Delete CapMIT1003.py

CapMIT1003.py

@@ -1,167 +0,0 @@
-import os
-import warnings
-from shutil import unpack_archive
-from typing import Union, List
-from urllib.request import urlretrieve
-
-import pandas as pd
-import sqlite3
-import datasets
-
-_CITATION = """```@article{zanca2023contrastive,
-  title={Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors},
-  author={Zanca, Dario and Zugarini, Andrea and Dietz, Simon and Altstidl, Thomas R and Ndjeuha, Mark A Turban and Schwinn, Leo and Eskofier, Bjoern},
-  journal={arXiv preprint arXiv:2305.12380},
-  year={2023}```
-}"""
-
-_DESCRIPTION = """CapMIT1003 is a dataset of captions and click-contingent image explorations collected during captioning tasks. 
-CapMIT1003 is based on the same stimuli from the well-known MIT1003 benchmark, for which eye-tracking data
-under free-viewing conditions is available, which offers a promising opportunity to concurrently study human attention under both tasks.
-"""
-
-_HOMEPAGE = "https://github.com/mad-lab-fau/CapMIT1003/"
-MIT1003_URL = "http://people.csail.mit.edu/tjudd/WherePeopleLook/ALLSTIMULI.zip"
-_VERSION = "1.0.0"
-
-logger = datasets.logging.get_logger(__name__)
-
-
-class CapMIT1003DB:
-    """
-    Lightweight wrapper around CapMIT1003 SQLite3 database.
-
-    It provides utility functions for loading labeled images with captions and their associated click paths. To use it,
-    you first need to download the database from https://redacted.com/scanpath.db.
-    """
-
-    def __init__(self, db_path: Union[str, bytes, os.PathLike] = 'capmit1003.db',
-                 img_path: Union[str, bytes, os.PathLike] = os.path.join('mit1003', 'ALLSTIMULI')):
-        """
-
-        Parameters
-        ----------
-        db_path: str or bytes or os.PathLike
-            Path pointing to the location of the `scanpath.db` SQLite3 database.
-        img_path: str or bytes or os.PathLike
-            Path pointing to the location of the MIT1003 stimuli images.
-        """
-        self.db_path = db_path
-        self.img_path = os.path.join(img_path, '')
-        if not os.path.exists(db_path) and not os.path.isfile(db_path):
-            warnings.warn('Could not find database at {}'.format(db_path))
-        if not os.path.exists(img_path) and not os.path.isdir(img_path):
-            warnings.warn('Could not find images at {}'.format(img_path))
-
-    def __enter__(self):
-        self.cnx = sqlite3.connect(self.db_path)
-        return self
-
-    def __exit__(self, type, value, traceback):
-        self.cnx.close()
-
-    def get_captions(self) -> pd.DataFrame:
-        """ Retrieve image-caption pairs of CapMIT1003 database.
-
-        Returns
-        -------
-        pd.DataFrame
-            Data frame with columns `obs_uid`, `usr_uid`, `start_time`, `caption`, `img_uid`, and `img_path`. See
-            accompanying readme for full documentation of columns.
-        """
-        captions = pd.read_sql_query('SELECT * FROM captions o LEFT JOIN images i USING(img_uid)', self.cnx)
-        captions['img_path'] = self.img_path + captions['img_path']
-        return captions
-
-    def get_click_path(self, obs_uid: str) -> pd.DataFrame:
-        """ Retrieve click path for a specific image-caption pair.
-
-        Parameters
-        ----------
-        obs_uid: str
-            The unique id of the image-caption pair for which to retrieve the click path.
-
-        Returns
-        -------
-        pd.DataFrame
-            Data frame with columns `click_id`, `obs_uid`, `x`, `y`, and `click_time`. See accompanying readme for full
-            documentation of columns.
-        """
-        return pd.read_sql_query('SELECT x, y, click_time, usr_uid AS time FROM clicks WHERE obs_uid = ?', self.cnx,
-                                 params=[obs_uid])
-
-    @staticmethod
-    def download_images(quiet=False):
-        """ Download stimuli images for MIT1003.
-
-        Parameters
-        ----------
-        quiet: bool
-            Flag that suppresses command-line outputs.
-        """
-        if not os.path.exists('mit1003'):
-            if not os.path.exists('mit1003.zip'):
-                print('Downloading MIT1003 Stimuli') if not quiet else None
-                urlretrieve(MIT1003_URL, 'mit1003.zip')
-            print('Extracting MIT1003 Stimuli') if not quiet else None
-            unpack_archive('mit1003.zip', 'mit1003')
-
-
-class CapMIT1003(datasets.GeneratorBasedBuilder):
-    _URLS = [MIT1003_URL]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "obs_uid": datasets.Value("string"),
-                    "usr_uid": datasets.Value("string"),
-                    # "start_time": datasets.Value("timestamp"),
-                    "caption": datasets.Value("string"),
-                    # "img_uid": datasets.Value("string"),
-                    # "img_path": datasets.Value("string"),
-                    "image": datasets.Image(),
-                    #"click_id": datasets.Value("int32"),
-                    # "x": datasets.Value("int16"),
-                    # "y": datasets.Value("int16"),
-                    # "click_time": datasets.Value("timestamp")
-                }
-            ),
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    # def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-    #     urls_to_download = self._URLS
-    #     downloaded_files = dl_manager.download_and_extract(urls_to_download)
-    #
-    #     return [
-    #         datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-    #     ]
-    #
-
-    def _generate_examples(self, file_path):
-        CapMIT1003DB.download_images()
-        with CapMIT1003DB('capmit1003.db') as db:
-            image_captions = db.get_captions()
-            for pair in image_captions.itertuples(index=False):
-                caption = pair.caption
-                obs_uid = pair.obs_uid
-                click_path = db.get_click_path(obs_uid)
-                xy_coordinates = click_path[['x', 'y']].values
-                click_times = click_path["click_time"].values
-                usr_uid = click_path["usr_uid"].values
-                example = {
-                    "obs_uid": obs_uid,
-                    "usr_uid": usr_uid,
-                    "image": pair.img_path,
-                    "caption": caption,
-                    # "click_path": xy_coordinates,
-                    # "click_times": click_times
-                }
-
-                yield obs_uid, example