Upload aphantasia_drawing_dataset.py

aphantasia_drawing_dataset.py

@@ -7,7 +7,7 @@ Original file is located at
     https://colab.research.google.com/drive/1DYVroeFqoNK7DDiw_3OIczPeqEME-rbh
 """
 
-# !pip install -q datasets
+
 
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
@@ -27,13 +27,9 @@ Original file is located at
 
 
 
-import base64
-from PIL import Image
 import pandas as pd
 import numpy as np
 import io
-import json
-import os
 from typing import List
 import datasets
 import logging
@@ -87,7 +83,7 @@ class AphantasiaDrawingDataset(datasets.GeneratorBasedBuilder):
           description=_DESCRIPTION,
           features=datasets.Features({
                   "subject_id": datasets.Value("int32"),
-                  "treatment": datasets.Value("string"),#datasets.ClassLabel(names=["aphantasia", "control"]),
+                  "treatment": datasets.Value("string"),
                   "demographics": {
                       "country": datasets.Value("string"),
                       "age": datasets.Value("int32"),
@@ -145,49 +141,10 @@ class AphantasiaDrawingDataset(datasets.GeneratorBasedBuilder):
           image = Image.open(image_buffer)
           return image
         return None
-        
+
       with open(filepath, "rb") as subjects_file:
         subjects_data = pd.read_parquet(subjects_file)
-        #idx = 0
         for idx, sub_row in subjects_data.iterrows():
-          # 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
-            
-          # try:
-          #   age = int(sub_row["demographics.age"])
-          # except:
-          #   age = -1
-          # try:
-          #   art_ability = int(sub_row["demographics.art_ability"])
-          # except:
-          #   art_ability = -1
-          # try:
-          #   vviq_score = int(sub_row["demographics.vviq_score"])
-          # except:
-          #   vviq_score = -1
-          # try:
-          #   osiq_score = int(sub_row["demographics.osiq_score"])
-          # except:
-          #   osiq_score = -1
-          # age = int(sub_row["demographics.age"]) if sub_row["demographics.age"] and sub_row["demographics.age"] is not np.nan else None
           yield idx, {
               "subject_id": sub_row["subject_id"],
               "treatment": sub_row["treatment"],
@@ -224,4 +181,5 @@ class AphantasiaDrawingDataset(datasets.GeneratorBasedBuilder):
                   "livingroom": byt_to_image(sub_row["image.livingroom"]),
                   "bedroom": byt_to_image(sub_row["image.bedroom"])
                   }
-              }
+              }
+