dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# undefined > original-images
+https://public.roboflow.ai/object-detection/undefined
+
+Provided by undefined
+License: CC BY 4.0
+
+# Overview
+The Numberplate Dataset is a collection of Licence Plates that can easily be used for Automatic Number Plate Detection. 
+# Example Footage!
+![Licese Plate Detection](https://media0.giphy.com/media/5l6FAJoHuT0YiM3oZG/giphy.gif?cid=790b7611013999dd479d98f81ddbaaf2a0563c41cea2413b&rid=giphy.gif&ct=g)
+
+# Training and Deployment
+The Number Plate model has been trained in Roboflow, and available for inference on the Dataset tab.
+One could also build a Automatic Number Plate Recognition [ANPR] App using YOLOR and EasyOCR. This is achieved using the Roboflow Platform which you can deploy the model for robust and real-time ANPR.
+
+# About Augmented Startups
+We are at the forefront of Artificial Intelligence in computer vision. With over 92k subscribers on YouTube, we embark on fun and innovative projects in this field and create videos and courses so that everyone can be an expert in this field. Our vision is to create a world full of inventors that can turn their dreams into reality

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+---
+
+### Roboflow Dataset Page
+https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk/dataset/1
+
+### Dataset Labels
+
+```
+['license_plate']
+```
+
+### Citation
+
+```
+@misc{ vehicle-registration-plates-trudk_dataset,
+    title = { Vehicle Registration Plates Dataset },
+    type = { Open Source Dataset },
+    author = { Augmented Startups },
+    howpublished = { \\url{ https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk } },
+    url = { https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { jun },
+    note = { visited on 2023-01-01 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.ai on January 13, 2022 at 5:20 PM GMT
+
+It includes 8823 images.
+VRP are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+Vehicle Registration Plates - v1 original-images
+==============================
+
+This dataset was exported via roboflow.ai on January 13, 2022 at 5:20 PM GMT
+
+It includes 8823 images.
+VRP are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0da8f2a4a703d126b4c39416940aa93f1fc7c9c4c75d7d86a2c5e245f8918258
+size 21917681

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1275562c0716aee2e9f377c86f74fe76a80a9057a30d1d518b88cfbc3f0785e8
+size 163307791

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ed8ccebeb8c09fbc19836eaf88c45105fddf27e4909c0e8ed79cabcbd85742d4
+size 44930828

license-plate-object-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk/dataset/1"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ vehicle-registration-plates-trudk_dataset,
+    title = { Vehicle Registration Plates Dataset },
+    type = { Open Source Dataset },
+    author = { Augmented Startups },
+    howpublished = { \\url{ https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk } },
+    url = { https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { jun },
+    note = { visited on 2023-01-01 },
+}
+"""
+_URLS = {
+    "train": "https://huggingface.co/datasets/keremberke/license-plate-object-detection/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/keremberke/license-plate-object-detection/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/keremberke/license-plate-object-detection/resolve/main/data/test.zip",
+}
+
+_CATEGORIES = ['license_plate']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class LICENSEPLATEOBJECTDETECTION(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("int64"),
+                "image": datasets.Image(),
+                "width": datasets.Value("int32"),
+                "height": datasets.Value("int32"),
+                "objects": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int64"),
+                        "area": datasets.Value("int64"),
+                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                        "category": datasets.ClassLabel(names=_CATEGORIES),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "folder_dir": data_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "folder_dir": data_files["validation"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "folder_dir": data_files["test"],
+                },
+            ),
+]
+
+    def _generate_examples(self, folder_dir):
+        def process_annot(annot, category_id_to_category):
+            return {
+                "id": annot["id"],
+                "area": annot["area"],
+                "bbox": annot["bbox"],
+                "category": category_id_to_category[annot["category_id"]],
+            }
+
+        image_id_to_image = {}
+        idx = 0
+        
+        annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
+        with open(annotation_filepath, "r") as f:
+            annotations = json.load(f)
+        category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
+        image_id_to_annotations = collections.defaultdict(list)
+        for annot in annotations["annotations"]:
+            image_id_to_annotations[annot["image_id"]].append(annot)
+        image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
+
+        for filename in os.listdir(folder_dir):
+            filepath = os.path.join(folder_dir, filename)
+            if filename in image_id_to_image:
+                image = image_id_to_image[filename]
+                objects = [
+                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
+                ]
+                with open(filepath, "rb") as f:
+                    image_bytes = f.read()
+                yield idx, {
+                    "image_id": image["id"],
+                    "image": {"path": filepath, "bytes": image_bytes},
+                    "width": image["width"],
+                    "height": image["height"],
+                    "objects": objects,
+                }
+                idx += 1