Welcome to DOFBOT-JetsonNANO repository

Gesture Recognition

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. Gesture Recognition

The gesture recognition is designed based on the right hand and can accurately recognize when specific conditions are met. Recognizable gestures include: [Zero, One, Two, Three, Four, Five, Six, Seven, Eight, Ok, Rock, Thumb_up (thumbs up), Thumb_down (thumbs down), Heart_single (single hand heart)], a total of 14 categories.

2.1. Launch

• Enter the following command to start the program

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ros2 run dofbot_mediapipe 10_GestureRecognition 

2.2. Source Code

Source code location:

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# need to enter the docker container to view
~/dofbot_ws/src/dofbot_mediapipe/dofbot_mediapipe/10_GestureRecognition.py

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#!/usr/bin/env python3
# encoding: utf-8
​
import math
import time
import cv2 as cv
import numpy as np
import mediapipe as mp
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
from dofbot_interface.msg import PointArray
from Arm_Lib import Arm_Device
Arm = 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 HandDetector:
    def __init__(self, mode=False, maxHands=2, detectorCon=0.5, trackCon=0.5):
        self.tipIds = [4, 8, 12, 16, 20]
        self.mpHand = mp.solutions.hands
        self.mpDraw = mp.solutions.drawing_utils
        self.hands = self.mpHand.Hands(
            static_image_mode=mode,
            max_num_hands=maxHands,
            min_detection_confidence=detectorCon,
            min_tracking_confidence=trackCon
        )
        self.lmList = []
        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)
​
    def get_dist(self, point1, point2):
        x1, y1 = point1
        x2, y2 = point2
        return abs(math.sqrt(math.pow(abs(y1 - y2), 2) + math.pow(abs(x1 - x2), 2)))
​
    def calc_angle(self, pt1, pt2, pt3):
        point1 = self.lmList[pt1][1], self.lmList[pt1][2]
        point2 = self.lmList[pt2][1], self.lmList[pt2][2]
        point3 = self.lmList[pt3][1], self.lmList[pt3][2]
        a = self.get_dist(point1, point2)
        b = self.get_dist(point2, point3)
        c = self.get_dist(point1, point3)
        try:
            radian = math.acos((math.pow(a, 2) + math.pow(b, 2) - math.pow(c, 2)) / (2 * a * b))
            angle = radian / math.pi * 180
        except:
            angle = 0
        return abs(angle)
​
    def findHands(self, frame, draw=True):
        self.lmList = []
        img = np.zeros(frame.shape, np.uint8)
        img_RGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
        self.results = self.hands.process(img_RGB)
        if self.results.multi_hand_landmarks:
            for i in range(len(self.results.multi_hand_landmarks)):
                if draw: self.mpDraw.draw_landmarks(frame, self.results.multi_hand_landmarks[i], self.mpHand.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec)
                self.mpDraw.draw_landmarks(img, self.results.multi_hand_landmarks[i], self.mpHand.HAND_CONNECTIONS, self.lmDrawSpec, self.drawSpec)
                for id, lm in enumerate(self.results.multi_hand_landmarks[i].landmark):
                    h, w, c = frame.shape
                    cx, cy = int(lm.x * w), int(lm.y * h)
                    self.lmList.append([id, cx, cy])
        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 fingersUp(self):
        fingers = []
        # Thumb
        if (self.calc_angle(self.tipIds[0], self.tipIds[0] - 1, self.tipIds[0] - 2) > 150.0) and (
                self.calc_angle(self.tipIds[0] - 1, self.tipIds[0] - 2, self.tipIds[0] - 3) > 150.0):
            fingers.append(1)
        else:
            fingers.append(0)
        # 4 fingers
        for id in range(1, 5):
            if self.lmList[self.tipIds[id]][2] < self.lmList[self.tipIds[id] - 2][2]:
                fingers.append(1)
            else:
                fingers.append(0)
        return fingers
​
    def get_gesture(self):
        gesture = ""
        fingers = self.fingersUp()
        if self.lmList[self.tipIds[0]][2] > self.lmList[self.tipIds[1]][2] and \
                self.lmList[self.tipIds[0]][2] > self.lmList[self.tipIds[2]][2] and \
                self.lmList[self.tipIds[0]][2] > self.lmList[self.tipIds[3]][2] and \
