added ds download script

plastic_in_river.py

@@ -0,0 +1,93 @@
+# from https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py
+
+import numpy as np
+
+import datasets
+from PIL import Image
+
+
+_DESCRIPTION = """
+    This dataset contains photos of rivers on which there may be waste. The waste items are annotated 
+    through bounding boxes, and are assigned to one of the 4 following categories: plastic bottle, plastic bag, 
+    another plastic waste, or non-plastic waste. Note that some photos may not contain any waste.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+# # TODO: Add link to the official dataset URLs here
+# # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://storage.googleapis.com/kili-datasets-public/plastic-in-river/"
+
+_URLS = {
+    "train_images": f"{_URL}train/images.tar.gz",
+    "train_annotations": f"{_URL}train/annotations.tar.gz",
+    "validation_images": f"{_URL}validation/images.tar.gz",
+    "validation_annotations": f"{_URL}validation/annotations.tar.gz",
+    "test_images": f"{_URL}test/images.tar.gz",
+    "test_annotations": f"{_URL}test/annotations.tar.gz"
+}
+
+class PlasticInRiver(datasets.GeneratorBasedBuilder):
+    """Download script for the Plastic In River dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "litter": datasets.Sequence(
+                        {
+                            "label": datasets.ClassLabel(num_classes=3, names=["PLASTIC_BAG", "PLASTIC_BOTTLE", "OTHER_PLASTIC_WASTE"]),
+                            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                        }
+                    )
+                }
+            )
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "image_files": dl_manager.iter_archive(downloaded_files[f"{split_name}_images"]),
+                    "annotations_files":  dl_manager.iter_archive(downloaded_files[f"{split_name}_annotations"]),
+                    "split": split_name,
+                },
+            ) for split, split_name in [
+                (datasets.Split.TRAIN, "train"),
+                (datasets.Split.TEST, "test"),
+                (datasets.Split.VALIDATION, "validation"),
+                ]
+        ]
+
+    def _generate_examples(self, image_files, annotations_files, split):
+
+        for idx, (image_file, annotations_file) in enumerate(zip(image_files, annotations_files)):
+            image_array = np.array(Image.open(image_file[1]))
+            data = {
+                "image": image_array,
+                "litter": []
+            }
+
+            for l in annotations_file[1].readlines():
+                numbers = l.decode("utf-8").split(" ")
+
+                data["litter"].append({
+                    "label": int(numbers[0]),
+                    "bbox": [float(n) for n in numbers[1:]]
+                })
+
+            yield idx, data
+