Welcome to NUWA-HP60C Depth Camera repository

4. FaceMesh

4.1. 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 demonstrates the full capabilities of the framework.
• Free and open source: framework and solution under Apache2.0, fully extensible and customizable.

4.2, Dlib

DLIB is a modern C++ toolkit that contains machine learning algorithms and tools for creating complex software in C++ to solve real-world problems. It is widely used by industry and academia in fields such as robotics, embedded devices, mobile phones and large high-performance computing environments. The dlib library uses 68 points to mark important parts of the face, such as 18-22 points mark the right eyebrow, 51-68 marks the mouth. Use the get_frontal_face_detector module of the dlib library to detect faces, and use the shape_predictor_68_face_landmarks.dat feature data to predict facial feature values.

4.3, FaceMesh

4.3.1, Start

Start the camera

roslaunch ascamera hp60c.launch

Terminal input,

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roslaunch yahboomcar_mediapipe 04_FaceMesh.launch

4.3.2, Source code

Source code location: /home/yahboom/ascam_ws/src/yahboomcar_mediapipe/scripts/04_FaceMesh.py

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#!/usr/bin/env python3
# encoding: utf-8
import time
import rospy
import cv2 as cv
import numpy as np
import mediapipe as mp
from geometry_msgs.msg import Point
from yahboomcar_msgs.msg import PointArray
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
​
​
class FaceMesh:
    def __init__(self, staticMode=False, maxFaces=2, minDetectionCon=0.5, minTrackingCon=0.5):
        self.mpDraw = mp.solutions.drawing_utils
        self.mpFaceMesh = mp.solutions.face_mesh
        self.faceMesh = self.mpFaceMesh.FaceMesh(
            static_image_mode=staticMode,
            max_num_faces=maxFaces,
            min_detection_confidence=minDetectionCon,
            min_tracking_confidence=minTrackingCon )
        self.pub_point = rospy.Publisher('/mediapipe/points', PointArray, queue_size=1000)
        self.lmDrawSpec = mp.solutions.drawing_utils.DrawingSpec(color=(0, 0, 255), thickness=-1, circle_radius=3)
        self.drawSpec = self.mpDraw.DrawingSpec(color=(0, 255, 0), thickness=1, circle_radius=1)
        self.bridge = CvBridge()
        self.sub_image = rospy.Subscriber('/ascamera_hp60c/rgb0/image', Image, self.image_callback, queue_size=1)
        self.pTime = 0
​
    def pubFaceMeshPoint(self, frame, draw=True):
        pointArray = PointArray()
        img = np.zeros(frame.shape, np.uint8)
        imgRGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
        self.results = self.faceMesh.process(imgRGB)
        if self.results.multi_face_landmarks:
            for i in range(len(self.results.multi_face_landmarks)):
                if draw: self.mpDraw.draw_landmarks(frame, self.results.multi_face_landmarks[i], self.mpFaceMesh.FACEMESH_CONTOURS, self.lmDrawSpec, self.drawSpec)
                self.mpDraw.draw_landmarks(img, self.results.multi_face_landmarks[i], self.mpFaceMesh.FACEMESH_CONTOURS, self.lmDrawSpec, self.drawSpec)
                for id, lm in enumerate(self.results.multi_face_landmarks[i].landmark):
                        point = Point()
                        point.x, point.y, point.z = lm.x, lm.y, lm.z
                        pointArray.points.append(point)
        self.pub_point.publish(pointArray)
        return frame, img
​
    def frame_combine(slef,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 image_callback(self, msg):
        try:
            # Convert ROS image messages to OpenCV images
            frame = self.bridge.imgmsg_to_cv2(msg, desired_encoding="bgr8")
​
            # Process images and publish keypoints
            frame, img = self.pubFaceMeshPoint(frame, draw=True)
​
            # Calculate and display frame rate
            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), 2)
​
            # Merge and display images
            dst = self.frame_combine(frame, img)
            cv.imshow('dst', dst)
​
            # Check for 'q' key pressed to quit
            if cv.waitKey(1) & 0xFF == ord('q'):
                rospy.signal_shutdown("User requested shutdown")
        except Exception as e:
            rospy.logerr("Could not process image: %s" % e)
​
​
if __name__ == '__main__':
    rospy.init_node('FaceMesh', anonymous=True)
    face_mesh = FaceMesh(maxFaces=2)
    rospy.spin()
    cv.destroyAllWindows()