Create renovation.py

renovation.py

@@ -0,0 +1,109 @@
+import os
+import glob
+import random
+
+import datasets
+from datasets.tasks import ImageClassification
+
+_HOMEPAGE = "https://github.com/your-github/renovation"
+
+_CITATION = """\
+@ONLINE {renovationdata,
+    author="Your Name",
+    title="Renovation dataset",
+    month="January",
+    year="2023",
+    url="https://github.com/your-github/renovation"
+}
+"""
+
+_DESCRIPTION = """\
+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 = {
+    '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"
+}
+
+
+_NAMES = ['Not Applicable', 'Very Poor', 'Poor', 'Fair', 'Good', 'Excellent', 'Exceptional']
+
+class Renovations(datasets.GeneratorBasedBuilder):
+    """Renovations house images dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image_file_path": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    "labels": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("image", "labels"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            task_templates=[ImageClassification(image_column="image", label_column="labels")],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_files": data_files,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_files": data_files,
+                    "split": "val",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_files": data_files,
+                    "split": "test",
+                },
+            ),
+        ]
+    def _generate_examples(self, data_files, split):
+        all_files_and_labels = []
+        for label, path in data_files.items():
+            files = glob.glob(path + '/*.jpeg', recursive=True)
+            all_files_and_labels.extend((file, label) for file in files)
+    
+        random.seed(43)  # ensure reproducibility
+        random.shuffle(all_files_and_labels)
+    
+        num_files = len(all_files_and_labels)
+        train_data = all_files_and_labels[:int(num_files*0.9)]
+        val_test_data = all_files_and_labels[int(num_files*0.9):] # This will be used for both val and test
+    
+        if split == "train":
+            data_to_use = train_data
+        else: # "val" or "test" split
+            data_to_use = val_test_data
+    
+        for idx, (file, label) in enumerate(data_to_use):
+            yield idx, {
+                "image_file_path": file,
+                "image": file,
+                "labels": label,
+        }
+
+        
+