Welcome to DOFBOT SE repository

Hand Detection

1.1 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 multiple 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 (Raspberry Pi and similar), mobile devices (iOS and Android), workstations and servers, with support for 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 Java and Objective C. The main concepts of MediaPipe include Packet, Stream, Calculator, Graph, and Subgraph.

MediaPipe features:

• End-to-end acceleration: Built-in fast ML inference and processing accelerate even on ordinary hardware.
• Build once, deploy anywhere: Unified solutions work on Android, iOS, desktop/cloud, web, and IoT.
• Ready-to-use solutions: Cutting-edge ML solutions that showcase all framework features.
• Free and open source: Framework and solutions under Apache 2.0, fully extensible and customizable.

1.2 MediaPipe Hands

MediaPipe Hands is a high-fidelity hand and finger tracking solution. It uses machine learning (ML) to infer 21 3D hand coordinates from a single frame.

After detecting the hand palm across the entire image, the hand landmark model performs precise keypoint localization of 21 3D hand joint coordinates within the detected hand region through regression, i.e., direct coordinate prediction. The model learns a consistent internal hand pose representation and is robust even for partially visible hands and self-occlusion.

To obtain ground truth data, approximately 30K real-world images were manually annotated with 21 3D coordinates (Z values were obtained from depth maps when available for each corresponding coordinate). To better cover possible hand poses and provide additional supervision on the nature of hand geometry, high-quality synthetic hand models were rendered in various backgrounds and mapped to corresponding 3D coordinates.

1.3 Hand Detection

1.3.1 Launch

Enter in terminal:

xxxxxxxxxx
ros2 run dofbot_mediapipe 01_HandDetector

1.3.2 Source Code

Source location: /home/yahboom/dofbot_ws/src/dofbot_mediapipe/dofbot_mediapipe/01_HandDetector.py

​x
#!/usr/bin/env python3
# encoding: utf-8
import rclpy
import cv2 as cv
import numpy as np
import mediapipe as mp
import time
from rclpy.node import Node
from geometry_msgs.msg import Point
from dofbot_msgs.msg import PointArray
​
class HandDetector(Node):
    def __init__(self):
        super().__init__('hand_detector')
        self.mpHand = mp.solutions.hands
        self.mpDraw = mp.solutions.drawing_utils
        self.hands = self.mpHand.Hands(
            static_image_mode=False,
            max_num_hands=2,
            min_detection_confidence=0.5,
            min_tracking_confidence=0.5
        )
​
        self.publisher_ = self.create_publisher(PointArray, '/mediapipe/points', 10)
        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)
​
        # Initialize the camera
        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)
​
        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()
​
        # Create a timer to process frames approximately at 30 FPS
        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.pubHandsPoint(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)
​
        HandDetector = self.frame_combine(frame, img)
        cv.imshow('HandDetector', HandDetector)
​
        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 pubHandsPoint(self, frame, draw=True):
        pointArray = PointArray()
        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 lm in self.results.multi_hand_landmarks[i].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 = HandDetector()
    try:
        rclpy.spin(node)
    except KeyboardInterrupt:
        pass
    finally:
        node.capture.release()
        cv.destroyAllWindows()
        node.destroy_node()
        rclpy.shutdown()
​
if __name__ == '__main__':
    main()