feat(social): personality system — SOUL.md persona, mood engine, relationship DB (Issue #84)

New packages:
- saltybot_social_msgs: PersonalityState.msg + QueryMood.srv custom interfaces
- saltybot_social_personality: full personality node

Features:
- SOUL.md YAML/Markdown persona file: name, humor_level (0-10), sass_level (0-10),
  base_mood, per-tier greeting templates, mood prefix strings
- Hot-reload: SoulWatcher polls SOUL.md every reload_interval seconds, applies
  changes live without restarting the node
- Per-person relationship memory in SQLite: score, interaction_count,
  first/last_seen, learned preferences (JSON), full interaction log
- Mood engine (pure functions): happy | curious | annoyed | playful
  driven by relationship score, interaction count, recent event window (120s)
- Greeting personalisation: stranger | regular | favorite tiers
  keyed on interaction count thresholds from SOUL.md
- Publishes /social/personality/state (PersonalityState) at publish_rate Hz
- /social/personality/query_mood (QueryMood) service for on-demand mood query
- Full ROS2 dynamic reconfigure: soul_file, db_path, reload_interval, publish_rate
- 52 unit tests, no ROS2 runtime required

ROS2 interfaces:
  Sub: /social/person_detected  (std_msgs/String JSON)
  Pub: /social/personality/state (saltybot_social_msgs/PersonalityState)
  Srv: /social/personality/query_mood (saltybot_social_msgs/QueryMood)

esp32/social_expression/social_expression.ino

@@ -0,0 +1,206 @@
+/*
+ * social_expression.ino — LED mood display for saltybot social layer.
+ *
+ * Hardware
+ * ────────
+ *   MCU  : ESP32-C3 (e.g. Seeed XIAO ESP32-C3 or equivalent)
+ *   LEDs : NeoPixel ring or strip, DATA_PIN → GPIO5, power via 5V rail
+ *
+ * Serial protocol (from Orin Nano, 115200 8N1)
+ * ─────────────────────────────────────────────
+ *   One JSON line per command, newline-terminated:
+ *     {"mood":"happy","intensity":0.8}\n
+ *
+ *   mood values   : "happy" | "curious" | "annoyed" | "playful" | "idle"
+ *   intensity     : 0.0 (off) .. 1.0 (full brightness)
+ *
+ *   If no command arrives for IDLE_TIMEOUT_MS, the node enters the
+ *   breathing idle animation automatically.
+ *
+ * Mood → colour mapping
+ * ──────────────────────
+ *   happy    →  green   #00FF00
+ *   curious  →  blue    #0040FF
+ *   annoyed  →  red     #FF1000
+ *   playful  →  rainbow (cycling hue across all pixels)
+ *   idle     →  soft white breathing (sine modulated)
+ *
+ * Dependencies (Arduino Library Manager)
+ * ───────────────────────────────────────
+ *   Adafruit NeoPixel  ≥ 1.11.0
+ *   ArduinoJson        ≥ 7.0.0
+ *
+ * Build: Arduino IDE 2 / arduino-cli with board "ESP32C3 Dev Module"
+ *   Board manager URL: https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_index.json
+ */
+
+#include <Adafruit_NeoPixel.h>
+#include <ArduinoJson.h>
+
+// ── Hardware config ──────────────────────────────────────────────────────────
+#define LED_PIN          5      // GPIO connected to NeoPixel data line
+#define LED_COUNT        16     // pixels in ring / strip — adjust as needed
+#define LED_BRIGHTNESS   120    // global cap 0-255 (≈47%), protects PSU
+
+// ── Timing constants ─────────────────────────────────────────────────────────
+#define IDLE_TIMEOUT_MS  3000   // fall back to breathing after 3 s silence
+#define LOOP_INTERVAL_MS 20     // animation tick ≈ 50 Hz
+
+// ── Colours (R, G, B) ────────────────────────────────────────────────────────
+static const uint8_t COL_HAPPY[3]   = {  0, 220,   0 };  // green
+static const uint8_t COL_CURIOUS[3] = {  0,  64, 255 };  // blue
+static const uint8_t COL_ANNOYED[3] = {255,  16,   0 };  // red
+
+// ── State ────────────────────────────────────────────────────────────────────
+enum Mood { MOOD_IDLE, MOOD_HAPPY, MOOD_CURIOUS, MOOD_ANNOYED, MOOD_PLAYFUL };
+
+static Mood    g_mood         = MOOD_IDLE;
+static float   g_intensity    = 1.0f;
+static uint32_t g_last_cmd_ms = 0;   // millis() of last received command
+
+// Animation counters
+static uint16_t g_rainbow_hue = 0;   // 0..65535, cycles for playful
+static uint32_t g_last_tick   = 0;
+
+// Serial receive buffer
+static char     g_serial_buf[128];
+static uint8_t  g_buf_pos = 0;
+
+// ── NeoPixel object ──────────────────────────────────────────────────────────
+Adafruit_NeoPixel strip(LED_COUNT, LED_PIN, NEO_GRB + NEO_KHZ800);
+
+// ── Helpers ──────────────────────────────────────────────────────────────────
+
+static uint8_t scale(uint8_t v, float intensity) {
+    return (uint8_t)(v * intensity);
+}
+
+/*
+ * Breathing: sine envelope over time.
+ * Returns brightness factor 0.0..1.0, period ~4 s.
+ */
+static float breath_factor(uint32_t now_ms) {
+    float phase = (float)(now_ms % 4000) / 4000.0f;  // 0..1 per period
+    return 0.08f + 0.35f * (0.5f + 0.5f * sinf(2.0f * M_PI * phase));
+}
+
+static void set_all(uint8_t r, uint8_t g, uint8_t b) {
+    for (int i = 0; i < LED_COUNT; i++) {
+        strip.setPixelColor(i, strip.Color(r, g, b));
+    }
+}
+
+// ── Animation drivers ────────────────────────────────────────────────────────
+
+static void animate_solid(const uint8_t col[3], float intensity) {
+    set_all(scale(col[0], intensity),
+            scale(col[1], intensity),
+            scale(col[2], intensity));
+}
+
+static void animate_breathing(uint32_t now_ms, float intensity) {
+    float bf = breath_factor(now_ms) * intensity;
+    // Warm white: R=255 G=200 B=120
+    set_all(scale(255, bf), scale(200, bf), scale(120, bf));
+}
+
+static void animate_rainbow(float intensity) {
+    // Spread full wheel across all pixels
+    for (int i = 0; i < LED_COUNT; i++) {
+        uint16_t hue = g_rainbow_hue + (uint16_t)((float)i / LED_COUNT * 65536.0f);
+        uint32_t rgb = strip.ColorHSV(hue, 255,
+                            (uint8_t)(255.0f * intensity));
+        strip.setPixelColor(i, rgb);
+    }
+    // Advance hue each tick (full cycle in ~6 s at 50 Hz)
+    g_rainbow_hue += 218;
+}
+
+// ── Serial parser ────────────────────────────────────────────────────────────
+
+static void parse_command(const char *line) {
+    StaticJsonDocument<128> doc;
+    DeserializationError err = deserializeJson(doc, line);
+    if (err) return;
+
+    const char *mood_str = doc["mood"] | "";
+    float intensity      = doc["intensity"] | 1.0f;
+    if (intensity < 0.0f) intensity = 0.0f;
+    if (intensity > 1.0f) intensity = 1.0f;
+
+    if      (strcmp(mood_str, "happy")   == 0) g_mood = MOOD_HAPPY;
+    else if (strcmp(mood_str, "curious") == 0) g_mood = MOOD_CURIOUS;
+    else if (strcmp(mood_str, "annoyed") == 0) g_mood = MOOD_ANNOYED;
+    else if (strcmp(mood_str, "playful") == 0) g_mood = MOOD_PLAYFUL;
+    else                                        g_mood = MOOD_IDLE;
+
+    g_intensity    = intensity;
+    g_last_cmd_ms  = millis();
+}
+
+static void read_serial(void) {
+    while (Serial.available()) {
+        char c = (char)Serial.read();
+        if (c == '\n' || c == '\r') {
+            if (g_buf_pos > 0) {
+                g_serial_buf[g_buf_pos] = '\0';
+                parse_command(g_serial_buf);
+                g_buf_pos = 0;
+            }
+        } else if (g_buf_pos < (sizeof(g_serial_buf) - 1)) {
+            g_serial_buf[g_buf_pos++] = c;
+        } else {
+            // Buffer overflow — discard line
+            g_buf_pos = 0;
+        }
+    }
+}
+
+// ── Arduino entry points ─────────────────────────────────────────────────────
+
+void setup(void) {
+    Serial.begin(115200);
+
+    strip.begin();
+    strip.setBrightness(LED_BRIGHTNESS);
+    strip.clear();
+    strip.show();
+
+    g_last_tick = millis();
+}
+
+void loop(void) {
+    uint32_t now = millis();
+
+    read_serial();
+
+    // Fall back to idle if no command for IDLE_TIMEOUT_MS
+    if ((now - g_last_cmd_ms) > IDLE_TIMEOUT_MS) {
+        g_mood = MOOD_IDLE;
+    }
+
+    // Throttle animation ticks
+    if ((now - g_last_tick) < LOOP_INTERVAL_MS) return;
+    g_last_tick = now;
+
+    switch (g_mood) {
+    case MOOD_HAPPY:
+        animate_solid(COL_HAPPY,   g_intensity);
+        break;
+    case MOOD_CURIOUS:
+        animate_solid(COL_CURIOUS, g_intensity);
+        break;
+    case MOOD_ANNOYED:
+        animate_solid(COL_ANNOYED, g_intensity);
+        break;
+    case MOOD_PLAYFUL:
+        animate_rainbow(g_intensity);
+        break;
+    case MOOD_IDLE:
+    default:
+        animate_breathing(now, g_intensity > 0.0f ? g_intensity : 1.0f);
+        break;
+    }
+
+    strip.show();
+}

jetson/ros2_ws/src/saltybot_social/config/attention_params.yaml

@@ -0,0 +1,30 @@
+# attention_params.yaml — Social attention controller configuration.
+#
+# ── Proportional gain ─────────────────────────────────────────────────────────
+# angular_z = clamp(kp_angular * bearing_rad, ±max_angular_vel)
+#
+# kp_angular  : 1.0 → 1.0 rad/s per 1 rad error (57° → full speed).
+#   Increase for snappier tracking; decrease if robot oscillates.
+kp_angular:       1.0         # rad/s per rad
+
+# ── Velocity limits ──────────────────────────────────────────────────────────
+# Hard cap on rotation output. In social mode the balance loop must not be
+# disturbed by large sudden angular commands; keep this ≤ 1.0 rad/s.
+max_angular_vel:  0.8         # rad/s
+
+# ── Dead zone ────────────────────────────────────────────────────────────────
+# If |bearing| ≤ dead_zone_rad, rotation is suppressed.
+# 0.15 rad ≈ 8.6° — prevents chattering when person is roughly centred.
+dead_zone_rad:    0.15        # radians
+
+# ── Control loop ─────────────────────────────────────────────────────────────
+control_rate:     20.0        # Hz
+
+# ── Lost-target timeout ───────────────────────────────────────────────────────
+# If no /social/persons message arrives for this many seconds, publish zero
+# and enter IDLE state. The cmd_vel bridge deadman will also kick in.
+lost_timeout_s:   2.0         # seconds
+
+# ── Master enable ─────────────────────────────────────────────────────────────
+# Toggle at runtime: ros2 param set /attention_node attention_enabled false
+attention_enabled: true

jetson/ros2_ws/src/saltybot_social/config/expression_params.yaml

@@ -0,0 +1,22 @@
+# expression_params.yaml — LED mood display bridge configuration.
+#
+# serial_port  : udev symlink or device path for the ESP32-C3 USB-CDC port.
+#   Recommended: create a udev rule:
+#     SUBSYSTEM=="tty", ATTRS{idVendor}=="303a", ATTRS{idProduct}=="1001",
+#     SYMLINK+="esp32-social"
+#
+# baud_rate    : must match social_expression.ino (default 115200)
+#
+# idle_timeout_s : if no /social/mood message arrives for this many seconds,
+#   the node sends {"mood":"idle","intensity":1.0} so the ESP32 breathing
+#   animation is synchronised with node awareness.
+#
+# control_rate : how often to check the idle timeout (Hz).
+#   Does NOT gate the mood messages — those are forwarded immediately.
+
+expression_node:
+  ros__parameters:
+    serial_port:    /dev/esp32-social
+    baud_rate:      115200
+    idle_timeout_s: 3.0
+    control_rate:   10.0

jetson/ros2_ws/src/saltybot_social/launch/social.launch.py

@@ -1,3 +1,18 @@
+"""
+social.launch.py — Launch the full saltybot social stack.
+
+Includes:
+  person_state_tracker — multi-modal person identity fusion (Issue #82)
+  expression_node      — /social/mood → ESP32-C3 NeoPixel serial (Issue #86)
+  attention_node       — /social/persons → /cmd_vel rotation (Issue #86)
+
+Usage:
+  ros2 launch saltybot_social social.launch.py
+  ros2 launch saltybot_social social.launch.py serial_port:=/dev/ttyUSB1
+"""
+
+import os
+from ament_index_python.packages import get_package_share_directory
 from launch import LaunchDescription
 from launch.actions import DeclareLaunchArgument
 from launch.substitutions import LaunchConfiguration
@@ -5,7 +20,12 @@ from launch_ros.actions import Node
 
 
 def generate_launch_description():
+    pkg = get_package_share_directory("saltybot_social")
+    exp_cfg = os.path.join(pkg, "config", "expression_params.yaml")
+    att_cfg = os.path.join(pkg, "config", "attention_params.yaml")
+
     return LaunchDescription([
+        # person_state_tracker args (Issue #82)
         DeclareLaunchArgument(
             'engagement_distance',
             default_value='2.0',
@@ -21,6 +41,19 @@ def generate_launch_description():
             default_value='false',
             description='Whether UWB anchor data is available'
         ),
+
+        # expression_node args (Issue #86)
+        DeclareLaunchArgument("serial_port",    default_value="/dev/esp32-social"),
+        DeclareLaunchArgument("baud_rate",      default_value="115200"),
+        DeclareLaunchArgument("idle_timeout_s", default_value="3.0"),
+
+        # attention_node args (Issue #86)
+        DeclareLaunchArgument("kp_angular",       default_value="1.0"),
+        DeclareLaunchArgument("max_angular_vel",  default_value="0.8"),
+        DeclareLaunchArgument("dead_zone_rad",     default_value="0.15"),
+        DeclareLaunchArgument("lost_timeout_s",    default_value="2.0"),
+        DeclareLaunchArgument("attention_enabled", default_value="true"),
+
         Node(
             package='saltybot_social',
             executable='person_state_tracker',
@@ -32,4 +65,36 @@ def generate_launch_description():
                 'uwb_enabled': LaunchConfiguration('uwb_enabled'),
             }],
         ),
+
+        Node(
+            package="saltybot_social",
+            executable="expression_node",
+            name="expression_node",
+            output="screen",
+            parameters=[
+                exp_cfg,
+                {
+                    "serial_port":    LaunchConfiguration("serial_port"),
+                    "baud_rate":      LaunchConfiguration("baud_rate"),
+                    "idle_timeout_s": LaunchConfiguration("idle_timeout_s"),
+                },
+            ],
+        ),
+
+        Node(
+            package="saltybot_social",
+            executable="attention_node",
+            name="attention_node",
+            output="screen",
+            parameters=[
+                att_cfg,
+                {
+                    "kp_angular":       LaunchConfiguration("kp_angular"),
+                    "max_angular_vel":  LaunchConfiguration("max_angular_vel"),
+                    "dead_zone_rad":    LaunchConfiguration("dead_zone_rad"),
+                    "lost_timeout_s":   LaunchConfiguration("lost_timeout_s"),
+                    "attention_enabled": LaunchConfiguration("attention_enabled"),
+                },
+            ],
+        ),
     ])

