Create mini_car_bikes_detection.py

mini_car_bikes_detection.py

@@ -0,0 +1,70 @@
+from PIL import Image
+import numpy as np
+
+import json
+import os
+
+import datasets
+
+
+_DESCRIPTION = """
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+_URL = "https://huggingface.co/datasets/alexrods/mini_car_bikes_detection/resolve/main"
+
+_URLS = {
+    "train_images": f"{_URL}/data/train.zip",
+    "test_images": f"{_URL}/data/test.zip",
+}
+
+_CATEGORIES = ['Car', 'bike']
+
+
+class MiniCarBikesDetection(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "image_name": datasets.Value("string"),
+            }
+        )
+       
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+
+
+    def _split_generators(self, dl_manager):
+            data_files = dl_manager.download(_URLS)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "image_files": dl_manager.iter_archive(data_files["train_images"]),
+                        "split": "train"
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "image_files": dl_manager.iter_archive(data_files["test_images"]),
+                        "split": "test"
+                    },
+                ),
+            ]
+        
+    def _generate_examples(self, image_files, split): 
+        for image_file in image_files:
+            yield np.array(Image.open(image_file[1])), image_file[0].split("/")[1]
+
+