def ReadAffinityData(prefix):
filename, dataset = meta_data.MetaData(prefix).AffinityFilename()
affinities = ReadH5File(filename, dataset).astype(np.float32)
# create the dataset so it is (z, y, x, c)
if affinities.shape[0] == 3: affinities = np.moveaxis(affinities, 0, 3)
return affinities
def SpawnMetaFile(prefix, rhoana_filename, rhoana_dataset):
meta = meta_data.MetaData(prefix)
# get the new prefix for the data from the rhoana file
new_prefix = rhoana_filename.split('/')[1][:-3]
# update the values for this meta data
meta.prefix = new_prefix
meta.rhoana_filename = '{} {}'.format(rhoana_filename, rhoana_dataset)
meta.WriteMetaFile()
def ReadImageData(prefix):
filename, dataset = meta_data.MetaData(prefix).ImageFilename()
return ReadH5File(filename, dataset)
def ReadGoldData(prefix):
filename, dataset = meta_data.MetaData(prefix).GoldFilename()
return ReadH5File(filename, dataset).astype(np.int64)
def ReadSegmentationData(prefix):
filename, dataset = meta_data.MetaData(prefix).SegmentationFilename()
return ReadH5File(filename, dataset).astype(np.int64)
def Resolution(prefix):
# return the resolution for this prefix
return meta_data.MetaData(prefix).Resolution()
def ReadMetaData(prefix):
# return the meta data for this prefix
return meta_data.MetaData(prefix)
def CroppingBox(prefix):
# return which locations are valid for training and validation
return meta_data.MetaData(prefix).CroppingBox()
def GridSize(prefix):
# return the size of this dataset
return meta_data.MetaData(prefix).GridSize()
def GetWorldBBox(prefix):
# return the bounding box for this segment
return meta_data.MetaData(prefix).WorldBBox()
def ReadAffinityData(prefix):
filename, dataset = meta_data.MetaData(prefix).AffinityFilename()
return ReadH5File(filename, dataset).astype(np.float32)
def GetGoldFilename(prefix):
filename, _ = meta_data.MetaData(prefix).GoldFilename()
return filename
