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import os
import logging
from detectron2.config.config import get_cfg
from detectron2.data.catalog import MetadataCatalog
from detectron2.utils.logger import setup_logger
import pandas as pd
from cubercnn import data, util, vis
from cubercnn.config.config import get_cfg_defaults
from cubercnn.data.datasets import simple_register
logger = logging.getLogger("cubercnn")
def get_config_and_filter_settings(config_file='configs/Base_Omni3D.yaml'):
# we must load the config file to get the filter settings
cfg = get_cfg()
get_cfg_defaults(cfg)
cfg.merge_from_file(config_file)
# must setup logger to get info about filtered out annotations
setup_logger(output=cfg.OUTPUT_DIR, name="cubercnn")
filter_settings = data.get_filter_settings_from_cfg(cfg)
return cfg, filter_settings
def priors_of_objects(dataset):
cfg, filter_settings = get_config_and_filter_settings()
dataset_names = ['SUNRGBD_train','SUNRGBD_val', 'SUNRGBD_test']
for dataset_name in dataset_names:
simple_register(dataset_name, filter_settings, filter_empty=True)
dataset_paths = ['datasets/Omni3D/'+dataset_name+'.json' for dataset_name in dataset_names]
datasets = data.Omni3D(dataset_paths, filter_settings=filter_settings)
# determine the meta data given the datasets used.
data.register_and_store_model_metadata(datasets, cfg.OUTPUT_DIR, filter_settings)
thing_classes = MetadataCatalog.get('omni3d_model').thing_classes
dataset_id_to_contiguous_id = MetadataCatalog.get('omni3d_model').thing_dataset_id_to_contiguous_id
'''
It may be useful to keep track of which categories are annotated/known
for each dataset in use, in case a method wants to use this information.
'''
infos = datasets.dataset['info']
if type(infos) == dict:
infos = [datasets.dataset['info']]
dataset_id_to_unknown_cats = {}
possible_categories = set(i for i in range(cfg.MODEL.ROI_HEADS.NUM_CLASSES + 1))
dataset_id_to_src = {}
dset_classes = []
for info in infos:
dataset_id = info['id']
known_category_training_ids = set()
if not dataset_id in dataset_id_to_src:
dataset_id_to_src[dataset_id] = info['source']
for id in info['known_category_ids']:
if id in dataset_id_to_contiguous_id:
known_category_training_ids.add(dataset_id_to_contiguous_id[id])
# determine and store the unknown categories.
unknown_categories = possible_categories - known_category_training_ids
dataset_id_to_unknown_cats[dataset_id] = unknown_categories
# log the per-dataset categories
avail_cats = [thing_classes[i] for i in (possible_categories & known_category_training_ids)]
logger.info('Available categories for {}'.format(info['name']))
logger.info(avail_cats)
dset_classes.append(avail_cats)
# set difference between the available categories for each dataset.
dset1 = set(dset_classes[0])
dset2 = set(dset_classes[1])
logger.info(f'Categories in {dataset_names[0]} missing from {dataset_names[1]}:')
logger.info(dset1 - dset2)
# compute priors given the training data.
# interested in priors['priors_dims_per_cat'], some of them have [1,1,1], as they are invalid categories, e.g. car and bus which are not present SUNRGBD
# priors are w / h / d, std for (w / h / d)
priors = util.compute_priors(cfg, datasets)
priors_bins = util.compute_priors(cfg, datasets,n_bins=5)
# TODO: can we emulate the behaviour of this function
# without ever having access to 3D annotations?
priors2 = pd.read_csv('datasets/typical sizes of 3d items.csv')
print(priors)
pass
if __name__ == "__main__":
priors_of_objects('SUNRGBD')
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