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@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+capmit1003.db filter=lfs diff=lfs merge=lfs -text

CapMIT1003.py

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+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

capmit1003.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e96f060bc51f932414a9a31c13a67c06e413563b71a57992e88dbefefc880400
+size 6443008