seo网站做推广公司,网站建设的前期投入,大型门户网站制作流程,wordpress 获取附件前面的都是使用了mediapipe框架。后面的这两节采用了opencv\dlib的框架。
一 脸部检测
核心#xff1a;opencv
detectMultiScale函数
detectMultiScale(image, scaleFactor, minNeighbors, flags, minSize, maxSize) image--待检测图片#xff0c;一般为灰度图像加快检测…前面的都是使用了mediapipe框架。后面的这两节采用了opencv\dlib的框架。
一 脸部检测
核心opencv
detectMultiScale函数
detectMultiScale(image, scaleFactor, minNeighbors, flags, minSize, maxSize) image--待检测图片一般为灰度图像加快检测速度
scaleFactor参数控制每个图像序列的缩放比例。该参数决定了在每个图像序列中检测窗口的大小。默认值为1.1表示每次图像被缩小10%。较小的值可以捕捉更多的细节但也会增加计算量。较大的值可以加快检测速度但可能会错过一些目标。
minNeighbors参数定义了每个目标至少应该有多少个邻居才能被认为是一个目标。该参数用于过滤检测到的目标。
flags参数用于定义检测模式。它可以是以下几个值的组合
CASCADE_SCALE_IMAGE使用缩放图像进行检测默认值。CASCADE_FIND_BIGGEST_OBJECT只检测最大的目标。CASCADE_DO_ROUGH_SEARCH快速搜索模式。
minSize、maxSize参数用于指定检测目标的最小、最大尺寸。 src/yahboom_esp32_mediapipe/yahboom_esp32_mediapipe/目录下新建05_FaceEyeDetection.py代码如下
#!/usr/bin/env python2
# encoding: utf-8
#import ros lib
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import Point
import mediapipe as mp
from cv_bridge import CvBridge
from sensor_msgs.msg import CompressedImage,Image
#import define msg
from yahboomcar_msgs.msg import PointArray
#import commom lib
import cv2 as cv
import numpy as np
import time
from cv_bridge import CvBridge
from sensor_msgs.msg import Image, CompressedImagefrom rclpy.time import Time
import datetimeprint(import done)class FaceEyeDetection(Node):def __init__(self,name):super().__init__(name)self.bridge CvBridge()#加载分类器self.eyeDetect cv.CascadeClassifier( /home/bohu/yahboomcar/yahboomcar_ws/src/yahboom_esp32_mediapipe/resource/haarcascade_eye.xml)self.faceDetect cv.CascadeClassifier(/home/bohu/yahboomcar/yahboomcar_ws/src/yahboom_esp32_mediapipe/resource/haarcascade_frontalface_default.xml)self.pub_rgb self.create_publisher(Image,/FaceEyeDetection/image, 500)def cancel(self):self.pub_rgb.unregister()def face(self, frame):gray cv.cvtColor(frame, cv.COLOR_BGR2GRAY)#灰度faces self.faceDetect.detectMultiScale(gray, 1.3)#检测for face in faces: frame self.faceDraw(frame, face)return framedef eye(self, frame):gray cv.cvtColor(frame, cv.COLOR_BGR2GRAY)#灰度eyes self.eyeDetect.detectMultiScale(gray, 1.3)#检测for eye in eyes:cv.circle(frame, (int(eye[0] eye[2] / 2), int(eye[1] eye[3] / 2)), (int(eye[3] / 2)), (0, 0, 255), 2)return frame#画框显示def faceDraw(self, frame, bbox, l30, t10):x, y, w, h bboxx1, y1 x w, y hcv.rectangle(frame, (x, y), (x w, y h), (255, 0, 255), 2)# Top left x,ycv.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,ycv.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,y1cv.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,y1cv.line(frame, (x1, y1), (x1 - l, y1), (255, 0, 255), t)cv.line(frame, (x1, y1), (x1, y1 - l), (255, 0, 255), t)return framedef pub_img(self, frame):self.pub_rgb.publish(self.bridge.cv2_to_imgmsg(frame, bgr8))class MY_Picture(Node):def __init__(self, name):super().__init__(name)self.bridge CvBridge()self.sub_img self.create_subscription(CompressedImage, /espRos/esp32camera, self.handleTopic, 1) #获取esp32传来的图像self.last_stamp Noneself.new_seconds 0self.fps_seconds 1self.face_eye_detection FaceEyeDetection(face_eye_detection)self.content [face, eye, face_eye]self.content_index 0#回调函数def handleTopic(self, msg):self.last_stamp msg.header.stamp if self.last_stamp:total_secs Time(nanosecondsself.last_stamp.nanosec, secondsself.last_stamp.sec).nanosecondsdelta datetime.timedelta(secondstotal_secs * 1e-9)seconds delta.total_seconds()*100if self.new_seconds ! 