Welcome to NUWA-HP60C Depth Camera repository

1. Hand detection

1.1. 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 supports 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 commodity 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.

1.2, MediaPipe Hands

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

After palm detection for the entire image, the 21 3D hand joint coordinates in the detected hand area are accurately located by regression based on the hand marker model, that is, direct coordinate prediction. The model learns a consistent internal hand pose representation that is robust even to partially visible hands and self-occlusions.

To obtain ground truth data, ~30K real-world images were manually annotated with 21 3D coordinates, as shown below (Z values were taken from the image depth map, if available for each corresponding coordinate). To provide better coverage of possible hand poses and provide additional supervision on the properties of the hand geometry, high-quality synthetic hand models were also drawn against various backgrounds and mapped to the corresponding 3D coordinates.

1.3, Hand detection

1.3.1, Start

Start the camera

ros2 launch ascamera hp60c.launch.py

Open a new terminal and enter,

xxxxxxxxxx
ros2 run yahboomcar_mediapipe 01_HandDetector

1.3.2, Source code

Source code location: ~/ascam_ros2_ws/src/yahboomcar_mediapipe/yahboomcar_mediapipe/01_HandDetector.py

​x
#!/usr/bin/env python3
# encoding: utf-8
​
#import ros lib
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import Point
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import mediapipe as mp
#import define msg
from yahboomcar_msgs.msg import PointArray
​
#import common lib
import cv2 as cv
import numpy as np
import time
import os
print("import done")
​
​
class HandDetector(Node):
    def __init__(self, name, mode=False, maxHands=2, detectorCon=0.5, trackCon=0.5):
        super().__init__(name)
        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.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)
        # Create publisher to publish hand points
        self.pub_point = self.create_publisher(PointArray, '/mediapipe/points', 1000)
        
        # Initialize CvBridge to convert ROS images to OpenCV format
        self.bridge = CvBridge()
​
        # Subscribe to the image topic
        self.create_subscription(Image, '/ascamera_hp60c/camera_publisher/rgb0/image', self.image_callback, 10)
​
        self.pTime = 0  # Initialize pTime to calculate FPS
        self.exit_flag = False  # Flag to indicate exit condition
​
    def image_callback(self, msg):
        # Convert ROS image message to OpenCV format
        frame = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
        frame, img = self.pubHandsPoint(frame, draw=False)
        
        # Display FPS and combine frames
        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)
        
        # Combine the frames (original and landmark drawn image)
        dist = self.frame_combine(frame, img)
        
        # Check if 'q' is pressed to exit the program
        if cv.waitKey(1) & 0xFF == ord('q'):
            self.exit_flag = True
        
        # Display the final image
        cv.imshow('dist', dist)
​
    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[
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