百度云地址，dlib人脸特征库分类器，194点，由helen 库(2000张)训练而成 dlib人脸特征库分类器，194点，由helen 库(2000张)训练而成 dlib人脸特征库分类器，194点，由helen 库(2000张)训练而成

dlib人脸特征库分类器，81个点 包含使用代码，通过摄像头识别人脸 import cv2 import dlib from skimage import io import numpy as np # 使用特征提取器get_frontal_face_detector detector = dlib.get_frontal_face_detector() # dlib的68点模型，使用作者训练好的特征预测器 predictor = dlib.shape_predictor("shape_predictor_81_face_landmarks.dat") cap=cv2.VideoCapture(0) while True: ret,img=cap.read() dets = detector(img, 1) for k, d in enumerate(dets): print("第", k+1, "个人脸d的坐标：", "left:", d.left(), "right:", d.right(), "top:", d.top(), "bottom:", d.bottom()) width = d.right() - d.left() heigth = d.bottom() - d.top() print('人脸面积为：',(width*heigth)) # 利用预测器预测 #shape = predictor(img, d) cv2.rectangle(img,(d.left(),d.top()),(d.right(),d.bottom()),(0,255,0),1) shape = predictor(img, d) landmarks = np.matrix([[p.x, p.y] for p in shape.parts()]) for num in range(shape.num_parts): cv2.circle(img, (shape.parts()[num].x, shape.parts()[num].y), 3, (0,255,0), -1) #cv2.putText(img, str(i), (shape.part(i).x, shape.part(i).y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255)) cv2.imshow("img",img) if cv2.waitKey(1) & 0xFF == ord('q'): break