def warp_patch(self, ng_path, z, bbox, res_mip_range, mip):
influence_bbox = deepcopy(bbox)
influence_bbox.uncrop(self.max_displacement, mip=0)
agg_flow = influence_bbox.identity(mip=mip)
agg_flow = np.expand_dims(agg_flow, axis=0)
agg_res = data_handler.get_aggregate_rel_flow(
z, influence_bbox, res_mip_range, mip, self.process_low_mip,
self.process_high_mip, self.x_res_ng_paths, self.y_res_ng_paths)
agg_flow += agg_res
raw_data = data_handler.get_image_data(ng_path, z, influence_bbox, mip)
#no need to warp if flow is identity
#warp introduces noise
if not influence_bbox.is_identity_flow(agg_flow, mip=mip):
warped = warp(raw_data, agg_flow)
else:
#print ("not warping")
warped = raw_data[0]
mip_disp = int(self.max_displacement / 2**mip)
cropped = crop(warped, mip_disp)
result = data_handler.preprocess_data(cropped * 256)
#preprocess divides by 256 and puts it into right dimensions
#this data range is good already, so mult by 256
data_handler.save_image_patch(self.dst_ng_path, result, z, bbox, mip)
def chunkwise(patch_bbox):
print("Downsampling {} to mip {}".format(
patch_bbox.__str__(mip=0), m))
downsampled_patch = self.downsample_patch(
self.dst_ng_path, z, patch_bbox, m)
data_handler.save_image_patch(self.dst_ng_path,
downsampled_patch, z,
patch_bbox, m)
def chunkwise(patch_bbox):
downsampled_patch = self.downsample_patch(self.dst_ng_path, z,
patch_bbox, mip)
data_handler.save_image_patch(self.dst_ng_path, downsampled_patch,
z, patch_bbox, mip)
def chunkwise(patch_bbox):
raw_patch = data_handler.get_image_data(source, z, patch_bbox, mip)
data_handler.save_image_patch(dest, raw_patch, z, patch_bbox, mip)