reload(latent_svm)
from rwsegment import svm_worker
reload(svm_worker)
from rwsegment import loss_functions
reload(loss_functions)
from rwsegment.rwsegment import BaseAnchorAPI
from segmentation_utils import load_or_compute_prior_and_mask
from segmentation_utils import compute_dice_coef
import svm_rw_api
reload(svm_rw_api)
from svm_rw_api import SVMRWMeanAPI
from svm_rw_api import MetaAnchor, MetaLaplacianFunction
from rwsegment import mpi
from rwsegment import utils_logging
logger = utils_logging.get_logger('learn_svm_batch',utils_logging.DEBUG)
class SVMSegmenter(object):
def __init__(self,
use_parallel=True,
**kwargs
):
## paths
self.dir_reg = config.dir_reg
self.dir_inf = config.dir_inf
self.dir_svm = config.dir_svm
self.training_vols = config.vols
## params
import numpy as np
from scipy import ndimage
from rwsegment import io_analyze
from rwsegment import rwsegment_prior
from rwsegment import rwsegment_pca_prior
from rwsegment import loss_functions
reload(rwsegment_prior)
reload(rwsegment_pca_prior)
reload(loss_functions)
import config
reload(config)
from rwsegment import utils_logging
logger = utils_logging.get_logger('segmentation_utils',utils_logging.DEBUG)
def compute_losses(z,y,mask):
## loss 0 : 1 - Dice(y,z)
loss0 = loss_functions.ideal_loss(z,y,mask=mask)
logger.info('Tloss = {}'.format(loss0))
## loss2: squared difference with ztilde
loss1 = loss_functions.anchor_loss(z,y,mask=mask)
logger.info('SDloss = {}'.format(loss1))
## loss3: laplacian loss
loss2 = loss_functions.laplacian_loss(z,y,mask=mask)
logger.info('LAPloss = {}'.format(loss2))
import numpy as np
import sys
from rwsegment import rwsegment
from rwsegment.rwsegment import BaseAnchorAPI
from rwsegment import loss_functions
reload(rwsegment)
reload(loss_functions)
from rwsegment import utils_logging
logger = utils_logging.get_logger('svm_rw_api',utils_logging.DEBUG)
## combine all prior models
class MetaAnchor():
def __init__(
self,
prior,
prior_models,
prior_weights,
loss=None,
loss_weight=None,
image=None,
):
self.prior = prior
self.prior_models = prior_models
self.prior_weights = prior_weights
self.loss = loss
self.loss_weight = loss_weight
self.image = image
self.labelset = prior['labelset']
def get_labelset(self):
np.testing.assert_allclose(w, w_gold, err_msg='w mismatch')
np.testing.assert_allclose(xi, xi_gold, err_msg='xi mismatch')
except Exception as e:
print str(type(e)) + ', ' + e.message
else:
print 'PASS: learning tests'
else:
w = np.loadtxt(outdir + 'w.test.txt')
self.w = w
## inference
self.run_svm_inference(test, w)
def process_all_samples(self, sample_list):
for test in sample_list:
self.process_sample(test)
##------------------------------------------------------------------------------
from rwsegment import utils_logging
logger = utils_logging.get_logger('learn_svm_batch', utils_logging.DEBUG)
if __name__ == '__main__':
''' start script '''
svm_segmenter = SVMSegmenter()
sample_list = ['01/']
# sample_list = config.vols
svm_segmenter.process_all_samples(sample_list)
import numpy as np
from scipy import ndimage
from rwsegment import io_analyze
from rwsegment import rwsegment_prior
from rwsegment import rwsegment_pca_prior
from rwsegment import loss_functions
reload(rwsegment_prior)
reload(rwsegment_pca_prior)
reload(loss_functions)
import config
reload(config)
from rwsegment import utils_logging
logger = utils_logging.get_logger('segmentation_utils', utils_logging.DEBUG)
def compute_losses(z, y, mask):
## loss 0 : 1 - Dice(y,z)
loss0 = loss_functions.ideal_loss(z, y, mask=mask)
logger.info('Tloss = {}'.format(loss0))
## loss2: squared difference with ztilde
loss1 = loss_functions.anchor_loss(z, y, mask=mask)
