Update photo_dataset.py

photo_dataset.py

@@ -0,0 +1,61 @@
+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}")