def predict_in_directory(self,
spath,
fold,
stage,
cb,
data,
limit=-1,
batchSize=32,
ttflips=False):
with tqdm.tqdm(total=len(generic.dir_list(spath)),
unit="files",
desc="segmentation of images from " +
str(spath)) as pbar:
for v in self.predict_on_directory(spath,
fold=fold,
stage=stage,
limit=limit,
batch_size=batchSize,
ttflips=ttflips):
b: imgaug.Batch = v
for i in range(len(b.data)):
id = b.data[i]
entry = self.toEntry(b, i)
cb(id, entry, data)
pbar.update(batchSize)
def predict_in_directory(self,
spath,
fold,
stage,
cb,
data,
limit=-1,
batchSize=32,
ttflips=False):
with tqdm.tqdm(total=len(generic.dir_list(spath)),
unit="files",
desc="segmentation of images from " +
str(spath)) as pbar:
for v in self.predict_on_directory(spath,
fold=fold,
stage=stage,
limit=limit,
batch_size=batchSize,
ttflips=ttflips):
b: imgaug.Batch = v
for i in range(len(b.data)):
id = b.data[i]
orig = b.images[i]
map = b.segmentation_maps_aug[i]
scaledMap = imgaug.augmenters.Scale({
"height": orig.shape[0],
"width": orig.shape[1]
}).augment_segmentation_maps([map])
cb(id, scaledMap[0], data)
pbar.update(batchSize)
def predict_to_directory(self,
spath,
tpath,
fold=0,
stage=0,
limit=-1,
batchSize=32,
binaryArray=False,
ttflips=False):
generic.ensure(tpath)
with tqdm.tqdm(total=len(generic.dir_list(spath)),
unit="files",
desc="segmentation of images from " + str(spath) +
" to " + str(tpath)) as pbar:
for v in self.predict_on_directory(spath,
fold=fold,
stage=stage,
limit=limit,
batch_size=batchSize,
ttflips=ttflips):
b: imgaug.Batch = v
for i in range(len(b.data)):
id = b.data[i]
entry = self.toEntry(b, i)
if isinstance(tpath, datasets.ConstrainedDirectory):
tp = tpath.path
else:
tp = tpath
p = os.path.join(tp, id[0:id.index('.')] + ".npy")
save(p, entry)
pbar.update(batchSize)
def predict_to_directory(self,
spath,
tpath,
fold=0,
stage=0,
limit=-1,
batchSize=32,
binaryArray=False,
ttflips=False):
generic.ensure(tpath)
with tqdm.tqdm(total=len(generic.dir_list(spath)),
unit="files",
desc="segmentation of images from " + str(spath) +
" to " + str(tpath)) as pbar:
for v in self.predict_on_directory(spath,
fold=fold,
stage=stage,
limit=limit,
batch_size=batchSize,
ttflips=ttflips):
b: imgaug.Batch = v
for i in range(len(b.data)):
id = b.data[i]
orig = b.images[i]
map = b.segmentation_maps_aug[i]
scaledMap = imgaug.augmenters.Scale({
"height": orig.shape[0],
"width": orig.shape[1]
}).augment_segmentation_maps([map])
if isinstance(tpath, datasets.ConstrainedDirectory):
tp = tpath.path
else:
tp = tpath
if binaryArray:
np.save(os.path.join(tp, id[0:id.index('.')]),
scaledMap[0].arr)
else:
imageio.imwrite(
os.path.join(tp, id[0:id.index('.')] + ".png"),
(scaledMap[0].arr * 255).astype(np.uint8))
pbar.update(batchSize)