jetson/ros2_ws/src/saltybot_social/package.xml

@@ -3,7 +3,12 @@
 <package format="3">
   <name>saltybot_social</name>
   <version>0.1.0</version>
-  <description>Multi-modal person identity fusion and state tracking for saltybot</description>
+  <description>
+    Social interaction layer for saltybot.
+    person_state_tracker: multi-modal person identity fusion (Issue #82).
+    expression_node: bridges /social/mood to ESP32-C3 NeoPixel ring over serial (Issue #86).
+    attention_node: rotates robot toward active speaker via /social/persons bearing (Issue #86).
+  </description>
   <maintainer email="seb@vayrette.com">seb</maintainer>
   <license>MIT</license>
   <depend>rclpy</depend>

jetson/ros2_ws/src/saltybot_social/saltybot_social/attention_node.py

@@ -0,0 +1,205 @@
+"""
+attention_node.py — Social attention controller for saltybot.
+
+Rotates the robot to face the active speaker or most confident detected person.
+Publishes rotation-only cmd_vel (linear.x = 0) so balance is not disturbed.
+
+Subscribes
+──────────
+  /social/persons  (saltybot_social_msgs/PersonArray)
+    Contains all tracked persons; active_id marks the current speaker.
+    bearing_rad: signed bearing in base_link (+ve = left/CCW).
+
+Publishes
+─────────
+  /cmd_vel  (geometry_msgs/Twist)
+    angular.z = clamp(kp_angular * bearing, ±max_angular_vel)
+    linear.x  = 0 always (social mode, not follow mode)
+
+Control law
+───────────
+  1. Pick target: person with active_id; fall back to highest-confidence person.
+  2. Dead zone: if |bearing| ≤ dead_zone_rad → angular_z = 0.
+  3. Proportional: angular_z = kp_angular * bearing.
+  4. Clamp to ±max_angular_vel.
+  5. If no fresh person data for lost_timeout_s → publish zero and stay idle.
+
+State machine
+─────────────
+  ATTENDING  — fresh target; publishing proportional rotation
+  IDLE       — no target; publishing zero
+
+Parameters
+──────────
+  kp_angular      (float)  1.0        proportional gain (rad/s per rad error)
+  max_angular_vel (float)  0.8        hard cap (rad/s)
+  dead_zone_rad   (float)  0.15       ~8.6° dead zone around robot heading
+  control_rate    (float)  20.0       Hz
+  lost_timeout_s  (float)  2.0        seconds before going idle
+  attention_enabled (bool) True       runtime kill switch
+"""
+
+import math
+import time
+
+import rclpy
+from rclpy.node import Node
+from geometry_msgs.msg import Twist
+
+from saltybot_social_msgs.msg import PersonArray
+
+
+# ── Pure helpers (used by tests) ──────────────────────────────────────────────
+
+def clamp(v: float, lo: float, hi: float) -> float:
+    return max(lo, min(hi, v))
+
+
+def select_target(persons, active_id: int):
+    """
+    Pick the attention target from a PersonArray's persons list.
+
+    Priority:
+      1. Person whose track_id == active_id (speaking / explicitly active).
+      2. Person with highest confidence (fallback when no speaker declared).
+
+    Returns the selected Person message, or None if persons is empty.
+    """
+    if not persons:
+        return None
+
+    # Prefer the declared active speaker
+    for p in persons:
+        if p.track_id == active_id:
+            return p
+
+    # Fall back to most confident detection
+    return max(persons, key=lambda p: p.confidence)
+
+
+def compute_attention_cmd(
+    bearing_rad: float,
+    dead_zone_rad: float,
+    kp_angular: float,
+    max_angular_vel: float,
+) -> float:
+    """
+    Proportional rotation command toward bearing.
+
+    Returns angular_z (rad/s).
+    Zero inside dead zone; proportional + clamped outside.
+    """
+    if abs(bearing_rad) <= dead_zone_rad:
+        return 0.0
+    return clamp(kp_angular * bearing_rad, -max_angular_vel, max_angular_vel)
+
+
+# ── ROS2 node ─────────────────────────────────────────────────────────────────
+
+class AttentionNode(Node):
+
+    def __init__(self):
+        super().__init__("attention_node")
+
+        self.declare_parameter("kp_angular",        1.0)
+        self.declare_parameter("max_angular_vel",   0.8)
+        self.declare_parameter("dead_zone_rad",     0.15)
+        self.declare_parameter("control_rate",      20.0)
+        self.declare_parameter("lost_timeout_s",    2.0)
+        self.declare_parameter("attention_enabled", True)
+
+        self._reload_params()
+
+        # State
+        self._last_persons_t = 0.0    # monotonic; 0 = never received
+        self._persons        = []
+        self._active_id      = -1
+        self._state          = "idle"
+
+        self.create_subscription(
+            PersonArray, "/social/persons", self._persons_cb, 10
+        )
+
+        self._cmd_pub = self.create_publisher(Twist, "/cmd_vel", 10)
+
+        rate = self._p["control_rate"]
+        self._timer = self.create_timer(1.0 / rate, self._control_cb)
+
+        self.get_logger().info(
+            f"AttentionNode ready  kp={self._p['kp_angular']} "
+            f"dead_zone={math.degrees(self._p['dead_zone_rad']):.1f}° "
+            f"max_ω={self._p['max_angular_vel']} rad/s"
+        )
+
+    def _reload_params(self):
+        self._p = {
+            "kp_angular":        self.get_parameter("kp_angular").value,
+            "max_angular_vel":   self.get_parameter("max_angular_vel").value,
+            "dead_zone_rad":     self.get_parameter("dead_zone_rad").value,
+            "control_rate":      self.get_parameter("control_rate").value,
+            "lost_timeout_s":    self.get_parameter("lost_timeout_s").value,
+            "attention_enabled": self.get_parameter("attention_enabled").value,
+        }
+
+    # ── Callbacks ─────────────────────────────────────────────────────────────
+
+    def _persons_cb(self, msg: PersonArray):
+        self._persons        = msg.persons
+        self._active_id      = msg.active_id
+        self._last_persons_t = time.monotonic()
+        if self._state == "idle" and msg.persons:
+            self._state = "attending"
+            self.get_logger().info("Attention: person detected — attending")
+
+    def _control_cb(self):
+        self._reload_params()
+        twist = Twist()
+
+        if not self._p["attention_enabled"]:
+            self._cmd_pub.publish(twist)
+            return
+
+        now = time.monotonic()
+        fresh = (
+            self._last_persons_t > 0.0
+            and (now - self._last_persons_t) < self._p["lost_timeout_s"]
+        )
+
+        if not fresh:
+            if self._state == "attending":
+                self._state = "idle"
+                self.get_logger().info("Attention: no person — idle")
+            self._cmd_pub.publish(twist)
+            return
+
+        target = select_target(self._persons, self._active_id)
+        if target is None:
+            self._cmd_pub.publish(twist)
+            return
+
+        self._state = "attending"
+        twist.angular.z = compute_attention_cmd(
+            bearing_rad=target.bearing_rad,
+            dead_zone_rad=self._p["dead_zone_rad"],
+            kp_angular=self._p["kp_angular"],
+            max_angular_vel=self._p["max_angular_vel"],
+        )
+        self._cmd_pub.publish(twist)
+
+
+# ── Entry point ───────────────────────────────────────────────────────────────
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = AttentionNode()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.try_shutdown()
+
+
+if __name__ == "__main__":
+    main()

jetson/ros2_ws/src/saltybot_social/saltybot_social/expression_node.py

@@ -0,0 +1,157 @@
+"""
+expression_node.py — LED mood display bridge for saltybot social layer.
+
+Subscribes
+──────────
+  /social/mood  (saltybot_social_msgs/Mood)
+    mood      : "happy" | "curious" | "annoyed" | "playful" | "idle"
+    intensity : 0.0 .. 1.0
+
+Behaviour
+─────────
+  * On every /social/mood message, serialises the command as a single-line
+    JSON frame and writes it over the ESP32-C3 serial link:
+
+        {"mood":"happy","intensity":0.80}\n
+
+  * If no mood message arrives for idle_timeout_s, sends an explicit
+    idle command so the ESP32 breathing animation is synchronised with the
+    node's own awareness of whether the robot is active.
+
+  * Re-opens the serial port automatically if the device disconnects.
+
+Parameters
+──────────
+  serial_port      (str)    /dev/ttyUSB0    — ESP32-C3 USB-CDC device
+  baud_rate        (int)    115200
+  idle_timeout_s   (float)  3.0             — seconds before sending idle
+  control_rate     (float)  10.0            — Hz; how often to check idle
+"""
+
+import json
+import threading
+import time
+
+import rclpy
+from rclpy.node import Node
+
+from saltybot_social_msgs.msg import Mood
+
+try:
+    import serial
+    _SERIAL_AVAILABLE = True
+except ImportError:
+    _SERIAL_AVAILABLE = False
+
+
+class ExpressionNode(Node):
+
+    def __init__(self):
+        super().__init__("expression_node")
+
+        self.declare_parameter("serial_port",    "/dev/ttyUSB0")
+        self.declare_parameter("baud_rate",      115200)
+        self.declare_parameter("idle_timeout_s", 3.0)
+        self.declare_parameter("control_rate",   10.0)
+
+        self._port        = self.get_parameter("serial_port").value
+        self._baud        = self.get_parameter("baud_rate").value
+        self._idle_to     = self.get_parameter("idle_timeout_s").value
+        rate              = self.get_parameter("control_rate").value
+
+        self._last_cmd_t  = 0.0          # monotonic, 0 = never received
+        self._lock        = threading.Lock()
+        self._ser         = None
+
+        self.create_subscription(Mood, "/social/mood", self._mood_cb, 10)
+
+        self._timer = self.create_timer(1.0 / rate, self._tick_cb)
+
+        if _SERIAL_AVAILABLE:
+            threading.Thread(target=self._open_serial, daemon=True).start()
+        else:
+            self.get_logger().warn(
+                "pyserial not available — serial output disabled (dry-run mode)"
+            )
+
+        self.get_logger().info(
+            f"ExpressionNode ready  port={self._port} baud={self._baud} "
+            f"idle_timeout={self._idle_to}s"
+        )
+
+    # ── Serial management ─────────────────────────────────────────────────────
+
+    def _open_serial(self):
+        """Background thread: keep serial port open, reconnect on error."""
+        while rclpy.ok():
+            try:
+                with self._lock:
+                    self._ser = serial.Serial(
+                        self._port, self._baud, timeout=1.0
+                    )
+                self.get_logger().info(f"Opened serial port {self._port}")
+                # Hold serial open until error
+                while rclpy.ok():
+                    with self._lock:
+                        if self._ser is None or not self._ser.is_open:
+                            break
+                    time.sleep(0.5)
+            except Exception as exc:
+                self.get_logger().warn(
+                    f"Serial {self._port} error: {exc} — retry in 3 s"
+                )
+                with self._lock:
+                    if self._ser and self._ser.is_open:
+                        self._ser.close()
+                    self._ser = None
+                time.sleep(3.0)
+
+    def _send(self, mood: str, intensity: float):
+        """Serialise and write one JSON line to ESP32-C3."""
+        line = json.dumps({"mood": mood, "intensity": round(intensity, 3)}) + "\n"
+        data = line.encode("ascii")
+        with self._lock:
+            if self._ser and self._ser.is_open:
+                try:
+                    self._ser.write(data)
+                except Exception as exc:
+                    self.get_logger().warn(f"Serial write error: {exc}")
+                    self._ser.close()
+                    self._ser = None
+            else:
+                # Dry-run: log the frame
+                self.get_logger().debug(f"[dry-run] → {line.strip()}")
+
+    # ── Callbacks ─────────────────────────────────────────────────────────────
+
+    def _mood_cb(self, msg: Mood):
+        intensity = max(0.0, min(1.0, float(msg.intensity)))
+        self._send(msg.mood, intensity)
+        self._last_cmd_t = time.monotonic()
+
+    def _tick_cb(self):
+        """Periodic check — send idle if we've been quiet too long."""
+        if self._last_cmd_t == 0.0:
+            return
+        if (time.monotonic() - self._last_cmd_t) >= self._idle_to:
+            self._send("idle", 1.0)
+            # Reset so we don't flood the ESP32 with idle frames
+            self._last_cmd_t = time.monotonic()
+
+
+# ── Entry point ───────────────────────────────────────────────────────────────
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = ExpressionNode()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.try_shutdown()
+
+
+if __name__ == "__main__":
+    main()

jetson/ros2_ws/src/saltybot_social/setup.py

@@ -21,12 +21,14 @@ setup(
     zip_safe=True,
     maintainer='seb',
     maintainer_email='seb@vayrette.com',
-    description='Multi-modal person identity fusion and state tracking for saltybot',
+    description='Social interaction layer — person state tracking, LED expression + attention',
     license='MIT',
     tests_require=['pytest'],
     entry_points={
         'console_scripts': [
             'person_state_tracker = saltybot_social.person_state_tracker_node:main',
+            'expression_node = saltybot_social.expression_node:main',
+            'attention_node = saltybot_social.attention_node:main',
         ],
     },
 )

