Merge movement tracking from oak_mcp_1 + face recognition

Integrates Day 83 movement tracking (static poster filtering) with
Day 86 face recognition. Poster filter now skips recognized faces —
a known person sitting still won't be filtered out. Adds
oak_reset_tracking tool for camera repositioning.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

oak_mcp.py

@@ -5,6 +5,7 @@ OAK MCP - MCP server interface for OAK-D Vision Service.
 Vixy's eyes! Allows Claude to see through the OAK-D camera.
 Built by Vixy on Day 74 🦊👀
 Day 82 - SPATIAL UPGRADE! Now with real 3D depth! 📏
+Day 83 - MOVEMENT TRACKING! No more falling for posters! 🖼️❌
 Day 86 - FACE RECOGNITION! Coral Edge TPU + FaceNet! 🧑‍🤝‍🧑
 
 Connects to oak-service running on head-vixy.local:8100
@@ -28,6 +29,20 @@ SNAPSHOT_DIR = os.environ.get("OAK_SNAPSHOT_DIR", "/Users/alex/Documents/Vixy/oa
 # Ensure snapshot directory exists
 os.makedirs(SNAPSHOT_DIR, exist_ok=True)
 
+# Movement tracking state
+_last_detection = {
+    "x_mm": None,
+    "y_mm": None,
+    "z_mm": None,
+    "timestamp": None,
+    "static_count": 0,  # how many consecutive readings with same coords
+}
+
+# Movement detection thresholds
+MOVEMENT_THRESHOLD_MM = 50  # must move 5cm to count as "moving"
+STATIC_THRESHOLD = 3  # this many static readings = probably not a real person
+HIGH_CONFIDENCE_THRESHOLD = 0.85  # above this, trust detection even if static
+
 # Create MCP server
 mcp = FastMCP("oak-mcp")
 
@@ -77,6 +92,38 @@ async def api_delete(endpoint: str) -> dict:
         return response.json()
 
 
+def check_movement(x_mm: float, y_mm: float, z_mm: float) -> tuple[bool, int]:
+    """
+    Check if detection coordinates have moved since last reading.
+
+    Returns:
+        (is_moving, static_count)
+    """
+    global _last_detection
+
+    is_moving = False
+
+    if _last_detection["x_mm"] is not None:
+        delta = (abs(x_mm - _last_detection["x_mm"]) +
+                 abs(y_mm - _last_detection["y_mm"]) +
+                 abs(z_mm - _last_detection["z_mm"]))
+        is_moving = delta > MOVEMENT_THRESHOLD_MM
+
+        if not is_moving:
+            _last_detection["static_count"] += 1
+        else:
+            _last_detection["static_count"] = 0
+    else:
+        _last_detection["static_count"] = 0
+
+    _last_detection.update({
+        "x_mm": x_mm, "y_mm": y_mm, "z_mm": z_mm,
+        "timestamp": time.time()
+    })
+
+    return is_moving, _last_detection["static_count"]
+
+
 @mcp.tool()
 async def oak_health() -> str:
     """
@@ -184,22 +231,24 @@ async def oak_snapshot(save: bool = True, filename: str = None) -> str:
 @mcp.tool()
 async def oak_presence() -> str:
     """
-    Check if Foxy is present (person detection via OAK-D) with SPATIAL data!
+    Check if Foxy is present with SPATIAL data, movement tracking, and face recognition!
+
+    Static detections at moderate confidence are filtered out
+    (goodbye, Spinal Nerves poster! 🖼️❌)
 
     Returns:
-        Presence status, person count, confidence, distance, and 3D position.
+        Presence status, person count, confidence, distance, 3D position,
+        movement state, and recognized name if known.
 
