/
batch_rwpca.py
171 lines (121 loc) · 5.39 KB
/
batch_rwpca.py
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import os
import numpy as np
from scipy import ndimage
from rwsegment import io_analyze
from rwsegment import rwsegment_pca
from rwsegment import rwsegment_prior_models as prior_models
reload(rwsegment_pca)
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',
'rtol' : 1e-6,
'maxiter' : 2e3,
}
self.prior_models = [
prior_models.Constant,
prior_models.Entropy_no_D,
prior_models.Intensity,
prior_models.Variance_no_D,
prior_models.Variance_no_D_Cmap,
]
self.prior_weights = prior_weights
logger.info('Model name = {}, using prior weights={}'\
.format(self.model_name, self.prior_weights))
def process_sample(self,test, fold=None):
## get prior
prior, mask = load_or_compute_prior_and_mask(
test,force_recompute=self.force_recompute_prior, pca=True, fold=fold)
seeds = (-1)*mask
mask = mask.astype(bool)
## load image
file_name = config.dir_reg + test + 'gray.hdr'
logger.info('segmenting data: {}'.format(file_name))
im = io_analyze.load(file_name)
file_gt = config.dir_reg + test + 'seg.hdr'
seg = io_analyze.load(file_gt)
seg.flat[~np.in1d(seg, self.labelset)] = self.labelset[0]
## normalize image
nim = im/np.std(im)
## init anchor_api
anchor_api = MetaAnchor(
prior=prior,
prior_models=self.prior_models,
prior_weights=self.prior_weights,
image=nim,
)
## start segmenting
# import ipdb; ipdb.set_trace()
sol,impca = rwsegment_pca.segment(
nim,
anchor_api,
seeds=seeds,
labelset=self.labelset,
**self.params
)
## compute Dice coefficient per label
dice = compute_dice_coef(sol, seg, labelset=self.labelset)
logger.info('Dice: {}'.format(dice))
dice_pca = compute_dice_coef(impca, seg, labelset=self.labelset)
logger.info('Dice pca only: {}'.format(dice_pca))
if not config.debug:
if fold is not None:
test_name = 'f{}_{}'.format(fold[0][:2],test)
else:
test_name = test
outdir = config.dir_seg + \
'/{}/{}'.format(self.model_name,test_name)
logger.info('saving data in: {}'.format(outdir))
if not os.path.isdir(outdir):
os.makedirs(outdir)
io_analyze.save(outdir + 'sol.hdr', sol.astype(np.int32))
io_analyze.save(outdir + 'solpca.hdr', impca.astype(np.int32))
np.savetxt(
outdir + 'dice.txt', np.c_[dice.keys(),dice.values()],fmt='%d %.8f')
np.savetxt(
outdir + 'dice_pca.txt', np.c_[dice.keys(),dice_pca.values()],fmt='%d %.8f')
def process_all_samples(self,sample_list, fold=None):
for test in sample_list:
self.process_sample(test, fold=fold)
if __name__=='__main__':
import sys
if '-s' not in sys.argv: sys.exit(0)
''' start script '''
for fold in config.folds:
for w in [1e-3, 1e-2, 1e-1, 1e0, 1e1]:
segmenter = SegmentationBatch(prior_weights=[w, 0, 0, 0,0], name='constant{}'.format(w))
segmenter.process_all_samples(fold)
segmenter = SegmentationBatch(prior_weights=[0, w, 0, 0,0], name='entropy{}'.format(w))
segmenter.process_all_samples(fold)
segmenter = SegmentationBatch(prior_weights=[1e-2, 0, w, 0,0], name='entropy1e-2_intensity{}'.format(w))
segmenter.process_all_samples(fold)
segmenter = SegmentationBatch(prior_weights=[0, 0, 0, w, 0], name='variance}'.format(w))
segmenter.process_all_samples(fold)
segmenter = SegmentationBatch(prior_weights=[0, 0, 0, 0, w], name='variance_cmap{}'.format(w))
segmenter.process_all_samples(fold)
## constant prior
#segmenter = SegmentationBatch(prior_weights=[1e-1, 0, 0, 0,0], name='constant1e-1')
#segmenter.process_all_samples(['01/'])
## constant prior
#segmenter = SegmentationBatch(prior_weights=[1e-0, 0, 0, 0,0], name='constant1e0')
#segmenter.process_all_samples(['01/'])
#
## entropy prior
#segmenter = SegmentationBatch(prior_weights=[0, 1e-2, 0,0,0], name='entropy1e-2')
#segmenter.process_all_samples(['01/'])