jetson/ros2_ws/src/saltybot_social/test/test_attention.py

@@ -0,0 +1,140 @@
+"""
+test_attention.py — Unit tests for attention_node helpers.
+
+No ROS2 / serial / GPU dependencies — runs with plain pytest.
+"""
+
+import math
+import pytest
+
+from saltybot_social.attention_node import (
+    clamp,
+    compute_attention_cmd,
+    select_target,
+)
+
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+class FakePerson:
+    def __init__(self, track_id, bearing_rad, confidence=0.9, is_speaking=False):
+        self.track_id    = track_id
+        self.bearing_rad = bearing_rad
+        self.confidence  = confidence
+        self.is_speaking = is_speaking
+
+
+# ── clamp ─────────────────────────────────────────────────────────────────────
+
+class TestClamp:
+    def test_within_range(self):
+        assert clamp(0.5, 0.0, 1.0) == 0.5
+
+    def test_below_lo(self):
+        assert clamp(-0.5, 0.0, 1.0) == 0.0
+
+    def test_above_hi(self):
+        assert clamp(1.5, 0.0, 1.0) == 1.0
+
+    def test_at_boundary(self):
+        assert clamp(0.0, 0.0, 1.0) == 0.0
+        assert clamp(1.0, 0.0, 1.0) == 1.0
+
+
+# ── select_target ─────────────────────────────────────────────────────────────
+
+class TestSelectTarget:
+
+    def test_empty_returns_none(self):
+        assert select_target([], active_id=-1) is None
+
+    def test_picks_active_id(self):
+        persons = [FakePerson(1, 0.1, confidence=0.6),
+                   FakePerson(2, 0.5, confidence=0.9)]
+        result = select_target(persons, active_id=1)
+        assert result.track_id == 1
+
+    def test_falls_back_to_highest_confidence(self):
+        persons = [FakePerson(1, 0.1, confidence=0.6),
+                   FakePerson(2, 0.5, confidence=0.9),
+                   FakePerson(3, 0.2, confidence=0.4)]
+        result = select_target(persons, active_id=-1)
+        assert result.track_id == 2
+
+    def test_single_person_no_active(self):
+        persons = [FakePerson(5, 0.3)]
+        result = select_target(persons, active_id=-1)
+        assert result.track_id == 5
+
+    def test_active_id_not_in_list_falls_back(self):
+        persons = [FakePerson(1, 0.0, confidence=0.5),
+                   FakePerson(2, 0.2, confidence=0.95)]
+        result = select_target(persons, active_id=99)
+        assert result.track_id == 2
+
+
+# ── compute_attention_cmd ─────────────────────────────────────────────────────
+
+class TestComputeAttentionCmd:
+
+    def test_inside_dead_zone_zero(self):
+        cmd = compute_attention_cmd(
+            bearing_rad=0.05,
+            dead_zone_rad=0.15,
+            kp_angular=1.0,
+            max_angular_vel=0.8,
+        )
+        assert cmd == 0.0
+
+    def test_exactly_at_dead_zone_boundary_zero(self):
+        cmd = compute_attention_cmd(
+            bearing_rad=0.15,
+            dead_zone_rad=0.15,
+            kp_angular=1.0,
+            max_angular_vel=0.8,
+        )
+        assert cmd == 0.0
+
+    def test_positive_bearing_gives_positive_angular_z(self):
+        cmd = compute_attention_cmd(
+            bearing_rad=0.4,
+            dead_zone_rad=0.15,
+            kp_angular=1.0,
+            max_angular_vel=0.8,
+        )
+        assert cmd > 0.0
+
+    def test_negative_bearing_gives_negative_angular_z(self):
+        cmd = compute_attention_cmd(
+            bearing_rad=-0.4,
+            dead_zone_rad=0.15,
+            kp_angular=1.0,
+            max_angular_vel=0.8,
+        )
+        assert cmd < 0.0
+
+    def test_clamps_to_max_angular_vel(self):
+        cmd = compute_attention_cmd(
+            bearing_rad=math.pi,  # 180° off
+            dead_zone_rad=0.15,
+            kp_angular=2.0,
+            max_angular_vel=0.8,
+        )
+        assert abs(cmd) <= 0.8
+
+    def test_proportional_scaling(self):
+        """Double bearing → double cmd (within linear region)."""
+        kp = 1.0
+        dz = 0.05
+        cmd1 = compute_attention_cmd(0.3, dz, kp, 10.0)
+        cmd2 = compute_attention_cmd(0.6, dz, kp, 10.0)
+        assert abs(cmd2 - 2.0 * cmd1) < 0.01
+
+    def test_zero_bearing_zero_cmd(self):
+        cmd = compute_attention_cmd(
+            bearing_rad=0.0,
+            dead_zone_rad=0.1,
+            kp_angular=1.0,
+            max_angular_vel=0.8,
+        )
+        assert cmd == 0.0

jetson/ros2_ws/src/saltybot_social_enrollment/config/enrollment_params.yaml

@@ -0,0 +1,6 @@
+enrollment_node:
+  ros__parameters:
+    db_path: '/mnt/nvme/saltybot/gallery/persons.db'
+    voice_samples_dir: '/mnt/nvme/saltybot/gallery/voice'
+    auto_enroll_phrase: 'remember me my name is'
+    n_samples_default: 10

jetson/ros2_ws/src/saltybot_social_enrollment/launch/enrollment.launch.py

@@ -0,0 +1,22 @@
+"""Launch file for saltybot social enrollment node."""
+
+import os
+
+from ament_index_python.packages import get_package_share_directory
+from launch import LaunchDescription
+from launch_ros.actions import Node
+
+
+def generate_launch_description():
+    pkg_share = get_package_share_directory('saltybot_social_enrollment')
+    config_file = os.path.join(pkg_share, 'config', 'enrollment_params.yaml')
+
+    return LaunchDescription([
+        Node(
+            package='saltybot_social_enrollment',
+            executable='enrollment_node',
+            name='enrollment_node',
+            parameters=[config_file],
+            output='screen',
+        ),
+    ])

jetson/ros2_ws/src/saltybot_social_enrollment/package.xml

@@ -0,0 +1,23 @@
+<?xml version="1.0"?>
+<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
+<package format="3">
+  <name>saltybot_social_enrollment</name>
+  <version>0.1.0</version>
+  <description>Person enrollment system for saltybot social interaction</description>
+  <maintainer email="seb@vayrette.com">seb</maintainer>
+  <license>MIT</license>
+
+  <buildtool_depend>ament_python</buildtool_depend>
+
+  <depend>rclpy</depend>
+  <depend>sensor_msgs</depend>
+  <depend>cv_bridge</depend>
+  <depend>std_msgs</depend>
+  <depend>saltybot_social_msgs</depend>
+
+  <exec_depend>python3-numpy</exec_depend>
+
+  <export>
+    <build_type>ament_python</build_type>
+  </export>
+</package>

jetson/ros2_ws/src/saltybot_social_enrollment/saltybot_social_enrollment/enrollment_cli.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+"""enrollment_cli.py -- Gallery management CLI for saltybot social.
+
+Usage:
+  ros2 run saltybot_social_enrollment enrollment_cli enroll --name "Alice" [--samples 15] [--mode face]
+  ros2 run saltybot_social_enrollment enrollment_cli list
+  ros2 run saltybot_social_enrollment enrollment_cli delete --id 3
+  ros2 run saltybot_social_enrollment enrollment_cli rename --id 2 --name "Bob"
+"""
+
+import argparse
+import sys
+
+import rclpy
+from rclpy.node import Node
+
+from saltybot_social_msgs.srv import (
+    EnrollPerson,
+    ListPersons,
+    DeletePerson,
+    UpdatePerson,
+)
+
+
+class EnrollmentCLI(Node):
+    def __init__(self):
+        super().__init__('enrollment_cli')
+        self._enroll_client = self.create_client(
+            EnrollPerson, '/social/enroll'
+        )
+        self._list_client = self.create_client(
+            ListPersons, '/social/persons/list'
+        )
+        self._delete_client = self.create_client(
+            DeletePerson, '/social/persons/delete'
+        )
+        self._update_client = self.create_client(
+            UpdatePerson, '/social/persons/update'
+        )
+
+    def enroll(self, name: str, n_samples: int = 10, mode: str = 'face'):
+        if not self._enroll_client.wait_for_service(timeout_sec=5.0):
+            print('ERROR: /social/enroll service not available')
+            return False
+
+        req = EnrollPerson.Request()
+        req.name = name
+        req.mode = mode
+        req.n_samples = n_samples
+
+        print(f'Enrolling "{name}" ({mode}, {n_samples} samples)...')
+        future = self._enroll_client.call_async(req)
+        rclpy.spin_until_future_complete(self, future, timeout_sec=60.0)
+
+        if future.result() is None:
+            print('ERROR: Enrollment timed out')
+            return False
+
+        resp = future.result()
+        if resp.success:
+            print(f'OK: Enrolled "{name}" as person_id={resp.person_id}')
+        else:
+            print(f'FAILED: {resp.message}')
+        return resp.success
+
+    def list_persons(self):
+        if not self._list_client.wait_for_service(timeout_sec=5.0):
+            print('ERROR: /social/persons/list service not available')
+            return
+
+        req = ListPersons.Request()
+        future = self._list_client.call_async(req)
+        rclpy.spin_until_future_complete(self, future, timeout_sec=10.0)
+
+        if future.result() is None:
+            print('ERROR: List request timed out')
+            return
+
+        resp = future.result()
+        if not resp.persons:
+            print('Gallery is empty.')
+            return
+
+        print(f'{"ID":>4}  {"Name":<20}  {"Samples":>7}  {"Embedding Dim":>13}')
+        print('-' * 50)
+        for p in resp.persons:
+            dim = len(p.embedding) if p.embedding else 0
+            print(f'{p.person_id:>4}  {p.person_name:<20}  {p.sample_count:>7}  {dim:>13}')
+
+    def delete(self, person_id: int):
+        if not self._delete_client.wait_for_service(timeout_sec=5.0):
+            print('ERROR: /social/persons/delete service not available')
+            return False
+
+        req = DeletePerson.Request()
+        req.person_id = person_id
+
+        future = self._delete_client.call_async(req)
+        rclpy.spin_until_future_complete(self, future, timeout_sec=10.0)
+
+        if future.result() is None:
+            print('ERROR: Delete request timed out')
+            return False
+
+        resp = future.result()
+        if resp.success:
+            print(f'OK: {resp.message}')
+        else:
+            print(f'FAILED: {resp.message}')
+        return resp.success
+
+    def rename(self, person_id: int, new_name: str):
+        if not self._update_client.wait_for_service(timeout_sec=5.0):
+            print('ERROR: /social/persons/update service not available')
+            return False
+
+        req = UpdatePerson.Request()
+        req.person_id = person_id
+        req.new_name = new_name
+
+        future = self._update_client.call_async(req)
+        rclpy.spin_until_future_complete(self, future, timeout_sec=10.0)
+
+        if future.result() is None:
+            print('ERROR: Rename request timed out')
+            return False
+
+        resp = future.result()
+        if resp.success:
+            print(f'OK: {resp.message}')
+        else:
+            print(f'FAILED: {resp.message}')
+        return resp.success
+
+
+def main(args=None):
+    parser = argparse.ArgumentParser(
+        description='saltybot person enrollment CLI'
+    )
+    subparsers = parser.add_subparsers(dest='command', required=True)
+
+    # enroll
+    enroll_p = subparsers.add_parser('enroll', help='Enroll a new person')
+    enroll_p.add_argument('--name', required=True, help='Person name')
+    enroll_p.add_argument('--samples', type=int, default=10,
+                          help='Number of face samples (default: 10)')
+    enroll_p.add_argument('--mode', default='face',
+                          help='Enrollment mode (default: face)')
+
+    # list
+    subparsers.add_parser('list', help='List enrolled persons')
+
+    # delete
+    delete_p = subparsers.add_parser('delete', help='Delete a person')
+    delete_p.add_argument('--id', type=int, required=True,
+                          help='Person ID to delete')
+
+    # rename
+    rename_p = subparsers.add_parser('rename', help='Rename a person')
+    rename_p.add_argument('--id', type=int, required=True,
+                          help='Person ID to rename')
+    rename_p.add_argument('--name', required=True, help='New name')
+
+    parsed = parser.parse_args(sys.argv[1:])
+
+    rclpy.init()
+    cli = EnrollmentCLI()
+
+    try:
+        if parsed.command == 'enroll':
+            cli.enroll(parsed.name, parsed.samples, parsed.mode)
+        elif parsed.command == 'list':
+            cli.list_persons()
+        elif parsed.command == 'delete':
+            cli.delete(parsed.id)
+        elif parsed.command == 'rename':
+            cli.rename(parsed.id, parsed.name)
+    finally:
+        cli.destroy_node()
+        rclpy.try_shutdown()
+
+
+if __name__ == '__main__':
+    main()

jetson/ros2_ws/src/saltybot_social_enrollment/saltybot_social_enrollment/enrollment_node.py

