(pose_estimation) allways use ~/data/pytorch, when just model provided

image_recognition_pose_estimation/scripts/detect_poses

@@ -40,7 +40,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Detect poses in an image", formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
-    parser.add_argument("--pose_model", help="What pose model to use", default="~/data/pytorch_models/yolov8n-pose.pt")
+    parser.add_argument("--pose_model", help="What pose model to use", default="yolov8n-pose.pt")
     parser.add_argument("--conf", help="Minimal confidence level for detection", default=0.25, type=float)
     parser.add_argument(
         "--verbose",

image_recognition_pose_estimation/src/image_recognition_pose_estimation/yolo_pose_wrapper.py

@@ -1,4 +1,5 @@
 import re
+from pathlib import Path
 from typing import List, Tuple
 
 import numpy as np
@@ -48,14 +49,20 @@
 ]
 
 
-YOLO_POSE_PATTERN = re.compile(r"^.*yolov8(?:([nsml])|(x))-pose(?(2)-p6|)?.pt$")
+YOLO_POSE_PATTERN = re.compile(r"^yolov8(?:([nsml])|(x))-pose(?(2)-p6|)?.pt$")
 
 
 class YoloPoseWrapper:
     def __init__(self, model_name: str = "yolov8n-pose.pt", verbose: bool = False):
-        if not YOLO_POSE_PATTERN.match(model_name):
+        # Validate model name
+        model_name_path = Path(model_name)
+        model_basename = model_name_path.name
+        if not YOLO_POSE_PATTERN.match(model_basename):
             raise ValueError(f"Model name '{model_name}' does not match pattern '{YOLO_POSE_PATTERN.pattern}'")
 
+        if len(model_name_path.parts) == 1:
+            model_name = str(Path.home() / "data" / "pytorch_models" / model_name)
+
         self._model = YOLO(model=model_name, task="pose", verbose=verbose)
 
     def detect_poses(self, image: np.ndarray, conf: float = 0.25) -> Tuple[List[Recognition], np.ndarray]: