def __init__(self,
use_pretrained_weights=True,
model_image_shape=(128, 128, 1)):
model = PanopticNet('resnet50',
input_shape=model_image_shape,
norm_method='whole_image',
num_semantic_heads=3,
num_semantic_classes=[1, 1, 2],
location=True,
include_top=True,
lite=True,
interpolation='bilinear')
if use_pretrained_weights:
weights_path = get_file(
os.path.basename(WEIGHTS_PATH),
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='50614f04d5dbc4b3eadd897fa5fb0e23'
)
model.load_weights(weights_path)
else:
weights_path = None
super(CytoplasmSegmentation, self).__init__(model,
model_image_shape=model_image_shape,
model_mpp=0.65,
preprocessing_fn=phase_preprocess,
postprocessing_fn=deep_watershed,
dataset_metadata=self.dataset_metadata,
model_metadata=self.model_metadata)
def __init__(self,
use_pretrained_weights=True,
model_image_shape=(256, 256, 2)):
model = PanopticNet('resnet50',
input_shape=model_image_shape,
norm_method=None,
num_semantic_heads=4,
num_semantic_classes=[1, 1, 2, 3],
location=True,
include_top=True,
use_imagenet=False)
if use_pretrained_weights:
weights_path = get_file(
os.path.basename(WEIGHTS_PATH),
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='66fec859eacc5222b5e7d2baa105f3e3')
model.load_weights(weights_path)
else:
weights_path = None
super(MultiplexSegmentation,
self).__init__(model,
model_image_shape=model_image_shape,
model_mpp=2.0,
preprocessing_fn=phase_preprocess,
postprocessing_fn=deep_watershed_mibi,
dataset_metadata=self.dataset_metadata,
model_metadata=self.model_metadata)
def __init__(self,
use_pretrained_weights=True,
model_image_shape=(128, 128, 1)):
model = PanopticNet('resnet50',
input_shape=model_image_shape,
norm_method='whole_image',
num_semantic_heads=2,
num_semantic_classes=[1, 1],
location=True,
include_top=True,
lite=True,
use_imagenet=use_pretrained_weights,
interpolation='bilinear')
if use_pretrained_weights:
weights_path = get_file(
os.path.basename(WEIGHTS_PATH),
WEIGHTS_PATH,
cache_subdir='models',
file_hash='104a7d7884c80c37d2bce6d1c3a17c7a')
model.load_weights(weights_path, by_name=True)
else:
weights_path = None
super(CytoplasmSegmentation,
self).__init__(model,
model_image_shape=model_image_shape,
model_mpp=0.65,
preprocessing_fn=phase_preprocess,
postprocessing_fn=deep_watershed,
dataset_metadata=self.dataset_metadata,
model_metadata=self.model_metadata)
def __init__(self,
use_pretrained_weights=True,
model_image_shape=(128, 128, 1)):
model = PanopticNet('resnet50',
input_shape=model_image_shape,
norm_method='whole_image',
num_semantic_heads=2,
num_semantic_classes=[1, 1],
location=True,
include_top=True,
lite=True,
interpolation='bilinear')
if use_pretrained_weights:
weights_path = get_file(
os.path.basename(WEIGHTS_PATH),
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='42ca0ebe4b7b0f782eaa4733cdddad88'
)
model.load_weights(weights_path, by_name=True)
else:
weights_path = None
super(NuclearSegmentation, self).__init__(model,
model_image_shape=model_image_shape,
model_mpp=0.65,
preprocessing_fn=None,
postprocessing_fn=deep_watershed,
dataset_metadata=self.dataset_metadata,
model_metadata=self.model_metadata)
def __init__(self,
use_pretrained_weights=True,
model_image_shape=(128, 128, 1)):
model = PanopticNet('resnet50',
input_shape=model_image_shape,
norm_method='whole_image',
num_semantic_heads=3,
num_semantic_classes=[1, 1, 2],
location=True,
include_top=True)
if use_pretrained_weights:
weights_path = get_file(
os.path.basename(WEIGHTS_PATH),
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='eb29808ef2f662fb3bcda6986e47f91a')
model.load_weights(weights_path)
else:
weights_path = None
super(NuclearSegmentation,
self).__init__(model,
model_image_shape=model_image_shape,
model_mpp=0.65,
preprocessing_fn=None,
postprocessing_fn=deep_watershed,
dataset_metadata=self.dataset_metadata,
model_metadata=self.model_metadata)
def __init__(self,
use_pretrained_weights=True,
model_image_shape=(256, 256, 2)):
whole_cell_classes = [1, 1, 2, 3]
nuclear_classes = [1, 1, 2, 3]
num_semantic_classes = whole_cell_classes + nuclear_classes
num_semantic_heads = len(num_semantic_classes)
model = PanopticNet('resnet50',
input_shape=model_image_shape,
norm_method=None,
num_semantic_heads=num_semantic_heads,
num_semantic_classes=num_semantic_classes,
location=True,
include_top=True,
use_imagenet=False)
if use_pretrained_weights:
weights_path = get_file(
os.path.basename(WEIGHTS_PATH),
WEIGHTS_PATH,
cache_subdir='models',
file_hash='ff24e821c6056cf847e58e8e52916814')
model.load_weights(weights_path)
else:
weights_path = None
super(MultiplexSegmentation,
self).__init__(model,
model_image_shape=model_image_shape,
model_mpp=0.5,
preprocessing_fn=phase_preprocess,
postprocessing_fn=deep_watershed_subcellular,
format_model_output_fn=format_output_multiplex,
dataset_metadata=self.dataset_metadata,
model_metadata=self.model_metadata)