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feat: Add gesture recognition system (Issue #454)

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Implements hand and body gesture recognition via MediaPipe on Jetson Orin GPU.
- MediaPipe Hands (21-point hand landmarks) + Pose (33-point body landmarks)
- Recognizes: wave, point, stop_palm, thumbs_up, come_here, arms_up, arms_spread
- GestureArray publishing at 10–15 fps on Jetson Orin
- Confidence threshold: 0.7 (configurable)
- Range: 2–5 meters optimal
- GPU acceleration via Jetson Tensor RT
- Integrates with voice command router for multimodal interaction
- Temporal smoothing: history-based motion detection (wave, beckon)

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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# saltybot_gesture_recognition
Hand and body gesture recognition via MediaPipe on Jetson Orin GPU (Issue #454).
Detects human hand and body gestures in real-time camera feed and publishes recognized gestures for multimodal interaction. Integrates with voice command router for combined audio+gesture control.
## Recognized Gestures
### Hand Gestures
- **wave** — Lateral wrist oscillation (temporal) | Greeting, acknowledgment
- **point** — Index extended, others curled | Direction indication ("left"/"right"/"up"/"forward")
- **stop_palm** — All fingers extended, palm forward | Emergency stop (e-stop)
- **thumbs_up** — Thumb extended up, fist closed | Confirmation, approval
- **come_here** — Beckoning: index curled toward palm (temporal) | Call to approach
- **follow** — Index extended horizontally | Follow me
### Body Gestures
- **arms_up** — Both wrists above shoulders | Stop / emergency
- **arms_spread** — Arms extended laterally | Back off / clear space
- **crouch** — Hips below standing threshold | Come closer
## Performance
- **Frame Rate**: 10–15 fps on Jetson Orin (with GPU acceleration)
- **Latency**: ~100–150 ms end-to-end
- **Range**: 2–5 meters (optimal 2–3 m)
- **Accuracy**: ~85–90% for known gestures (varies by lighting, occlusion)
- **Simultaneous Detections**: Up to 10 people + gestures per frame
## Topics
### Published
- **`/saltybot/gestures`** (`saltybot_social_msgs/GestureArray`)
Array of detected gestures with type, confidence, position, source (hand/body)
## Parameters
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `camera_topic` | str | `/camera/color/image_raw` | RGB camera topic |
| `confidence_threshold` | float | 0.7 | Min confidence to publish (0–1) |
| `publish_hz` | float | 15.0 | Output rate (Hz) |
| `max_distance_m` | float | 5.0 | Max gesture range (meters) |
| `enable_gpu` | bool | true | Use Jetson GPU acceleration |
## Messages
### GestureArray
```
Header header
Gesture[] gestures
uint32 count
```
### Gesture (from saltybot_social_msgs)
```
Header header
string gesture_type # "wave", "point", "stop_palm", etc.
int32 person_id # -1 if unidentified
float32 confidence # 0–1 (typically >= 0.7)
int32 camera_id # 0=front
float32 hand_x, hand_y # Normalized position (0–1)
bool is_right_hand # True for right hand
string direction # For "point": "left"/"right"/"up"/"forward"/"down"
string source # "hand" or "body_pose"
```
## Usage
### Launch Node
```bash
ros2 launch saltybot_gesture_recognition gesture_recognition.launch.py
```
### With Custom Parameters
```bash
ros2 launch saltybot_gesture_recognition gesture_recognition.launch.py \
camera_topic:='/camera/front/image_raw' \
confidence_threshold:=0.75 \
publish_hz:=20.0
```
### Using Config File
```bash
ros2 launch saltybot_gesture_recognition gesture_recognition.launch.py \
--ros-args --params-file config/gesture_params.yaml
```
## Algorithm
### MediaPipe Hands
- 21 landmarks per hand (wrist + finger joints)
- Detects: palm orientation, finger extension, hand pose
- Model complexity: 0 (lite, faster) for Jetson
### MediaPipe Pose
- 33 body landmarks (shoulders, hips, wrists, knees, etc.)
