Carla-COCO-Object-Detection-Dataset.py

# coding=utf-8
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"""Carla-COCO-Object-Detection-Dataset"""


import collections
import json
import os

import datasets


_DESCRIPTION = """\
This dataset contains 1028 images each 640x380 pixels.
The dataset is split into 249 test and 779 training examples.
Every image comes with MS COCO format annotations.
The dataset was collected in Carla Simulator, driving around in autopilot mode in various environments
(Town01, Town02, Town03, Town04, Town05) and saving every i-th frame.
The labels where then automatically generated using the semantic segmentation information.
"""

_HOMEPAGE = "https://github.com/yunusskeete/Carla-COCO-Object-Detection-Dataset"

_LICENSE = "MIT"

_URL = "https://huggingface.co/datasets/yunusskeete/Carla-COCO-Object-Detection-Dataset/resolve/main/Carla-COCO-Object-Detection-Dataset.tar.gz"

_CATEGORIES = ["automobile", "bike", "motorbike", "traffic_light", "traffic_sign"]


class CARLA_COCO(datasets.GeneratorBasedBuilder):
    """Carla-COCO-Object-Detection-Dataset"""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        """This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset"""

        features = datasets.Features(
            {
                "image_id": datasets.Value("int64"),
                "image": datasets.Image(),
                "width": datasets.Value("int32"),
                "height": datasets.Value("int32"),
                "file_name": datasets.Value("string"),
                "license": datasets.Value(dtype="int32"),
                "url": datasets.Value("string"),
                "date_captured": datasets.Value("string"),
                "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(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        """This method is tasked with downloading/extracting the data and defining the splits depending on the configuration"""

        archive = dl_manager.download(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "annotation_file_path": "annotations/train.json",
                    "files": dl_manager.iter_archive(archive),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "annotation_file_path": "annotations/test.json",
                    "files": dl_manager.iter_archive(archive),
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, annotation_file_path, files):
        """
        This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        """

        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
        # This loop relies on the ordering of the files in the archive:
        # Annotation files come first, then the images.
        for path, f in files:
            file_name = os.path.basename(path)
            if path == annotation_file_path:
                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"]}
            elif file_name in image_id_to_image:
                image = image_id_to_image[file_name]
                objects = [
                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
                ]
                yield idx, {
                    "image_id": image["id"],
                    "image": {"path": path, "bytes": f.read()},
                    "width": image["width"],
                    "height": image["height"],
                    "file_name": image["file_name"],
                    "license": image["license"],
                    "url": image["url"],
                    "date_captured": image["date_captured"],
                    "objects": objects,
                }
                idx += 1



# class CARLA_COCO(datasets.GeneratorBasedBuilder):
#     """Carla-COCO-Object-Detection-Dataset"""

#     VERSION = datasets.Version("1.1.0")

#     def _info(self):
#         """This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset"""

#         features = datasets.Features(
#             {
#                 "id": datasets.Value("int64"),
#                 "image_id": datasets.Value("string"),
#                 "image": datasets.Image(),
#                 "width": datasets.Value("int32"),
#                 "height": datasets.Value("int32"),
#                 "file_name": datasets.Value("string"),
#                 "url": datasets.Value("string"),
#                 "objects": datasets.Sequence(
#                     {
#                         "id": datasets.Sequence(datasets.Value("int64")),
#                         "area": datasets.Sequence(datasets.Value("int64")),
#                         "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
#                         "category": datasets.Sequence(datasets.ClassLabel(names=_CATEGORIES)),
#                     }
#                 ),
#             }
#         )
#         return datasets.DatasetInfo(
#             description=_DESCRIPTION,
#             features=features,
#             homepage=_HOMEPAGE,
#             license=_LICENSE,
#         )

#     def _split_generators(self, dl_manager):
#         """This method is tasked with downloading/extracting the data and defining the splits depending on the configuration"""

#         archive = dl_manager.download_and_extract(_URL)

#         return [
#             datasets.SplitGenerator(
#                 name=datasets.Split.TRAIN,
#                 # These kwargs will be passed to _generate_examples
#                 gen_kwargs={
#                     "annotation_file_path": "annotations/train.json",
#                     "files": dl_manager.iter_archive(archive),
#                 }
#             ),
#             datasets.SplitGenerator(
#                 name=datasets.Split.TEST,
#                 # These kwargs will be passed to _generate_examples
#                 gen_kwargs={
#                     "annotation_file_path": "annotations/test.json",
#                     "files": dl_manager.iter_archive(archive),
#                 }
#             ),
#         ]

#     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
#     def _generate_examples(self, annotation_file_path, files):
#         """
#         This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
#         The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
#         """

#         logger.info("generating examples from = %s", annotation_file_path)

#         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
#         # This loop relies on the ordering of the files in the archive:
#         # Annotation files come first, then the images.
#         for path, f in files:
#             file_name = os.path.basename(path)
#             if path == annotation_file_path:
#                 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"]}
#             elif file_name in image_id_to_image:
#                 image = image_id_to_image[file_name]
#                 objects = [
#                     process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
#                 ]
#                 yield idx, {
#                     "image_id": image["id"],
#                     "image": {"path": path, "bytes": f.read()},
#                     "width": image["width"],
#                     "height": image["height"],
#                     "objects": objects,
#                 }
#                 idx += 1