Upload anime_object_detection/detection/person.py with huggingface_hub

anime_object_detection/detection/person.py

@@ -0,0 +1,53 @@
+import os.path
+import re
+from typing import List, Tuple
+
+from hfutils.operate import get_hf_fs
+from hfutils.utils import hf_fs_path, parse_hf_fs_path
+from imgutils.data import ImageTyping
+from imgutils.detect import detect_person
+
+from .base import ObjectDetection
+
+_VERSIONS = {
+    '': 'v0',
+    'plus_': 'v1',
+    'plus_v1.1_': 'v1.1',
+}
+
+
+def _parse_model_name(model_name: str):
+    matching = re.fullmatch(r'^person_detect_(?P<content>[\s\S]+?)best_(?P<level>[\s\S]+?)$', model_name)
+    return _VERSIONS[matching.group('content')], matching.group('level')
+
+
+class PersonDetection(ObjectDetection):
+    def __init__(self):
+        self.repo_id = 'deepghs/imgutils-models'
+
+    def _get_default_model(self) -> str:
+        return 'person_detect_plus_v1.1_best_m'
+
+    def _list_models(self) -> List[str]:
+        hf_fs = get_hf_fs()
+        return [
+            os.path.splitext(os.path.basename(parse_hf_fs_path(path).filename))[0]
+            for path in hf_fs.glob(hf_fs_path(
+                repo_id=self.repo_id,
+                repo_type='model',
+                filename='person_detect/*.onnx',
+            ))
+        ]
+
+    def _get_default_iou_and_score(self, model_name: str) -> Tuple[float, float]:
+        return 0.5, 0.3
+
+    def _get_labels(self, model_name: str) -> List[str]:
+        return ['person']
+
+    def detect(self, image: ImageTyping, model_name: str,
+               iou_threshold: float = 0.7, score_threshold: float = 0.25) -> \
+            List[Tuple[Tuple[float, float, float, float], str, float]]:
+        version, level = _parse_model_name(model_name)
+        return detect_person(image=image, level=level, version=version,
+                             iou_threshold=iou_threshold, conf_threshold=score_threshold)