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python利用dlib获取人脸的68个landmark

更新时间:2020-08-10 16:54:01 作者:startmvc
(1)单人脸情况importcv2importdlibpath="1.jpg"img=cv2.imread(path)gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#人

(1) 单人脸情况


import cv2
import dlib

path = "1.jpg"
img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#人脸检测画框
detector = dlib.get_frontal_face_detector()
# 获取人脸关键点检测器
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
#获取人脸框位置信息
dets = detector(gray, 1)#1表示采样(upsample)次数 0识别的人脸少点,1识别的多点,2识别的更多,小脸也可以识别
for face in dets:
 shape = predictor(img, face) # 寻找人脸的68个标定点
 # 遍历所有点,打印出其坐标,并圈出来
 for pt in shape.parts():
 pt_pos = (pt.x, pt.y)
 cv2.circle(img, pt_pos, 2, (0, 0, 255), 1)#img, center, radius, color, thickness

 cv2.imshow("image", img)

cv2.waitKey(0)
cv2.destroyAllWindows()

(2) 多人脸情况


import cv2
import dlib

path1 = "zxc.jpg"
img = cv2.imread(path1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#人脸检测画框
detector = dlib.get_frontal_face_detector()
# 获取人脸关键点检测器
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
#获取人脸框位置信息
dets = detector(gray, 1)#1表示采样(upsample)次数 0识别的人脸少点,1识别的多点,2识别的更多,小脸也可以识别

for i in range(len(dets)):
 shape = predictor(img, dets[i]) # 寻找人脸的68个标定点
 # 遍历所有点,打印出其坐标,并圈出来
 for pt in shape.parts():
 pt_pos = (pt.x, pt.y)
 cv2.circle(img, pt_pos, 2, (0, 0, 255), 1)#img, center, radius, color, thickness

cv2.imshow("image", img)

cv2.waitKey(0)#等待键盘输入
cv2.destroyAllWindows()

(3) 获取电脑摄像头实时识别标定


import cv2
import dlib
import numpy as np

cap = cv2.VideoCapture(0)#打开笔记本的内置摄像头,若参数是视频文件路径则打开视频
cap.isOpened()

def key_points(img):
 points_keys = []
 PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
 detector = dlib.get_frontal_face_detector()
 predictor = dlib.shape_predictor(PREDICTOR_PATH)
 rects = detector(img,1)

 for i in range(len(rects)):
 landmarks = np.matrix([[p.x,p.y] for p in predictor(img,rects[i]).parts()])
 for point in landmarks:
 pos = (point[0,0],point[0,1])
 points_keys.append(pos)
 cv2.circle(img,pos,2,(255,0,0),-1)
 return img

while(True):
 ret, frame = cap.read()#按帧读取视频,ret,frame是cap.read()方法的两个返回值。其中ret是布尔值,如果读取帧是正确的则返回True,如果文件读取到结尾,它的返回值就为False。frame就是每一帧的图像,是个三维矩阵。
 # gray = cv2.cvtColor(frame)
 face_key = key_points(frame)
 cv2.imshow('frame',face_key)
 if cv2.waitKey(1) & 0xFF == ord('q'):
 break

cap.release()#释放摄像头
cv2.destroyAllWindows()#关闭所有图像窗口

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持脚本之家。

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