Add pose estimation to spatial service (production file)

Integrates MoveNet Lightning on Coral 2 into oak_service_spatial.py,
which is the actual production service running on head-vixy. Reuses
the existing RGB frame grab (shared with face recognition) for pose
estimation. Adds /pose and /pose/summary endpoints.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

oak_service_spatial.py

@@ -21,6 +21,7 @@ import cv2
 import numpy as np
 
 from face_recognition import FaceRecognizer
+from pose_estimator import PoseEstimator
 
 logger = logging.getLogger("oak-service")
 logging.basicConfig(level=logging.INFO)
@@ -42,6 +43,10 @@ FACE_DETECT_MODEL = MODELS_DIR / "ssd_mobilenet_v2_face_quant_postprocess_edgetp
 FACE_EMBED_MODEL = MODELS_DIR / "facenet.tflite"
 FACE_DB_PATH = Path(__file__).parent / "faces.db"
 
+# Pose estimation
+POSE_MODEL_PATH = MODELS_DIR / "movenet_single_pose_lightning_ptq_edgetpu.tflite"
+POSE_CORAL_DEVICE = 1  # Second Coral (device 0 is headmic/YAMNet)
+
 # ============== Global State ==============
 pipeline_ctx = None
 detection_queue = None
@@ -51,6 +56,7 @@ detection_thread = None
 running = False
 labels = []
 face_recognizer = None
+pose_estimator = None
 
 presence_state = {
     "present": False,
@@ -69,6 +75,16 @@ presence_state = {
     "recognition_confidence": None,
 }
 
+pose_state = {
+    "active": False,
+    "keypoints": [],
+    "posture": {},
+    "num_valid": 0,
+    "mean_confidence": 0.0,
+    "inference_ms": 0.0,
+    "last_update": None,
+}
+
 
 def init_face_recognition():
     """Initialize Coral face detection + FaceNet embedding."""
@@ -147,6 +163,10 @@ def init_oak():
         pipeline_ctx = pipeline
         
         print("✅ OAK-D initialized with SPATIAL person detection!")
+
+        # Initialize pose estimator on Coral 2
+        _init_pose_estimator()
+
         return True
         
     except Exception as e:
@@ -174,6 +194,48 @@ def cleanup_oak():
     pipeline_ctx = None
 
 
+def _init_pose_estimator():
+    """Initialize MoveNet Lightning on the second Coral Edge TPU."""
+    global pose_estimator
+
+    if not POSE_MODEL_PATH.exists():
+        print(f"⚠️ Pose model not found: {POSE_MODEL_PATH}")
+        return
+
+    try:
+        pose_estimator = PoseEstimator(
+            model_path=str(POSE_MODEL_PATH),
+            device_index=POSE_CORAL_DEVICE,
+        )
+        print("✅ Pose estimator initialized on Coral 2!")
+    except Exception as e:
+        print(f"⚠️ Pose estimator failed to initialize: {e}")
+        pose_estimator = None
+
+
+def _run_pose_estimation(rgb_frame):
+    """Run pose estimation on an RGB frame via Coral 2."""
+    global pose_state
+
+    if pose_estimator is None:
+        return
+
+    try:
+        result = pose_estimator.estimate(rgb_frame)
+        posture = pose_estimator.derive_posture(result["keypoints"])
+
+        pose_state["active"] = True
+        pose_state["keypoints"] = result["keypoints"]
+        pose_state["posture"] = posture
+        pose_state["num_valid"] = result["num_valid"]
+        pose_state["mean_confidence"] = result["mean_confidence"]
+        pose_state["inference_ms"] = result["inference_ms"]
+        pose_state["last_update"] = result["timestamp"]
+
+    except Exception as e:
+        print(f"Pose estimation error: {e}")
+
+
 def detection_loop():
     """Background thread for SPATIAL presence detection."""
     global running, presence_state, detection_queue
@@ -212,12 +274,16 @@ def detection_loop():
                     presence_state["spatial_z"] = best.spatialCoordinates.z
                     presence_state["distance_mm"] = best.spatialCoordinates.z
 