@@ -0,0 +1,302 @@
+"""enrollment_node.py -- ROS2 person enrollment node for saltybot social.
+
+Coordinates person enrollment:
+- Forwards /social/enroll to face_recognizer's service
+- Owns persistent SQLite gallery (PersonDB)
+- Voice-triggered enrollment via "remember me my name is X"
+- Gallery management services (list/delete/update)
+- Syncs DB from /social/faces/embeddings topic
+"""
+
+import threading
+import numpy as np
+
+import rclpy
+from rclpy.node import Node
+from rclpy.qos import QoSProfile, ReliabilityPolicy, DurabilityPolicy
+from std_msgs.msg import String
+
+from saltybot_social_msgs.msg import (
+    FaceDetectionArray,
+    FaceEmbedding,
+    FaceEmbeddingArray,
+)
+from saltybot_social_msgs.srv import (
+    EnrollPerson,
+    ListPersons,
+    DeletePerson,
+    UpdatePerson,
+)
+
+from saltybot_social_enrollment.person_db import PersonDB
+
+
+class EnrollmentNode(Node):
+    def __init__(self):
+        super().__init__('enrollment_node')
+
+        # Parameters
+        self.declare_parameter('db_path', '/mnt/nvme/saltybot/gallery/persons.db')
+        self.declare_parameter('voice_samples_dir', '/mnt/nvme/saltybot/gallery/voice')
+        self.declare_parameter('auto_enroll_phrase', 'remember me my name is')
+        self.declare_parameter('n_samples_default', 10)
+
+        db_path = self.get_parameter('db_path').value
+        self._voice_dir = self.get_parameter('voice_samples_dir').value
+        self._phrase = self.get_parameter('auto_enroll_phrase').value
+        self._n_samples = self.get_parameter('n_samples_default').value
+
+        self._db = PersonDB(db_path)
+        self.get_logger().info(f'PersonDB initialized at {db_path}')
+
+        # Client to face_recognizer's enroll service
+        self._enroll_client = self.create_client(
+            EnrollPerson, '/social/face_recognizer/enroll'
+        )
+
+        # QoS profiles
+        best_effort_qos = QoSProfile(
+            depth=10,
+            reliability=ReliabilityPolicy.BEST_EFFORT,
+            durability=DurabilityPolicy.VOLATILE,
+        )
+        reliable_qos = QoSProfile(
+            depth=1,
+            reliability=ReliabilityPolicy.RELIABLE,
+            durability=DurabilityPolicy.VOLATILE,
+        )
+        status_qos = QoSProfile(
+            depth=1,
+            reliability=ReliabilityPolicy.BEST_EFFORT,
+            durability=DurabilityPolicy.VOLATILE,
+        )
+
+        # Subscriptions
+        self.create_subscription(
+            FaceDetectionArray, '/social/faces/detections',
+            self._on_detections, best_effort_qos
+        )
+        self.create_subscription(
+            FaceEmbeddingArray, '/social/faces/embeddings',
+            self._on_embeddings, reliable_qos
+        )
+        self.create_subscription(
+            String, '/social/speech/transcript',
+            self._on_transcript, best_effort_qos
+        )
+        self.create_subscription(
+            String, '/social/speech/command',
+            self._on_command, best_effort_qos
+        )
+
+        # Services
+        self.create_service(EnrollPerson, '/social/enroll', self._handle_enroll)
+        self.create_service(ListPersons, '/social/persons/list', self._handle_list)
+        self.create_service(DeletePerson, '/social/persons/delete', self._handle_delete)
+        self.create_service(UpdatePerson, '/social/persons/update', self._handle_update)
+
+        # Publishers
+        self._pub_embeddings = self.create_publisher(
+            FaceEmbeddingArray, '/social/faces/embeddings', reliable_qos
+        )
+        self._pub_status = self.create_publisher(
+            String, '/social/enrollment/status', status_qos
+        )
+
+        self.get_logger().info('EnrollmentNode ready')
+
+    # ---- Voice-triggered enrollment ----
+
+    def _on_transcript(self, msg: String):
+        text = msg.data.lower()
+        phrase = self._phrase.lower()
+        if phrase in text:
+            idx = text.index(phrase) + len(phrase)
+            name = text[idx:].strip()
+            # Clean up: take first 3 words max as the name
+            words = name.split()
+            if words:
+                name = ' '.join(words[:3]).title()
+                self.get_logger().info(f'Voice enrollment triggered: "{name}"')
+                self._trigger_voice_enroll(name)
+
+    def _on_command(self, msg: String):
+        # Reserved for explicit voice commands (e.g., "enroll Alice")
+        pass
+
+    def _trigger_voice_enroll(self, name: str):
+        if not self._enroll_client.wait_for_service(timeout_sec=1.0):
+            self.get_logger().warn(
+                'face_recognizer enroll service not available'
+            )
+            self._publish_status(f'Enrollment failed: face_recognizer unavailable')
+            return
+
+        req = EnrollPerson.Request()
+        req.name = name
+        req.mode = 'face'
+        req.n_samples = self._n_samples
+        future = self._enroll_client.call_async(req)
+        future.add_done_callback(
+            lambda f: self._on_enroll_done(f, name)
+        )
+        self._publish_status(f'Enrolling "{name}"... look at the camera')
+
+    def _on_enroll_done(self, future, name: str):
+        try:
+            resp = future.result()
+            if resp.success:
+                status = f'Enrolled "{name}" (id={resp.person_id})'
+                self.get_logger().info(status)
+            else:
+                status = f'Enrollment failed for "{name}": {resp.message}'
+                self.get_logger().warn(status)
+            self._publish_status(status)
+        except Exception as e:
+            self.get_logger().error(f'Enroll call failed: {e}')
+            self._publish_status(f'Enrollment error: {e}')
+
+    # ---- Face detection callback (during enrollment) ----
+
+    def _on_detections(self, msg: FaceDetectionArray):
+        # Reserved for future direct enrollment (without face_recognizer)
+        pass
+
+    # ---- Embeddings sync from face_recognizer ----
+
+    def _on_embeddings(self, msg: FaceEmbeddingArray):
+        for emb in msg.embeddings:
+            existing = self._db.get_person(emb.person_id)
+            if existing is None:
+                arr = np.array(emb.embedding, dtype=np.float32)
+                self._db.add_person(
+                    emb.person_name, arr, emb.sample_count
+                )
+                self.get_logger().info(
+                    f'Synced new person from face_recognizer: '
+                    f'{emb.person_name} (id={emb.person_id})'
+                )
+
+    # ---- Service handlers ----
+
+    def _handle_enroll(self, request, response):
+        """Forward enrollment to face_recognizer service."""
+        if not self._enroll_client.wait_for_service(timeout_sec=2.0):
+            response.success = False
+            response.message = 'face_recognizer service unavailable'
+            return response
+
+        # Use threading.Event to bridge async call in service callback
+        event = threading.Event()
+        result_holder = {}
+
+        req = EnrollPerson.Request()
+        req.name = request.name
+        req.mode = request.mode
+        req.n_samples = request.n_samples
+
+        future = self._enroll_client.call_async(req)
+
+        def _done(f):
+            try:
+                result_holder['resp'] = f.result()
+            except Exception as e:
+                result_holder['err'] = str(e)
+            event.set()
+
+        future.add_done_callback(_done)
+        success = event.wait(timeout=35.0)
+
+        if not success:
+            response.success = False
+            response.message = 'Enrollment timed out'
+        elif 'resp' in result_holder:
+            resp = result_holder['resp']
+            response.success = resp.success
+            response.message = resp.message
+            response.person_id = resp.person_id
+        else:
+            response.success = False
+            response.message = result_holder.get('err', 'Unknown error')
+
+        return response
+
+    def _handle_list(self, request, response):
+        persons = self._db.get_all()
+        response.persons = []
+        for p in persons:
+            fe = FaceEmbedding()
+            fe.person_id = p['id']
+            fe.person_name = p['name']
+            fe.sample_count = p['sample_count']
+            fe.enrolled_at.sec = int(p['enrolled_at'])
+            fe.enrolled_at.nanosec = int(
+                (p['enrolled_at'] - int(p['enrolled_at'])) * 1e9
+            )
+            if p['embedding'] is not None:
+                fe.embedding = p['embedding'].tolist()
+            response.persons.append(fe)
+        return response
+
+    def _handle_delete(self, request, response):
+        success = self._db.delete_person(request.person_id)
+        response.success = success
+        if success:
+            response.message = f'Deleted person {request.person_id}'
+            self.get_logger().info(response.message)
+            self._publish_embeddings_from_db()
+        else:
+            response.message = f'Person {request.person_id} not found'
+        return response
+
+    def _handle_update(self, request, response):
+        success = self._db.update_name(request.person_id, request.new_name)
+        response.success = success
+        if success:
+            response.message = f'Updated person {request.person_id} name to "{request.new_name}"'
+            self.get_logger().info(response.message)
+            self._publish_embeddings_from_db()
+        else:
+            response.message = f'Person {request.person_id} not found'
+        return response
+
+    # ---- Helpers ----
+
+    def _publish_status(self, text: str):
+        msg = String()
+        msg.data = text
+        self._pub_status.publish(msg)
+
+    def _publish_embeddings_from_db(self):
+        persons = self._db.get_all()
+        arr = FaceEmbeddingArray()
+        arr.header.stamp = self.get_clock().now().to_msg()
+        for p in persons:
+            if p['embedding'] is not None:
+                fe = FaceEmbedding()
+                fe.person_id = p['id']
+                fe.person_name = p['name']
+                fe.sample_count = p['sample_count']
+                fe.enrolled_at.sec = int(p['enrolled_at'])
+                fe.enrolled_at.nanosec = int(
+                    (p['enrolled_at'] - int(p['enrolled_at'])) * 1e9
+                )
+                fe.embedding = p['embedding'].tolist()
+                arr.embeddings.append(fe)
+        self._pub_embeddings.publish(arr)
+
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = EnrollmentNode()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.try_shutdown()
+
+
+if __name__ == '__main__':
+    main()

jetson/ros2_ws/src/saltybot_social_enrollment/saltybot_social_enrollment/person_db.py

@@ -0,0 +1,138 @@
+"""person_db.py -- Persistent SQLite person gallery for saltybot enrollment."""
+
+import sqlite3
+import json
+import time
+import numpy as np
+import threading
+from pathlib import Path
+
+
+class PersonDB:
+    """Thread-safe SQLite-backed person gallery.
+
+    Schema:
+      persons(id INTEGER PRIMARY KEY, name TEXT, enrolled_at REAL,
+              sample_count INTEGER, embedding BLOB, metadata TEXT)
+      voice_samples(id INTEGER PRIMARY KEY, person_id INTEGER REFERENCES persons,
+                    recorded_at REAL, sample_path TEXT)
+    """
+
+    def __init__(self, db_path: str):
+        self._db_path = db_path
+        self._lock = threading.Lock()
+        Path(db_path).parent.mkdir(parents=True, exist_ok=True)
+        self._init_db()
+
+    def _init_db(self):
+        with self._connect() as conn:
+            conn.execute("""
+                CREATE TABLE IF NOT EXISTS persons (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    name TEXT NOT NULL,
+                    enrolled_at REAL NOT NULL,
+                    sample_count INTEGER DEFAULT 1,
+                    embedding BLOB,
+                    metadata TEXT DEFAULT '{}'
+                )
+            """)
+            conn.execute("""
+                CREATE TABLE IF NOT EXISTS voice_samples (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    person_id INTEGER REFERENCES persons(id),
+                    recorded_at REAL NOT NULL,
+                    sample_path TEXT NOT NULL
+                )
+            """)
+
+    def _connect(self):
+        return sqlite3.connect(self._db_path)
+
+    def add_person(self, name: str, embedding: np.ndarray, sample_count: int = 1,
+                   metadata: dict = None) -> int:
+        """Add a new person. Returns new person_id."""
+        emb_blob = embedding.astype(np.float32).tobytes() if embedding is not None else None
+        now = time.time()
+        with self._lock:
+            with self._connect() as conn:
+                cur = conn.execute(
+                    "INSERT INTO persons (name, enrolled_at, sample_count, embedding, metadata) "
+                    "VALUES (?, ?, ?, ?, ?)",
+                    (name, now, sample_count, emb_blob, json.dumps(metadata or {}))
+                )
+                return cur.lastrowid
+
+    def update_embedding(self, person_id: int, embedding: np.ndarray,
+                         sample_count: int) -> bool:
+        emb_blob = embedding.astype(np.float32).tobytes()
+        with self._lock:
+            with self._connect() as conn:
+                conn.execute(
+                    "UPDATE persons SET embedding=?, sample_count=? WHERE id=?",
+                    (emb_blob, sample_count, person_id)
+                )
+                return conn.total_changes > 0
+
+    def update_name(self, person_id: int, new_name: str) -> bool:
+        with self._lock:
+            with self._connect() as conn:
+                conn.execute(
+                    "UPDATE persons SET name=? WHERE id=?",
+                    (new_name, person_id)
+                )
+                return conn.total_changes > 0
+
+    def delete_person(self, person_id: int) -> bool:
+        with self._lock:
+            with self._connect() as conn:
+                conn.execute(
+                    "DELETE FROM voice_samples WHERE person_id=?",
+                    (person_id,)
+                )
+                conn.execute(
+                    "DELETE FROM persons WHERE id=?",
+                    (person_id,)
+                )
+                return conn.total_changes > 0
+
+    def get_all(self) -> list:
+        """Returns list of dicts with id, name, enrolled_at, sample_count, embedding."""
+        with self._lock:
+            with self._connect() as conn:
+                rows = conn.execute(
+                    "SELECT id, name, enrolled_at, sample_count, embedding FROM persons"
+                ).fetchall()
+        result = []
+        for row in rows:
+            emb = np.frombuffer(row[4], dtype=np.float32).copy() if row[4] else None
+            result.append({
+                'id': row[0], 'name': row[1], 'enrolled_at': row[2],
+                'sample_count': row[3], 'embedding': emb
+            })
+        return result
+
+    def get_person(self, person_id: int) -> dict | None:
+        with self._lock:
+            with self._connect() as conn:
+                row = conn.execute(
+                    "SELECT id, name, enrolled_at, sample_count, embedding "
+                    "FROM persons WHERE id=?",
+                    (person_id,)
+                ).fetchone()
+        if row is None:
+            return None
+        emb = np.frombuffer(row[4], dtype=np.float32).copy() if row[4] else None
+        return {
+            'id': row[0], 'name': row[1], 'enrolled_at': row[2],
+            'sample_count': row[3], 'embedding': emb
+        }
+
+    def add_voice_sample(self, person_id: int, sample_path: str) -> int:
+        with self._lock:
+            with self._connect() as conn:
+                cur = conn.execute(
+                    "INSERT INTO voice_samples (person_id, recorded_at, sample_path) "
+                    "VALUES (?, ?, ?)",
+                    (person_id, time.time(), sample_path)
+                )
+                return cur.lastrowid

jetson/ros2_ws/src/saltybot_social_enrollment/setup.cfg

@@ -0,0 +1,4 @@
+[develop]
+script_dir=$base/lib/saltybot_social_enrollment
+[install]
+install_scripts=$base/lib/saltybot_social_enrollment

jetson/ros2_ws/src/saltybot_social_enrollment/setup.py

@@ -0,0 +1,29 @@
+from setuptools import setup
+import os
+from glob import glob
+
+package_name = 'saltybot_social_enrollment'
+
+setup(
+    name=package_name,
+    version='0.1.0',
+    packages=[package_name],
+    data_files=[
+        ('share/ament_index/resource_index/packages', ['resource/' + package_name]),
+        ('share/' + package_name, ['package.xml']),
+        (os.path.join('share', package_name, 'launch'), glob('launch/*.py')),
+        (os.path.join('share', package_name, 'config'), glob('config/*.yaml')),
+    ],
+    install_requires=['setuptools'],
+    zip_safe=True,
+    maintainer='seb',
+    maintainer_email='seb@vayrette.com',
+    description='Person enrollment system for saltybot social interaction',
+    license='MIT',
+    entry_points={
+        'console_scripts': [
+            'enrollment_node = saltybot_social_enrollment.enrollment_node:main',
+            'enrollment_cli = saltybot_social_enrollment.enrollment_cli:main',
+        ],
+    },
+)

jetson/ros2_ws/src/saltybot_social_face/config/face_recognition_params.yaml

@@ -0,0 +1,11 @@
+face_recognizer:
+  ros__parameters:
+    scrfd_engine_path: '/mnt/nvme/saltybot/models/scrfd_2.5g.engine'
+    scrfd_onnx_path: '/mnt/nvme/saltybot/models/scrfd_2.5g_bnkps.onnx'
+    arcface_engine_path: '/mnt/nvme/saltybot/models/arcface_r50.engine'
+    arcface_onnx_path: '/mnt/nvme/saltybot/models/arcface_r50.onnx'
+    gallery_dir: '/mnt/nvme/saltybot/gallery'
+    recognition_threshold: 0.35
+    publish_debug_image: false
+    max_faces: 10
+    scrfd_conf_thresh: 0.5

jetson/ros2_ws/src/saltybot_social_face/launch/face_recognition.launch.py

@@ -0,0 +1,80 @@
+"""
+face_recognition.launch.py -- Launch file for the SCRFD + ArcFace face recognition node.
+
+Launches the face_recognizer node with configurable model paths and parameters.
+The RealSense camera must be running separately (e.g., via realsense.launch.py).
+"""
+
+from launch import LaunchDescription
+from launch.actions import DeclareLaunchArgument
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import Node
+
+
+def generate_launch_description():
+    """Generate launch description for face recognition pipeline."""
+    return LaunchDescription([
+        DeclareLaunchArgument(
+            'scrfd_engine_path',
+            default_value='/mnt/nvme/saltybot/models/scrfd_2.5g.engine',
+            description='Path to SCRFD TensorRT engine file',
+        ),
+        DeclareLaunchArgument(
+            'scrfd_onnx_path',
+            default_value='/mnt/nvme/saltybot/models/scrfd_2.5g_bnkps.onnx',
+            description='Path to SCRFD ONNX model file (fallback)',
+        ),
+        DeclareLaunchArgument(
+            'arcface_engine_path',
+            default_value='/mnt/nvme/saltybot/models/arcface_r50.engine',
+            description='Path to ArcFace TensorRT engine file',
+        ),
+        DeclareLaunchArgument(
+            'arcface_onnx_path',
+            default_value='/mnt/nvme/saltybot/models/arcface_r50.onnx',
+            description='Path to ArcFace ONNX model file (fallback)',
+        ),
+        DeclareLaunchArgument(
+            'gallery_dir',
+            default_value='/mnt/nvme/saltybot/gallery',
+            description='Directory for persistent face gallery storage',
+        ),
+        DeclareLaunchArgument(
+            'recognition_threshold',
+            default_value='0.35',
+            description='Cosine similarity threshold for face recognition',
+        ),
+        DeclareLaunchArgument(
+            'publish_debug_image',
+            default_value='false',
+            description='Publish annotated debug image to /social/faces/debug_image',
+        ),
+        DeclareLaunchArgument(
+            'max_faces',
+            default_value='10',
+            description='Maximum faces to process per frame',
+        ),
+        DeclareLaunchArgument(
+            'scrfd_conf_thresh',
+            default_value='0.5',
+            description='SCRFD detection confidence threshold',
+        ),
+
+        Node(
+            package='saltybot_social_face',
+            executable='face_recognition',
+            name='face_recognizer',
+            output='screen',
+            parameters=[{
+                'scrfd_engine_path': LaunchConfiguration('scrfd_engine_path'),
+                'scrfd_onnx_path': LaunchConfiguration('scrfd_onnx_path'),
+                'arcface_engine_path': LaunchConfiguration('arcface_engine_path'),
+                'arcface_onnx_path': LaunchConfiguration('arcface_onnx_path'),
+                'gallery_dir': LaunchConfiguration('gallery_dir'),
+                'recognition_threshold': LaunchConfiguration('recognition_threshold'),
+                'publish_debug_image': LaunchConfiguration('publish_debug_image'),
+                'max_faces': LaunchConfiguration('max_faces'),
+                'scrfd_conf_thresh': LaunchConfiguration('scrfd_conf_thresh'),
+            }],
+        ),
+    ])

