Update renovation.py

renovation.py

@@ -1,44 +1,37 @@
-import csv
-import random
-import datasets
-import requests
 import os
-import py7zr
-import numpy as np
-from pathlib import Path
 
-from PIL import Image
-from io import BytesIO
+import datasets
 from datasets.tasks import ImageClassification
 
-_HOMEPAGE = "https://huggingface.co/datasets/rshrott/renovation"
+_HOMEPAGE = "https://github.com/your-github/renovation"
 
 _CITATION = """\
-@ONLINE {renovationquality,
+@ONLINE {renovationdata,
     author="Your Name",
-    title="Renovation Quality Dataset",
-    month="Your Month",
-    year="Your Year",
-    url="https://huggingface.co/datasets/rshrott/renovation"
+    title="Renovation dataset",
+    month="January",
+    year="2023",
+    url="https://github.com/your-github/renovation"
 }
 """
 
 _DESCRIPTION = """\
-This dataset contains images of various properties, along with labels indicating the quality of renovation - 'cheap', 'average', 'expensive'.
+Renovations is a dataset of images of houses taken in the field using smartphone
+cameras. It consists of 3 classes: cheap, average, and expensive renovations.
+Data was collected by the your research lab.
 """
 
 _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",
+    "cheap": "https://huggingface.co/datasets/renovation/resolve/main/data/cheap.7z",
+    "average": "https://huggingface.co/datasets/renovation/resolve/main/data/average.7z",
+    "expensive": "https://huggingface.co/datasets/renovation/resolve/main/data/expensive.7z",
 }
 
 _NAMES = ["cheap", "average", "expensive"]
 
-class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
-    """Renovation Quality Dataset."""
 
-    VERSION = datasets.Version("1.0.0")
+class Renovations(datasets.GeneratorBasedBuilder):
+    """Renovations house images dataset."""
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -57,57 +50,34 @@ class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # Download and extract images
-        image_paths = []
-        for label, url in _URLS.items():
-            extract_path = dl_manager.download_and_extract(url)
-            print(f"Extracted files for label {label} to path: {extract_path}")
-
-            # Get image paths
-            for image_file_path in Path(extract_path).rglob("*"):
-                if image_file_path.suffix in [".jpg", ".jpeg", ".png"]:  # add or remove image file types as needed
-                    image_paths.append((str(image_file_path), label))
-
-        print(f"Collected a total of {len(image_paths)} image paths.")
-
-
-        # Shuffle image paths
-        random.shuffle(image_paths)
-
-        # 80% for training, 10% for validation, 10% for testing
-        train_end = int(0.8 * len(image_paths))
-        val_end = int(0.9 * len(image_paths))
-
+        data_files = dl_manager.download_and_extract(_URLS)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "rows": image_paths[:train_end],
+                    "files": dl_manager.iter_files([data_files["cheap"]]),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "rows": image_paths[train_end:val_end],
+                    "files": dl_manager.iter_files([data_files["average"]]),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "rows": image_paths[val_end:],
+                    "files": dl_manager.iter_files([data_files["expensive"]]),
                 },
             ),
         ]
 
-    def _generate_examples(self, rows):
-        def file_to_image(file_path):
-            img = Image.open(file_path)
-            return np.array(img)
-
-        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,
-            }
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith(".jpg"):
+                yield i, {
+                    "image_file_path": path,
+                    "image": path,
+                    "labels": os.path.basename(os.path.dirname(path)).lower(),
+                }