ifp.addfromfile(params['intermedfolder'] + params['largeobjfile'], image_names=params['largeobjname'], ids='largeobj') ifp.startlogger(filename=params['intermedfolder'] + 'paths_within_labels.log', type='a') # ifp.code2log(__file__) ifp.code2log(inspect.stack()[0][1]) ifp.logging('') ifp.logging('yamlfile = {}', yamlfile) ifp.logging('ifp.get_data().keys() = {}', ifp.get_data().keys()) ifp.logging('ifp.shape() = {}', ifp.shape()) ifp.logging('{}', ifp.amax()) hfp = Hdf5Processing() c = 0 for lblo in ifp.label_bounds_iterator('largeobj', 'curlabel', ids=('locmax', 'disttransf'), targetids=('curlocmax', 'curdisttransf'), maskvalue=0, value=0, background=0): ifp.logging('------------\nCurrent label {} in iteration {}', lblo['label'], c) ifp.logging('Bounding box = {}', lblo['bounds'])
if __name__ == "__main__": # sys.stdout = open('/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/log.txt', "a") # Parameters tapering_tolerance_rel = 0.5 tapering_tolerance_abs = 5 # object = 191 ifp = ImageFileProcessing( "/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/", "multicut_segmentation.h5", asdict=True, keys=('labels',)) # for obj in [191]: for obj in xrange(1, ifp.amax(ids=('labels',))['labels'] + 1): # sys.stdout = open('/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/log.txt', "a") print '_____________________________________________________________________' print 'Object label: ' + str(obj) ifp.deepcopy_entry('labels', 'disttransf') if True: # Start with one object only ifp.getlabel(obj, ids=('disttransf',)) if ifp.amax(ids=('disttransf',)) == 0: print 'Skiping object ' + str(obj) + ': Not found.' # sys.exit()
ifp.addfromfile(params['intermedfolder'] + params['largeobjfile'], image_names=params['largeobjname'], ids='largeobj') ifp.startlogger(filename=params['intermedfolder'] + 'paths_of_partners.log', type='a') # ifp.code2log(__file__) ifp.code2log(inspect.stack()[0][1]) ifp.logging('') ifp.logging('yamlfile = {}', yamlfile) ifp.logging('ifp.get_data().keys() = {}', ifp.get_data().keys()) ifp.logging('ifp.shape() = {}', ifp.shape()) ifp.logging('ifp.amax() = {}', ifp.amax()) hfp = Hdf5Processing() c = 0 # These are the labels which were merged with a respective partner # labellist = ifp.get_image('mergeids_all')[:, 0] labellist = ifp.get_image('mergeids_all') # labellist = [9988] ifp.logging('labellist = {}', labellist) # for lblo in ifp.label_bounds_iterator('largeobjm', 'curlabel', # ids=('locmax', 'disttransf', 'largeobj'), # targetids=('curlocmax', 'curdisttransf', 'curlargeobj'), # maskvalue=0, value=0, background=0, labellist=labellist, # forcecontinue=True): for lblp in ifp.labelpair_bounds_iterator('largeobj',
ifp.logging('Starting label iterator for {} labels', len(ifp.anytask_rtrn(np.unique, ids='labels'))) c = 0 for lblo in ifp.label_bounds_iterator('labels', 'curlabel', ids='disttransf', targetids='curdisttransf', maskvalue=0, value=0): ifp.logging('------------\nCurrent label {} in iteration {}', lblo['label'], c) ifp.logging('Bounding box = {}', lblo['bounds']) local_maxima_found = find_local_maxima(ifp) ifp.logging('Local maxima found: {}', local_maxima_found) if local_maxima_found: if ifp.amax('locmax') != 0: find_shortest_path(ifp) ifp.write(filename='{0}input_{1}.h5'.format(resultfolder, lblo['label']), ids=('curdisttransf', 'curlabel', 'smoothed')) ifp.write(filename='{0}paths_over_dist_{1}.h5'.format(resultfolder, lblo['label']), ids=('paths_over_dist',)) else: ifp.logging('No maxima found!') else: ifp.logging('Maxima detection not successful!')
