def perform_annotation(som: str,
save: bool = True,
resume: bool = False,
results: str = None):
"""A simple driver to perform annotation of a `SOM`
Arguments:
som {str} -- Path to desired SOM for annotation
Keyword Arguments:
save {bool} -- Save the annotations as they are being performed (default: {True})
resume {bool} -- Continue the annotation process from the first un-annotated neuron (default: {False})
"""
annotator = pu.Annotator(som, save=save, results=results)
annotator.interactive_annotate(resume=resume)
def update_annotation(som: str, key: Tuple[Any, ...], results: str = None):
"""Load an existing annotation set and update a single neuron. To do this
a annotation set has to be loaded.
TODO: Build in a custom path for the `results` argument
Arguments:
som {str} -- Path to SOM binary file
key {Tuple[Any, ...]} -- Key of the `results` attribute to update
Keyword Arguments:
results {str} -- Path to existing results Annotator set. Default will atempt to automatically find one. (default: {None})
"""
print(results)
results_path = True if results is None else results
print(results_path)
annotator = pu.Annotator(som, results=results_path)
annotator.annotate_neuron(key, update=True, labeling=True)
results_path = None if results_path == True else results
annotator.save_annotation_results(results_path)
# /// Select Sample (single tile) \\\
print("Loading the VLASS catalogue, specifying complex components")
radio_cat_file = os.path.join(radio_path, radio_component_catalogue)
radio_cat = vdl.load_vlass_catalogue(radio_cat_file,
complex=True,
NN_dist=72,
SNR_min=None)
print("Creating the tile catalogue")
subtile_file = os.path.join(radio_path, vlass_subtiles)
subtile_cat = Table.read(subtile_file, format="csv").to_pandas()
tile_cat = vdl.vlass_tile(subtile_cat)
print("Loading the SOM and annotations")
som = pu.SOM(som_file)
annotation = pu.Annotator(som.path, results=annotations_file)
# sdss = sky_chunk(df, (120, 240), (-10, 50))
tiles = tile_cat.Tile.values
ti = int(sys.argv[1]) if len(sys.argv) >= 2 else 0
num_tiles = int(sys.argv[2]) if len(sys.argv) >= 3 else 1
tiles = tiles[ti:ti + num_tiles]
for tile_id in tiles:
print(f"Processing tile {tile_id}")
imbin_file, map_file, trans_file = cxc.binary_names(tile_id,
path=out_bin_path)
# Load VLASS and unWISE catalogues
radio_sample, ir_cat = load_tile_catalogues(out_cat_path, radio_cat,
def run_all(
catalogues,
som_file,
unique_id,
image_cutout_path,
bin_path="",
img_size=(2, 150, 150),
numthreads=10,
annotation=None,
annotations_file=None,
remove_tile_cutouts=False,
cpu_only=False,
sorter_mode="area_ratio",
pix_scale=0.6,
som_mask=None,
):
"""Run the preprocess, map, and collate steps for a single sample.
Args:
catalogues (tuple): DataFrames of the radio and ir catalogues
som_file (str): Name of the SOM file
unique_id (str): Unique identifier for the sample (tile id, ssid, etc)
image_cutout_path (str): Path to the directory containing the image cutouts
annotations_file (str, optional): Name of the SOM annotations file. Defaults to a name based on the SOM file name.
Returns:
[type]: [description]
"""
radio_cat, ir_cat = catalogues
# Preprocess
imbin_file, map_file, trans_file = binary_names(unique_id, bin_path)
print(f"Preprocessing the sample: {imbin_file}")
imgs = preprocess(
radio_cat,
imbin_file,
img_size=img_size,
tile_cutout_path=image_cutout_path,
remove_tile_cutouts=remove_tile_cutouts,
)
print("...done")
# Map
print(f"Mapping...")
som = pu.SOM(som_file)
w, h, d = som.som_shape
somset = map(
imbin_file,
som_file,
map_file,
trans_file,
w,
h,
numthreads=numthreads,
cpu_only=cpu_only,
)
somset.som.bmu_mask = som_mask
print("...done")
# Collate
if annotation is None:
if annotations_file is None:
annotations_file = f"{som_file}.results.pkl"
annotation = pu.Annotator(som.path, results=annotations_file)
print(f"Collating...")
comp_cat, src_cat = collate(
radio_cat,
ir_cat,
imgs,
somset,
annotation,
sorter_mode=sorter_mode,
pix_scale=pix_scale,
)
print("...done")
return comp_cat, src_cat