|
|
|
"""aphantasia_drawing_dataset.ipynb |
|
|
|
Automatically generated by Colab. |
|
|
|
Original file is located at |
|
https://colab.research.google.com/drive/1DYVroeFqoNK7DDiw_3OIczPeqEME-rbh |
|
""" |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
import pandas as pd |
|
import numpy as np |
|
import io |
|
from typing import List |
|
import datasets |
|
import logging |
|
from PIL import Image |
|
|
|
|
|
_CITATION = """\ |
|
@misc{Bainbridge_Pounder_Eardley_Baker_2023, |
|
title={Quantifying Aphantasia through drawing: Those without visual imagery show deficits in object but not spatial memory}, |
|
url={osf.io/cahyd}, |
|
publisher={OSF}, |
|
author={Bainbridge, Wilma A and Pounder, Zoë and Eardley, Alison and Baker, Chris I}, |
|
year={2023}, |
|
month={Sep} |
|
} |
|
""" |
|
|
|
_DESCRIPTION = """\ |
|
This dataset comes from the Brain Bridge Lab from the University of Chicago. |
|
It is from an online memory drawing experiment with 61 individuals with aphantasia |
|
and 52 individuals with normal imagery. In the experiment participants 1) studied 3 separate |
|
scene photographs presented one after the other, 2) then drew them from memory, |
|
3) completed a recognition task, 4) copied the images while viewing them, 5) filled out |
|
a VVIQ and OSIQ questionnaire and also demographics questions. The data from the experiment |
|
was made available on the OSF website linked above. It was created July 31, 2020 and last |
|
updated September 27, 2023. |
|
""" |
|
|
|
|
|
_HOMEPAGE = "https://osf.io/cahyd/" |
|
|
|
|
|
url = "https://drive.google.com/file/d/1v1oaZog5j5dD_vIElOEWLCZUrXvJ3jzx/view?usp=drive_link" |
|
|
|
def _get_drive_url(url): |
|
base_url = 'https://drive.google.com/uc?id=' |
|
split_url = url.split('/') |
|
return base_url + split_url[5] |
|
|
|
_URL = {"train": _get_drive_url(url)} |
|
|
|
|
|
|
|
class AphantasiaDrawingDataset(datasets.GeneratorBasedBuilder): |
|
|
|
_URL = _URL |
|
VERSION = datasets.Version("1.1.0") |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features({ |
|
"subject_id": datasets.Value("int32"), |
|
"treatment": datasets.Value("string"), |
|
"demographics": { |
|
"country": datasets.Value("string"), |
|
"age": datasets.Value("int32"), |
|
"gender": datasets.Value("string"), |
|
"occupation": datasets.Value("string"), |
|
"art_ability": datasets.Value("int32"), |
|
"art_experience": datasets.Value("string"), |
|
"device": datasets.Value("string"), |
|
"input": datasets.Value("string"), |
|
"difficult": datasets.Value("string"), |
|
"diff_explanation": datasets.Value("string"), |
|
"vviq_score": datasets.Value("int32"), |
|
"osiq_score": datasets.Value("int32") |
|
}, |
|
"drawings": { |
|
"kitchen": { |
|
"perception": datasets.Image(decode=True), |
|
"memory": datasets.Image(decode=True) |
|
}, |
|
"livingroom": { |
|
"perception": datasets.Image(decode=True), |
|
"memory": datasets.Image(decode=True) |
|
}, |
|
"bedroom": { |
|
"perception": datasets.Image(decode=True), |
|
"memory": datasets.Image(decode=True) |
|
} |
|
}, |
|
"image": { |
|
"kitchen": datasets.Image(decode=True), |
|
"livingroom": datasets.Image(decode=True), |
|
"bedroom": datasets.Image(decode = True) |
|
} |
|
}), |
|
supervised_keys=None, |
|
homepage=_HOMEPAGE, |
|
citation=_CITATION |
|
) |
|
|
|
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
|
url_to_download = self._URL |
|
downloaded_file = dl_manager.download_and_extract(url_to_download) |
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={ |
|
"filepath": downloaded_file["train"] |
|
}) |
|
] |
|
|
|
def _generate_examples(self, filepath): |
|
"""This function returns the examples in the raw (text) form.""" |
|
logging.info("generating examples from = %s", filepath) |
|
def byt_to_image(image_bytes): |
|
if image_bytes is not None: |
|
image_buffer = io.BytesIO(image_bytes) |
|
image = Image.open(image_buffer) |
|
return image |
|
return None |
|
|
|
with open(filepath, "rb") as subjects_file: |
|
subjects_data = pd.read_parquet(subjects_file) |
|
for idx, sub_row in subjects_data.iterrows(): |
|
yield idx, { |
|
"subject_id": sub_row["subject_id"], |
|
"treatment": sub_row["treatment"], |
|
"demographics": { |
|
"country": sub_row["demographics.country"], |
|
"age": sub_row["demographics.age"], |
|
"gender": sub_row["demographics.gender"], |
|
"occupation": sub_row["demographics.occupation"], |
|
"art_ability": sub_row["demographics.art_ability"], |
|
"art_experience": sub_row["demographics.art_experience"], |
|
"device": sub_row["demographics.device"], |
|
"input": sub_row["demographics.input"], |
|
"difficult": sub_row["demographics.difficult"], |
|
"diff_explanation": sub_row["demographics.diff_explanation"], |
|
"vviq_score": sub_row["demographics.vviq_score"], |
|
"osiq_score": sub_row["demographics.osiq_score"] |
|
}, |
|
"drawings": { |
|
"kitchen": { |
|
"perception": byt_to_image(sub_row["drawings.kitchen.perception"]), |
|
"memory": byt_to_image(sub_row["drawings.kitchen.memory"]) |
|
}, |
|
"livingroom": { |
|
"perception": byt_to_image(sub_row["drawings.livingroom.perception"]), |
|
"memory": byt_to_image(sub_row["drawings.livingroom.memory"]) |
|
}, |
|
"bedroom": { |
|
"perception": byt_to_image(sub_row["drawings.bedroom.perception"]), |
|
"memory": byt_to_image(sub_row["drawings.bedroom.memory"]) |
|
} |
|
}, |
|
"image": { |
|
"kitchen": byt_to_image(sub_row["image.kitchen"]), |
|
"livingroom": byt_to_image(sub_row["image.livingroom"]), |
|
"bedroom": byt_to_image(sub_row["image.bedroom"]) |
|
} |
|
} |
|
|