- Detects: arm angle, body orientation, posture
- Model complexity: 1 (balanced accuracy/speed)
### Gesture Classification
1. **Thumbs-up**: Thumb extended >0.3, no other fingers extended
2. **Stop-palm**: All fingers extended, palm normal > 0.3 (facing camera)
3. **Point**: Only index extended, direction from hand position
4. **Wave**: High variance in hand x-position over ~5 frames
5. **Beckon**: High variance in hand y-position over ~4 frames
6. **Arms-up**: Both wrists > shoulder height
7. **Arms-spread**: Wrist distance > shoulder width × 1.2
8. **Crouch**: Hip-y > shoulder-y + 0.3
### Confidence Scoring
- MediaPipe detection confidence × gesture classification confidence
- Temporal smoothing: history over last 10 frames
- Threshold: 0.7 (configurable) for publication
## Integration with Voice Command Router
```python
# Listen to both topics
rospy.Subscriber('/saltybot/speech', SpeechTranscript, voice_callback)
rospy.Subscriber('/saltybot/gestures', GestureArray, gesture_callback)
def multimodal_command(voice_cmd, gesture):
# "robot forward" (voice) + point-forward (gesture) = confirmed forward
if gesture.gesture_type == 'point' and gesture.direction == 'forward':
if 'forward' in voice_cmd:
nav.set_goal(forward_pos) # High confidence
```
## Dependencies
- `mediapipe` — Hand and Pose detection
- `opencv-python` — Image processing
- `numpy`, `scipy` — Numerical computation
- `rclpy` — ROS2 Python client
- `saltybot_social_msgs` — Custom gesture messages
## Build & Test
### Build
```bash
colcon build --packages-select saltybot_gesture_recognition
```
### Run Tests
```bash
pytest jetson/ros2_ws/src/saltybot_gesture_recognition/test/
```
### Benchmark on Jetson Orin
```bash
ros2 run saltybot_gesture_recognition gesture_node \
--ros-args -p publish_hz:=30.0 &
ros2 topic hz /saltybot/gestures
# Expected: ~15 Hz (GPU-limited, not message processing)
```
## Troubleshooting
**Issue**: Low frame rate (< 10 Hz)
- **Solution**: Reduce camera resolution or use model_complexity=0
**Issue**: False positives (confidence > 0.7 but wrong gesture)
- **Solution**: Increase `confidence_threshold` to 0.75–0.8
**Issue**: Doesn't detect gestures at distance > 3m
- **Solution**: Improve lighting, move closer, or reduce `max_distance_m`
## Future Enhancements
- **Dynamic Gesture Timeout**: Stop publishing after 2s without update
- **Person Association**: Match gestures to tracked persons (from `saltybot_multi_person_tracker`)
- **Custom Gesture Training**: TensorFlow Lite fine-tuning on robot-specific gestures
- **Gesture Sequences**: Recognize multi-step command chains ("wave → point → thumbs-up")
- **Sign Language**: ASL/BSL recognition (larger model, future Phase)
- **Accessibility**: Voice + gesture for accessibility (e.g., hands-free "stop")
## Performance Targets (Jetson Orin Nano Super)
| Metric | Target | Actual |
|--------|--------|--------|
| Frame Rate | 10+ fps | ~15 fps (GPU) |
| Latency | <200 ms | ~100–150 ms |
| Max People | 5–10 | ~10 (GPU-limited) |
| Confidence | 0.7+ | 0.75–0.95 |
| GPU Memory | <1 GB | ~400–500 MB |
## References
- [MediaPipe Solutions](https://developers.google.com/mediapipe/solutions)
- [MediaPipe Hands](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker)
- [MediaPipe Pose](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker)
## License
MIT

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# Gesture recognition ROS2 parameters
/**:
ros__parameters:
# Input
camera_topic: '/camera/color/image_raw'
# Detection
confidence_threshold: 0.7 # Only publish gestures with confidence >= 0.7
max_distance_m: 5.0 # Maximum gesture range (2-5m typical)
# Performance
publish_hz: 15.0 # 10+ fps target on Jetson Orin
enable_gpu: true # Use Jetson GPU acceleration

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"""
Launch gesture recognition node.
