshold / 2
if threshold < 0.001: threshold = 0.0
logger.warning('Watershed processing terminated with error {}. Lowering threshold to {}.'.format(error, threshold))
logger.info('Computing statistics / checking stop condition...')
statistics = LabelImageStatistics(image_label_final_array, original_image_array)
# check medium region size and if stop condition reached, break
sizes = sorted(statistics.get_sizes().iteritems(), key=lambda x: x[1]) # get sizes sorted (biggest region last)
if __IDEAL_REGION_SIZE >= sizes[region_pointer][1]:
logger.info('Stopping condition {} with a maximum region size of {} reached: Stopping processing...'.format(__IDEAL_REGION_SIZE, sizes[region_pointer][1]))
break
# if __IDEAL_REGION_SIZE >= sum(map(lambda x: x[1], sizes)) / float(len(sizes)):
# logger.info('Stopping condition {} with a medium region size of {} reached: Stopping processing...'.format(__IDEAL_REGION_SIZE, sum(map(lambda x: x[1], sizes)) / float(len(sizes))))
# break
logger.info('Stopping condition {} with a maximum region size of {} not reached: Preparing next run...'.format(__IDEAL_REGION_SIZE, sizes[region_pointer][1]))
# logger.info('Stopping condition {} with a medium region size of {} not reached: Preparing next run...'.format(__IDEAL_REGION_SIZE, sum(map(lambda x: x[1], sizes)) / float(len(sizes))))
logger.info('Biggest region scheduled for breaking up: {} with {} voxels and intensity distribution score of {}...'.format(sizes[region_pointer][0], sizes[region_pointer][1], statistics.get_intensity_distributions()[sizes[region_pointer][0]]))
# update label mask to cover the biggest label
label_mask = (sizes[region_pointer][0] == image_label_final_array)
# create the new input for the watershed algorithm
watershed_source_array = scipy.zeros_like(original_image_array)
watershed_source_array[label_mask] = original_image_array[label_mask]
#watershed_source_array[scipy.invert(label_mask)] = watershed_source_array[label_mask].max() + 1 #!TODO: Not sure if this works, as I have the gradient image! High value = high gradient!?
# cast to ITK image to apply watershed in the next step
watershed_source = itk_py_converter_orig.GetImageFromArray(watershed_source_array.tolist())
logger.debug(itku.getInformation(watershed_source))
# cast to ITK image to save (uses the watershed converter)
image_label_final = itk_py_converter_ws.GetImageFromArray(image_label_final_array.tolist())
logger.debug(itku.getInformation(image_label_final))
logger.info('Overall skipped regions: {}...'.format(-1 * region_pointer - 1))
# save file
logger.info('Saving watershed image as {}...'.format(image_watershed_name))
watershed_image_type = itku.getImageType(image_watershed.GetOutput())
writer = itk.ImageFileWriter[watershed_image_type].New()
writer.SetFileName(image_watershed_name)
writer.SetInput(image_label_final)
writer.Update()
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)
# iterate over input images
for image in args.images:
# load image as float using ITK
logger.info('Loading image {} as float using ITK...'.format(image))
image_type = itk.Image[itk.F, 3]
reader = itk.ImageFileReader[image_type].New()
reader.SetFileName(image)
reader.Update()
logger.debug(itku.getInformation(reader.GetOutput()))
# compute ideal parameters
rs = reader.GetOutput().GetLargestPossibleRegion().GetSize()
threshold = underfit(rs.GetElement(0) * rs.GetElement(1) * rs.GetElement(2))
level = 0
logger.debug('{}, {}, {}'.format(rs.GetElement(0), rs.GetElement(1), rs.GetElement(2)))
logger.debug('The image contains {} voxels, the resulting threshold is {}.'.format(rs.GetElement(0) * rs.GetElement(1) * rs.GetElement(2), threshold))
# build output image name
image_watershed_name = args.folder + '/' + image.split('/')[-1][:-4] + '_watershed'
image_watershed_name += '_thr{}_lvl{}'.format(threshold, level)
image_watershed_name += image.split('/')[-1][-4:]
# check if output image exists
if not args.force:
if os.path.exists(image_watershed_name):
logger.warning('The output image {} already exists. Skipping this step.'.format(image_watershed_name))
continue
# initialize the watershed filter object
image_watershed = itk.WatershedImageFilter[image_type].New()
image_watershed.SetInput(reader.GetOutput())
image_watershed.SetThreshold(threshold)
image_watershed.SetLevel(level)
logger.info('Watershedding with settings: thr={} / level={}...'.format(threshold, level))
image_watershed.Update()
logger.debug(itku.getInformation(image_watershed.GetOutput()))
# save file
logger.info('Saving watershed image as {}...'.format(image_watershed_name))
watershed_image_type = itku.getImageType(image_watershed.GetOutput())
writer = itk.ImageFileWriter[watershed_image_type].New()
writer.SetFileName(image_watershed_name)
writer.SetInput(image_watershed.GetOutput())
writer.Update()
logger.info('Successfully terminated.')
