dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# wlots > 2022-05-06 10:52pm
+https://universe.roboflow.com/asd-culfr/wlots
+
+Provided by a Roboflow user
+License: CC BY 4.0
+

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+---
+
+### Roboflow Dataset Page
+https://universe.roboflow.com/asd-culfr/wlots
+
+### Citation
+```
+@misc{ wlots_dataset,
+    title = { wlots Dataset },
+    type = { Open Source Dataset },
+    author = { asd },
+    howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
+    url = { https://universe.roboflow.com/asd-culfr/wlots },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { may },
+    note = { visited on 2022-12-29 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.com on December 28, 2022 at 8:08 PM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+It includes 4454 images.
+Ct-cthead-t-thead are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 416x416 (Fill (with center crop))
+
+The following augmentation was applied to create 3 versions of each source image:
+* Random brigthness adjustment of between -15 and +15 percent
+
+
+

README.roboflow.txt

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+
+wlots - v1 2022-05-06 10:52pm
+==============================
+
+This dataset was exported via roboflow.com on December 28, 2022 at 8:08 PM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+It includes 4454 images.
+Ct-cthead-t-thead are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 416x416 (Fill (with center crop))
+
+The following augmentation was applied to create 3 versions of each source image:
+* Random brigthness adjustment of between -15 and +15 percent
+
+

csgo-object-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/asd-culfr/wlots"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ wlots_dataset,
+    title = { wlots Dataset },
+    type = { Open Source Dataset },
+    author = { asd },
+    howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
+    url = { https://universe.roboflow.com/asd-culfr/wlots },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { may },
+    note = { visited on 2022-12-29 },
+}
+"""
+_URLS = {
+    "train": "https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/data/test.zip",
+}
+
+_CATEGORIES = ['thead', 't', 'cthead', 'ct']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class CSGOOBJECTDETECTION(datasets.GeneratorBasedBuilder):
+    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(
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "folder_dir": data_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "folder_dir": data_files["validation"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "folder_dir": data_files["test"],
+                },
+            ),
+]
+
+    def _generate_examples(self, folder_dir):
+        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
+        
+        annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
+        with open(annotation_filepath, "r") as f:
+            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"]}
+
+        for filename in os.listdir(folder_dir):
+            filepath = os.path.join(folder_dir, filename)
+            if filename in image_id_to_image:
+                image = image_id_to_image[filename]
+                objects = [
+                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
+                ]
+                with open(filepath, "rb") as f:
+                    image_bytes = f.read()
+                yield idx, {
+                    "image_id": image["id"],
+                    "image": {"path": filepath, "bytes": image_bytes},
+                    "width": image["width"],
+                    "height": image["height"],
+                    "objects": objects,
+                }
+                idx += 1

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fd5b254e5840a3c8f560bf0b27c2e175dffea93c2d3cb99f94108fa04fead5af
+size 4941492

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b30e5753bf1fa91af52d9af058888a75243aa7442433258b5fce1d2730fe5dc0
+size 101266440

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:79421a3a866a7ae8a179ac72a51a507aa30feb58cc2541ba487b95b341ac5602
+size 10147480