File size: 9,699 Bytes
478fb4a 986dc0b 478fb4a 6404fdb 4fbf53e 478fb4a 986dc0b 478fb4a 30d8bff 478fb4a ae658d3 a2eb370 478fb4a a2eb370 ae658d3 478fb4a a2eb370 478fb4a a2eb370 ae658d3 478fb4a 0511667 478fb4a 0511667 478fb4a 18c25e2 478fb4a 27b68d2 478fb4a 986dc0b 478fb4a c5edc51 48e6885 9b7c19e 30d8bff 9b7c19e c5edc51 27b68d2 c5edc51 48e6885 478fb4a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
# -*- coding: utf-8 -*-
"""aphantasia_drawing_dataset.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1FHMQJWfjSzSrtEpARqh7IKVrfF7LwyRA
"""
#!pip install datasets -q
#from google.colab import drive
#drive.mount('/content/drive')
#path = os.getcwd() + "/drive/MyDrive/Duke/huggingface_project/aphantasia_drawing"
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
#import csv
import base64
from PIL import Image
import numpy as np
import io
import json
import os
from typing import List
import datasets
import logging
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_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}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_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.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://osf.io/cahyd/"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
url = "https://drive.google.com/file/d/1aRhQlKPDk29yYPkx2kPhqaMwec5QZ4JE/view?usp=sharing"
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)}
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class AphantasiaDrawingDataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
_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"),#datasets.ClassLabel(names=["aphantasia", "control"]),
"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)
}
}),
# 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]:
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)
with open(filepath, "r") as subjects_file:
subjects_data = json.load(subjects_file)
idx = 0
for sub in subjects_data:
for room in subjects_data[sub]["drawings"].keys():
if subjects_data[sub]["drawings"][room]["perception"] != "":
img_byt = base64.b64decode(subjects_data[sub]["drawings"][room]["perception"])
img = Image.open(io.BytesIO(img_byt))
subjects_data[sub]["drawings"][room]["perception"] = img
else:
subjects_data[sub]["drawings"][room]["perception"] = None
if subjects_data[sub]["drawings"][room]["memory"] != "":
img_byt = base64.b64decode(subjects_data[sub]["drawings"][room]["memory"])
img = Image.open(io.BytesIO(img_byt))
subjects_data[sub]["drawings"][room]["memory"] = img
else:
subjects_data[sub]["drawings"][room]["memory"] = None
for room in subjects_data[sub]["image"].keys():
img_byt = base64.b64decode(subjects_data[sub]["image"][room])
img = Image.open(io.BytesIO(img_byt))
subjects_data[sub]["image"][room] = img.resize((500,500))
idx += 1
age = int(subjects_data[sub]["demographics"]["age"]) if subjects_data[sub]["demographics"]["age"] else np.nan
yield idx-1, {
"subject_id": subjects_data[sub]["subject_id"],
"treatment": subjects_data[sub]["treatment"],
"demographics": {
"country": subjects_data[sub]["demographics"]["country"],
"age": subjects_data[sub]["demographics"]["age"],
"gender": subjects_data[sub]["demographics"]["gender"],
"occupation": subjects_data[sub]["demographics"]["occupation"],
"art_ability": subjects_data[sub]["demographics"]["art_ability"],
"art_experience": subjects_data[sub]["demographics"]["art_experience"],
"device": subjects_data[sub]["demographics"]["device"],
"input": subjects_data[sub]["demographics"]["input"],
"difficult": subjects_data[sub]["demographics"]["difficult"],
"diff_explanation": subjects_data[sub]["demographics"]["diff_explanation"],
"vviq_score": subjects_data[sub]["demographics"]["vviq_score"],
"osiq_score": subjects_data[sub]["demographics"]["osiq_score"]
},
"drawings": {
"kitchen": {
"perception": subjects_data[sub]["drawings"]["kitchen"]["perception"],
"memory": subjects_data[sub]["drawings"]["kitchen"]["memory"]
},
"livingroom": {
"perception": subjects_data[sub]["drawings"]["livingroom"]["perception"],
"memory": subjects_data[sub]["drawings"]["livingroom"]["memory"]
},
"bedroom": {
"perception": subjects_data[sub]["drawings"]["bedroom"]["perception"],
"memory": subjects_data[sub]["drawings"]["bedroom"]["memory"]
}
},
"image": {
"kitchen": subjects_data[sub]["image"]["kitchen"],
"livingroom": subjects_data[sub]["image"]["livingroom"],
"bedroom": subjects_data[sub]["image"]["bedroom"]
}
}
|