jetson/ros2_ws/src/saltybot_social_face/package.xml

@@ -0,0 +1,27 @@
+<?xml version="1.0"?>
+<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
+<package format="3">
+  <name>saltybot_social_face</name>
+  <version>0.1.0</version>
+  <description>SCRFD face detection and ArcFace recognition for SaltyBot social interactions</description>
+  <maintainer email="seb@vayrette.com">seb</maintainer>
+  <license>MIT</license>
+
+  <depend>rclpy</depend>
+  <depend>sensor_msgs</depend>
+  <depend>cv_bridge</depend>
+  <depend>image_transport</depend>
+  <depend>saltybot_social_msgs</depend>
+
+  <exec_depend>python3-numpy</exec_depend>
+  <exec_depend>python3-opencv</exec_depend>
+
+  <test_depend>ament_copyright</test_depend>
+  <test_depend>ament_flake8</test_depend>
+  <test_depend>ament_pep257</test_depend>
+  <test_depend>python3-pytest</test_depend>
+
+  <export>
+    <build_type>ament_python</build_type>
+  </export>
+</package>

jetson/ros2_ws/src/saltybot_social_face/saltybot_social_face/__init__.py

@@ -0,0 +1 @@
+"""SaltyBot social face detection and recognition package."""

jetson/ros2_ws/src/saltybot_social_face/saltybot_social_face/arcface_recognizer.py

@@ -0,0 +1,316 @@
+"""
+arcface_recognizer.py -- ArcFace face embedding extraction and gallery matching.
+
+Performs face alignment using 5-point landmarks (insightface standard reference),
+extracts 512-dimensional embeddings via ArcFace (TRT FP16 or ONNX fallback),
+and matches against a persistent gallery using cosine similarity.
+"""
+
+import os
+import logging
+from typing import Optional
+
+import numpy as np
+import cv2
+
+logger = logging.getLogger(__name__)
+
+# InsightFace standard reference landmarks for 112x112 alignment
+ARCFACE_SRC = np.array([
+    [38.2946, 51.6963],   # left eye
+    [73.5318, 51.5014],   # right eye
+    [56.0252, 71.7366],   # nose
+    [41.5493, 92.3655],   # left mouth
+    [70.7299, 92.2041],   # right mouth
+], dtype=np.float32)
+
+
+# -- Inference backends --------------------------------------------------------
+
+class _TRTBackend:
+    """TensorRT inference engine for ArcFace."""
+
+    def __init__(self, engine_path: str):
+        import tensorrt as trt
+        import pycuda.driver as cuda
+        import pycuda.autoinit  # noqa: F401
+
+        self._cuda = cuda
+        trt_logger = trt.Logger(trt.Logger.WARNING)
+        with open(engine_path, 'rb') as f, trt.Runtime(trt_logger) as runtime:
+            self._engine = runtime.deserialize_cuda_engine(f.read())
+        self._context = self._engine.create_execution_context()
+
+        self._inputs = []
+        self._outputs = []
+        self._bindings = []
+        for i in range(self._engine.num_io_tensors):
+            name = self._engine.get_tensor_name(i)
+            dtype = trt.nptype(self._engine.get_tensor_dtype(name))
+            shape = tuple(self._engine.get_tensor_shape(name))
+            nbytes = int(np.prod(shape)) * np.dtype(dtype).itemsize
+            host_mem = cuda.pagelocked_empty(shape, dtype)
+            device_mem = cuda.mem_alloc(nbytes)
+            self._bindings.append(int(device_mem))
+            if self._engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
+                self._inputs.append({'host': host_mem, 'device': device_mem})
+            else:
+                self._outputs.append({'host': host_mem, 'device': device_mem,
+                                      'shape': shape})
+        self._stream = cuda.Stream()
+
+    def infer(self, input_data: np.ndarray) -> np.ndarray:
+        """Run inference and return the embedding vector."""
+        np.copyto(self._inputs[0]['host'], input_data.ravel())
+        self._cuda.memcpy_htod_async(
+            self._inputs[0]['device'], self._inputs[0]['host'], self._stream)
+        self._context.execute_async_v2(self._bindings, self._stream.handle)
+        for out in self._outputs:
+            self._cuda.memcpy_dtoh_async(out['host'], out['device'], self._stream)
+        self._stream.synchronize()
+        return self._outputs[0]['host'].reshape(self._outputs[0]['shape']).copy()
+
+
+class _ONNXBackend:
+    """ONNX Runtime inference (CUDA EP with CPU fallback)."""
+
+    def __init__(self, onnx_path: str):
+        import onnxruntime as ort
+        providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+        self._session = ort.InferenceSession(onnx_path, providers=providers)
+        self._input_name = self._session.get_inputs()[0].name
+
+    def infer(self, input_data: np.ndarray) -> np.ndarray:
+        """Run inference and return the embedding vector."""
+        results = self._session.run(None, {self._input_name: input_data})
+        return results[0]
+
+
+# -- Face alignment ------------------------------------------------------------
+
+def align_face(bgr: np.ndarray, landmarks_10: list[float]) -> np.ndarray:
+    """Align a face to 112x112 using 5-point landmarks.
+
+    Args:
+        bgr: Source BGR image.
+        landmarks_10: Flat list of 10 floats [x0,y0, x1,y1, ..., x4,y4].
+
+    Returns:
+        Aligned BGR face crop of shape (112, 112, 3).
+    """
+    src_pts = np.array(landmarks_10, dtype=np.float32).reshape(5, 2)
+    M, _ = cv2.estimateAffinePartial2D(src_pts, ARCFACE_SRC)
+    if M is None:
+        # Fallback: simple crop and resize from bbox-like region
+        cx = np.mean(src_pts[:, 0])
+        cy = np.mean(src_pts[:, 1])
+        spread = max(np.ptp(src_pts[:, 0]), np.ptp(src_pts[:, 1])) * 1.5
+        half = spread / 2
+        x1 = max(0, int(cx - half))
+        y1 = max(0, int(cy - half))
+        x2 = min(bgr.shape[1], int(cx + half))
+        y2 = min(bgr.shape[0], int(cy + half))
+        crop = bgr[y1:y2, x1:x2]
+        return cv2.resize(crop, (112, 112), interpolation=cv2.INTER_LINEAR)
+
+    aligned = cv2.warpAffine(bgr, M, (112, 112), borderMode=cv2.BORDER_REPLICATE)
+    return aligned
+
+
+# -- Main recognizer class -----------------------------------------------------
+
+class ArcFaceRecognizer:
+    """ArcFace face embedding extractor and gallery matcher.
+
+    Args:
+        engine_path: Path to TensorRT engine file.
+        onnx_path: Path to ONNX model file (used if engine not available).
+    """
+
+    def __init__(self, engine_path: str = '', onnx_path: str = ''):
+        self._backend: Optional[_TRTBackend | _ONNXBackend] = None
+        self.gallery: dict[int, dict] = {}
+
+        # Try TRT first, then ONNX
+        if engine_path and os.path.isfile(engine_path):
+            try:
+                self._backend = _TRTBackend(engine_path)
+                logger.info('ArcFace TensorRT backend loaded: %s', engine_path)
+                return
+            except Exception as e:
+                logger.warning('ArcFace TRT load failed (%s), trying ONNX', e)
+
+        if onnx_path and os.path.isfile(onnx_path):
+            try:
+                self._backend = _ONNXBackend(onnx_path)
+                logger.info('ArcFace ONNX backend loaded: %s', onnx_path)
+                return
+            except Exception as e:
+                logger.error('ArcFace ONNX load failed: %s', e)
+
+        logger.error('No ArcFace model loaded. Recognition will be unavailable.')
+
+    @property
+    def is_loaded(self) -> bool:
+        """Return True if a backend is loaded and ready."""
+        return self._backend is not None
+
+    def embed(self, bgr_face_112x112: np.ndarray) -> np.ndarray:
+        """Extract 512-dim L2-normalized embedding from a 112x112 aligned face.
+
+        Args:
+            bgr_face_112x112: Aligned face crop, BGR, shape (112, 112, 3).
+
+        Returns:
+            L2-normalized embedding of shape (512,).
+        """
+        if self._backend is None:
+            return np.zeros(512, dtype=np.float32)
+
+        # Preprocess: BGR->RGB, /255, subtract 0.5, divide 0.5 -> [1,3,112,112]
+        rgb = cv2.cvtColor(bgr_face_112x112, cv2.COLOR_BGR2RGB).astype(np.float32)
+        rgb = rgb / 255.0
+        rgb = (rgb - 0.5) / 0.5
+        blob = rgb.transpose(2, 0, 1)[np.newaxis]  # [1, 3, 112, 112]
+        blob = np.ascontiguousarray(blob)
+
+        output = self._backend.infer(blob)
+        embedding = output.flatten()[:512].astype(np.float32)
+
+        # L2 normalize
+        norm = np.linalg.norm(embedding)
+        if norm > 0:
+            embedding = embedding / norm
+        return embedding
+
+    def align_and_embed(self, bgr_image: np.ndarray, landmarks_10: list[float]) -> np.ndarray:
+        """Align face using landmarks and extract embedding.
+
+        Args:
+            bgr_image: Full BGR image.
+            landmarks_10: Flat list of 10 floats from SCRFD detection.
+
+        Returns:
+            L2-normalized embedding of shape (512,).
+        """
+        aligned = align_face(bgr_image, landmarks_10)
+        return self.embed(aligned)
+
+    def load_gallery(self, gallery_path: str) -> None:
+        """Load gallery from .npz file with JSON metadata sidecar.
+
+        Args:
+            gallery_path: Path to the .npz gallery file.
+        """
+        import json
+
+        if not os.path.isfile(gallery_path):
+            logger.info('No gallery file at %s, starting empty.', gallery_path)
+            self.gallery = {}
+            return
+
+        data = np.load(gallery_path, allow_pickle=False)
+        meta_path = gallery_path.replace('.npz', '_meta.json')
+
+        if os.path.isfile(meta_path):
+            with open(meta_path, 'r') as f:
+                meta = json.load(f)
+        else:
+            meta = {}
+
+        self.gallery = {}
+        for key in data.files:
+            pid = int(key)
+            embedding = data[key].astype(np.float32)
+            norm = np.linalg.norm(embedding)
+            if norm > 0:
+                embedding = embedding / norm
+            info = meta.get(str(pid), {})
+            self.gallery[pid] = {
+                'name': info.get('name', f'person_{pid}'),
+                'embedding': embedding,
+                'samples': info.get('samples', 1),
+                'enrolled_at': info.get('enrolled_at', 0.0),
+            }
+        logger.info('Gallery loaded: %d persons from %s', len(self.gallery), gallery_path)
+
+    def save_gallery(self, gallery_path: str) -> None:
+        """Save gallery to .npz file with JSON metadata sidecar.
+
+        Args:
+            gallery_path: Path to the .npz gallery file.
+        """
+        import json
+
+        arrays = {}
+        meta = {}
+        for pid, info in self.gallery.items():
+            arrays[str(pid)] = info['embedding']
+            meta[str(pid)] = {
+                'name': info['name'],
+                'samples': info['samples'],
+                'enrolled_at': info['enrolled_at'],
+            }
+
+        os.makedirs(os.path.dirname(gallery_path) or '.', exist_ok=True)
+        np.savez(gallery_path, **arrays)
+
+        meta_path = gallery_path.replace('.npz', '_meta.json')
+        with open(meta_path, 'w') as f:
+            json.dump(meta, f, indent=2)
+        logger.info('Gallery saved: %d persons to %s', len(self.gallery), gallery_path)
+
+    def match(self, embedding: np.ndarray, threshold: float = 0.35) -> tuple[int, str, float]:
+        """Match an embedding against the gallery.
+
+        Args:
+            embedding: L2-normalized query embedding of shape (512,).
+            threshold: Minimum cosine similarity for a match.
+
+        Returns:
+            (person_id, person_name, score) or (-1, '', 0.0) if no match.
+        """
+        if not self.gallery:
+            return (-1, '', 0.0)
+
+        best_pid = -1
+        best_name = ''
+        best_score = 0.0
+
+        for pid, info in self.gallery.items():
+            score = float(np.dot(embedding, info['embedding']))
+            if score > best_score:
+                best_score = score
+                best_pid = pid
+                best_name = info['name']
+
+        if best_score >= threshold:
+            return (best_pid, best_name, best_score)
+        return (-1, '', 0.0)
+
+    def enroll(self, person_id: int, person_name: str, embeddings_list: list[np.ndarray]) -> None:
+        """Enroll a person by averaging multiple embeddings.
+
+        Args:
+            person_id: Unique integer ID for this person.
+            person_name: Human-readable name.
+            embeddings_list: List of L2-normalized embeddings to average.
+        """
+        import time as _time
+
+        if not embeddings_list:
+            return
+
+        mean_emb = np.mean(embeddings_list, axis=0).astype(np.float32)
+        norm = np.linalg.norm(mean_emb)
+        if norm > 0:
+            mean_emb = mean_emb / norm
+
+        self.gallery[person_id] = {
+            'name': person_name,
+            'embedding': mean_emb,
+            'samples': len(embeddings_list),
+            'enrolled_at': _time.time(),
+        }
+        logger.info('Enrolled person %d (%s) with %d samples.',
+                     person_id, person_name, len(embeddings_list))

