Datasets:

alexrods
/

mini_car_bikes_detection

@@ -1,149 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# TODO: Address all TODOs and remove all explanatory comments
-"""Cars and bikes detection small dataset."""
-import collections
-import numpy as np
-from PIL import Image
-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"),
-                "width": datasets.Value("int32"),
-                "height": datasets.Value("int32"),
-                # "objects": datasets.Sequence(
-                #     {
-                #         "name": datasets.ClassLabel(names=_CATEGORIES),
-                #         "bbox": datasets.Sequence(datasets.Value("float32"), length=4)
-                #     }
-                # ),
-            }
-        )
-        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"]),
-                    "annotation_files": f"{_URL}/annotations/train_annotations.json",
-                    "split": "train"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    # "image_files": dl_manager.iter_archive(data_files["test_images"]),
-                    "annotation_files": f"{_URL}/annotations/test_annotations.json",
-                    "split": "test"
-                },
-            ),
-        ]
-    def _generate_examples(self, annotation_files, split):
-        with open(annotation_files, "r") as jf:
-            annotations = json.load(jf)
-        for annotation in annotations:
-            yield {
-                    "width": annotation["width"],
-                    "height": annotation["height"],
-                    # "objects": [
-                    #     {
-                    #     "name": annotation["name"],
-                    #     "bbox": annotation["bbox"]
-                    #     }
-                    #     ]
-                    }
-    # def _generate_examples(self, image_files, annotation_files, split):
-    #     # for image_file in image_files:
-    #     #     return np.array(Image.open(image_file[1]))
-    #     with open(annotation_files, "r") as jf:
-    #         annotations = json.load(jf)
-    #     for image_file in image_files:
-    #         image_name = image_file[0].split("/")[1]
-    #         image_array = np.array(Image.open(image_file[1]))
-    #         for annotation in annotations:
-    #             if annotation['image'] == image_name[0]:
-    #                 data = {
-    #                     "image": image_array,
-    #                     "width": annotation["width"],
-    #                     "height": annotation["height"],
-    #                     "objects": []
-    #                 }
-    #                 data["objects"].append(
-    #                     {
-    #                     "name": annotation["name"],
-    #                     "bbox": annotation["bbox"]
-    #                     }
-    #                 )
-    #             yield data
-                # yield {
-                #         "image": image_array,
-                #         "width": annotation["width"],
-                #         "height": annotation["height"],
-                #         "objects": [
-                #             {
-                #             "name": [name for name in annotation["name"]],
-                #             "bbox": [bbox for bbox in annotation["bbox"]]
-                #             }
-                #         ]
-                #     }