Skip to main content

Mediapipe palm arm target positioning

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 (such as Raspberry Pi), 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 showcases the full capabilities of the framework.
  • Free and open source: framework and solution under Apache 2.0, fully extensible and customizable.

2. Start​

2.1 Program description​

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

2.2 Start program​

  • Input following command to start the program
roscore
rosrun jetcobot_mediapipe Find_Hand.py

2.3 About code​

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

#!/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()