Face Detection

1. Course Content

• Run the example program to detect faces using the mediapipe framework.

2. Program Startup

• Run the face detection node in the terminal

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ros2 launch yahboomcar_bringup mediapipe_demo.launch.py demo:=FaceDetector

After the program runs, as shown in the figure below, a detected face will be framed and the detection score will be displayed. The higher the score, the more likely it is recognized as a face.

3. Core Source Code Analysis

Program code path:

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~/yahboomcar_ros2_ws/yahboomcar_ws/src/common_demo/common_demo/mediapipe/05_FaceDetection.py

Import the required library files

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import time
import cv2 as cv
import numpy as np
import rclpy
from rclpy.node import Node
#Import the mediapipe library
import mediapipe as mp
from cv_bridge import CvBridge
from sensor_msgs.msg import Image
from arm_msgs.msg import ArmJoints
import cv2

Initialize the data and define the Publisher and Subscriber,

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def __init__(self, name):
    super().__init__(name)
    self.minDetectionCon=0.5
    #Use the class in the mediapipe library to define a face detection object
    self.mpFaceDetection = mp.solutions.face_detection
    self.mpDraw = mp.solutions.drawing_utils
    self.facedetection = self.mpFaceDetection.FaceDetection(min_detection_confidence=self.minDetectionCon)
    self.rgb_bridge = CvBridge()

    #Define the Subscriber for the color image topic
    self.sub_rgb = self.create_subscription(Image,"/camera/color/image_raw",self.get_RGBImageCallBack,100)

Color image callback function,

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def get_RGBImageCallBack(self,msg):
    #Use CvBridge to convert the color image message data into image data
    rgb_image = self.rgb_bridge.imgmsg_to_cv2(msg, "bgr8")
    #Pass the obtained image into the defined findFaces function for the face detection program
    frame,_ = self.findFaces(rgb_image)
    key = cv2.waitKey(1)
    cv.imshow('frame', frame)

findFaces function,

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def findFaces(self, frame):
    #Convert the color space of the input image from BGR to RGB for subsequent image processing
    img_RGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
    #Call the process function in the mediapipe library for image processing; during init, the self.facedetection object was created and initialized
    self.results = self.facedetection.process(img_RGB)
    bboxs = []
    #Determine whether self.results.detections exists, that is, whether a face has been recognized
    if self.results.detections:
        #Iterate through the id and detection data
        for id, detection in enumerate(self.results.detections):
            #Obtain the bounding box coordinates
            bboxC = detection.location_data.relative_bounding_box
            ih, iw, ic = frame.shape
            bbox = int(bboxC.xmin * iw), int(bboxC.ymin * ih), \
            int(bboxC.width * iw), int(bboxC.height * ih)
            #Store the detection result
            bboxs.append([id, bbox, detection.score])
            #Call the fancyDraw function to draw the bounding box
            frame = self.fancyDraw(frame, bbox)
            #Display the face recognition score on the image
            cv.putText(frame, f'{int(detection.score[0] * 100)}%',(bbox[0], bbox[1] - 20), cv.FONT_HERSHEY_PLAIN,3, (255, 0, 255), 2)
    return frame, bboxs

fancyDraw function, draws the bounding box according to the value of the detection result bbox

def fancyDraw(self, frame, bbox, l=30, t=10):
    x, y, w, h = bbox
    x1, y1 = x + w, y + h
    cv.rectangle(frame, (x, y),(x + w, y + h), (255, 0, 255), 2)
    # Top left x,y
    cv.line(frame, (x, y), (x + l, y), (255, 0, 255), t)
    cv.line(frame, (x, y), (x, y + l), (255, 0, 255), t)
    # Top right x1,y
    cv.line(frame, (x1, y), (x1 - l, y), (255, 0, 255), t)
    cv.line(frame, (x1, y), (x1, y + l), (255, 0, 255), t)
    # Bottom left x1,y1
    cv.line(frame, (x, y1), (x + l, y1), (255, 0, 255), t)
    cv.line(frame, (x, y1), (x, y1 - l), (255, 0, 255), t)
    # Bottom right x1,y1
    cv.line(frame, (x1, y1), (x1 - l, y1), (255, 0, 255), t)
    cv.line(frame, (x1, y1), (x1, y1 - l), (255, 0, 255), t)
    return frame