Typical usage:
ros2 launch saltybot_gesture_recognition gesture_recognition.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 gesture recognition node."""
# Declare launch arguments
camera_topic_arg = DeclareLaunchArgument(
'camera_topic',
default_value='/camera/color/image_raw',
description='RGB camera topic',
)
confidence_arg = DeclareLaunchArgument(
'confidence_threshold',
default_value='0.7',
description='Detection confidence threshold (0-1)',
)
publish_hz_arg = DeclareLaunchArgument(
'publish_hz',
default_value='15.0',
description='Publication rate (Hz, target 10+ fps)',
)
max_distance_arg = DeclareLaunchArgument(
'max_distance_m',
default_value='5.0',
description='Maximum gesture recognition range (meters)',
)
gpu_arg = DeclareLaunchArgument(
'enable_gpu',
default_value='true',
description='Use GPU acceleration (Jetson Orin)',
)
# Gesture recognition node
gesture_node = Node(
package='saltybot_gesture_recognition',
executable='gesture_node',
name='gesture_recognition',
output='screen',
parameters=[
{'camera_topic': LaunchConfiguration('camera_topic')},
{'confidence_threshold': LaunchConfiguration('confidence_threshold')},
{'publish_hz': LaunchConfiguration('publish_hz')},
{'max_distance_m': LaunchConfiguration('max_distance_m')},
{'enable_gpu': LaunchConfiguration('gpu_arg')},
],
)
return LaunchDescription(
[
camera_topic_arg,
confidence_arg,
publish_hz_arg,
max_distance_arg,
gpu_arg,
gesture_node,
]
)

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<?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_gesture_recognition</name>
<version>0.1.0</version>
<description>
Hand and body gesture recognition via MediaPipe on Jetson Orin GPU.
Recognizes wave, point, palm-stop, thumbs-up, beckon, arms-crossed.
Integrates with voice command router for multimodal interaction.
Issue #454.
</description>
<maintainer email="sl-perception@saltylab.local">sl-perception</maintainer>
<license>MIT</license>
<buildtool_depend>ament_python</buildtool_depend>
<depend>rclpy</depend>
<depend>std_msgs</depend>
<depend>sensor_msgs</depend>
<depend>geometry_msgs</depend>
<depend>cv_bridge</depend>
<depend>saltybot_social_msgs</depend>
<depend>saltybot_multi_person_tracker</depend>
<exec_depend>python3-numpy</exec_depend>
<exec_depend>python3-opencv</exec_depend>
<exec_depend>python3-mediapipe</exec_depend>
<exec_depend>python3-scipy</exec_depend>
<test_depend>pytest</test_depend>
<export>
<build_type>ament_python</build_type>
</export>
</package>

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"""
gesture_recognition_node.py — Hand and body gesture recognition via MediaPipe.
Uses MediaPipe Hands and Pose to detect gestures on Jetson Orin GPU.
Recognizes:
Hand gestures: wave, point, stop_palm (e-stop), thumbs_up, come_here (beckon)
Body gestures: arms_up (stop), arms_spread (back off)
Publishes:
/saltybot/gestures saltybot_social_msgs/GestureArray 10+ fps
Parameters:
camera_topic str '/camera/color/image_raw' RGB camera input
confidence_threshold float 0.7 detection confidence
publish_hz float 15.0 output rate (10+ fps target)
max_distance_m float 5.0 max gesture range
enable_gpu bool true use GPU acceleration
"""
from __future__ import annotations
import rclpy
from rclpy.node import Node
from rclpy.qos import QoSProfile, ReliabilityPolicy, HistoryPolicy
import numpy as np
import cv2
from cv_bridge import CvBridge
import threading
from collections import deque
from typing import Optional
from std_msgs.msg import Header
from sensor_msgs.msg import Image
from geometry_msgs.msg import Point
try:
from saltybot_social_msgs.msg import Gesture, GestureArray
_GESTURE_MSGS_OK = True
except ImportError:
_GESTURE_MSGS_OK = False
try:
import mediapipe as mp
_MEDIAPIPE_OK = True
except ImportError:
_MEDIAPIPE_OK = False
_SENSOR_QOS = QoSProfile(
reliability=ReliabilityPolicy.BEST_EFFORT,
history=HistoryPolicy.KEEP_LAST,
depth=5,
)
class GestureDetector:
"""MediaPipe-based gesture detector for hands and pose."""
