[update]add data

.gitignore

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+.idea/
+.git/
+
+**/__pycache__/
+
+**/*.tmp

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b417be9196db8eb252f8f31759504aea6ffdfbc84987b010eb4c3fabd0056cc3
+size 15934756

main.py

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+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "wechat_or_qq_icon_detection.py",
+    split="train",
+)
+print(dataset)
+
+for sample in dataset:
+    print(sample)
+
+
+if __name__ == '__main__':
+    pass

requirements.txt

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+datasets

wechat_or_qq_icon_detection.py

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+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+from glob import glob
+import os
+from pathlib import Path
+
+import datasets
+
+
+_URL = "data/train.zip"
+
+
+_CITATION = """\
+@dataset{early_media,
+  author       = {Xing Tian},
+  title        = {wechat_or_qq_icon_detection},
+  month        = aug,
+  year         = 2023,
+  publisher    = {Xing Tian},
+  version      = {1.0},
+}
+"""
+
+
+_CATEGORIES = [
+    "红黑白QQ图标", "绿边白色微信图标", "淡蓝底全白QQ图标",
+    "绿底全白微信图标", "大绿小白微信图标", "纯色微信图标", "纯色QQ图标"
+]
+
+
+class TelemarketingVoiceClassification(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="default",
+            version=VERSION,
+            description="wechat_or_qq_icon_detection",
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("string"),
+                "image": datasets.Image(),
+                "objects": datasets.Sequence({
+                    "object_id": datasets.Value("int64"),
+                    "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                    "category": datasets.ClassLabel(names=_CATEGORIES)
+                }),
+                "source": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            features=features,
+            supervised_keys=None,
+            homepage="",
+            license="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_path = dl_manager.download_and_extract(_URL)
+        archive_path = os.path.join(dl_path, self.config.name)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"archive_path": archive_path, "split": "train"},
+            ),
+        ]
+
+    def _generate_examples(self, archive_path, split):
+        """Yields examples."""
+        archive_path = Path(archive_path)
+        archive_path = archive_path.parent
+
+        annotation_file = archive_path / split / "annotation.txt"
+
+        idx = 0
+        source = 0
+        image_path = None
+        objects = list()
+        with open(annotation_file, "r", encoding="utf-8") as f:
+            for row in f:
+                row = str(row).strip()
+
+                if len(row) == 0:
+                    image_path_ = archive_path / split / image_path
+
+                    with open(image_path_.as_posix(), "rb") as f_image:
+                        image_bytes = f_image.read()
+                    yield idx, {
+                        "image_id": image_path.name,
+                        "image": {"path": image_path.as_posix(), "bytes": image_bytes},
+                        "objects": objects,
+                        "source": source,
+                    }
+                    idx += 1
+                    source = 0
+                    image_path = None
+                    objects = list()
+
+                elif image_path is None:
+                    image_path = Path(row)
+                else:
+                    splits = row.split(",")
+                    splits = [int(str(split).strip()) for split in splits]
+                    objects.append({
+                        "object_id": len(objects),
+                        "bbox": splits[:4],
+                        "category": _CATEGORIES[splits[4]],
+                    })
+                    source = splits[5]
+
+
+if __name__ == '__main__':
+    pass