     Example:
-        oak_presence()  # "Present: ✅ Yes (1 person, 87% confidence) at 1.24m"
+        oak_presence()  # "Present: ✅ Yes (1 person, 87%, moving) at 1.24m — Alex"
     """
     try:
         data = await api_get("/presence")
         present = data.get("present", False)
         count = data.get("person_count", 0)
-        confidence = data.get("confidence", 0) * 100
+        confidence = data.get("confidence", 0)
         last_seen = data.get("seconds_since_seen")
-        
-        # Spatial data
         distance_m = data.get("distance_m")
         spatial = data.get("spatial")
 
@@ -207,45 +256,52 @@ async def oak_presence() -> str:
         recognized = data.get("recognized_name")
         recog_conf = data.get("recognition_confidence")
 
-        if present:
+        # Movement tracking
-            dist_str = f" at {distance_m:.2f}m" if distance_m else ""
+        is_moving = False
-            name_str = f" — {recognized}" if recognized else ""
+        static_count = 0
-            status = f"✅ Yes ({count} person{'s' if count != 1 else ''}, {confidence:.0f}% confidence){dist_str}{name_str}"
+        filtered_out = False
-        elif last_seen is not None:
-            status = f"❌ No (last seen {last_seen:.0f}s ago)"
-        else:
-            status = "❌ No (never seen)"
 
-        result = f"""👀 Presence Detection:
-• Present: {status}
-• Detection model: yolov6-nano"""
-
-        if recognized:
-            result += f"\n• Recognized: {recognized} ({recog_conf*100:.0f}% match)" if recog_conf else f"\n• Recognized: {recognized}"
-
-        # Add spatial info if available
         if spatial and present:
             x_mm = spatial.get("x_mm", 0)
             y_mm = spatial.get("y_mm", 0)
             z_mm = spatial.get("z_mm", 0)
 
-            # Determine position description
+            is_moving, static_count = check_movement(x_mm, y_mm, z_mm)
-            h_pos = "center"
-            if x_mm < -100:
-                h_pos = "left"
-            elif x_mm > 100:
-                h_pos = "right"
 
-            v_pos = "center"
+            # Filter: moderate confidence + static + unrecognized = probably poster
-            if y_mm < -100:
+            if (confidence < HIGH_CONFIDENCE_THRESHOLD
-                v_pos = "below"
+                    and static_count >= STATIC_THRESHOLD
-            elif y_mm > 100:
+                    and not recognized):
-                v_pos = "above"
+                present = False
+                filtered_out = True
+                logger.info(f"Filtered static: conf={confidence:.0%}, static={static_count}")
 
-            result += f"""
+        conf_pct = confidence * 100
-• Distance: {distance_m:.2f}m ({int(z_mm)}mm)
+
-• Position: {h_pos}, {v_pos} center
+        if present:
-• 3D coords: X={int(x_mm)}mm, Y={int(y_mm)}mm, Z={int(z_mm)}mm"""
+            dist_str = f" at {distance_m:.2f}m" if distance_m else ""
+            move_str = "moving" if is_moving else "still"
+            name_str = f" — {recognized}" if recognized else ""
+            status = f"✅ Yes ({count} person{'s' if count != 1 else ''}, {conf_pct:.0f}%, {move_str}){dist_str}{name_str}"
+        elif filtered_out:
+            status = f"🖼️ Filtered (static at {conf_pct:.0f}% - probably poster)"
+        elif last_seen is not None:
+            status = f"❌ No (last seen {last_seen:.0f}s ago)"
+        else:
+            status = "❌ No (never seen)"
+
+        result = f"👀 Present: {status}"
+
+        if recognized:
+            result += f"\n• Recognized: {recognized} ({recog_conf*100:.0f}% match)" if recog_conf else f"\n• Recognized: {recognized}"
+
+        if spatial and (present or filtered_out):
+            x_mm = spatial.get("x_mm", 0)
+            y_mm = spatial.get("y_mm", 0)
+            h_pos = "left" if x_mm < -100 else "right" if x_mm > 100 else "center"
+            v_pos = "below" if y_mm < -100 else "above" if y_mm > 100 else "center"
+            result += f"\n• Position: {h_pos}, {v_pos} @ {distance_m:.2f}m"
+            result += f"\n• Movement: {'✅ moving' if is_moving else f'⏸️ static ({static_count})'}"
 
         return result
     except httpx.HTTPError as e:
@@ -369,6 +425,17 @@ async def oak_depth(save: bool = True, filename: str = None) -> str:
         return f"❌ Error: {e}"
 
 
+@mcp.tool()
+async def oak_reset_tracking() -> str:
+    """Reset movement tracking state (use after repositioning camera)."""
+    global _last_detection
+    _last_detection = {
+        "x_mm": None, "y_mm": None, "z_mm": None,
+        "timestamp": None, "static_count": 0,
+    }
+    return "🔄 Movement tracking reset"
+
+
 # ============== Face Recognition Tools ==============