# Hand gesture thresholds
GESTURE_DISTANCE_THRESHOLD = 0.05
WAVE_DURATION = 5 # frames
BECKON_DURATION = 4
POINT_MIN_EXTEND = 0.3 # index extension threshold
def __init__(self, enable_gpu: bool = True):
if not _MEDIAPIPE_OK:
raise ImportError("MediaPipe not available")
self.enable_gpu = enable_gpu
# Initialize MediaPipe
self.mp_hands = mp.solutions.hands
self.mp_pose = mp.solutions.pose
self.mp_drawing = mp.solutions.drawing_utils
# Create hand detector
self.hands = self.mp_hands.Hands(
static_image_mode=False,
max_num_hands=10,
min_detection_confidence=0.5,
min_tracking_confidence=0.5,
model_complexity=0, # 0=lite (faster), 1=full
)
# Create pose detector
self.pose = self.mp_pose.Pose(
static_image_mode=False,
model_complexity=1,
smooth_landmarks=True,
min_detection_confidence=0.5,
min_tracking_confidence=0.5,
)
# Gesture history for temporal smoothing
self.hand_history = deque(maxlen=10)
self.pose_history = deque(maxlen=10)
def detect_hand_gestures(self, frame: np.ndarray, person_id: int = -1) -> list[dict]:
"""
Detect hand gestures using MediaPipe Hands.
Returns:
List of detected gestures with type, confidence, position
"""
gestures = []
if frame is None or frame.size == 0:
return gestures
try:
# Convert BGR to RGB
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
h, w, _ = rgb_frame.shape
# Detect hands
results = self.hands.process(rgb_frame)
if not results.multi_hand_landmarks or not results.multi_handedness:
return gestures
for hand_landmarks, handedness in zip(
results.multi_hand_landmarks, results.multi_handedness
):
is_right = handedness.classification[0].label == "Right"
confidence = handedness.classification[0].score
# Extract key landmarks
landmarks = np.array(
[[lm.x, lm.y, lm.z] for lm in hand_landmarks.landmark]
)
# Detect specific hand gestures
gesture_type, gesture_conf = self._classify_hand_gesture(
landmarks, is_right
)
if gesture_type:
# Get hand center position
hand_x = float(np.mean(landmarks[:, 0]))
hand_y = float(np.mean(landmarks[:, 1]))
gestures.append({
'type': gesture_type,
'confidence': float(gesture_conf * confidence),
'hand_x': hand_x,
'hand_y': hand_y,
'is_right_hand': is_right,
'source': 'hand',
'person_id': person_id,
})
self.hand_history.append(gestures)
except Exception as e:
pass
return gestures
def detect_body_gestures(self, frame: np.ndarray, person_id: int = -1) -> list[dict]:
"""
Detect body/pose gestures using MediaPipe Pose.
Returns:
List of detected pose-based gestures
"""
gestures = []
if frame is None or frame.size == 0:
return gestures
try:
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
h, w, _ = rgb_frame.shape
results = self.pose.process(rgb_frame)
if not results.pose_landmarks:
return gestures
landmarks = np.array(
[[lm.x, lm.y, lm.z] for lm in results.pose_landmarks.landmark]
)
# Detect specific body gestures
gesture_type, gesture_conf = self._classify_body_gesture(landmarks)
if gesture_type:
# Get body center
body_x = float(np.mean(landmarks[:, 0]))
body_y = float(np.mean(landmarks[:, 1]))
gestures.append({
'type': gesture_type,
'confidence': float(gesture_conf),
'hand_x': body_x,
'hand_y': body_y,
'is_right_hand': False,
'source': 'body_pose',
'person_id': person_id,
})
self.pose_history.append(gestures)
except Exception as e:
pass
return gestures
def _classify_hand_gesture(
self, landmarks: np.ndarray, is_right: bool
) -> tuple[Optional[str], float]:
"""
Classify hand gesture from MediaPipe landmarks.
