Rename photo_dataset.py to photos.py

photo_dataset.py

@@ -1,61 +0,0 @@
-import os
-import random
-import zipfile
-from pathlib import Path
-from datasets import load_dataset, Features, ClassLabel, Image
-
-# Define the URLs for the zip files
-_URLS = {
-    'Not Applicable': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Not Applicable.zip",
-    'Very Poor': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Very Poor.zip",
-    'Poor': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Poor.zip",
-    'Fair': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Fair.zip",
-    'Good': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Good.zip",
-    'Excellent': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Excellent.zip",
-    'Exceptional': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Exceptional.zip"
-}
-
-# Define the features of the dataset
-features = Features({
-    'image': Image(),
-    'label': ClassLabel(names=list(_URLS.keys()))
-})
-
-def extract_and_load_images(data_dir):
-    # Extract all zip files and load images
-    images = []
-    for label, url in _URLS.items():
-        zip_path = os.path.join(data_dir, os.path.basename(url))
-        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-            zip_ref.extractall(data_dir)
-            extracted_dir = Path(zip_path).stem  # Remove .zip extension
-            for img_path in Path(os.path.join(data_dir, extracted_dir)).glob('*.jpg'):
-                images.append({'image': str(img_path), 'label': label})
-    return images
-
-def split_dataset(images, split_ratios=(0.7, 0.2, 0.1)):
-    # Shuffle and split the dataset
-    random.shuffle(images)
-    total = len(images)
-    train_end = int(total * split_ratios[0])
-    val_end = train_end + int(total * split_ratios[1])
-    return {
-        'train': images[:train_end],
-        'validation': images[train_end:val_end],
-        'test': images[val_end:]
-    }
-
-def photos_dataset(data_dir):
-    images = extract_and_load_images(data_dir)
-    splits = split_dataset(images)
-    return splits
-
-if __name__ == '__main__':
-    data_dir = './photos_data'
-    os.makedirs(data_dir, exist_ok=True)
-    dataset_splits = photos_dataset(data_dir)
-
-    # Create Hugging Face datasets
-    for split, data in dataset_splits.items():
-        ds = load_dataset('image', data_files={split: data}, split=split, features=features)
-        print(f"{split} dataset: {ds}")

photos.py

@@ -0,0 +1,48 @@
+import os
+import zipfile
+from pathlib import Path
+import datasets
+
+class Photos(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features({
+                "image": datasets.Image(),
+                "label": datasets.ClassLabel(names=["Not Applicable", "Very Poor", "Poor", "Fair", "Good", "Excellent", "Exceptional"]),
+            }),
+            supervised_keys=("image", "label"),
+        )
+
+    def _split_generators(self, dl_manager):
+        # Define the URLs for the zip files
+        urls = {
+            'Not Applicable': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Not Applicable.zip",
+            'Very Poor': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Very Poor.zip",
+            'Poor': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Poor.zip",
+            'Fair': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Fair.zip",
+            'Good': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Good.zip",
+            'Excellent': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Excellent.zip",
+            'Exceptional': "https://huggingface.co/datasets/rshrott/photos/resolve/main/Exceptional.zip"
+        }
+
+        # Download and extract the zip files
+        downloaded_files = dl_manager.download_and_extract(urls)
+        extracted_dirs = {label: Path(file).stem for label, file in downloaded_files.items()}  # Remove .zip extension
+
+        # Here you would split the dataset into train, validation, and test sets
+        # For simplicity, we'll assume all images are used for training
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"extracted_dirs": extracted_dirs}),
+        ]
+
+    def _generate_examples(self, extracted_dirs):
+        # Iterate over the images in the extracted directories and yield examples
+        for label, dir in extracted_dirs.items():
+            label_dir = os.path.join(self.config.data_dir, dir)
+            for img_path in Path(label_dir).glob('*.jpg'):
+                yield str(img_path), {
+                    "image": str(img_path),
+                    "label": label,
+                }