| | import os
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| | import warnings
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| | from shutil import unpack_archive
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| | from typing import Union, List
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| | from urllib.request import urlretrieve
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| |
|
| | import pandas as pd
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| | import sqlite3
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| | import datasets
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| |
|
| | _CITATION = """@article{zanca2023contrastive,
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| | title={Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors},
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| | author={Zanca, Dario and Zugarini, Andrea and Dietz, Simon and Altstidl, Thomas R and Ndjeuha, Mark A Turban and Schwinn, Leo and Eskofier, Bjoern},
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| | journal={arXiv preprint arXiv:2305.12380},
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| | year={2023}
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| | }"""
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| |
|
| | _DESCRIPTION = """CapMIT1003 is a dataset of captions and click-contingent image explorations collected during captioning tasks.
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| | CapMIT1003 is based on the same stimuli from the well-known MIT1003 benchmark, for which eye-tracking data
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| | under free-viewing conditions is available, which offers a promising opportunity to concurrently study human attention under both tasks.
|
| | """
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| |
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| | _HOMEPAGE = "https://github.com/mad-lab-fau/CapMIT1003/"
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| | MIT1003_URL = "http://people.csail.mit.edu/tjudd/WherePeopleLook/ALLSTIMULI.zip"
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| | _VERSION = "1.0.0"
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| |
|
| | logger = datasets.logging.get_logger(__name__)
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| |
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| |
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| | class CapMIT1003DB:
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| | """
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| | Lightweight wrapper around CapMIT1003 SQLite3 database.
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| |
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| | It provides utility functions for loading labeled images with captions and their associated click paths. To use it,
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| | you first need to download the database from https://redacted.com/scanpath.db.
|
| | """
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| |
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| | def __init__(self, db_path: Union[str, bytes, os.PathLike] = 'capmit1003.db',
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| | img_path: Union[str, bytes, os.PathLike] = os.path.join('mit1003', 'ALLSTIMULI')):
|
| | """
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| |
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| | Parameters
|
| | ----------
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| | db_path: str or bytes or os.PathLike
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| | Path pointing to the location of the `scanpath.db` SQLite3 database.
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| | img_path: str or bytes or os.PathLike
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| | Path pointing to the location of the MIT1003 stimuli images.
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| | """
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| | self.db_path = db_path
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| | self.img_path = os.path.join(img_path, '')
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| | if not os.path.exists(db_path) and not os.path.isfile(db_path):
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| | warnings.warn('Could not find database at {}'.format(db_path))
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| | if not os.path.exists(img_path) and not os.path.isdir(img_path):
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| | warnings.warn('Could not find images at {}'.format(img_path))
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| |
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| | def __enter__(self):
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| | self.cnx = sqlite3.connect(self.db_path)
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| | return self
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| |
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| | def __exit__(self, type, value, traceback):
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| | self.cnx.close()
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| |
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| | def get_captions(self) -> pd.DataFrame:
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| | """ Retrieve image-caption pairs of CapMIT1003 database.
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| |
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| | Returns
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| | -------
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| | pd.DataFrame
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| | Data frame with columns `obs_uid`, `usr_uid`, `start_time`, `caption`, `img_uid`, and `img_path`. See
|
| | accompanying readme for full documentation of columns.
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| | """
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| | captions = pd.read_sql_query('SELECT * FROM captions o LEFT JOIN images i USING(img_uid)', self.cnx)
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| | captions['img_path'] = self.img_path + captions['img_path']
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| | return captions
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| |
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| | def get_click_path(self, obs_uid: str) -> pd.DataFrame:
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| | """ Retrieve click path for a specific image-caption pair.
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| |
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| | Parameters
|
| | ----------
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| | obs_uid: str
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| | The unique id of the image-caption pair for which to retrieve the click path.
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| |
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| | Returns
|
| | -------
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| | pd.DataFrame
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| | Data frame with columns `click_id`, `obs_uid`, `x`, `y`, and `click_time`. See accompanying readme for full
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| | documentation of columns.
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| | """
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| | return pd.read_sql_query('SELECT x, y, click_time AS time FROM clicks WHERE obs_uid = ?', self.cnx,
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| | params=[obs_uid])
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| |
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| |
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| | class CapMIT1003(datasets.GeneratorBasedBuilder):
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| | _URLS = [MIT1003_URL]
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| |
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| | def _info(self):
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| | return datasets.DatasetInfo(
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| | description=_DESCRIPTION,
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| | features=datasets.Features(
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| | {
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| | "obs_uid": datasets.Value("string"),
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| | "usr_uid": datasets.Value("string"),
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| | "caption": datasets.Value("string"),
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| | "image": datasets.Image(),
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| | "clicks_path": datasets.Sequence(datasets.Sequence(datasets.Value("int32"), length=2)),
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| | "clicks_time": datasets.Sequence(datasets.Value("timestamp[s]"))
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| | }
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| | ),
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| |
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| |
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| | supervised_keys=None,
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| | homepage=_HOMEPAGE,
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| | citation=_CITATION,
|
| | )
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| |
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| | def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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| | urls_to_download = {"mit1003": self._URLS}
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| | downloaded_files = dl_manager.download_and_extract(urls_to_download)
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| | downloaded_db = dl_manager.download({"cap1003": ["./capmit1003.db"]})
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| |
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| | return [
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| | datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"mit1003_path": downloaded_files["mit1003"], "capmit1003_db_path": downloaded_db["cap1003"]}),
|
| | ]
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| |
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| |
|
| | def _generate_examples(self, mit1003_path, capmit1003_db_path):
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| | with CapMIT1003DB(os.path.join(capmit1003_db_path[0]), os.path.join(mit1003_path[0], "ALLSTIMULI")) as db:
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| | image_captions = db.get_captions()
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| | for pair in image_captions.itertuples(index=False):
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| | obs_uid = pair.obs_uid
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| | click_path = db.get_click_path(obs_uid)
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| | xy_coordinates = click_path[['x', 'y']].values
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| | clicks_time = click_path["time"].values
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| | example = {
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| | "obs_uid": obs_uid,
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| | "usr_uid": pair.usr_uid,
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| | "image": pair.img_path,
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| | "caption": pair.caption,
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| | "clicks_path": xy_coordinates,
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| | "clicks_time": clicks_time
|
| | }
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| |
|
| | yield obs_uid, example
|
| |
|
| |
|
| | |
