Welcome to DOFBOT-JetsonNANO repository
02.Posture detection
Pose Detection
1. Introduction
MediaPipe is a data stream processing machine learning application development framework developed and open-sourced by Google. It is a graph-based data processing pipeline used to build applications that use various forms of data sources such as video, audio, sensor data, and any time series data. MediaPipe is cross-platform and can run on embedded platforms (Jetson nano, etc.), mobile devices (iOS and Android), workstations and servers, and supports mobile GPU acceleration. MediaPipe provides cross-platform, customizable ML solutions for real-time and streaming media.
The core framework of MediaPipe is implemented in C++ and provides support for languages such as Java and Objective C. The main concepts of MediaPipe include Packet, Stream, Calculator, Graph, and Subgraph.
Features of MediaPipe:
- End-to-end acceleration: Built-in fast ML inference and processing accelerates even on ordinary hardware.
- Build once, deploy anywhere: Unified solution for Android, iOS, desktop/cloud, web and IoT.
- Ready-to-use solutions: Cutting-edge ML solutions that showcase the full capabilities of the framework.
- Free and open source: Framework and solutions under Apache2.0, fully scalable and customizable.
2. MediaPipe Pose
MediaPipe Pose is an ML solution for high-fidelity body pose tracking, utilizing BlazePose research to infer 33 3D coordinates and full-body background segmentation masks from RGB video frames. This research also powers the ML Kit pose detection API.
The landmark model in MediaPipe Pose predicts the location of 33 pose coordinates (see the image below).

3. Pose Detection
3.1. Launch
- Enter the following command to start the program
ros2 run dofbot_mediapipe 02_PoseDetector

3.2. Source Code
Source code location:
xxxxxxxxxx# need to enter the docker container to view~/dofbot_ws/src/dofbot_mediapipe/dofbot_mediapipe/02_PoseDetector.py
x#!/usr/bin/env python3# encoding: utf-8import timeimport rclpyfrom rclpy.node import Nodeimport cv2 as cvimport numpy as npimport mediapipe as mpfrom geometry_msgs.msg import Pointfrom sensor_msgs.msg import Imagefrom cv_bridge import CvBridgefrom dofbot_interface.msg import PointArrayfrom Arm_Lib import Arm_DeviceArm = Arm_Device()init_joints = [90.0, 150.0, 10.0, 10.0, 90.0, 0.0]Arm.Arm_serial_servo_write6_array(init_joints,2000)class PoseDetector(Node): def __init__(self): super().__init__('pose_detector') self.publisher_ = self.create_publisher(PointArray, '/mediapipe/points', 10) self.bridge = CvBridge() self.mpPose = mp.solutions.pose self.mpDraw = mp.solutions.drawing_utils self.pose = self.mpPose.Pose( static_image_mode=False, smooth_landmarks=True, min_detection_confidence=0.5, min_tracking_confidence=0.5 ) self.lmDrawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 0, 255), thickness=-1, circle_radius=6) self.drawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 255, 0), thickness=2, circle_radius=2) self.capture = cv.VideoCapture(0, cv.CAP_V4L2) self.capture.set(6, cv.VideoWriter.fourcc('M', 'J', 'P', 'G')) self.capture.set(cv.CAP_PROP_FRAME_WIDTH, 640) self.capture.set(cv.CAP_PROP_FRAME_HEIGHT, 480) if not self.capture.isOpened(): self.get_logger().error("Failed to open the camera") return self.get_logger().info(f"Camera FPS: {self.capture.get(cv.CAP_PROP_FPS)}") self.pTime = time.time() self.timer = self.create_timer(0.03, self.process_frame) def process_frame(self): ret, frame = self.capture.read() if not ret: self.get_logger().error("Failed to read frame") return frame, img = self.pubPosePoint(frame, draw=True) cTime = time.time() fps = 1 / (cTime - self.pTime) self.pTime = cTime text = "FPS : " + str(int(fps)) cv.putText(frame, text, (20, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1) combined_frame = self.frame_combine(frame, img) cv.imshow('PoseDetector', combined_frame) if cv.waitKey(1) & 0xFF == ord('q'): self.get_logger().info("Exiting program") self.capture.release() cv.destroyAllWindows() self.destroy_node() rclpy.shutdown() exit(0) def pubPosePoint(self, frame, draw=True): pointArray = PointArray() img = np.zeros(frame.shape, np.uint8) img_RGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB) self.results = self.pose.process(img_RGB) if self.results.pose_landmarks: if draw: self.mpDraw.draw_landmarks(frame, self.results.pose_landmarks, self.mpPose.POSE_CONNECTIONS, self.lmDrawSpec, self.drawSpec) self.mpDraw.draw_landmarks(img, self.results.pose_landmarks, self.mpPose.POSE_CONNECTIONS, self.lmDrawSpec, self.drawSpec) for id, lm in enumerate(self.results.pose_landmarks.landmark): point = Point() point.x, point.y, point.z = lm.x, lm.y, lm.z pointArray.points.append(point) self.publisher_.publish(pointArray) return frame, img def frame_combine(self, frame, src): if len(frame.shape) == 3: frameH, frameW = frame.shape[:2] srcH, srcW = src.shape[:2] dst = np.zeros((max(frameH, srcH), frameW + srcW, 3), np.uint8) dst[:, :frameW] = frame[:, :] dst[:, frameW:] = src[:, :] else: src = cv.cvtColor(src, cv.COLOR_BGR2GRAY) frameH, frameW = frame.shape[:2] imgH, imgW = src.shape[:2] dst = np.zeros((frameH, frameW + imgW), np.uint8) dst[:, :frameW] = frame[:, :] dst[:, frameW:] = src[:, :] return dstdef main(args=None): rclpy.init(args=args) node = PoseDetector() try: rclpy.spin(node) except KeyboardInterrupt: pass finally: node.capture.release() cv.destroyAllWindows() node.destroy_node() rclpy.shutdown()if __name__ == '__main__': main()