人脸特征点检测——基于DLib库

1.人脸特征点检测

         dlib关键点模型官方有两种&＃xff0c;分别为5个点和68个点的&＃xff0c;下载地址&＃xff1a; 

        http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2

        人脸68个关键点如下&＃xff1a;

         主要函数&＃xff1a;

        dlib.get_frontal_face_detector()获取人脸检测器

        dlib.shape_predictor()预测人脸关键点。   

2.人脸5个关键点检测——图片

# 1.导入库
import cv2
import dlib
import numpy as np
import matplotlib.pyplot as plt# 2.读取一张照片
img&＃61;cv2.imread(&＃39;img_1.jpg&＃39;)# 3.调用人脸检测器
detector&＃61;dlib.get_frontal_face_detector()# 4.加载预测关键点模型&＃xff08;5个关键点&＃xff09;
predictor&＃61;dlib.shape_predictor("shape_predictor_5_face_landmarks.dat")# 5.灰度转换
gray&＃61;cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)# 6.人脸检测
faces&＃61;detector(gray,1)# 7.循环&＃xff0c;遍历每一张人脸&＃xff0c;给人脸绘制矩形和关键点
for face in faces:# 8.绘制矩形框cv2.rectangle(img,(face.left(),face.top()),(face.right(),face.bottom()),(0,0,255),3)# 9.预测关键点shape&＃61;predictor(img,face)# 10.获取关键点坐标for pt in shape.parts():# 获取横纵坐标pt_position&＃61;(pt.x,pt.y)# 11.显示/检测关键点cv2.circle(img,pt_position,3,(0,255,0),-1)# 12.显示整个效果图
img_RGB&＃61;img[:,:,::-1]
plt.imshow(img_RGB)
plt.axis(&＃39;off&＃39;)
plt.show()

3.人脸68个关键点检测——图片

# 4.加载预测关键点模型&＃xff08;68个关键点&＃xff09;
predictor&＃61;dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

4.人脸68个关键点检测——视频

# 1.导入库
import cv2
import dlib
import numpy as np
import matplotlib.pyplot as plt# 2.打开摄像头
capture&＃61;cv2.VideoCapture(0)# 3.调用人脸检测器
detector&＃61;dlib.get_frontal_face_detector()# 4.加载预测关键点模型&＃xff08;68个关键点&＃xff09;
predictor&＃61;dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")while True:# 6.读取视频流ret,img&＃61;capture.read()# 6.灰度转换gray&＃61;cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)# 7.人脸检测faces&＃61;detector(gray,1)# 8.循环&＃xff0c;遍历每一张人脸&＃xff0c;给人脸绘制矩形和关键点for face in faces:# 8.1.绘制矩形框cv2.rectangle(img,(face.left(),face.top()),(face.right(),face.bottom()),(0,0,255),3)# 8.2.预测关键点shape&＃61;predictor(img,face)# 8.3.获取关键点坐标for pt in shape.parts():# 获取横纵坐标pt_position&＃61;(pt.x,pt.y)# 8.4.显示/检测关键点cv2.circle(img,pt_position,3,(0,255,0),-1)if cv2.waitKey(1) & 0xff &＃61;&＃61; ord(&＃39;q&＃39;):break# 9.显示结果cv2.imshow(&＃39;face detection landmark&＃39;,img)capture.release()
cv2.destroyAllWindows()