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Crowd human

class alodataset.crowd_human_dataset.CrowdHumanDataset(img_folder=None, ann_file=None, boxes_limit=None, **kwargs)

Bases: Generic[torch.utils.data.dataset.T_co]

getitem(idx)

load_gt(dict_input)

load_json_lines(fpath)

prepare()

Prepare the dataset. The human crowd dataset has a lot of huge 4k images that drasticly slow down the training. To be more effective, this method will go through all images from the dataset and will save a new version of the dataset under {self.dataset_dir_prepared}. Once the dataset is prepared, the path to the dir in /.aloception/alodataset_config.json will be replace by the new prepared one.

Notes

If the dataset is already prepared, this method will simply check that all file are prepared and stored into the prepared folder. Otherwise, if the original directory is no longer on the disk, the method will simply use the prepared dir as it is and the prepare step will be skiped.

alodataset.crowd_human_dataset.main()

Main