Welcome to DOFBOT Pro repository

Overall Detection

Orin board users can directly open the terminal and input the tutorial commands to run directly. Jetson-Nano board users need to enter the docker container first, then input the tutorial commands in the docker to start the program.

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 Hands

Refer to the content in section 1.2 Hand Detection for details, which will not be repeated here.

3. MediaPipe Pose

Refer to the content in section 2.2 Hand Detection for details, which will not be repeated here.

4. Overall Detection

Combining the content from the previous two sections, this example implements the functionality to detect both hands and body.

4.1. Launch

• Enter the following command to start the program

ros2 run dofbot_pro_mediapipe 03_Holistic

4.2. Source Code

Source code location: ~/dofbot_pro_ws/src/dofbot_pro_mediapipe/dofbot_pro_mediapipe/03_Holistic.py

​x
#!/usr/bin/env python3
# encoding: utf-8
import time
import rclpy
from rclpy.node import Node
import cv2 as cv
import numpy as np
import mediapipe as mp
from geometry_msgs.msg import Point
from dofbot_pro_msgs.msg import PointArray
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
​
class Holistic(Node):
    def __init__(self, staticMode=False, landmarks=True, detectionCon=0.5, trackingCon=0.5):
        super().__init__('holistic_detector')
        self.publisher_ = self.create_publisher(PointArray, '/mediapipe/points', 10)
        self.bridge = CvBridge()
        
        self.mpHolistic = mp.solutions.holistic
        self.mpFaceMesh = mp.solutions.face_mesh
        self.mpHands = mp.solutions.hands
        self.mpPose = mp.solutions.pose
        self.mpDraw = mp.solutions.drawing_utils
        self.mpholistic = self.mpHolistic.Holistic(
            static_image_mode=staticMode,
            smooth_landmarks=landmarks,
            min_detection_confidence=detectionCon,
            min_tracking_confidence=trackingCon)
        
        self.lmDrawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 0, 255), thickness=-1, circle_radius=3)
        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.findHolistic(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, 0.8, (0, 0, 255), 2)
​
        combined_frame = self.frame_combine(frame, img)
        cv.imshow('HolisticDetector', 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 findHolistic(self, frame, draw=True):
        pointArray = PointArray()
        img = np.zeros(frame.shape, np.uint8)
        img_RGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
        self.results = self.mpholistic.process(img_RGB)
​
        if self.results.face_landmarks:
            try:
                if draw: self.mpDraw.draw_landmarks(frame, self.results.face_landmarks, self.mpFaceMesh.FACE_CONNECTIONS, self.lmDrawSpec, self.drawSpec)
                self.mpDraw.draw_landmarks(img, self.results.face_landmarks, self.mpFaceMesh.FACE_CONNECTIONS, self.lmDrawSpec, self.drawSpec)
            except:
                if draw: self.mpDraw.draw_landmarks(frame, self.results.face_landmarks, self.mpFaceMesh.FACEMESH_CONTOURS, self.lmDrawSpec, self.drawSpec)
                self.mpDraw.draw_landmarks(img, self.results.face_landmarks, self.mpFaceMesh.FACEMESH_CONTOURS, self.lmDrawSpec, self.drawSpec)
​
            for id, lm in enumerate(self.results.face_landmarks.landmark):
                point = Point()
                point.x, point.y, point.z = lm.x, lm.y, lm.z
                pointArray.points.append(point)
        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)
        if self.results.left_hand_landmarks:
            if draw: self.mpDraw.draw_landmarks(frame, self.results.left_hand_landmarks, self.mpHands.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec)
            self.mpDraw.draw_landmarks(img, self.results.left_hand_landmarks, self.mpHands.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec)
            for id, lm in enumerate(self.results.left_hand_landmarks.landmark):
                point = Point()
                point.x, point.y, point.z = lm.x, lm.y, lm.z
                pointArray.points.append(point)
        if self.results.right_hand_landmarks:
            if draw: self.mpDraw.draw_landmarks(frame, self.results.right_hand_landmarks, self.mpHands.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec)
            self.mpDraw.draw_landmarks(img, self.results.right_hand_landmarks, self.mpHands.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec)
            for id, lm in enumerate(self.results.right_hand_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 dst
​
def main(args=None):
    rclpy.init(args=args)
    node = Holistic()
    try:
        rclpy.spin(node)
    except KeyboardInterrupt:
        pass
    finally:
        node.capture.release()
        cv.destroyAllWindows()
        node.destroy_node()
        rclpy.shutdown()
​
if __name__ == '__main__':
    main()