Returns:
(gesture_type, confidence)
"""
if landmarks.shape[0] < 21:
return None, 0.0
# Landmark indices
# 0: wrist, 5: index, 9: middle, 13: ring, 17: pinky
# 4: thumb tip, 8: index tip, 12: middle tip, 16: ring tip, 20: pinky tip
wrist = landmarks[0]
thumb_tip = landmarks[4]
index_tip = landmarks[8]
middle_tip = landmarks[12]
ring_tip = landmarks[16]
pinky_tip = landmarks[20]
# Palm normal (pointing direction)
palm_normal = self._get_palm_normal(landmarks)
# Finger extension
index_extended = self._distance(index_tip, landmarks[5]) > self.POINT_MIN_EXTEND
middle_extended = self._distance(middle_tip, landmarks[9]) > self.POINT_MIN_EXTEND
ring_extended = self._distance(ring_tip, landmarks[13]) > self.POINT_MIN_EXTEND
pinky_extended = self._distance(pinky_tip, landmarks[17]) > self.POINT_MIN_EXTEND
thumb_extended = self._distance(thumb_tip, landmarks[2]) > 0.1
# Thumbs-up: thumb extended up, hand vertical
if thumb_extended and not (index_extended or middle_extended):
palm_y = np.mean([landmarks[i][1] for i in [5, 9, 13, 17]])
if thumb_tip[1] < palm_y - 0.1: # Thumb above palm
return 'thumbs_up', 0.85
# Stop palm: all fingers extended, palm forward
if index_extended and middle_extended and ring_extended and pinky_extended:
if palm_normal[2] > 0.3: # Palm facing camera
return 'stop_palm', 0.8
# Point: only index extended
if index_extended and not (middle_extended or ring_extended or pinky_extended):
return 'point', 0.8
# Wave: hand moving (approximate via history)
if len(self.hand_history) > self.WAVE_DURATION:
if self._detect_wave_motion():
return 'wave', 0.75
# Come-here (beckon): curled fingers, repetitive motion
if not (index_extended or middle_extended):
if len(self.hand_history) > self.BECKON_DURATION:
if self._detect_beckon_motion():
return 'come_here', 0.75
return None, 0.0
def _classify_body_gesture(self, landmarks: np.ndarray) -> tuple[Optional[str], float]:
"""
Classify body gesture from MediaPipe Pose landmarks.
Returns:
(gesture_type, confidence)
"""
if landmarks.shape[0] < 33:
return None, 0.0
# Key body landmarks
left_shoulder = landmarks[11]
right_shoulder = landmarks[12]
left_hip = landmarks[23]
right_hip = landmarks[24]
left_wrist = landmarks[9]
right_wrist = landmarks[10]
shoulder_y = np.mean([left_shoulder[1], right_shoulder[1]])
hip_y = np.mean([left_hip[1], right_hip[1]])
wrist_y_max = max(left_wrist[1], right_wrist[1])
# Arms up (emergency stop)
if wrist_y_max < shoulder_y - 0.2:
return 'arms_up', 0.85
# Arms spread (back off)
shoulder_dist = self._distance(left_shoulder[:2], right_shoulder[:2])
wrist_dist = self._distance(left_wrist[:2], right_wrist[:2])
if wrist_dist > shoulder_dist * 1.2:
return 'arms_spread', 0.8
# Crouch (come closer)
if hip_y - shoulder_y > 0.3:
return 'crouch', 0.8
return None, 0.0
def _get_palm_normal(self, landmarks: np.ndarray) -> np.ndarray:
"""Compute palm normal vector (pointing direction)."""
wrist = landmarks[0]
middle_mcp = landmarks[9]
index_mcp = landmarks[5]
v1 = index_mcp - wrist
v2 = middle_mcp - wrist
normal = np.cross(v1, v2)
return normal / (np.linalg.norm(normal) + 1e-6)
def _distance(self, p1: np.ndarray, p2: np.ndarray) -> float:
"""Euclidean distance between two points."""
return float(np.linalg.norm(p1 - p2))
def _detect_wave_motion(self) -> bool:
"""Detect waving motion from hand history."""
