Welcome to JetCobot repository

9. MediaPipe palm arm target positioning

9.1. Introduction

MediaPipe is an open source data stream processing machine learning application development framework developed by Google. It is a graph-based data processing pipeline used to build data sources in various forms, such as video, audio, sensor data, and any time series data.

MediaPipe is cross-platform and can run on embedded platforms (Raspberry Pi, 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 packets, streams, calculators, graphs, and subgraphs.

Features of MediaPipe:

• End-to-end acceleration: built-in fast ML inference and processing can be accelerated even on ordinary hardware.

• Build once, deploy anywhere: unified solution for Android, iOS, desktop/cloud, web and IoT.

• Ready-to-use solution: cutting-edge ML solution that demonstrates the full capabilities of the framework.

• Free and open source: framework and solution under Apache2.0, fully extensible and customizable.

9.2, Startup

9.2.1, Program Description

After the program runs, the camera captures the image screen, the program detects the palm, and prints the center coordinates of the palm to the terminal.

9.2.2, Program Startup

• Enter the following command to start the program

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ros2 run jetcobot_mediapipe Find_Hand

9.2.3, Source Code

Code path: ~/jetcobot_ws/src/jetcobot_mediapipe/jetcobot_mediapipe/Find_Hand.py

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#!/usr/bin/env python3
# encoding: utf-8
import threading
import numpy as np
from time import sleep, time
from media_library import *

class HandCtrlArm:
    def __init__(self):
        self.hand_detector = HandDetector()
        self.arm_status = True
        self.locking = True
        self.init = True
        self.pTime = 0
        self.add_lock = self.remove_lock = 0
        self.Joy_active = True
        self.event = threading.Event()
        self.event.set()
        self.Joy_active = True


    def process(self, frame):
        frame = cv.flip(frame, 1)
        if self.Joy_active:
            frame, lmList, bbox = self.hand_detector.findHands(frame)
            if len(lmList) != 0 and self.Joy_active:
                threading.Thread(target=self.find_hand_threading, args=(lmList, bbox)).start()
        self.cTime = time()
        fps = 1 / (self.cTime - self.pTime)
        self.pTime = self.cTime
        text = "FPS : " + str(int(fps))
        cv.putText(frame, text, (20, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1)
        return frame

    def find_hand_threading(self, lmList, bbox):
        fingers = self.hand_detector.fingersUp(lmList)
        self.hand_detector.draw = True
        angle = self.hand_detector.ThumbTOforefinger(lmList)
        value = np.interp(angle, [0, 70], [185, 20])
        indexX = (bbox[0] + bbox[2]) / 2
        indexY = (bbox[1] + bbox[3]) / 2
        print("indexX: ",indexX)
        print("indexY: ",indexY)
        
if __name__ == '__main__':
    capture = cv.VideoCapture(0)
    capture.set(6, cv.VideoWriter.fourcc('M', 'J', 'P', 'G'))
    capture.set(cv.CAP_PROP_FRAME_WIDTH, 640)
    capture.set(cv.CAP_PROP_FRAME_HEIGHT, 480)
    print("capture get FPS : ", capture.get(cv.CAP_PROP_FPS))
    ctrl_arm = HandCtrlArm()
    while capture.isOpened():
        ret, frame = capture.read()
        action = cv.waitKey(1) & 0xFF
        frame = ctrl_arm.process(frame)
        if action == ord('q'):
            #ctrl_arm.media_ros.cancel()
            break
        cv.imshow('frame', frame)
    capture.release()
    cv.destroyAllWindows()