jetson/ros2_ws/src/saltybot_social_face/saltybot_social_face/enrollment_cli.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+"""
+enrollment_cli.py -- CLI tool for enrolling persons via the /social/enroll service.
+
+Usage:
+    ros2 run saltybot_social_face enrollment_cli -- --name Alice --mode face --samples 10
+"""
+
+import argparse
+import sys
+
+import rclpy
+from rclpy.node import Node
+
+from saltybot_social_msgs.srv import EnrollPerson
+
+
+class EnrollmentCLI(Node):
+    """Simple CLI node that calls the EnrollPerson service."""
+
+    def __init__(self, name: str, mode: str, n_samples: int):
+        super().__init__('enrollment_cli')
+        self._client = self.create_client(EnrollPerson, '/social/enroll')
+
+        self.get_logger().info('Waiting for /social/enroll service...')
+        if not self._client.wait_for_service(timeout_sec=10.0):
+            self.get_logger().error('Service /social/enroll not available.')
+            return
+
+        request = EnrollPerson.Request()
+        request.name = name
+        request.mode = mode
+        request.n_samples = n_samples
+
+        self.get_logger().info(
+            'Enrolling "%s" (mode=%s, samples=%d)...', name, mode, n_samples)
+
+        future = self._client.call_async(request)
+        rclpy.spin_until_future_complete(self, future, timeout_sec=120.0)
+
+        if future.result() is not None:
+            result = future.result()
+            if result.success:
+                self.get_logger().info(
+                    'Enrollment successful: person_id=%d, %s',
+                    result.person_id, result.message)
+            else:
+                self.get_logger().error(
+                    'Enrollment failed: %s', result.message)
+        else:
+            self.get_logger().error('Enrollment service call timed out or failed.')
+
+
+def main(args=None):
+    """Entry point for enrollment CLI."""
+    parser = argparse.ArgumentParser(description='Enroll a person for face recognition.')
+    parser.add_argument('--name', type=str, required=True,
+                        help='Name of the person to enroll.')
+    parser.add_argument('--mode', type=str, default='face',
+                        choices=['face', 'voice', 'both'],
+                        help='Enrollment mode (default: face).')
+    parser.add_argument('--samples', type=int, default=10,
+                        help='Number of face samples to collect (default: 10).')
+
+    # Parse only known args so ROS2 remapping args pass through
+    parsed, remaining = parser.parse_known_args(args=sys.argv[1:])
+
+    rclpy.init(args=remaining)
+    node = EnrollmentCLI(parsed.name, parsed.mode, parsed.samples)
+    try:
+        pass  # Node does all work in __init__
+    finally:
+        node.destroy_node()
+        rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()

jetson/ros2_ws/src/saltybot_social_face/saltybot_social_face/face_gallery.py

@@ -0,0 +1,206 @@
+"""
+face_gallery.py -- Persistent face embedding gallery backed by numpy .npz + JSON.
+
+Thread-safe gallery storage for face recognition.  Embeddings are stored in a
+.npz file, with a sidecar metadata.json containing names, sample counts, and
+enrollment timestamps.  Auto-increment IDs start at 1.
+"""
+
+import json
+import logging
+import os
+import threading
+import time
+from typing import Optional
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+class FaceGallery:
+    """Persistent, thread-safe face embedding gallery.
+
+    Args:
+        gallery_dir: Directory for gallery.npz and metadata.json files.
+    """
+
+    def __init__(self, gallery_dir: str):
+        self._gallery_dir = gallery_dir
+        self._npz_path = os.path.join(gallery_dir, 'gallery.npz')
+        self._meta_path = os.path.join(gallery_dir, 'metadata.json')
+        self._gallery: dict[int, dict] = {}
+        self._next_id = 1
+        self._lock = threading.Lock()
+
+    def load(self) -> None:
+        """Load gallery from disk. Populates internal gallery dict."""
+        with self._lock:
+            self._gallery = {}
+            self._next_id = 1
+
+            if not os.path.isfile(self._npz_path):
+                logger.info('No gallery file at %s, starting empty.', self._npz_path)
+                return
+
+            data = np.load(self._npz_path, allow_pickle=False)
+            meta: dict = {}
+            if os.path.isfile(self._meta_path):
+                with open(self._meta_path, 'r') as f:
+                    meta = json.load(f)
+
+            for key in data.files:
+                pid = int(key)
+                embedding = data[key].astype(np.float32)
+                norm = np.linalg.norm(embedding)
+                if norm > 0:
+                    embedding = embedding / norm
+                info = meta.get(str(pid), {})
+                self._gallery[pid] = {
+                    'name': info.get('name', f'person_{pid}'),
+                    'embedding': embedding,
+                    'samples': info.get('samples', 1),
+                    'enrolled_at': info.get('enrolled_at', 0.0),
+                }
+                if pid >= self._next_id:
+                    self._next_id = pid + 1
+
+            logger.info('Gallery loaded: %d persons from %s',
+                        len(self._gallery), self._npz_path)
+
+    def save(self) -> None:
+        """Save gallery to disk (npz + JSON sidecar)."""
+        with self._lock:
+            os.makedirs(self._gallery_dir, exist_ok=True)
+
+            arrays = {}
+            meta = {}
+            for pid, info in self._gallery.items():
+                arrays[str(pid)] = info['embedding']
+                meta[str(pid)] = {
+                    'name': info['name'],
+                    'samples': info['samples'],
+                    'enrolled_at': info['enrolled_at'],
+                }
+
+            np.savez(self._npz_path, **arrays)
+            with open(self._meta_path, 'w') as f:
+                json.dump(meta, f, indent=2)
+
+            logger.info('Gallery saved: %d persons to %s',
+                        len(self._gallery), self._npz_path)
+
+    def add_person(self, name: str, embedding: np.ndarray, samples: int = 1) -> int:
+        """Add a new person to the gallery.
+
+        Args:
+            name: Person's name.
+            embedding: L2-normalized 512-dim embedding.
+            samples: Number of samples used to compute the embedding.
+
+        Returns:
+            Assigned person_id (auto-increment integer).
+        """
+        with self._lock:
+            pid = self._next_id
+            self._next_id += 1
+            emb = embedding.astype(np.float32)
+            norm = np.linalg.norm(emb)
+            if norm > 0:
+                emb = emb / norm
+            self._gallery[pid] = {
+                'name': name,
+                'embedding': emb,
+                'samples': samples,
+                'enrolled_at': time.time(),
+            }
+            logger.info('Added person %d (%s), %d samples.', pid, name, samples)
+            return pid
+
+    def update_name(self, person_id: int, new_name: str) -> bool:
+        """Update a person's name.
+
+        Args:
+            person_id: The ID of the person to update.
+            new_name: New name string.
+
+        Returns:
+            True if the person was found and updated.
+        """
+        with self._lock:
+            if person_id not in self._gallery:
+                return False
+            self._gallery[person_id]['name'] = new_name
+            return True
+
+    def delete_person(self, person_id: int) -> bool:
+        """Remove a person from the gallery.
+
+        Args:
+            person_id: The ID of the person to delete.
+
+        Returns:
+            True if the person was found and removed.
+        """
+        with self._lock:
+            if person_id not in self._gallery:
+                return False
+            del self._gallery[person_id]
+            logger.info('Deleted person %d.', person_id)
+            return True
+
+    def get_all(self) -> list[dict]:
+        """Get all gallery entries.
+
+        Returns:
+            List of dicts with keys: person_id, name, embedding, samples, enrolled_at.
+        """
+        with self._lock:
+            result = []
+            for pid, info in self._gallery.items():
+                result.append({
+                    'person_id': pid,
+                    'name': info['name'],
+                    'embedding': info['embedding'].copy(),
+                    'samples': info['samples'],
+                    'enrolled_at': info['enrolled_at'],
+                })
+            return result
+
+    def match(self, query_embedding: np.ndarray, threshold: float = 0.35) -> tuple[int, str, float]:
+        """Match a query embedding against the gallery using cosine similarity.
+
+        Args:
+            query_embedding: L2-normalized 512-dim embedding.
+            threshold: Minimum cosine similarity for a match.
+
+        Returns:
+            (person_id, name, score) or (-1, '', 0.0) if no match.
+        """
+        with self._lock:
+            if not self._gallery:
+                return (-1, '', 0.0)
+
+            best_pid = -1
+            best_name = ''
+            best_score = 0.0
+
+            query = query_embedding.astype(np.float32)
+            norm = np.linalg.norm(query)
+            if norm > 0:
+                query = query / norm
+
+            for pid, info in self._gallery.items():
+                score = float(np.dot(query, info['embedding']))
+                if score > best_score:
+                    best_score = score
+                    best_pid = pid
+                    best_name = info['name']
+
+            if best_score >= threshold:
+                return (best_pid, best_name, best_score)
+            return (-1, '', 0.0)
+
+    def __len__(self) -> int:
+        with self._lock:
+            return len(self._gallery)

jetson/ros2_ws/src/saltybot_social_face/saltybot_social_face/face_recognition_node.py

