carla

Carla-COCO-Object-Detection-Dataset.py

@@ -1,158 +1,19 @@
-# coding=utf-8
-# Copyright 2021 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.
-"""CPPE-5 dataset."""
-
-
-import collections
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@misc{dagli2021cppe5,
-      title={CPPE-5: Medical Personal Protective Equipment Dataset},
-      author={Rishit Dagli and Ali Mustufa Shaikh},
-      year={2021},
-      eprint={2112.09569},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-"""
-
-_DESCRIPTION = """\
-CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
-to allow the study of subordinate categorization of medical personal protective equipments,
-which is not possible with other popular data sets that focus on broad level categories.
-"""
-
-_HOMEPAGE = "https://sites.google.com/view/cppe5"
-
-_LICENSE = "Unknown"
-
-_URL = "https://drive.google.com/uc?id=1QeveFt1jDNrafJeeCV1N_KoIKQEZyhuf"
-
-_CATEGORIES = ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"]
-
-
-class CPPE5(datasets.GeneratorBasedBuilder):
-    """CPPE - 5 dataset."""
-
-    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(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        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),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, annotation_file_path, files):
-        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
-
 # # 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"""
+# # Copyright 2021 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.
+# """CPPE-5 dataset."""
+
 
 # import collections
 # import json
@@ -161,48 +22,50 @@ class CPPE5(datasets.GeneratorBasedBuilder):
 # import datasets
 
 
-# logger = datasets.logging.get_logger(__name__)
+# _CITATION = """\
+# @misc{dagli2021cppe5,
+#       title={CPPE-5: Medical Personal Protective Equipment Dataset},
+#       author={Rishit Dagli and Ali Mustufa Shaikh},
+#       year={2021},
+#       eprint={2112.09569},
+#       archivePrefix={arXiv},
+#       primaryClass={cs.CV}
+# }
+# """
 
 # _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.
+# CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
+# to allow the study of subordinate categorization of medical personal protective equipments,
+# which is not possible with other popular data sets that focus on broad level categories.
 # """
 
-# _HOMEPAGE = "https://github.com/yunusskeete/Carla-COCO-Object-Detection-Dataset"
+# _HOMEPAGE = "https://sites.google.com/view/cppe5"
 
-# _LICENSE = "MIT"
+# _LICENSE = "Unknown"
 
-# _URL = "https://drive.google.com/file/d/1xUPwrMBBrGFIapLx_fyLjmH4HN16A4iZ"
+# _URL = "https://drive.google.com/uc?id=1QeveFt1jDNrafJeeCV1N_KoIKQEZyhuf"
 
-# _CATEGORIES = ["automobile", "bike", "motorbike", "traffic_light", "traffic_sign"]
+# _CATEGORIES = ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"]
 
-# class CARLA_COCO(datasets.GeneratorBasedBuilder):
-#     """Carla-COCO-Object-Detection-Dataset"""
 
-#     VERSION = datasets.Version("1.1.0")
+# class CPPE5(datasets.GeneratorBasedBuilder):
+#     """CPPE - 5 dataset."""
 
-#     def _info(self):
-#         """This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset"""
+#     VERSION = datasets.Version("1.0.0")
 
+#     def _info(self):
 #         features = datasets.Features(
 #             {
-#                 "id": datasets.Value("int64"),
-#                 "image_id": datasets.Value("string"),
+#                 "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.Sequence(datasets.Value("int64")),
-#                         "area": datasets.Sequence(datasets.Value("int64")),
+#                         "id": datasets.Value("int64"),
+#                         "area": datasets.Value("int64"),
 #                         "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-#                         "category": datasets.Sequence(datasets.ClassLabel(names=_CATEGORIES)),
+#                         "category": datasets.ClassLabel(names=_CATEGORIES),
 #                     }
 #                 ),
 #             }
@@ -212,41 +75,29 @@ class CPPE5(datasets.GeneratorBasedBuilder):
 #             features=features,
 #             homepage=_HOMEPAGE,
 #             license=_LICENSE,
+#             citation=_CITATION,
 #         )
 
 #     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)
-
+#         archive = dl_manager.download(_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"],
@@ -281,3 +132,152 @@ class CPPE5(datasets.GeneratorBasedBuilder):
 #                     "objects": objects,
 #                 }
 #                 idx += 1
+
+# 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://drive.google.com/uc?id=1xUPwrMBBrGFIapLx_fyLjmH4HN16A4iZ"
+
+_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(
+            {
+                "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