Upload Carla-COCO-Object-Detection-Dataset.py

Carla-COCO-Object-Detection-Dataset.py

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+# coding=utf-8
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+"""Carla-COCO-Object-Detection-Dataset"""
+
+import collections
+import json
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_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://github.com/yunusskeete/Carla-COCO-Object-Detection-Dataset/raw/master/"
+_URLS = {
+    "train": _URL + "train.tar.gz",
+    "test": _URL + "test.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):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("int64"),
+                "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.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):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("generating examples from = %s", filepath)
+
+        with open(filepath, encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                yield idx, json.loads(line)