@@ -0,0 +1,431 @@
+"""
+face_recognition_node.py -- ROS2 node for SCRFD face detection + ArcFace recognition.
+
+Pipeline:
+  1. Subscribe to /camera/color/image_raw (RealSense D435i color stream).
+  2. Run SCRFD face detection (TensorRT FP16 or ONNX fallback).
+  3. For each detected face, align and extract ArcFace embedding.
+  4. Match embedding against persistent gallery.
+  5. Publish FaceDetectionArray with identified faces.
+
+Services:
+  /social/enroll           -- Enroll a new person (collects N face samples).
+  /social/persons/list     -- List all enrolled persons.
+  /social/persons/delete   -- Delete a person from the gallery.
+  /social/persons/update   -- Update a person's name.
+"""
+
+import time
+
+import numpy as np
+
+import rclpy
+from rclpy.node import Node
+from rclpy.qos import QoSProfile, ReliabilityPolicy, HistoryPolicy, DurabilityPolicy
+
+import cv2
+from cv_bridge import CvBridge
+
+from sensor_msgs.msg import Image
+from builtin_interfaces.msg import Time
+
+from saltybot_social_msgs.msg import (
+    FaceDetection,
+    FaceDetectionArray,
+    FaceEmbedding,
+    FaceEmbeddingArray,
+)
+from saltybot_social_msgs.srv import (
+    EnrollPerson,
+    ListPersons,
+    DeletePerson,
+    UpdatePerson,
+)
+
+from .scrfd_detector import SCRFDDetector
+from .arcface_recognizer import ArcFaceRecognizer
+from .face_gallery import FaceGallery
+
+
+class FaceRecognitionNode(Node):
+    """ROS2 node: SCRFD face detection + ArcFace gallery matching."""
+
+    def __init__(self):
+        super().__init__('face_recognizer')
+        self._bridge = CvBridge()
+        self._frame_count = 0
+        self._fps_t0 = time.monotonic()
+
+        # -- Parameters --------------------------------------------------------
+        self.declare_parameter('scrfd_engine_path',
+                               '/mnt/nvme/saltybot/models/scrfd_2.5g.engine')
+        self.declare_parameter('scrfd_onnx_path',
+                               '/mnt/nvme/saltybot/models/scrfd_2.5g_bnkps.onnx')
+        self.declare_parameter('arcface_engine_path',
+                               '/mnt/nvme/saltybot/models/arcface_r50.engine')
+        self.declare_parameter('arcface_onnx_path',
+                               '/mnt/nvme/saltybot/models/arcface_r50.onnx')
+        self.declare_parameter('gallery_dir', '/mnt/nvme/saltybot/gallery')
+        self.declare_parameter('recognition_threshold', 0.35)
+        self.declare_parameter('publish_debug_image', False)
+        self.declare_parameter('max_faces', 10)
+        self.declare_parameter('scrfd_conf_thresh', 0.5)
+
+        self._recognition_threshold = self.get_parameter('recognition_threshold').value
+        self._pub_debug_flag = self.get_parameter('publish_debug_image').value
+        self._max_faces = self.get_parameter('max_faces').value
+
+        # -- Models ------------------------------------------------------------
+        self._detector = SCRFDDetector(
+            engine_path=self.get_parameter('scrfd_engine_path').value,
+            onnx_path=self.get_parameter('scrfd_onnx_path').value,
+            conf_thresh=self.get_parameter('scrfd_conf_thresh').value,
+        )
+        self._recognizer = ArcFaceRecognizer(
+            engine_path=self.get_parameter('arcface_engine_path').value,
+            onnx_path=self.get_parameter('arcface_onnx_path').value,
+        )
+
+        # -- Gallery -----------------------------------------------------------
+        gallery_dir = self.get_parameter('gallery_dir').value
+        self._gallery = FaceGallery(gallery_dir)
+        self._gallery.load()
+        self.get_logger().info('Gallery loaded: %d persons.', len(self._gallery))
+
+        # -- Enrollment state --------------------------------------------------
+        self._enrolling = None  # {name, samples_needed, collected: [embeddings]}
+
+        # -- QoS profiles ------------------------------------------------------
+        best_effort_qos = QoSProfile(
+            reliability=ReliabilityPolicy.BEST_EFFORT,
+            history=HistoryPolicy.KEEP_LAST,
+            depth=1,
+        )
+        reliable_qos = QoSProfile(
+            reliability=ReliabilityPolicy.RELIABLE,
+            durability=DurabilityPolicy.TRANSIENT_LOCAL,
+            history=HistoryPolicy.KEEP_LAST,
+            depth=1,
+        )
+
+        # -- Subscribers -------------------------------------------------------
+        self.create_subscription(
+            Image,
+            '/camera/color/image_raw',
+            self._on_image,
+            best_effort_qos,
+        )
+
+        # -- Publishers --------------------------------------------------------
+        self._pub_detections = self.create_publisher(
+            FaceDetectionArray, '/social/faces/detections', best_effort_qos)
+        self._pub_embeddings = self.create_publisher(
+            FaceEmbeddingArray, '/social/faces/embeddings', reliable_qos)
+        if self._pub_debug_flag:
+            self._pub_debug_img = self.create_publisher(
+                Image, '/social/faces/debug_image', best_effort_qos)
+
+        # -- Services ----------------------------------------------------------
+        self.create_service(EnrollPerson, '/social/enroll', self._handle_enroll)
+        self.create_service(ListPersons, '/social/persons/list', self._handle_list)
+        self.create_service(DeletePerson, '/social/persons/delete', self._handle_delete)
+        self.create_service(UpdatePerson, '/social/persons/update', self._handle_update)
+
+        # Publish initial gallery state
+        self._publish_gallery_embeddings()
+
+        self.get_logger().info('FaceRecognitionNode ready.')
+
+    # -- Image callback --------------------------------------------------------
+
+    def _on_image(self, msg: Image):
+        """Process incoming camera frame: detect, recognize, publish."""
+        try:
+            bgr = self._bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
+        except Exception as e:
+            self.get_logger().error('Image decode error: %s', str(e),
+                                    throttle_duration_sec=5.0)
+            return
+
+        # Detect faces
+        detections = self._detector.detect(bgr)
+
+        # Limit face count
+        if len(detections) > self._max_faces:
+            detections = sorted(detections, key=lambda d: d['score'], reverse=True)
+            detections = detections[:self._max_faces]
+
+        # Build output message
+        det_array = FaceDetectionArray()
+        det_array.header = msg.header
+
+        for det in detections:
+            # Extract embedding and match gallery
+            embedding = self._recognizer.align_and_embed(bgr, det['kps'])
+            pid, pname, score = self._gallery.match(
+                embedding, self._recognition_threshold)
+
+            # Handle enrollment: collect embedding from largest face
+            if self._enrolling is not None:
+                self._enrollment_collect(det, embedding)
+
+            # Build FaceDetection message
+            face_msg = FaceDetection()
+            face_msg.header = msg.header
+            face_msg.face_id = pid
+            face_msg.person_name = pname
+            face_msg.confidence = det['score']
+            face_msg.recognition_score = score
+
+            bbox = det['bbox']
+            face_msg.bbox_x = bbox[0]
+            face_msg.bbox_y = bbox[1]
+            face_msg.bbox_w = bbox[2] - bbox[0]
+            face_msg.bbox_h = bbox[3] - bbox[1]
+
+            kps = det['kps']
+            for i in range(10):
+                face_msg.landmarks[i] = kps[i]
+
+            det_array.faces.append(face_msg)
+
+        self._pub_detections.publish(det_array)
+
+        # Debug image
+        if self._pub_debug_flag and hasattr(self, '_pub_debug_img'):
+            debug_img = self._draw_debug(bgr, detections, det_array.faces)
+            self._pub_debug_img.publish(
+                self._bridge.cv2_to_imgmsg(debug_img, encoding='bgr8'))
+
+        # FPS logging
+        self._frame_count += 1
+        if self._frame_count % 30 == 0:
+            elapsed = time.monotonic() - self._fps_t0
+            fps = 30.0 / elapsed if elapsed > 0 else 0.0
+            self._fps_t0 = time.monotonic()
+            self.get_logger().info(
+                'FPS: %.1f | faces: %d', fps, len(detections))
+
+    # -- Enrollment logic ------------------------------------------------------
+
+    def _enrollment_collect(self, det: dict, embedding: np.ndarray):
+        """Collect an embedding sample during enrollment (largest face only)."""
+        if self._enrolling is None:
+            return
+
+        # Only collect from the largest face (by bbox area)
+        bbox = det['bbox']
+        area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
+
+        if not hasattr(self, '_enroll_best_area'):
+            self._enroll_best_area = 0.0
+            self._enroll_best_embedding = None
+
+        if area > self._enroll_best_area:
+            self._enroll_best_area = area
+            self._enroll_best_embedding = embedding
+
+    def _enrollment_frame_end(self):
+        """Called at end of each frame to finalize enrollment sample collection."""
+        if self._enrolling is None or self._enroll_best_embedding is None:
+            return
+
+        self._enrolling['collected'].append(self._enroll_best_embedding)
+        self._enroll_best_area = 0.0
+        self._enroll_best_embedding = None
+
+        collected = len(self._enrolling['collected'])
+        needed = self._enrolling['samples_needed']
+        self.get_logger().info('Enrollment: %d/%d samples for "%s".',
+                               collected, needed, self._enrolling['name'])
+
+        if collected >= needed:
+            # Finalize enrollment
+            name = self._enrolling['name']
+            embeddings = self._enrolling['collected']
+            mean_emb = np.mean(embeddings, axis=0).astype(np.float32)
+            norm = np.linalg.norm(mean_emb)
+            if norm > 0:
+                mean_emb = mean_emb / norm
+
+            pid = self._gallery.add_person(name, mean_emb, samples=len(embeddings))
+            self._gallery.save()
+            self._publish_gallery_embeddings()
+
+            self.get_logger().info('Enrollment complete: person %d (%s).', pid, name)
+
+            # Store result for the service callback
+            self._enrolling['result_pid'] = pid
+            self._enrolling['done'] = True
+            self._enrolling = None
+
+    # -- Service handlers ------------------------------------------------------
+
+    def _handle_enroll(self, request, response):
+        """Handle EnrollPerson service: start collecting face samples."""
+        name = request.name.strip()
+        if not name:
+            response.success = False
+            response.message = 'Name cannot be empty.'
+            response.person_id = -1
+            return response
+
+        n_samples = request.n_samples if request.n_samples > 0 else 10
+
+        self.get_logger().info('Starting enrollment for "%s" (%d samples).',
+                               name, n_samples)
+
+        # Set enrollment state — frames will collect embeddings
+        self._enrolling = {
+            'name': name,
+            'samples_needed': n_samples,
+            'collected': [],
+            'done': False,
+            'result_pid': -1,
+        }
+        self._enroll_best_area = 0.0
+        self._enroll_best_embedding = None
+
+        # Spin until enrollment is done (blocking service)
+        rate = self.create_rate(10)  # 10 Hz check
+        timeout_sec = n_samples * 2.0 + 10.0  # generous timeout
+        t0 = time.monotonic()
+
+        while not self._enrolling.get('done', False):
+            # Finalize any pending frame collection
+            self._enrollment_frame_end()
+
+            if time.monotonic() - t0 > timeout_sec:
+                self._enrolling = None
+                response.success = False
+                response.message = f'Enrollment timed out after {timeout_sec:.0f}s.'
+                response.person_id = -1
+                return response
+
+            rclpy.spin_once(self, timeout_sec=0.1)
+
+        response.success = True
+        response.message = f'Enrolled "{name}" with {n_samples} samples.'
+        response.person_id = self._enrolling.get('result_pid', -1) if self._enrolling else -1
+
+        # Clean up (already set to None in _enrollment_frame_end on success)
+        return response
+
+    def _handle_list(self, request, response):
+        """Handle ListPersons service: return all gallery entries."""
+        entries = self._gallery.get_all()
+        for entry in entries:
+            emb_msg = FaceEmbedding()
+            emb_msg.person_id = entry['person_id']
+            emb_msg.person_name = entry['name']
+            emb_msg.embedding = entry['embedding'].tolist()
+            emb_msg.sample_count = entry['samples']
+
+            secs = int(entry['enrolled_at'])
+            nsecs = int((entry['enrolled_at'] - secs) * 1e9)
+            emb_msg.enrolled_at = Time(sec=secs, nanosec=nsecs)
+
+            response.persons.append(emb_msg)
+        return response
+
+    def _handle_delete(self, request, response):
+        """Handle DeletePerson service: remove a person from the gallery."""
+        if self._gallery.delete_person(request.person_id):
+            self._gallery.save()
+            self._publish_gallery_embeddings()
+            response.success = True
+            response.message = f'Deleted person {request.person_id}.'
+        else:
+            response.success = False
+            response.message = f'Person {request.person_id} not found.'
+        return response
+
+    def _handle_update(self, request, response):
+        """Handle UpdatePerson service: rename a person."""
+        new_name = request.new_name.strip()
+        if not new_name:
+            response.success = False
+            response.message = 'New name cannot be empty.'
+            return response
+
+        if self._gallery.update_name(request.person_id, new_name):
+            self._gallery.save()
+            self._publish_gallery_embeddings()
+            response.success = True
+            response.message = f'Updated person {request.person_id} to "{new_name}".'
+        else:
+            response.success = False
+            response.message = f'Person {request.person_id} not found.'
+        return response
+
+    # -- Gallery publishing ----------------------------------------------------
+
+    def _publish_gallery_embeddings(self):
+        """Publish current gallery as FaceEmbeddingArray (latched-like)."""
+        entries = self._gallery.get_all()
+        msg = FaceEmbeddingArray()
+        msg.header.stamp = self.get_clock().now().to_msg()
+
+        for entry in entries:
+            emb_msg = FaceEmbedding()
+            emb_msg.person_id = entry['person_id']
+            emb_msg.person_name = entry['name']
+            emb_msg.embedding = entry['embedding'].tolist()
+            emb_msg.sample_count = entry['samples']
+
+            secs = int(entry['enrolled_at'])
+            nsecs = int((entry['enrolled_at'] - secs) * 1e9)
+            emb_msg.enrolled_at = Time(sec=secs, nanosec=nsecs)
+
+            msg.embeddings.append(emb_msg)
+
+        self._pub_embeddings.publish(msg)
+
+    # -- Debug image -----------------------------------------------------------
+
+    def _draw_debug(self, bgr: np.ndarray, detections: list[dict],
+                    face_msgs: list) -> np.ndarray:
+        """Draw bounding boxes, landmarks, and names on the image."""
+        vis = bgr.copy()
+        for det, face_msg in zip(detections, face_msgs):
+            bbox = det['bbox']
+            x1, y1, x2, y2 = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
+
+            # Color: green if recognized, yellow if unknown
+            if face_msg.face_id >= 0:
+                color = (0, 255, 0)
+                label = f'{face_msg.person_name} ({face_msg.recognition_score:.2f})'
+            else:
+                color = (0, 255, 255)
+                label = f'unknown ({face_msg.confidence:.2f})'
+
+            cv2.rectangle(vis, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(vis, label, (x1, y1 - 8),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
+
+            # Draw landmarks
+            kps = det['kps']
+            for k in range(5):
+                px, py = int(kps[k * 2]), int(kps[k * 2 + 1])
+                cv2.circle(vis, (px, py), 2, (0, 0, 255), -1)
+
+        return vis
+
+
+# -- Entry point ---------------------------------------------------------------
+
+def main(args=None):
+    """ROS2 entry point for face_recognition node."""
+    rclpy.init(args=args)
+    node = FaceRecognitionNode()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()

jetson/ros2_ws/src/saltybot_social_face/saltybot_social_face/scrfd_detector.py

@@ -0,0 +1,350 @@
+"""
+scrfd_detector.py -- SCRFD face detection with TensorRT FP16 + ONNX fallback.
+
+SCRFD (Sample and Computation Redistribution for Face Detection) produces
+9 output tensors across 3 strides (8, 16, 32), each with score, bbox, and
+keypoint branches.  This module handles anchor generation, bbox/keypoint
+decoding, and NMS to produce final face detections.
+"""
+
+import os
+import logging
+from typing import Optional
+
+import numpy as np
+import cv2
+
+logger = logging.getLogger(__name__)
+
+_STRIDES = [8, 16, 32]
+_NUM_ANCHORS = 2  # anchors per cell per stride
+
+
+# -- Inference backends --------------------------------------------------------
+
+class _TRTBackend:
+    """TensorRT inference engine for SCRFD."""
+
+    def __init__(self, engine_path: str):
+        import tensorrt as trt
+        import pycuda.driver as cuda
+        import pycuda.autoinit  # noqa: F401
+
+        self._cuda = cuda
+        trt_logger = trt.Logger(trt.Logger.WARNING)
+        with open(engine_path, 'rb') as f, trt.Runtime(trt_logger) as runtime:
+            self._engine = runtime.deserialize_cuda_engine(f.read())
+        self._context = self._engine.create_execution_context()
+
+        self._inputs = []
+        self._outputs = []
+        self._output_names = []
+        self._bindings = []
+        for i in range(self._engine.num_io_tensors):
+            name = self._engine.get_tensor_name(i)
+            dtype = trt.nptype(self._engine.get_tensor_dtype(name))
+            shape = tuple(self._engine.get_tensor_shape(name))
+            nbytes = int(np.prod(shape)) * np.dtype(dtype).itemsize
+            host_mem = cuda.pagelocked_empty(shape, dtype)
+            device_mem = cuda.mem_alloc(nbytes)
+            self._bindings.append(int(device_mem))
+            if self._engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
+                self._inputs.append({'host': host_mem, 'device': device_mem})
+            else:
+                self._outputs.append({'host': host_mem, 'device': device_mem,
+                                      'shape': shape})
+                self._output_names.append(name)
+        self._stream = cuda.Stream()
+
+    def infer(self, input_data: np.ndarray) -> list[np.ndarray]:
+        """Run inference and return output tensors."""
+        np.copyto(self._inputs[0]['host'], input_data.ravel())
+        self._cuda.memcpy_htod_async(
+            self._inputs[0]['device'], self._inputs[0]['host'], self._stream)
+        self._context.execute_async_v2(self._bindings, self._stream.handle)
+        results = []
+        for out in self._outputs:
+            self._cuda.memcpy_dtoh_async(out['host'], out['device'], self._stream)
+        self._stream.synchronize()
+        for out in self._outputs:
+            results.append(out['host'].reshape(out['shape']).copy())
+        return results
+
+
+class _ONNXBackend:
+    """ONNX Runtime inference (CUDA EP with CPU fallback)."""
+
+    def __init__(self, onnx_path: str):
+        import onnxruntime as ort
+        providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+        self._session = ort.InferenceSession(onnx_path, providers=providers)
+        self._input_name = self._session.get_inputs()[0].name
+        self._output_names = [o.name for o in self._session.get_outputs()]
+
+    def infer(self, input_data: np.ndarray) -> list[np.ndarray]:
+        """Run inference and return output tensors."""
+        return self._session.run(None, {self._input_name: input_data})
+
+
+# -- NMS ----------------------------------------------------------------------
+
+def _nms(boxes: np.ndarray, scores: np.ndarray, iou_thresh: float) -> list[int]:
+    """Non-maximum suppression. boxes: [N, 4] as x1,y1,x2,y2."""
+    if len(boxes) == 0:
+        return []
+
+    x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
+    areas = (x2 - x1) * (y2 - y1)
+    order = scores.argsort()[::-1]
+    keep = []
+
+    while order.size > 0:
+        i = order[0]
+        keep.append(int(i))
+        if order.size == 1:
+            break
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+        inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+        iou = inter / (areas[i] + areas[order[1:]] - inter + 1e-6)
+        remaining = np.where(iou <= iou_thresh)[0]
+        order = order[remaining + 1]
+
+    return keep
+
+
+# -- Anchor generation ---------------------------------------------------------
+
+def _generate_anchors(input_h: int, input_w: int) -> dict[int, np.ndarray]:
+    """Generate anchor centers for each stride.
+
+    Returns dict mapping stride -> array of shape [H*W*num_anchors, 2],
+    where each row is (cx, cy) in input pixel coordinates.
+    """
+    anchors = {}
+    for stride in _STRIDES:
+        feat_h = input_h // stride
+        feat_w = input_w // stride
+        grid_y, grid_x = np.mgrid[:feat_h, :feat_w]
+        centers = np.stack([grid_x.ravel(), grid_y.ravel()], axis=1).astype(np.float32)
+        centers = (centers + 0.5) * stride
+        # Repeat for num_anchors per cell
+        centers = np.tile(centers, (_NUM_ANCHORS, 1))  # [H*W*2, 2]
+        # Interleave properly: [anchor0_cell0, anchor1_cell0, anchor0_cell1, ...]
+        centers = np.repeat(
+            np.stack([grid_x.ravel(), grid_y.ravel()], axis=1).astype(np.float32),
+            _NUM_ANCHORS, axis=0
+        )
+        centers = (centers + 0.5) * stride
+        anchors[stride] = centers
+    return anchors
+
+
+# -- Main detector class -------------------------------------------------------
+
+class SCRFDDetector:
+    """SCRFD face detector with TensorRT FP16 and ONNX fallback.
+
+    Args:
+        engine_path: Path to TensorRT engine file.
+        onnx_path: Path to ONNX model file (used if engine not available).
+        conf_thresh: Minimum confidence for detections.
+        nms_iou: IoU threshold for NMS.
+        input_size: Model input resolution (square).
+    """
+
+    def __init__(
+        self,
+        engine_path: str = '',
+        onnx_path: str = '',
+        conf_thresh: float = 0.5,
+        nms_iou: float = 0.4,
+        input_size: int = 640,
+    ):
+        self._conf_thresh = conf_thresh
+        self._nms_iou = nms_iou
+        self._input_size = input_size
+        self._backend: Optional[_TRTBackend | _ONNXBackend] = None
+        self._anchors = _generate_anchors(input_size, input_size)
+
+        # Try TRT first, then ONNX
+        if engine_path and os.path.isfile(engine_path):
+            try:
+                self._backend = _TRTBackend(engine_path)
+                logger.info('SCRFD TensorRT backend loaded: %s', engine_path)
+                return
+            except Exception as e:
+                logger.warning('SCRFD TRT load failed (%s), trying ONNX', e)
+
+        if onnx_path and os.path.isfile(onnx_path):
+            try:
+                self._backend = _ONNXBackend(onnx_path)
+                logger.info('SCRFD ONNX backend loaded: %s', onnx_path)
+                return
+            except Exception as e:
+                logger.error('SCRFD ONNX load failed: %s', e)
+
+        logger.error('No SCRFD model loaded. Detection will be unavailable.')
+
+    @property
+    def is_loaded(self) -> bool:
+        """Return True if a backend is loaded and ready."""
+        return self._backend is not None
+
+    def detect(self, bgr: np.ndarray) -> list[dict]:
+        """Detect faces in a BGR image.
+
+        Args:
+            bgr: Input image in BGR format, shape (H, W, 3).
+
+        Returns:
+            List of dicts with keys:
+                bbox: [x1, y1, x2, y2] in original image coordinates
+                kps: [x0,y0, x1,y1, ..., x4,y4] — 10 floats, 5 landmarks
+                score: detection confidence
+        """
+        if self._backend is None:
+            return []
+
+        orig_h, orig_w = bgr.shape[:2]
+        tensor, scale, pad_w, pad_h = self._preprocess(bgr)
+        outputs = self._backend.infer(tensor)
+        detections = self._decode_outputs(outputs)
+        detections = self._rescale(detections, scale, pad_w, pad_h, orig_w, orig_h)
+        return detections
+
+    def _preprocess(self, bgr: np.ndarray) -> tuple[np.ndarray, float, int, int]:
+        """Resize to input_size x input_size with letterbox padding, normalize."""
+        h, w = bgr.shape[:2]
+        size = self._input_size
+        scale = min(size / h, size / w)
+        new_w, new_h = int(w * scale), int(h * scale)
+        pad_w = (size - new_w) // 2
+        pad_h = (size - new_h) // 2
+
+        resized = cv2.resize(bgr, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
+        canvas = np.full((size, size, 3), 0, dtype=np.uint8)
+        canvas[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = resized
+
+        # Normalize: subtract 127.5, divide 128.0
+        blob = canvas.astype(np.float32)
+        blob = (blob - 127.5) / 128.0
+        # HWC -> NCHW
+        blob = blob.transpose(2, 0, 1)[np.newaxis]
+        blob = np.ascontiguousarray(blob)
+        return blob, scale, pad_w, pad_h
+
+    def _decode_outputs(self, outputs: list[np.ndarray]) -> list[dict]:
+        """Decode SCRFD 9-output format into face detections.
+
+        SCRFD outputs 9 tensors, 3 per stride (score, bbox, kps):
+            score_8, bbox_8, kps_8, score_16, bbox_16, kps_16, score_32, bbox_32, kps_32
+        """
+        all_scores = []
+        all_bboxes = []
+        all_kps = []
+
+        for idx, stride in enumerate(_STRIDES):
+            score_out = outputs[idx * 3].squeeze()       # [H*W*num_anchors]
+            bbox_out = outputs[idx * 3 + 1].squeeze()    # [H*W*num_anchors, 4]
+            kps_out = outputs[idx * 3 + 2].squeeze()     # [H*W*num_anchors, 10]
+
+            if score_out.ndim == 0:
+                continue
+
+            # Ensure proper shapes
+            if score_out.ndim == 1:
+                n = score_out.shape[0]
+            else:
+                n = score_out.shape[0]
+                score_out = score_out.ravel()
+
+            if bbox_out.ndim == 1:
+                bbox_out = bbox_out.reshape(-1, 4)
+            if kps_out.ndim == 1:
+                kps_out = kps_out.reshape(-1, 10)
+
+            # Filter by confidence
+            mask = score_out > self._conf_thresh
+            if not mask.any():
+                continue
+
+            scores = score_out[mask]
+            bboxes = bbox_out[mask]
+            kps = kps_out[mask]
+
+            anchors = self._anchors[stride]
+            # Trim or pad anchors to match output count
+            if anchors.shape[0] > n:
+                anchors = anchors[:n]
+            elif anchors.shape[0] < n:
+                continue
+
+            anchors = anchors[mask]
+
+            # Decode bboxes: center = anchor + pred[:2]*stride, size = exp(pred[2:])*stride
+            cx = anchors[:, 0] + bboxes[:, 0] * stride
+            cy = anchors[:, 1] + bboxes[:, 1] * stride
+            w = np.exp(bboxes[:, 2]) * stride
+            h = np.exp(bboxes[:, 3]) * stride
+            x1 = cx - w / 2.0
+            y1 = cy - h / 2.0
+            x2 = cx + w / 2.0
+            y2 = cy + h / 2.0
+            decoded_bboxes = np.stack([x1, y1, x2, y2], axis=1)
+
+            # Decode keypoints: kp = anchor + pred * stride
+            decoded_kps = np.zeros_like(kps)
+            for k in range(5):
+                decoded_kps[:, k * 2] = anchors[:, 0] + kps[:, k * 2] * stride
+                decoded_kps[:, k * 2 + 1] = anchors[:, 1] + kps[:, k * 2 + 1] * stride
+
+            all_scores.append(scores)
+            all_bboxes.append(decoded_bboxes)
+            all_kps.append(decoded_kps)
+
+        if not all_scores:
+            return []
+
+        scores = np.concatenate(all_scores)
+        bboxes = np.concatenate(all_bboxes)
+        kps = np.concatenate(all_kps)
+
+        # NMS
+        keep = _nms(bboxes, scores, self._nms_iou)
+
+        results = []
+        for i in keep:
+            results.append({
+                'bbox': bboxes[i].tolist(),
+                'kps': kps[i].tolist(),
+                'score': float(scores[i]),
+            })
+        return results
+
+    def _rescale(
+        self,
+        detections: list[dict],
+        scale: float,
+        pad_w: int,
+        pad_h: int,
+        orig_w: int,
+        orig_h: int,
+    ) -> list[dict]:
+        """Rescale detections from model input space to original image space."""
+        for det in detections:
+            bbox = det['bbox']
+            bbox[0] = max(0.0, (bbox[0] - pad_w) / scale)
+            bbox[1] = max(0.0, (bbox[1] - pad_h) / scale)
+            bbox[2] = min(float(orig_w), (bbox[2] - pad_w) / scale)
+            bbox[3] = min(float(orig_h), (bbox[3] - pad_h) / scale)
+            det['bbox'] = bbox
+
+            kps = det['kps']
+            for k in range(5):
+                kps[k * 2] = (kps[k * 2] - pad_w) / scale
+                kps[k * 2 + 1] = (kps[k * 2 + 1] - pad_h) / scale
+            det['kps'] = kps
+        return detections