-                    # Face recognition
+                    # Grab RGB frame for face recognition + pose estimation
                     face_results = []
-                    if face_recognizer and rgb_queue:
+                    rgb_frame = None
+                    if rgb_queue:
                         rgb_data = rgb_queue.tryGet()
                         if rgb_data is not None:
                             rgb_frame = rgb_data.getCvFrame()
+
+                    # Face recognition
+                    if face_recognizer and rgb_frame is not None:
                         try:
                             face_results = face_recognizer.process_frame(
                                 rgb_frame, persons
@@ -225,6 +291,10 @@ def detection_loop():
                         except Exception as e:
                             logger.warning("Face recognition error: %s", e)
 
+                    # Pose estimation (runs on Coral 2, parallel-safe)
+                    if rgb_frame is not None:
+                        _run_pose_estimation(rgb_frame)
+
                     det_list = []
                     best_recognized = None
                     best_recog_conf = 0.0
@@ -265,6 +335,14 @@ def detection_loop():
                     presence_state["recognized_name"] = None
                     presence_state["recognition_confidence"] = None
 
+                    # Clear pose when no person
+                    if pose_state["active"]:
+                        pose_state["active"] = False
+                        pose_state["keypoints"] = []
+                        pose_state["posture"] = {}
+                        pose_state["num_valid"] = 0
+                        pose_state["mean_confidence"] = 0.0
+
                     # Check timeout
                     if presence_state["last_seen"]:
                         if now - presence_state["last_seen"] > PRESENCE_TIMEOUT:
@@ -304,8 +382,8 @@ async def lifespan(app: FastAPI):
 
 app = FastAPI(
     title="OAK-D SPATIAL Vision Service",
-    description="Vixy's eyes with SPATIAL presence detection + face recognition! 🦊👀📏",
+    description="Vixy's eyes with SPATIAL presence detection + face recognition + pose estimation! 🦊👀📏",
-    version="0.5.0",
+    version="0.6.0",
     lifespan=lifespan
 )
 
@@ -316,11 +394,12 @@ async def health():
     return {
         "status": "healthy",
         "service": "oak-service",
-        "version": "0.5.0",
+        "version": "0.6.0",
         "oak_connected": pipeline_ctx is not None,
         "detection_model": DETECTION_MODEL,
         "spatial_enabled": True,
         "face_recognition_enabled": face_recognizer is not None,
+        "pose_model_loaded": pose_estimator is not None,
         "timestamp": time.time()
     }
 
@@ -415,6 +494,43 @@ async def depth_frame():
         raise HTTPException(status_code=500, detail=str(e))
 
 
+# ============== Pose Estimation API ==============
+
+
+@app.get("/pose")
+async def pose():
+    """Get current pose keypoints."""
+    if pose_estimator is None:
+        raise HTTPException(status_code=503, detail="Pose estimator not available")
+
+    return {
+        "active": pose_state["active"],
+        "keypoints": pose_state["keypoints"],
+        "num_valid": pose_state["num_valid"],
+        "mean_confidence": pose_state["mean_confidence"],
+        "inference_ms": pose_state["inference_ms"],
+        "last_update": pose_state["last_update"],
+        "timestamp": time.time(),
+    }
+
+
+@app.get("/pose/summary")
+async def pose_summary():
+    """Get derived posture summary."""
+    if pose_estimator is None:
+        raise HTTPException(status_code=503, detail="Pose estimator not available")
+
+    return {
+        "active": pose_state["active"],
+        "posture": pose_state["posture"].get("posture", "unknown"),
+        "facing_camera": pose_state["posture"].get("facing_camera", False),
+        "arms_raised": pose_state["posture"].get("arms_raised", False),
+        "mean_confidence": pose_state["mean_confidence"],
+        "num_valid": pose_state["num_valid"],
+        "timestamp": time.time(),
+    }
+
+
 # ============== Face Enrollment API ==============