0:self.fps_seconds seconds - self.new_secondsself.new_seconds seconds#保留这次的值start time.time()frame self.bridge.compressed_imgmsg_to_cv2(msg)frame cv.resize(frame, (640, 480))cv.waitKey(10)action cv.waitKey(1) 0xFFif action ord(f) or action ord(F):self.content_index 1if self.content_index len(self.content): self.content_index 0if self.content[self.content_index] face: frame self.face_eye_detection.face(frame)elif self.content[self.content_index] eye: frame self.face_eye_detection.eye(frame)else: frame self.face_eye_detection.eye(self.face_eye_detection.face(frame))end time.time()fps 1 / ((end - start)self.fps_seconds)text FPS : str(int(fps))cv.putText(frame, text, (20, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1)cv.imshow(frame, frame)self.face_eye_detection.pub_img(frame)# print(frame)cv.waitKey(10)def main():print(start it)rclpy.init()esp_img MY_Picture(My_Picture)try:rclpy.spin(esp_img)except KeyboardInterrupt:passfinally:esp_img.destroy_node()rclpy.shutdown()主要流程跟之前类似这个不如mediapipe框架好尤其是画框比较麻烦。
这个识别效果对比之前的face_recognition我觉得不如那个好参见
ros2-4.2 用python实现人脸识别_ros2使用人脸检测-CSDN博客
构建后运行
ros2 run yahboom_esp32_mediapipe FaceEyeDetection
效果如下 二 人脸特效
人脸检测
使用了dlib库
.get_frontal_face_detector()
功能人脸检测画框 参数无 返回值默认的人脸检测器 shape_predictor()
功能标记人脸关键点 参数shape_predictor_68_face_landmarks.dat68个关键点模型地址 返回值人脸关键点预测器
import cv2
import mediapipe as mp
import dlibdetector dlib.get_frontal_face_detector()
predictor dlib.shape_predictor(/home/bohu/yahboomcar/yahboomcar_ws/src/yahboom_esp32_mediapipe/resource/shape_predictor_68_face_landmarks.dat)cap cv2.VideoCapture(0)#打开默认摄像头
while True:ret,frame cap.read()#读取一帧图像#图像格式转换frame cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)# 因为摄像头是镜像的所以将摄像头水平翻转# 不是镜像的可以不翻转frame cv2.flip(frame,1)#输出结果gray cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)faces detector(gray)print(ffaces:{len(faces)})for face in faces:# 利用预测器预测shape predictor(gray, face)# print(shape)# 标出68个点的位置for i in range(68):cv2.circle(frame, (shape.part(i).x, shape.part(i).y), 2, (0, 255, 0), -1, 1)cv2.putText(frame, str(i), (shape.part(i).x, shape.part(i).y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))cv2.imshow(opencv detectMultiScale, frame)if cv2.waitKey(1) 0xFF 27:break
cap.release() 人脸特效
基本思路就是在dlib人脸检测点上在额外使用opencv画线\和fillConvexPoly填充多边形函数。
这种就是比较麻烦没有官方的函数之间调用好。
src/yahboom_esp32_mediapipe/yahboom_esp32_mediapipe/目录下新建文件06_FaceLandmarks.py代码如下
#!/usr/bin/env python3
# encoding: utf-8
import rclpy
from rclpy.node import Node
import time
import dlib
import cv2 as cv
import numpy as np
from cv_bridge import CvBridge
from sensor_msgs.msg import Image, CompressedImagefrom rclpy.time import Time
import datetimeclass FaceLandmarks:def __init__(self, dat_file):self.hog_face_detector dlib.get_frontal_face_detector()self.dlib_facelandmark dlib.shape_predictor(dat_file)def get_face(self, frame, drawTrue):gray cv.cvtColor(frame, cv.COLOR_BGR2GRAY)self.faces self.hog_face_detector(gray)for face in self.faces:self.face_landmarks self.dlib_facelandmark(gray, face)if draw:for n in range(68):x self.face_landmarks.part(n).xy self.face_landmarks.part(n).ycv.circle(frame, (x, y), 2, (0, 255, 255), 2)cv.putText(frame, str(n), (x, y), cv.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)return framedef get_lmList(self, frame, p1, p2, drawTrue):lmList []if len(self.faces) ! 0:for n in range(p1, p2):x self.face_landmarks.part(n).xy self.face_landmarks.part(n).ylmList.append([x, y])if draw:next_point n 1if n p2 - 1: next_point p1x2 self.face_landmarks.part(next_point).xy2 self.face_landmarks.part(next_point).ycv.line(frame, (x, y), (x2, y2), (0, 255, 0), 1)return lmListdef get_lipList(self, frame, lipIndexlist, drawTrue):lmList []if len(self.faces) ! 