def execute(self):
src_translation, src_data_dict = self.src_stack.read_data_dict(
self.bcube,
mip=self.mip,
translation_adjuster=None,
stack_name="src")
if self.blackout_masks:
mask_layers = self.dst_stack.get_layers_of_type(["mask"])
mask = helpers.read_mask_list(mask_layers, self.bcube, self.mip)
else:
mask = None
if self.copy_masks:
write_layers = self.dst_stack.get_layers_of_type(["img", "mask"])
else:
write_layers = self.dst_stack.get_layers_of_type("img")
for l in write_layers:
src = src_data_dict[f"src_{l.name}"]
if mask is not None:
src[mask] = 0
l.write(src, bcube=self.bcube, mip=self.mip)
# copy fields
write_layers = self.dst_stack.get_layers_of_type("field")
for l in write_layers:
src = src_data_dict[f"src_{l.name}"]
l.write(src, bcube=self.bcube, mip=self.mip)
def execute(self):
src_data = self.src_layer.read(bcube=self.bcube, mip=self.mip)
mask_data = helpers.read_mask_list(mask_list=self.mask_layers,
bcube=self.bcube,
mip=self.mip)
if mask_data is not None:
src_data = src_data[mask_data == 0]
src_data = src_data[src_data != 0]
src_data = src_data[src_data == src_data]
if self.mean_layer is not None:
if len(src_data) == 0:
mean = torch.FloatTensor([0], device=src_data.device)
else:
mean = src_data.float().mean()
self.mean_layer.write(mean,
bcube=self.bcube,
mip=self.mip,
channel_start=self.write_channel,
channel_end=self.write_channel + 1)
if self.var_layer is not None:
if len(src_data) == 0:
var = torch.FloatTensor([0], device=src_data.device)
else:
var = src_data.float().var()
self.var_layer.write(var,
bcube=self.bcube,
mip=self.mip,
channel_start=self.write_channel,
channel_end=self.write_channel + 1)
def execute(self):
src_data = self.src_layer.read(bcube=self.bcube, mip=self.mip)
mask_data = helpers.read_mask_list(mask_list=self.mask_layers,
bcube=self.bcube,
mip=self.mip)
if mask_data is not None:
src_data = src_data[mask_data == 0]
if self.mean_layer is not None:
mean = src_data[src_data != 0].float().mean()
self.mean_layer.write(mean,
bcube=self.bcube,
mip=self.mip,
channel_start=self.write_channel,
channel_end=self.write_channel + 1)
if self.var_layer is not None:
var = src_data[src_data != 0].float().var()
self.var_layer.write(var,
bcube=self.bcube,
mip=self.mip,
channel_start=self.write_channel,
channel_end=self.write_channel + 1)
def execute(self):
corgie_logger.info(
f"Normalizing {self.src_layer} at MIP{self.mip}, region: {self.bcube}"
)
mean_data = self.mean_layer.read(self.bcube, mip=self.stats_mip)
var_data = self.var_layer.read(self.bcube, mip=self.stats_mip)
src_data = self.src_layer.read(self.bcube, mip=self.mip)
mask_data = helpers.read_mask_list(mask_list=self.mask_layers,
bcube=self.bcube,
mip=self.mip)
dst_data = (src_data - mean_data) / var_data.sqrt()
if mask_data is not None:
dst_data[mask_data] = self.mask_value
self.dst_layer.write(dst_data, self.bcube, mip=self.mip)
def execute(self):
padded_bcube = self.bcube.uncrop(self.pad, self.mip)
for f in self.additional_fields:
self.src_stack.add_layer(f)
src_translation, src_data_dict = self.src_stack.read_data_dict(
padded_bcube, mip=self.mip, stack_name='src')
agg_field = src_data_dict[f"src_agg_field"]
if self.blackout_masks:
mask_layers = self.dst_stack.get_layers_of_type(["img", "mask"])
mask = helpers.read_mask_list(mask_layers, self.bcube, self.mip)
else:
mask = None
if self.render_masks:
write_layers = self.dst_stack.get_layers_of_type(["img", "mask"])
else:
write_layers = self.dst_stack.get_layers_of_type("img")
for l in write_layers:
src = src_data_dict[f"src_{l.name}"]
if agg_field is not None:
warped_src = residuals.res_warp_img(src.float(), agg_field)
else:
warped_src = src
if mask is not None:
warped_src[mask] = 0.0
cropped_out = helpers.crop(warped_src, self.pad)
l.write(cropped_out, bcube=self.bcube, mip=self.mip)
for f in self.additional_fields:
self.src_stack.remove_layer(f.name)