def evaluate():
label_map = du.get_label_map(os.path.join(c.data_kaggle, 'train.csv'))
normdict = {'p1090': (10, 90), 'pMS': None}
for pstr in ['pMS', 'p1090']:
segment_fn = m.get_segmenter_function(os.path.join(
c.params_dir, 'fcn_v2_' + pstr),
c.fcn_img_size,
ensemble=True,
version=2)
dsets = []
for s in range(*c.fcn_eval_cases):
dset = du.CNN_Dataset(s)
if len(dset.slices_ver) < 5:
pass
dset.segment(
segment_fn, lambda x: du.segmenter_data_transform(
x, rotate=None, normalize_pctwise=normdict[pstr]))
sys_vol, dias_vol = du.calculate_areas(dset,
dset.counts,
dset.sys_time,
dset.dias_time,
end_slice_include=False)
sys_vol = max(sys_vol, 0.15 * dias_vol)
dset.sys_vol, dset.dias_vol = (sys_vol, dias_vol)
print '#{} {} {}'.format(dset.name, sys_vol, dias_vol)
dsets.append(dset)
dset.unload()
#vector_map = { int(ds.name):([1, ds.sys_vol], [1, ds.dias_vol]) for ds in dsets }
#w_func = du.optimize_w(vector_map, label_map, 2, max_w = 8, above_below=True)
du.write_outputs(dsets, c.output_dir, pstr)
def evaluate():
ch4_seg_fn = m.get_segmenter_function(os.path.join(c.params_dir, 'ch4seg_v2'),
c.fcn_img_size, ensemble=True, version=2)
label_map = du.get_label_map(os.path.join(c.data_kaggle, 'train.csv'))
# do 4ch segmentation and calculate volume as if it were circular
calc_map = {}
print 'segmenting 4ch images and calculating volumes'
for s in xrange(*c.ch4_eval_cases):
if s % 10 == 0:
print 'processing example {}'.format(s)
dset = du.CNN_Dataset(s, load_sax_images=False)
if dset.ch4_images is not None:
dset.segment_ch4(ch4_seg_fn, lambda x: du.segmenter_data_transform(x, rotate=None))
ch4counts = [np.count_nonzero(ch4s_) for ch4s_ in dset.ch4seg] # count for each time
if sum(ch4counts) > 0:
volumes = np.empty(dset.ch4seg.shape[0])
for t in xrange(dset.ch4seg.shape[0]):
diams = np.array([np.count_nonzero(dset.ch4seg[t,:,i])
for i in xrange(dset.ch4seg[t].shape[0])])
volumes[t] = sum((dset.ch4_line_mult**3)*np.pi*d**2/4 for d in diams)/1000.
calc_map[s] = min(volumes), max(volumes)
out_lines = ['{},{:.2f},{:.2f}\n'.format(s, calc[0], calc[1])
for s,calc in calc_map.iteritems()]
with open(os.path.join(c.output_dir, 'ch4_volumes_map.csv'), 'w') as wr:
wr.writelines(out_lines)
'''
def evaluate():
ch4_seg_fn = m.get_segmenter_function(os.path.join(c.params_dir,
'ch4seg_v2'),
c.fcn_img_size,
ensemble=True,
version=2)
label_map = du.get_label_map(os.path.join(c.data_kaggle, 'train.csv'))
# do 4ch segmentation and calculate volume as if it were circular
calc_map = {}
print 'segmenting 4ch images and calculating volumes'
for s in xrange(*c.ch4_eval_cases):
if s % 10 == 0:
print 'processing example {}'.format(s)
dset = du.CNN_Dataset(s, load_sax_images=False)
if dset.ch4_images is not None:
dset.segment_ch4(
ch4_seg_fn,
lambda x: du.segmenter_data_transform(x, rotate=None))
ch4counts = [np.count_nonzero(ch4s_)
for ch4s_ in dset.ch4seg] # count for each time
if sum(ch4counts) > 0:
volumes = np.empty(dset.ch4seg.shape[0])
for t in xrange(dset.ch4seg.shape[0]):
diams = np.array([
np.count_nonzero(dset.ch4seg[t, :, i])
for i in xrange(dset.ch4seg[t].shape[0])
])
volumes[t] = sum((dset.ch4_line_mult**3) * np.pi * d**2 / 4
for d in diams) / 1000.
calc_map[s] = min(volumes), max(volumes)
out_lines = [
'{},{:.2f},{:.2f}\n'.format(s, calc[0], calc[1])
for s, calc in calc_map.iteritems()
]
with open(os.path.join(c.output_dir, 'ch4_volumes_map.csv'), 'w') as wr:
wr.writelines(out_lines)
'''
tag = c.tag
vvv = "p2090_size{}_ss{}_noc{}_tag{}".format(c.fcn_img_size, ss, noc, tag)
res_file = 'v{}_'.format(version) + vvv + '_2norm'
train_CV = False
pct_norm = (10, 90)
pct_norm2 = (20, 99)
generate_contours = 0
if noc == 'auto':
generate_contours = 0
np.random.seed(2345)
#compare with 1 (10,95)
if train_CV:
segment_fn = m.get_segmenter_function(c.params_dir,
c.fcn_img_size,
NCV=5,
version=version,
param_file_key=vvv)
else:
segment_fn = m.get_segmenter_function(
c.params_dir + '/fcn_v{}_{}_f5.npz'.format(version, vvv),
c.fcn_img_size,
NCV=False,
version=version)
label_map = du.get_label_map(os.path.join(c.data_kaggle, 'train.csv'))
dsets = []
for s in range(M, N + 1):
start = time.time()
dset = du.CNN_Dataset(s)
ss = c.para_ss
noc = c.no_contour_type
tag = c.tag
vvv = "p2090_size{}_ss{}_noc{}_tag{}".format(c.fcn_img_size, ss, noc, tag)
res_file = "v{}_".format(version) + vvv + "_2norm"
train_CV = False
pct_norm = (10, 90)
pct_norm2 = (20, 99)
generate_contours = 0
if noc == "auto":
generate_contours = 0
np.random.seed(2345)
# compare with 1 (10,95)
if train_CV:
segment_fn = m.get_segmenter_function(c.params_dir, c.fcn_img_size, NCV=5, version=version, param_file_key=vvv)
else:
segment_fn = m.get_segmenter_function(
c.params_dir + "/fcn_v{}_{}_f5.npz".format(version, vvv), c.fcn_img_size, NCV=False, version=version
)
label_map = du.get_label_map(os.path.join(c.data_kaggle, "train.csv"))
dsets = []
for s in range(M, N + 1):
start = time.time()
dset = du.CNN_Dataset(s)
if len(dset.slices_ver) < 3:
pass
dset.segment(
segment_fn,