0:for n in range(len(lipIndexlist)):x self.face_landmarks.part(lipIndexlist[n]).xy self.face_landmarks.part(lipIndexlist[n]).ylmList.append([x, y])if draw:next_point n 1if n len(lipIndexlist) - 1: next_point 0x2 self.face_landmarks.part(lipIndexlist[next_point]).xy2 self.face_landmarks.part(lipIndexlist[next_point]).ycv.line(frame, (x, y), (x2, y2), (0, 255, 0), 1)return lmListdef prettify_face(self, frame, eyeTrue, lipsTrue, eyebrowTrue, drawTrue):if eye:leftEye landmarks.get_lmList(frame, 36, 42)rightEye landmarks.get_lmList(frame, 42, 48)if draw:if len(leftEye) ! 0: frame cv.fillConvexPoly(frame, np.mat(leftEye), (0, 0, 0))if len(rightEye) ! 0: frame cv.fillConvexPoly(frame, np.mat(rightEye), (0, 0, 0))if lips:lipIndexlistA [51, 52, 53, 54, 64, 63, 62]lipIndexlistB [48, 49, 50, 51, 62, 61, 60]lipsUpA landmarks.get_lipList(frame, lipIndexlistA, drawTrue)lipsUpB landmarks.get_lipList(frame, lipIndexlistB, drawTrue)lipIndexlistA [57, 58, 59, 48, 67, 66]lipIndexlistB [54, 55, 56, 57, 66, 65, 64]lipsDownA landmarks.get_lipList(frame, lipIndexlistA, drawTrue)lipsDownB landmarks.get_lipList(frame, lipIndexlistB, drawTrue)if draw:if len(lipsUpA) ! 0: frame cv.fillConvexPoly(frame, np.mat(lipsUpA), (249, 0, 226))if len(lipsUpB) ! 0: frame cv.fillConvexPoly(frame, np.mat(lipsUpB), (249, 0, 226))if len(lipsDownA) ! 0: frame cv.fillConvexPoly(frame, np.mat(lipsDownA), (249, 0, 226))if len(lipsDownB) ! 0: frame cv.fillConvexPoly(frame, np.mat(lipsDownB), (249, 0, 226))if eyebrow:lefteyebrow landmarks.get_lmList(frame, 17, 22)righteyebrow landmarks.get_lmList(frame, 22, 27)if draw:if len(lefteyebrow) ! 0: frame cv.fillConvexPoly(frame, np.mat(lefteyebrow), (255, 255, 255))if len(righteyebrow) ! 0: frame cv.fillConvexPoly(frame, np.mat(righteyebrow), (255, 255, 255))return frameclass MY_Picture(Node):def __init__(self, name,landmarkss):super().__init__(name)self.bridge CvBridge()self.sub_img self.create_subscription(CompressedImage, /espRos/esp32camera, self.handleTopic, 1) #获取esp32传来的图像self.landmarksros landmarkssself.last_stamp Noneself.new_seconds 0self.fps_seconds 1def handleTopic(self, msg):self.last_stamp msg.header.stamp if self.last_stamp:total_secs Time(nanosecondsself.last_stamp.nanosec, secondsself.last_stamp.sec).nanosecondsdelta datetime.timedelta(secondstotal_secs * 1e-9)seconds delta.total_seconds()*100if self.new_seconds ! 0:self.fps_seconds seconds - self.new_secondsself.new_seconds seconds#保留这次的值start time.time()frame self.bridge.compressed_imgmsg_to_cv2(msg)frame cv.resize(frame, (640, 480))cv.waitKey(10)frame self.landmarksros.get_face(frame, drawFalse)frame self.landmarksros.prettify_face(frame, eyeTrue, lipsTrue, eyebrowTrue, drawTrue)end time.time()fps 1 / ((end - start)self.fps_seconds)text FPS : str(int(fps))cv.putText(frame, text, (20, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 1)cv.imshow(frame, frame)landmarks None
def main():global landmarksprint(start it)#使用官方训练好的dlib 68点模型dat_file /home/bohu/yahboomcar/yahboomcar_ws/src/yahboom_esp32_mediapipe/resource/shape_predictor_68_face_landmarks.datlandmarks FaceLandmarks(dat_file)rclpy.init()esp_img MY_Picture(My_Picture,landmarks)try:rclpy.spin(esp_img)except KeyboardInterrupt:passfinally:esp_img.destroy_node()rclpy.shutdown()构建后运行 ros2 run yahboom_esp32_mediapipe FaceLandmarks
效果如下