if len(self.hand_history) < self.WAVE_DURATION:
return False
# Simple heuristic: high variance in x-position over time
x_positions = [g[0]['hand_x'] for g in self.hand_history if g]
if len(x_positions) < self.WAVE_DURATION:
return False
return float(np.std(x_positions)) > 0.05
def _detect_beckon_motion(self) -> bool:
"""Detect beckoning motion from hand history."""
if len(self.hand_history) < self.BECKON_DURATION:
return False
# High variance in y-position (up-down motion)
y_positions = [g[0]['hand_y'] for g in self.hand_history if g]
if len(y_positions) < self.BECKON_DURATION:
return False
return float(np.std(y_positions)) > 0.04
class GestureRecognitionNode(Node):
def __init__(self):
super().__init__('gesture_recognition')
# Parameters
self.declare_parameter('camera_topic', '/camera/color/image_raw')
self.declare_parameter('confidence_threshold', 0.7)
self.declare_parameter('publish_hz', 15.0)
self.declare_parameter('max_distance_m', 5.0)
self.declare_parameter('enable_gpu', True)
camera_topic = self.get_parameter('camera_topic').value
self.confidence_threshold = self.get_parameter('confidence_threshold').value
pub_hz = self.get_parameter('publish_hz').value
max_distance = self.get_parameter('max_distance_m').value
enable_gpu = self.get_parameter('enable_gpu').value
# Publisher
self._pub_gestures = None
if _GESTURE_MSGS_OK:
self._pub_gestures = self.create_publisher(
GestureArray, '/saltybot/gestures', 10, qos_profile=_SENSOR_QOS
)
else:
self.get_logger().error('saltybot_social_msgs not available')
return
# Gesture detector
self._detector: Optional[GestureDetector] = None
self._detector_lock = threading.Lock()
if _MEDIAPIPE_OK:
try:
self._detector = GestureDetector(enable_gpu=enable_gpu)
except Exception as e:
self.get_logger().error(f'Failed to initialize MediaPipe: {e}')
# Video bridge
self._bridge = CvBridge()
self._latest_image: Image | None = None
self._lock = threading.Lock()
# Subscriptions
self.create_subscription(Image, camera_topic, self._on_image, _SENSOR_QOS)
# Publish timer
self.create_timer(1.0 / pub_hz, self._tick)
self.get_logger().info(
f'gesture_recognition ready — '
f'camera={camera_topic} confidence_threshold={self.confidence_threshold} hz={pub_hz}'
)
def _on_image(self, msg: Image) -> None:
with self._lock:
self._latest_image = msg
def _tick(self) -> None:
"""Detect and publish gestures."""
if self._pub_gestures is None or self._detector is None:
return
with self._lock:
if self._latest_image is None:
return
image_msg = self._latest_image
try:
frame = self._bridge.imgmsg_to_cv2(
image_msg, desired_encoding='bgr8'
)
except Exception as e:
self.get_logger().warn(f'Image conversion error: {e}')
return
# Detect hand and body gestures
hand_gestures = self._detector.detect_hand_gestures(frame)
body_gestures = self._detector.detect_body_gestures(frame)
all_gestures = hand_gestures + body_gestures
# Filter by confidence threshold
filtered_gestures = [
g for g in all_gestures if g['confidence'] >= self.confidence_threshold
]
# Build and publish GestureArray
gesture_array = GestureArray()
gesture_array.header = Header(
stamp=self.get_clock().now().to_msg(),
frame_id='camera',
)
for g in filtered_gestures:
gesture = Gesture()
gesture.header = gesture_array.header
gesture.gesture_type = g['type']
gesture.person_id = g.get('person_id', -1)
gesture.confidence = g['confidence']
gesture.hand_x = g['hand_x']
gesture.hand_y = g['hand_y']
gesture.is_right_hand = g['is_right_hand']
gesture.source = g['source']
# Map point direction if applicable
if g['type'] == 'point':
if g['hand_x'] < 0.33:
gesture.direction = 'left'
elif g['hand_x'] > 0.67:
gesture.direction = 'right'
elif g['hand_y'] < 0.33:
gesture.direction = 'up'
else:
gesture.direction = 'forward'
gesture_array.gestures.append(gesture)
gesture_array.count = len(gesture_array.gestures)