params = ifp.get_params() thisparams = params['paths_of_partners'] ifp.addfromfile(params['intermedfolder']+params['largeobjmfile'], image_names=params['largeobjmnames'], ids=['largeobjm', 'mergeids_small', 'mergeids_random', 'mergeids_all']) ifp.addfromfile(params['intermedfolder']+params['largeobjfile'], image_names=params['largeobjname'], ids='largeobj') ifp.startlogger(filename=params['intermedfolder'] + 'paths_of_partners.log', type='a') # ifp.code2log(__file__) ifp.code2log(inspect.stack()[0][1]) ifp.logging('') ifp.logging('yamlfile = {}', yamlfile) ifp.logging('ifp.get_data().keys() = {}', ifp.get_data().keys()) ifp.logging('ifp.shape() = {}', ifp.shape()) ifp.logging('ifp.amax() = {}', ifp.amax()) hfp = Hdf5Processing() c = 0 # These are the labels which were merged with a respective partner # labellist = ifp.get_image('mergeids_all')[:, 0] labellist = ifp.get_image('mergeids_all') # labellist = [9988] ifp.logging('labellist = {}', labellist) # for lblo in ifp.label_bounds_iterator('largeobjm', 'curlabel', # ids=('locmax', 'disttransf', 'largeobj'), # targetids=('curlocmax', 'curdisttransf', 'curlargeobj'), # maskvalue=0, value=0, background=0, labellist=labellist, # forcecontinue=True): for lblp in ifp.labelpair_bounds_iterator('largeobj', 'curlabelpair',
# sys.stdout = open('/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/log.txt', "a") # Parameters tapering_tolerance_rel = 0.5 tapering_tolerance_abs = 5 # object = 191 ifp = ImageFileProcessing( "/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/", "multicut_segmentation.h5", asdict=True, keys=('labels', )) # for obj in [191]: for obj in xrange(1, ifp.amax(ids=('labels', ))['labels'] + 1): # sys.stdout = open('/media/julian/Daten/neuraldata/isbi_2013/mc_crop_cache/log.txt', "a") print '_____________________________________________________________________' print 'Object label: ' + str(obj) ifp.deepcopy_entry('labels', 'disttransf') if True: # Start with one object only ifp.getlabel(obj, ids=('disttransf', )) if ifp.amax(ids=('disttransf', )) == 0: print 'Skiping object ' + str(obj) + ': Not found.' # sys.exit()
ids='disttransf', targetids='curdisttransf', maskvalue=0, value=0): ifp.logging('------------\nCurrent label {} in iteration {}', lblo['label'], c) ifp.logging('Bounding box = {}', lblo['bounds']) local_maxima_found = find_local_maxima(ifp) ifp.logging('Local maxima found: {}', local_maxima_found) if local_maxima_found: if ifp.amax('locmax') != 0: find_shortest_path(ifp) ifp.write(filename='{0}input_{1}.h5'.format( resultfolder, lblo['label']), ids=('curdisttransf', 'curlabel', 'smoothed')) ifp.write(filename='{0}paths_over_dist_{1}.h5'.format( resultfolder, lblo['label']), ids=('paths_over_dist', )) else: ifp.logging('No maxima found!') else:
keys=('disttransf', 'locmax') ) params = ifp.get_params() thisparams = params['paths_within_labels'] ifp.addfromfile(params['intermedfolder']+params['largeobjfile'], image_names=params['largeobjname'], ids='largeobj') ifp.startlogger(filename=params['intermedfolder'] + 'paths_within_labels.log', type='a') # ifp.code2log(__file__) ifp.code2log(inspect.stack()[0][1]) ifp.logging('') ifp.logging('yamlfile = {}', yamlfile) ifp.logging('ifp.get_data().keys() = {}', ifp.get_data().keys()) ifp.logging('ifp.shape() = {}', ifp.shape()) ifp.logging('{}', ifp.amax()) hfp = Hdf5Processing() c = 0 for lblo in ifp.label_bounds_iterator('largeobj', 'curlabel', ids=('locmax', 'disttransf'), targetids=('curlocmax', 'curdisttransf'), maskvalue=0, value=0, background=0): ifp.logging('------------\nCurrent label {} in iteration {}', lblo['label'], c) ifp.logging('Bounding box = {}', lblo['bounds']) if ifp.amax('curlocmax') == 1: ps = find_shortest_path(ifp, thisparams['penaltypower'], lblo['bounds']) ifp.logging('Number of paths found: {}', len(ps)) if ps: hfp.setdata({lblo['label']: ps}, append=True) ifp.write(filename='paths_over_dist_true_{}.h5'.format(lblo['label']), ids='paths_over_dist')