feat: add dataset script

imvision.py

@@ -0,0 +1,147 @@
+# Copyright 2020 The HuggingFace Datasets Authors and Santiago Hincapie Potes.
+#
+# 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: Add a description here."""
+
+
+import csv
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{yu2019lytnet,
+  title = {LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired},
+  author = {Yu, Samuel and Lee, Heon and Kim, John},
+  booktitle = {Computer Analysis of Images and Patterns (CAIP)},
+  month = {Aug},
+  year = {2019}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/samuelyu2002/ImVisible"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "imags": "ptl_dataset.tar.gz",
+    "train": "training_file.csv",
+    "validation": "validation_file.csv",
+    "test": "testing_file.csv",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class ImVision(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "img": datasets.Image(),
+                "boxes": datasets.features.Sequence({
+                    "label": datasets.Value("int8"),                    
+                    "occluded": datasets.Value("bool"),
+                    "x_max": datasets.Value("float"),
+                    "x_min": datasets.Value("float"),
+                    "y_max": datasets.Value("float"),
+                    "y_min": datasets.Value("float"),
+                }),
+            }
+        )
+    
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "img_folder": data_dir["imgs"],
+                    "labels": data_dir["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "img_folder": data_dir["imgs"],
+                    "labels": data_dir["test"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "img_folder": data_dir["imgs"],
+                    "labels": data_dir["validation"],
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, img_folder, labels):
+        # TODO: 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.
+        with open(labels, encoding="utf-8") as f:
+            reader = csv.reader(f)
+            for key, row in enumerate(reader):
+                if key == 0:
+                    continue
+
+                fname, label, x_min, y_min, x_max, y_max, occluded = row
+                yield key - 1, {
+                    "img": os.path.join(img_folder, fname),
+                    "boxes": [
+                        {
+                            "label": int(label),
+                            "occluded": occluded != "not_blocked",
+                            "x_max": float(x_max),
+                            "x_min": float(x_min),
+                            "y_max": float(y_max),
+                            "y_min": float(y_min),
+                        }
+                    ]
+                }