gm.generate_ldd_model_csv( 'subjects_ldd_cut.lst', work_prefix='tmp_avg_SSD_ldd', options={ 'protocol': [ {'iter':4, 'level':8, 'blur_int': None, 'blur_vel': None }, {'iter':4, 'level':4, 'blur_int': None, 'blur_vel': None }, #{'iter':16, 'level':2, 'blur_int': None, 'blur_vel': None }, #{'iter':4, 'level':2, 'blur_int': None, 'blur_vel': 2 }, #{'iter':4, 'level':2, 'blur_int': None, 'blur_vel': 1 }, ], 'parameters': {'smooth_update':2, 'smooth_field':2, 'conf': { 8:200, 4:200, 2:40 }, 'LCC':False }, 'start_level': 8, 'refine': True, 'cleanup':False, 'debug': True, 'debias': True, 'qc': True, 'incremental': True }, #regress_model=['data/object_0_4.mnc'], model='data_ldd/object_0_0.mnc', mask='data_ldd/mask_0_0.mnc', )
from scoop import futures, shared import iplScoopGenerateModel as gm if __name__ == '__main__': # setup data for parallel processing os.environ['ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS']='1' gm.generate_ldd_model_csv('subjects.lst', work_prefix='tmp_ldd_nr', options={'symmetric':False, 'refine':False, 'protocol': [{'iter':4,'level':4}, {'iter':4,'level':2}, ], 'parameters': {'smooth_update':2, 'smooth_field':1, 'conf': { 32:40,16:40,8:40,4:40,2:40 } } }, model='ref.mnc', mask='mask.mnc' )
gm.generate_ldd_model_csv('subjects.lst', work_prefix='tmp_ldd_sym', options={ 'symmetric': True, 'refine': True, 'protocol': [ { 'iter': 4, 'level': 16 }, { 'iter': 4, 'level': 8 }, { 'iter': 4, 'level': 4 }, ], 'parameters': { 'smooth_update': 2, 'smooth_field': 2, 'conf': { 32: 20, 16: 20, 8: 20, 4: 20, 2: 20 } } }, model='test_data/ellipse_1.mnc', mask='test_data/mask.mnc')
# setup data for parallel processing gm.generate_ldd_model_csv('subjects_cut.lst', work_prefix='tmp_ldd', options={ 'symmetric': False, 'refine': True, 'protocol': [ { 'iter': 4, 'level': 8 }, { 'iter': 4, 'level': 4 }, ], 'parameters': { 'smooth_update': 2, 'smooth_field': 2, 'conf': { 32: 20, 16: 20, 8: 20, 4: 20, 2: 20, 1: 20 } } })