logger.info('SDloss = {}'.format(loss1))
## loss3: laplacian loss
loss2 = loss_functions.laplacian_loss(z, y, mask=mask)
logger.info('LAPloss = {}'.format(loss2))
from rwsegment import io_analyze
from rwsegment import rwsegment
from rwsegment.rwsegment import BaseAnchorAPI
reload(rwsegment)
import segmentation_utils
reload(segmentation_utils)
from segmentation_utils import load_or_compute_prior_and_mask
from segmentation_utils import compute_dice_coef
import config
reload(config)
from rwsegment import utils_logging
logger = utils_logging.get_logger('autoseeds', utils_logging.DEBUG)
class Autoseeds(object):
def __init__(self):
self.labelset = np.asarray(config.labelset)
self.nlabel = len(self.labelset)
self.force_recompute_prior = False
self.force_recompute_edges = False
self.step = [5, 10, 10]
self.sizex = 24
self.params = {
'beta': 50, # contrast parameter
import segmentation_utils
reload(segmentation_utils)
from segmentation_utils import load_or_compute_prior_and_mask
from segmentation_utils import compute_dice_coef
from svm_rw_api import MetaAnchor
import config
reload(config)
from rwsegment import utils_logging
logger = utils_logging.get_logger("batch_rwpca", utils_logging.INFO)
class SegmentationBatch(object):
def __init__(self, prior_weights, name="constant1"):
self.labelset = np.asarray(config.labelset)
self.force_recompute_prior = False
self.model_name = name
self.params = {
"beta": 50, # contrast parameter
"return_arguments": ["image", "impca"],
# optimization parameter
"per_label": True,
"optim_solver": "unconstrained",
reload(rwsegment)
reload(loss_functions)
reload(prior_models)
import segmentation_utils
reload(segmentation_utils)
from segmentation_utils import load_or_compute_prior_and_mask
from segmentation_utils import compute_dice_coef
import config
reload(config)
from rwsegment import utils_logging
logger = utils_logging.get_logger('segmentation_batch',utils_logging.INFO)
class SegmentationBatch(object):
def __init__(self, prior_weights=[1.,0,0], name='constant1'):
self.labelset = np.asarray(config.labelset)
self.model_name = name
self.force_recompute_prior = False
self.params = {
'beta' : 50, # contrast parameter
'return_arguments' :['image','y'],
# optimization parameter
'per_label': True,
'optim_solver':'unconstrained',
'rtol' : 1e-6,
from rwsegment import io_analyze
from rwsegment import rwsegment
from rwsegment.rwsegment import BaseAnchorAPI
reload(rwsegment)
import segmentation_utils
reload(segmentation_utils)
from segmentation_utils import load_or_compute_prior_and_mask
from segmentation_utils import compute_dice_coef
import config
reload(config)
from rwsegment import utils_logging
logger = utils_logging.get_logger('autoseeds',utils_logging.DEBUG)
class Autoseeds(object):
def __init__(self):
self.labelset = np.asarray(config.labelset)
self.nlabel = len(self.labelset)
self.force_recompute_prior = False
self.force_recompute_edges = False
self.step = [5,10,10]
self.sizex = 24
self.params = {
'beta' : 50, # contrast parameter
import numpy as np
import sys
from rwsegment import rwsegment
from rwsegment.rwsegment import BaseAnchorAPI
from rwsegment import loss_functions
reload(rwsegment)
reload(loss_functions)
from rwsegment import utils_logging
logger = utils_logging.get_logger('svm_rw_api', utils_logging.DEBUG)
## combine all prior models
class MetaAnchor():
def __init__(
self,
prior,
prior_models,
prior_weights,
loss=None,
loss_weight=None,
image=None,
):
self.prior = prior
self.prior_models = prior_models
self.prior_weights = prior_weights
self.loss = loss
self.loss_weight = loss_weight
self.image = image
self.labelset = prior['labelset']
