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