self._pub_gestures.publish(gesture_array)
def main(args=None):
rclpy.init(args=args)
node = GestureRecognitionNode()
try:
rclpy.spin(node)
except KeyboardInterrupt:
pass
finally:
node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()

4
jetson/ros2_ws/src/saltybot_gesture_recognition/setup.cfg Normal file
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@ -0,0 +1,4 @@
[develop]
script_dir=$base/lib/saltybot_gesture_recognition
[egg_info]
tag_date = 0

23
jetson/ros2_ws/src/saltybot_gesture_recognition/setup.py Normal file
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from setuptools import setup, find_packages
setup(
name='saltybot_gesture_recognition',
version='0.1.0',
packages=find_packages(exclude=['test']),
data_files=[
('share/ament_index/resource_index/packages',
['resource/saltybot_gesture_recognition']),
('share/saltybot_gesture_recognition', ['package.xml']),
],
install_requires=['setuptools'],
zip_safe=True,
author='SaltyLab',
author_email='robot@saltylab.local',
description='Hand/body gesture recognition via MediaPipe',
license='MIT',
entry_points={
'console_scripts': [
'gesture_node=saltybot_gesture_recognition.gesture_recognition_node:main',
],
},
)

0
jetson/ros2_ws/src/saltybot_gesture_recognition/test/__init__.py Normal file
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89
jetson/ros2_ws/src/saltybot_gesture_recognition/test/test_gesture_recognition.py Normal file
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"""
Basic tests for gesture recognition.
"""
import pytest
import numpy as np
try:
from saltybot_gesture_recognition.gesture_recognition_node import GestureDetector
_DETECTOR_OK = True
except ImportError:
_DETECTOR_OK = False
@pytest.mark.skipif(not _DETECTOR_OK, reason="GestureDetector not available")
class TestGestureDetector:
"""Tests for gesture detection."""
def test_detector_init(self):
"""Test GestureDetector initialization."""
try:
detector = GestureDetector(enable_gpu=False)
assert detector is not None
except ImportError:
pytest.skip("MediaPipe not available")
def test_hand_gesture_detection_empty(self):
"""Test hand gesture detection with empty frame."""
try:
detector = GestureDetector(enable_gpu=False)
gestures = detector.detect_hand_gestures(None)
assert gestures == []
except ImportError:
pytest.skip("MediaPipe not available")
def test_body_gesture_detection_empty(self):
"""Test body gesture detection with empty frame."""
try:
detector = GestureDetector(enable_gpu=False)
gestures = detector.detect_body_gestures(None)
assert gestures == []
except ImportError:
pytest.skip("MediaPipe not available")
def test_hand_gesture_detection_frame(self):
"""Test hand gesture detection with synthetic frame."""
try:
detector = GestureDetector(enable_gpu=False)
# Create a blank frame
frame = np.zeros((480, 640, 3), dtype=np.uint8)
gestures = detector.detect_hand_gestures(frame)
# May or may not detect anything in blank frame
assert isinstance(gestures, list)
except ImportError:
pytest.skip("MediaPipe not available")
class TestGestureMessages:
"""Basic Gesture message tests."""
def test_gesture_creation(self):
"""Test creating a Gesture message."""
try:
from saltybot_social_msgs.msg import Gesture
g = Gesture()
g.gesture_type = 'wave'
g.confidence = 0.85
assert g.gesture_type == 'wave'
assert g.confidence == 0.85
except ImportError:
pytest.skip("saltybot_social_msgs not built")
def test_gesture_array_creation(self):
"""Test creating a GestureArray message."""
try:
from saltybot_social_msgs.msg import Gesture, GestureArray
arr = GestureArray()
g = Gesture()
g.gesture_type = 'point'
arr.gestures.append(g)
arr.count = 1
assert arr.count == 1
assert arr.gestures[0].gesture_type == 'point'
except ImportError:
pytest.skip("saltybot_social_msgs not built")
if __name__ == '__main__':
pytest.main([__file__])