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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') | |