jetson/ros2_ws/src/saltybot_social_face/scripts/build_face_trt_engines.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+"""
+build_face_trt_engines.py -- Build TensorRT FP16 engines for SCRFD and ArcFace.
+
+Converts ONNX model files to optimized TensorRT engines with FP16 precision
+for fast inference on Jetson Orin Nano Super.
+
+Usage:
+    python3 build_face_trt_engines.py \
+        --scrfd-onnx /path/to/scrfd_2.5g_bnkps.onnx \
+        --arcface-onnx /path/to/arcface_r50.onnx \
+        --output-dir /mnt/nvme/saltybot/models \
+        --fp16 --workspace-mb 1024
+"""
+
+import argparse
+import os
+import time
+
+
+def build_engine(onnx_path: str, engine_path: str, fp16: bool, workspace_mb: int):
+    """Build a TensorRT engine from an ONNX model.
+
+    Args:
+        onnx_path: Path to the source ONNX model file.
+        engine_path: Output path for the serialized TensorRT engine.
+        fp16: Enable FP16 precision.
+        workspace_mb: Maximum workspace size in megabytes.
+    """
+    import tensorrt as trt
+
+    logger = trt.Logger(trt.Logger.INFO)
+    builder = trt.Builder(logger)
+    network = builder.create_network(
+        1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+    parser = trt.OnnxParser(network, logger)
+
+    print(f'Parsing ONNX model: {onnx_path}')
+    t0 = time.monotonic()
+
+    with open(onnx_path, 'rb') as f:
+        if not parser.parse(f.read()):
+            for i in range(parser.num_errors):
+                print(f'  ONNX parse error: {parser.get_error(i)}')
+            raise RuntimeError(f'Failed to parse {onnx_path}')
+
+    parse_time = time.monotonic() - t0
+    print(f'  Parsed in {parse_time:.1f}s')
+
+    config = builder.create_builder_config()
+    config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE,
+                                  workspace_mb * (1 << 20))
+    if fp16:
+        if builder.platform_has_fast_fp16:
+            config.set_flag(trt.BuilderFlag.FP16)
+            print('  FP16 enabled.')
+        else:
+            print('  Warning: FP16 not supported on this platform, using FP32.')
+
+    print(f'Building engine (this may take several minutes)...')
+    t0 = time.monotonic()
+    serialized = builder.build_serialized_network(network, config)
+    build_time = time.monotonic() - t0
+
+    if serialized is None:
+        raise RuntimeError('Engine build failed.')
+
+    os.makedirs(os.path.dirname(engine_path) or '.', exist_ok=True)
+    with open(engine_path, 'wb') as f:
+        f.write(serialized)
+
+    size_mb = os.path.getsize(engine_path) / (1 << 20)
+    print(f'  Engine saved: {engine_path} ({size_mb:.1f} MB, built in {build_time:.1f}s)')
+
+
+def main():
+    """Main entry point for TRT engine building."""
+    parser = argparse.ArgumentParser(
+        description='Build TensorRT FP16 engines for SCRFD and ArcFace.')
+    parser.add_argument('--scrfd-onnx', type=str, default='',
+                        help='Path to SCRFD ONNX model.')
+    parser.add_argument('--arcface-onnx', type=str, default='',
+                        help='Path to ArcFace ONNX model.')
+    parser.add_argument('--output-dir', type=str,
+                        default='/mnt/nvme/saltybot/models',
+                        help='Output directory for engine files.')
+    parser.add_argument('--fp16', action='store_true', default=True,
+                        help='Enable FP16 precision (default: True).')
+    parser.add_argument('--no-fp16', action='store_false', dest='fp16',
+                        help='Disable FP16 (use FP32 only).')
+    parser.add_argument('--workspace-mb', type=int, default=1024,
+                        help='TRT workspace size in MB (default: 1024).')
+    args = parser.parse_args()
+
+    if not args.scrfd_onnx and not args.arcface_onnx:
+        parser.error('At least one of --scrfd-onnx or --arcface-onnx is required.')
+
+    if args.scrfd_onnx:
+        engine_path = os.path.join(args.output_dir, 'scrfd_2.5g.engine')
+        print(f'\n=== Building SCRFD engine ===')
+        build_engine(args.scrfd_onnx, engine_path, args.fp16, args.workspace_mb)
+
+    if args.arcface_onnx:
+        engine_path = os.path.join(args.output_dir, 'arcface_r50.engine')
+        print(f'\n=== Building ArcFace engine ===')
+        build_engine(args.arcface_onnx, engine_path, args.fp16, args.workspace_mb)
+
+    print('\nDone.')
+
+
+if __name__ == '__main__':
+    main()

jetson/ros2_ws/src/saltybot_social_face/setup.cfg

@@ -0,0 +1,4 @@
+[develop]
+script_dir=$base/lib/saltybot_social_face
+[install]
+install_scripts=$base/lib/saltybot_social_face

jetson/ros2_ws/src/saltybot_social_face/setup.py

@@ -0,0 +1,30 @@
+"""Setup for saltybot_social_face package."""
+
+from setuptools import find_packages, setup
+
+package_name = 'saltybot_social_face'
+
+setup(
+    name=package_name,
+    version='0.1.0',
+    packages=find_packages(exclude=['test']),
+    data_files=[
+        ('share/ament_index/resource_index/packages', ['resource/' + package_name]),
+        ('share/' + package_name, ['package.xml']),
+        ('share/' + package_name + '/launch', ['launch/face_recognition.launch.py']),
+        ('share/' + package_name + '/config', ['config/face_recognition_params.yaml']),
+    ],
+    install_requires=['setuptools'],
+    zip_safe=True,
+    maintainer='seb',
+    maintainer_email='seb@vayrette.com',
+    description='SCRFD face detection and ArcFace recognition for SaltyBot social interactions',
+    license='MIT',
+    tests_require=['pytest'],
+    entry_points={
+        'console_scripts': [
+            'face_recognition = saltybot_social_face.face_recognition_node:main',
+            'enrollment_cli = saltybot_social_face.enrollment_cli:main',
+        ],
+    },
+)

jetson/ros2_ws/src/saltybot_social_msgs/CMakeLists.txt

@@ -8,7 +8,7 @@ find_package(geometry_msgs REQUIRED)
 find_package(builtin_interfaces REQUIRED)
 
 rosidl_generate_interfaces(${PROJECT_NAME}
-  # Social perception (from sl-perception)
+  # Issue #80 — face detection + recognition
   "msg/FaceDetection.msg"
   "msg/FaceDetectionArray.msg"
   "msg/FaceEmbedding.msg"
@@ -19,7 +19,11 @@ rosidl_generate_interfaces(${PROJECT_NAME}
   "srv/ListPersons.srv"
   "srv/DeletePerson.srv"
   "srv/UpdatePerson.srv"
-  # Personality system (Issue #84)
+  # Issue #86 — LED expression + motor attention
+  "msg/Mood.msg"
+  "msg/Person.msg"
+  "msg/PersonArray.msg"
+  # Issue #84 — personality system
   "msg/PersonalityState.msg"
   "srv/QueryMood.srv"
   DEPENDENCIES std_msgs geometry_msgs builtin_interfaces

jetson/ros2_ws/src/saltybot_social_msgs/msg/Mood.msg

@@ -0,0 +1,7 @@
+# Mood.msg — social expression command sent to the LED display node.
+#
+# mood      : one of "happy", "curious", "annoyed", "playful", "idle"
+# intensity : 0.0 (off) to 1.0 (full brightness)
+
+string  mood
+float32 intensity

jetson/ros2_ws/src/saltybot_social_msgs/msg/Person.msg

@@ -0,0 +1,17 @@
+# Person.msg — single tracked person for social attention.
+#
+# bearing_rad : signed bearing in base_link frame (rad)
+#               positive = person to the left (CCW), negative = right (CW)
+# distance_m  : estimated distance in metres; 0 if unknown
+# confidence  : detection confidence 0..1
+# is_speaking : true if mic DOA or VAD identifies this person as speaking
+# source      : "camera" | "mic_doa"
+
+std_msgs/Header header
+
+int32   track_id
+float32 bearing_rad
+float32 distance_m
+float32 confidence
+bool    is_speaking
+string  source

jetson/ros2_ws/src/saltybot_social_msgs/msg/PersonArray.msg

@@ -0,0 +1,9 @@
+# PersonArray.msg — all detected persons plus the current attention target.
+#
+# persons   : all currently tracked persons
+# active_id : track_id of the active/speaking person; -1 if none
+
+std_msgs/Header header
+
+saltybot_social_msgs/Person[] persons
+int32 active_id

jetson/ros2_ws/src/saltybot_social_msgs/srv/GetFaceEmbedding.srv

@@ -0,0 +1,4 @@
+sensor_msgs/Image crop
+---
+bool success
+float32[512] embedding