关于python:Python人脸识别微笑检测

import cv2 # 图像处理的库 OpenCv
import dlib # 人脸识别的库 dlib
import numpy as np # 数据处理的库 numpy
class face_emotion():

def __init__(self):    self.detector = dlib.get_frontal_face_detector()    self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")    self.cap = cv2.VideoCapture(0)    self.cap.set(3, 480)    self.cnt = 0  def learning_face(self):    line_brow_x = []    line_brow_y = []    while(self.cap.isOpened()):        flag, im_rd = self.cap.read()        k = cv2.waitKey(1)        # 取灰度        img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)          faces = self.detector(img_gray, 0)        font = cv2.FONT_HERSHEY_SIMPLEX        # 如果检测到人脸        if(len(faces) != 0):            #[ 外汇跟单](https://www.gendan5.com/)对每个人脸都标出68个特色点            for i in range(len(faces)):                for k, d in enumerate(faces):                    cv2.rectangle(im_rd, (d.left(), d.top()), (d.right(), d.bottom()), (0,0,255))                    self.face_width = d.right() - d.left()                    shape = self.predictor(im_rd, d)                    mouth_width = (shape.part(54).x - shape.part(48).x) / self.face_width                     mouth_height = (shape.part(66).y - shape.part(62).y) / self.face_width                    brow_sum = 0                     frown_sum = 0                     for j in range(17, 21):                        brow_sum += (shape.part(j).y - d.top()) + (shape.part(j + 5).y - d.top())                        frown_sum += shape.part(j + 5).x - shape.part(j).x                        line_brow_x.append(shape.part(j).x)                        line_brow_y.append(shape.part(j).y)                    tempx = np.array(line_brow_x)                    tempy = np.array(line_brow_y)                    z1 = np.polyfit(tempx, tempy, 1)                      self.brow_k = -round(z1[0], 3)                     brow_height = (brow_sum / 10) / self.face_width # 眉毛高度占比                    brow_width = (frown_sum / 5) / self.face_width  # 眉毛间隔占比                    eye_sum = (shape.part(41).y - shape.part(37).y + shape.part(40).y - shape.part(38).y +                                shape.part(47).y - shape.part(43).y + shape.part(46).y - shape.part(44).y)                    eye_hight = (eye_sum / 4) / self.face_width                    if round(mouth_height >= 0.03) and eye_hight<0.56:                        cv2.putText(im_rd, "smile", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 2,                                        (0,255,0), 2, 4)                    if round(mouth_height<0.03) and self.brow_k>-0.3:                        cv2.putText(im_rd, "unsmile", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 2,                                    (0,255,0), 2, 4)            cv2.putText(im_rd, "Face-" + str(len(faces)), (20,50), font, 0.6, (0,0,255), 1, cv2.LINE_AA)        else:            cv2.putText(im_rd, "No Face", (20,50), font, 0.6, (0,0,255), 1, cv2.LINE_AA)        im_rd = cv2.putText(im_rd, "S: screenshot", (20,450), font, 0.6, (255,0,255), 1, cv2.LINE_AA)        im_rd = cv2.putText(im_rd, "Q: quit", (20,470), font, 0.6, (255,0,255), 1, cv2.LINE_AA)        if (cv2.waitKey(1) & 0xFF) == ord('s'):            self.cnt += 1            cv2.imwrite("screenshoot" + str(self.cnt) + ".jpg", im_rd)        # 按下 q 键退出        if (cv2.waitKey(1)) == ord('q'):            break        # 窗口显示        cv2.imshow("Face Recognition", im_rd)    self.cap.release()    cv2.destroyAllWindows()

if name == "__main__":

my_face = face_emotion()my_face.learning_face()