| # coding=utf-8 | |
| # Permission is hereby granted, free of charge, to any person obtaining | |
| # a copy of this software and associated documentation files (the | |
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| # the following conditions: | |
| # The above copyright notice and this permission notice shall be | |
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| # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND | |
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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 | |