Update renovation.py

renovation.py

@@ -3,6 +3,7 @@ import random
 import datasets
 import requests
 import os
+import py7zr # for extracting .7z files
 
 from PIL import Image
 from io import BytesIO
@@ -24,7 +25,11 @@ _DESCRIPTION = """\
 This dataset contains images of various properties, along with labels indicating the quality of renovation - 'cheap', 'average', 'expensive'.
 """
 
-_URL = "https://huggingface.co/datasets/rshrott/renovation/raw/main/labels.csv"
+_URLS = {
+ "cheap": "https://huggingface.co/datasets/rshrott/renovation/raw/main/cheap.7z",
+ "average": "https://huggingface.co/datasets/rshrott/renovation/raw/main/average.7z",
+ "expensive": "https://huggingface.co/datasets/rshrott/renovation/raw/main/expensive.7z",
+}
 
 _NAMES = ["cheap", "average", "expensive"]
 
@@ -50,54 +55,55 @@ class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
  )
 
  def _split_generators(self, dl_manager):
- csv_path = dl_manager.download(_URL)
- with open(csv_path, "r") as f:
- reader = csv.reader(f)
- next(reader) # skip header
- rows = list(reader)
- 
- # Shuffle rows
- random.shuffle(rows)
+ # Download and extract images
+ image_paths = []
+ for label, url in _URLS.items():
+ download_path = dl_manager.download(url)
+ extract_path = dl_manager.extract(download_path)
+
+ # Get image paths
+ for root, _, files in os.walk(extract_path):
+ for file in files:
+ if file.endswith(".jpeg"): # Assuming all images are .jpeg
+ image_paths.append((os.path.join(root, file), label))
+
+ # Shuffle image paths
+ random.shuffle(image_paths)
 
  # 80% for training, 10% for validation, 10% for testing
- train_end = int(0.8 * len(rows))
- val_end = int(0.9 * len(rows))
+ train_end = int(0.8 * len(image_paths))
+ val_end = int(0.9 * len(image_paths))
 
  return [
  datasets.SplitGenerator(
  name=datasets.Split.TRAIN,
  gen_kwargs={
- "rows": rows[:train_end],
+ "rows": image_paths[:train_end],
  },
  ),
  datasets.SplitGenerator(
  name=datasets.Split.VALIDATION,
  gen_kwargs={
- "rows": rows[train_end:val_end],
+ "rows": image_paths[train_end:val_end],
  },
  ),
  datasets.SplitGenerator(
  name=datasets.Split.TEST,
  gen_kwargs={
- "rows": rows[val_end:],
+ "rows": image_paths[val_end:],
  },
  ),
  ]
 
  def _generate_examples(self, rows):
- def url_to_image(url):
- response = requests.get(url)
- img = Image.open(BytesIO(response.content))
+ def file_to_image(file_path):
+ img = Image.open(file_path)
  return img
  
- for id_, row in enumerate(rows):
- if len(row) < 2:
- print(f"Row with id {id_} has less than 2 elements: {row}")
- else:
- image_file_path = str(row[0])
- image = url_to_image(image_file_path)
- yield id_, {
- 'image_file_path': image_file_path,
- 'image': image,
- 'labels': row[1],
- }
+ for id_, (image_file_path, label) in enumerate(rows):
+ image = file_to_image(image_file_path)
+ yield id_, {
+ 'image_file_path': image_file_path,
+ 'image': image,
+ 'labels': label,
+ }