def data_generator(config, exams, aug):
xarray = np.zeros([
config['batchsize'], config['height'], config['width'],
config['depth'], config['channel']
])
yarray = np.zeros([config['batchsize'], config['patchheight'], 2])
bi = 0
while 1:
for ei in exams:
caseloader = CaseLoader(ei)
# print(caseloader)
for slicei in caseloader.slices:
polarstack = caseloader.loadstack(slicei,
'cart_patch',
nei=config['depth'] // 2)
if polarstack is None:
# print('skip',caseloader,slicei)
continue
polarcont = caseloader.loadstack(slicei,
'polar_cont',
nei=config['depth'] // 2)
polar_cont_center = polarcont[:, :, config['depth'] // 2]
xarray[bi] = polarstack[..., None]
yarray[bi] = polar_cont_center
bi += 1
if bi == config['batchsize']:
bi = 0
if aug == True:
for offi in random.sample(range(config['patchheight']),
config['rottimes']):
xarray_off = batch_polar_rot(xarray, offi)
yarray_off = batch_polar_rot(yarray, offi)
yield (xarray_off, yarray_off)
else:
yield xarray, yarray
def data_generator(config, exams, aug):
xarray = np.zeros([
config['batchsize'], config['height'], config['width'],
config['depth'], config['channel']
])
yarray = np.zeros([config['batchsize'], config['patchheight'], 2])
bi = 0
while 1:
for ei in exams:
caseloader = CaseLoader(ei)
#print(caseloader)
for slicei in caseloader.slices:
if caseloader.valid_slicei(slicei) == False:
continue
if caseloader.caselist['slices'][slicei]['augpatch'] == []:
continue
gen_aug_patch(caseloader, slicei)
print(caseloader, slicei, 'gen')
aug_polar_patch_batch = caseloader.load_aug_patch(
slicei, 'polar_patch')
aug_polar_cont_batch = caseloader.load_aug_patch(
slicei, 'polar_cont')
for augi in range(len(aug_polar_patch_batch)):
aug_cart_patch = aug_polar_patch_batch[augi]
xarray[bi] = aug_polar_patch_batch[augi, :, :, :, None]
aug_polar_cont = aug_polar_cont_batch[augi]
yarray[bi] = aug_polar_cont_batch[augi]
bi += 1
if bi == config['batchsize']:
bi = 0
if aug == True:
for offi in random.sample(range(config['height']),
config['rottimes']):
xarray_off = batch_polar_rot(xarray, offi)
yarray_off = batch_polar_rot(yarray, offi)
yield (xarray_off, yarray_off)
else:
yield (xarray, yarray)
def data_generator(config, exams, aug):
xarray = np.zeros([
config['batchsize'], config['height'], config['width'],
config['depth'], config['channel']
])
yarray = np.zeros([config['batchsize'], config['patchheight'], 2])
bi = 0
while 1:
for ei in exams:
caseloader = CaseLoader(ei)
# print(caseloader)
for slicei in caseloader.slices:
if caseloader.valid_slicei(slicei) == False:
continue
cartcont = caseloader.load_cart_cont(slicei)
min_dist_map = caseloader.load_cart_min_dist(slicei)
dcmstack = caseloader.loadstack(slicei, 'dcm')
ctx, cty = caseloader.loadct(slicei)
aug_polar_patch_batch = np.zeros((10, 256, 256, 3))
aug_polar_cont_batch = np.zeros((10, 256, 2))
offi = 0
mi = 0
while offi < 10 and mi < 50:
mi += 1
if offi == 0:
ofx = 0
ofy = 0
else:
ofx = random.randint(-20, 20)
ofy = random.randint(-20, 20)
cctx = ofx + ctx
ccty = ofy + cty
if min_dist_map[256 + ofx, 256 + ofy] < 0.5:
if mi > 40:
print('mi', mi)
# print('mind',min_dist_map[256+ofx,256+ofy])
continue
# print(ofx,ofy)
new_polar_cont_lumen = tocordpolar(cartcont[0], cctx, ccty)
new_polar_cont_wall = tocordpolar(cartcont[1], cctx, ccty)
aug_polar_cont_batch[offi, :, 0] = new_polar_cont_lumen
aug_polar_cont_batch[offi, :, 1] = new_polar_cont_wall
new_cart_patch = croppatch(dcmstack, ccty, cctx, 256, 256)
new_polar_patch = topolar(new_cart_patch, 256, 256)
aug_polar_patch_batch[offi] = new_polar_patch / np.max(
new_polar_patch)
offi += 1
# aug_polar_patch_batch = caseloader.load_aug_patch(slicei,'polar_patch')
# aug_polar_cont_batch = caseloader.load_aug_patch(slicei,'polar_cont')
for augi in range(len(aug_polar_patch_batch)):
xarray[bi] = aug_polar_patch_batch[augi, :, :, :, None]
yarray[bi] = aug_polar_cont_batch[augi]
bi += 1
if bi == config['batchsize']:
bi = 0
if aug == True:
for offi in random.sample(range(config['height']),
config['rottimes']):
xarray_off = batch_polar_rot(xarray, offi)
yarray_off = batch_polar_rot(yarray, offi)
yield (xarray_off, yarray_off)
else:
yield (xarray, yarray)