共计 2382 个字符，预计需要花费 6 分钟才能阅读完成。
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()正文完