Fix: inline self-contained MediaPipe pose (drop futheros.adapters dependency)

futheros/pipeline.py

@@ -271,11 +271,44 @@ def shooter_frame(holder, xy, goal, goal_f, n):
 
 # ----------------------------------------------------------------------------- pose
 _POSE = None
+_POSE_URL = ("https://storage.googleapis.com/mediapipe-models/pose_landmarker/"
+            "pose_landmarker_full/float16/latest/pose_landmarker_full.task")
+_POSE_FILE = "/tmp/pose_landmarker_full.task"
+
+
+class _MPPose:
+    """Self-contained MediaPipe Tasks PoseLandmarker (2D image-plane landmarks only)."""
+
+    def __init__(self):
+        import urllib.request
+        import mediapipe as mp
+        from mediapipe.tasks import python
+        from mediapipe.tasks.python import vision
+        self._mp = mp
+        if not os.path.exists(_POSE_FILE):
+            urllib.request.urlretrieve(_POSE_URL, _POSE_FILE)
+        opts = vision.PoseLandmarkerOptions(
+            base_options=python.BaseOptions(model_asset_path=_POSE_FILE),
+            running_mode=vision.RunningMode.IMAGE, num_poses=1,
+            min_pose_detection_confidence=0.5, min_pose_presence_confidence=0.5,
+            min_tracking_confidence=0.5)
+        self._lm = vision.PoseLandmarker.create_from_options(opts)
+
+    def xy(self, frame_bgr):
+        h, w = frame_bgr.shape[:2]
+        img = self._mp.Image(image_format=self._mp.ImageFormat.SRGB,
+                             data=np.ascontiguousarray(frame_bgr[:, :, ::-1]))
+        res = self._lm.detect(img)
+        if not res.pose_landmarks:
+            return None
+        lms = res.pose_landmarks[0]
+        return np.array([[lm.x * w, lm.y * h] for lm in lms], dtype=np.float32)
+
+
 def _pose():
     global _POSE
     if _POSE is None:
-        from .adapters.pose_mediapipe import MediaPipePose
-        _POSE = MediaPipePose()
+        _POSE = _MPPose()
     return _POSE
 
 
@@ -289,10 +322,10 @@ def shooter_pose(clip, players, xy, kicker, kf, W, H):
     cap = cv2.VideoCapture(clip); cap.set(cv2.CAP_PROP_POS_FRAMES, kf); ok, fr = cap.read(); cap.release()
     if not ok or x1-x0 < 8 or y1-y0 < 8:
         return None, (x0, y0, x1, y1), "?"
-    fp = _pose().estimate([fr[y0:y1, x0:x1]])[0]
-    if fp.xy is None:
+    fxy = _pose().xy(fr[y0:y1, x0:x1])
+    if fxy is None:
         return None, (x0, y0, x1, y1), "?"
-    pts = fp.xy + np.array([x0, y0]); foot = "?"
+    pts = fxy + np.array([x0, y0]); foot = "?"
     if not np.isnan(xy[kf, 0]):
         bx, by = xy[kf]
         foot = "L" if np.hypot(*(pts[L_ANKLE]-[bx, by])) < np.hypot(*(pts[R_ANKLE]-[bx, by])) else "R"