| 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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|
|
|
|
| class CapMIT1003DB:
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| """
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| Lightweight wrapper around CapMIT1003 SQLite3 database.
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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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| """
|
|
|
| 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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| ----------
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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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|
|
| def __enter__(self):
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| self.cnx = sqlite3.connect(self.db_path)
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| return self
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|
|
| def __exit__(self, type, value, traceback):
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| self.cnx.close()
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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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|
|
| 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
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| 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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|
|
| 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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|
|
| 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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|
|
| Returns
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| -------
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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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|
|
|
|
| class CapMIT1003(datasets.GeneratorBasedBuilder):
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| _URLS = [MIT1003_URL]
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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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|
|
|
|
| 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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|
|
| 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
|
|
|
|
|
| |