                self.lmList[self.tipIds[0]][2] > self.lmList[self.tipIds[4]][2]:
            gesture = "Thumb_down"
​
        elif self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[1]][2] and \
                self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[2]][2] and \
                self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[3]][2] and \
                self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[4]][2] and \
                self.calc_angle(self.tipIds[1] - 1, self.tipIds[1] - 2, self.tipIds[1] - 3) < 150.0:
            gesture = "Thumb_up"
        if fingers.count(1) == 3 or fingers.count(1) == 4:
            if fingers[0] == 1 and (
                    self.get_dist(self.lmList[4][1:], self.lmList[8][1:]) < self.get_dist(self.lmList[4][1:], self.lmList[5][1:])
            ):
                gesture = "OK"
            elif fingers[2] == fingers[3] == 0:
                gesture = "Rock"
            elif fingers.count(1) == 3:
                gesture = "Three"
            else:
                gesture = "Four"
        elif fingers.count(1) == 0:
            gesture = "Zero"
        elif fingers.count(1) == 1:
            gesture = "One"
        elif fingers.count(1) == 2:
            if fingers[0] == 1 and fingers[4] == 1:
                gesture = "Six"
            elif fingers[0] == 1 and self.calc_angle(4, 5, 8) > 90:
                gesture = "Eight"
            elif fingers[0] == fingers[1] == 1 and self.get_dist(self.lmList[4][1:], self.lmList[8][1:]) < 50:
                gesture = "Heart_single"
            else:
                gesture = "Two"
        elif fingers.count(1) == 5:
            gesture = "Five"
        if self.get_dist(self.lmList[4][1:], self.lmList[8][1:]) < 60 and \
                self.get_dist(self.lmList[4][1:], self.lmList[12][1:]) < 60 and \
                self.get_dist(self.lmList[4][1:], self.lmList[16][1:]) < 60 and \
                self.get_dist(self.lmList[4][1:], self.lmList[20][1:]) < 60:
            gesture = "Seven"
        if self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[1]][2] and \
                self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[2]][2] and \
                self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[3]][2] and \
                self.lmList[self.tipIds[0]][2] < self.lmList[self.tipIds[4]][2] and \
                self.calc_angle(self.tipIds[1] - 1, self.tipIds[1] - 2, self.tipIds[1] - 3) > 150.0:
            gesture = "Eight"
        return gesture
​
class HandGestureNode(Node):
    def __init__(self):
        super().__init__('hand_gesture_node')
        self.publisher_ = self.create_publisher(Image, 'hand_gesture_image', 10)
        self.timer = self.create_timer(0.1, self.timer_callback)
        self.bridge = CvBridge()
        self.hand_detector = HandDetector(detectorCon=0.75)
        self.capture = cv.VideoCapture(0, cv.CAP_V4L2)
        self.capture.set(cv.CAP_PROP_FRAME_WIDTH, 640)
        self.capture.set(cv.CAP_PROP_FRAME_HEIGHT, 480)
        self.pTime = 0
​
    def timer_callback(self):
        ret, frame = self.capture.read()
        if not ret:
            self.get_logger().error('Failed to capture frame')
            return
​
        frame, img = self.hand_detector.findHands(frame, draw=False)
        if len(self.hand_detector.lmList) != 0:
            totalFingers = self.hand_detector.get_gesture()
            cv.rectangle(frame, (0, 430), (230, 480), (0, 255, 0), cv.FILLED)
            cv.putText(frame, str(totalFingers), (10, 470), cv.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2)
​
        if cv.waitKey(1) & 0xFF == ord('q'):
            self.capture.release()
            cv.destroyAllWindows()
            rclpy.shutdown()
            return
​
        cTime = time.time()
        fps = 1 / (cTime - self.pTime)
        self.pTime = cTime
        text = "FPS : " + str(int(fps))
        cv.putText(frame, text, (10, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1)
        dist = self.hand_detector.frame_combine(frame, img)
        cv.imshow('dist', dist)
​
        msg = self.bridge.cv2_to_imgmsg(frame, "bgr8")
        self.publisher_.publish(msg)
​
def main(args=None):
    rclpy.init(args=args)
    hand_gesture_node = HandGestureNode()
    rclpy.spin(hand_gesture_node)
    hand_gesture_node.destroy_node()
    rclpy.shutdown()
​
if __name__ == '__main__':
    main()