import cv2
import dlib
import numpy as np
#根据人脸框bbox,从一张完整图片裁剪出人脸
def getface():
bgrImg = cv2.imread('1.jpg')
print bgrImg.shape
rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
detector=dlib.get_frontal_face_detector()
#img = io.imread('1.jpg')
faces = detector(rgbImg, 1)
if len(faces) > 0:
face=max(faces, key=lambda rect: rect.width() * rect.height())
[x1,x2,y1,y2]=[face.left(),face.right(),face.top(),face.bottom()]
人脸特征点定位:需要先从网上下载预训练模型
/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
import dlib
#根据人脸框bbox,从一张完整图片裁剪出人脸,并保存问文件名cropimgname
#如果未检测到人脸,那么返回false,否则返回true
face_detector=dlib.get_frontal_face_detector()
landmark_predictor=dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
def geteye_rect(imgpath):
bgrImg = cv2.imread(imgpath)
if bgrImg is None:
return False
rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
facesrect = face_detector(rgbImg, 1)
if len(facesrect) <=0:
return False
for k, d in enumerate(facesrect):
shape = landmark_predictor(rgbImg, d)
for i in range(68):
pt=shape.part(i)
plt.plot(pt.x,pt.y,'ro')
plt.imshow(rgbImg)
plt.show()