人脸表情判别，口罩识别
一、dlib,face_recognition以及opencv-python库安装
dlib安装：
需要安装cmake、boost、dlib
命令行安装
pip install cmake
pip install boost
pip install dlib
二、dlib的68点模型
链接: https://pan.baidu.com/s/10ZZNw86SqZL3-0D2XqC6tg
提取码: p2fc
三、python实现人脸识别和表情识别
import sys
import dlib # 人脸识别的库dlib
import numpy as np # 数据处理的库numpy
import cv2 # 图像处理的库OpenCv
class face_emotion():
def __init__(self):
# 使用特征提取器get_frontal_face_detector
self.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
self.predictor = dlib.shape_predictor("F:/face.dat")
# 建cv2摄像头对象，这里使用电脑自带摄像头，如果接了外部摄像头，则自动切换到外部摄像头
self.cap = cv2.VideoCapture(0)
# 设置视频参数，propId设置的视频参数，value设置的参数值
self.cap.set(3, 480)
# 截图screenshoot的计数器
self.cnt = 0
def learning_face(self):
# 眉毛直线拟合数据缓冲
line_brow_x = []
line_brow_y = []
# cap.isOpened（） 返回true/false 检查初始化是否成功
while (self.cap.isOpened()):
# cap.read()
# 返回两个值：
# 一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾
# 图像对象，图像的三维矩阵
flag, im_rd = self.cap.read()
# 每帧数据延时1ms，延时为0读取的是静态帧
k = cv2.waitKey(1)
# 取灰度
img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)
# 使用人脸检测器检测每一帧图像中的人脸。并返回人脸数rects
faces = self.detector(img_gray, 0)
# 待会要显示在屏幕上的字体
font = cv2.FONT_HERSHEY_SIMPLEX
# 如果检测到人脸
if (len(faces) != 0):
# 对每个人脸都标出68个特征点
for i in range(len(faces)):
# enumerate方法同时返回数据对象的索引和数据，k为索引，d为faces中的对象
for k, d in enumerate(faces):
# 用红色矩形框出人脸
cv2.rectangle(im_rd, (d.left(), d.top()), (d.right(), d.bottom()), (0, 0, 255))
# 计算人脸热别框边长
self.face_width = d.right() - d.left()
# 使用预测器得到68点数据的坐标
shape = self.predictor(im_rd, d)
# 圆圈显示每个特征点
for i in range(68):
cv2.circle(im_rd, (shape.part(i).x, shape.part(i).y), 2, (0, 255, 0), -1, 8)
# cv2.putText(im_rd, str(i), (shape.part(i).x, shape.part(i).y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
# (255, 255, 255))
# 分析任意n点的位置关系来作为表情识别的依据
mouth_width = (shape.part(54).x - shape.part(48).x) / self.face_width # 嘴巴咧开程度
mouth_higth = (shape.part(66).y - shape.part(62).y) / self.face_width # 嘴巴张开程度
# 通过两个眉毛上的10个特征点，分析挑眉程度和皱眉程度
brow_sum = 0 # 高度之和
frown_sum = 0 # 两边眉毛距离之和
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)
# self.brow_k, self.brow_d = self.fit_slr(line_brow_x, line_brow_y) # 计算眉毛的倾斜程度
tempx = np.array(line_brow_x)
tempy = np.array(line_brow_y)
z1 = np.polyfit(tempx, tempy, 1) # 拟合成一次直线
self.brow_k = -round(z1[0], 3) # 拟合出曲线的斜率和实际眉毛的倾斜方向是相反的
brow_hight = (brow_sum / 10) / self.face_width # 眉毛高度占比
brow_width = (frown_sum / 5) / self.face_width # 眉毛距离占比
# 眼睛睁开程度
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
# 分情况讨论
# 张嘴，可能是开心或者惊讶
if round(mouth_higth >= 0.03):
if eye_hight >= 0.056:
cv2.putText(im_rd, "amazing", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX,
0.8,
(0, 0, 255), 2, 4)
else:
cv2.putText(im_rd, "happy", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 255), 2, 4)
# 没有张嘴，可能是正常和生气
else:
if self.brow_k <= -0.3:
cv2.putText(im_rd, "angry", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 255), 2, 4)
else:
cv2.putText(im_rd, "nature", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(0, 0, 255), 2, 4)
# 标出人脸数
cv2.putText(im_rd, "Faces: " + str(len(faces)), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
else:
# 没有检测到人脸
cv2.putText(im_rd, "No Face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
# 添加说明
im_rd = cv2.putText(im_rd, "S: screenshot", (20, 400), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
im_rd = cv2.putText(im_rd, "Q: quit", (20, 450), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
# 按下s键截图保存
if (k == ord('s')):
self.cnt += 1
cv2.imwrite("screenshoot" + str(self.cnt) + ".jpg", im_rd)
# 按下q键退出
if (k == ord('q')):
break
# 窗口显示
cv2.imshow("camera", im_rd)
# 释放摄像头
self.cap.release()
# 删除建立的窗口
cv2.destroyAllWindows()
if __name__ == "__main__":
my_face = face_emotion()
my_face.learning_face()
四、基于CNN的表情识别
确定嘴唇的位置
# 显示嘴部特征点
# Draw the positions of someone's lip
import dlib # 人脸识别的库 Dlib
import cv2 # 图像处理的库 OpenCv
from get_features import get_features # return the positions of feature points
path_test_img = "C:\Users\ASUS\Desktop\timg.jpg"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('C:\Users\ASUS\Desktop\face.dat')
# Get lip's positions of features points
positions_lip = get_features(path_test_img)
img_rd = cv2.imread(path_test_img)
# Draw on the lip points
for i in range(0, len(positions_lip), 2):
print(positions_lip[i], positions_lip[i+1])
cv2.circle(img_rd, tuple([positions_lip[i], positions_lip[i+1]]), radius=1, color=(0, 255, 0))
cv2.namedWindow("img_read", 2)
cv2.imshow("img_read", img_rd)
cv2.waitKey(0)
判断人物是否微笑
# use the saved model
from sklearn.externals import joblib
from get_features import get_features
import ML_ways_sklearn
import cv2
# path of test img
path_test_img = "C:\Users\ASUS\Desktop\timg.jpg"
# 提取单张40维度特征
positions_lip_test = get_features(path_test_img)
# path of models
path_models = "F:/data/data_models/"
print("The result of"+path_test_img+":")
print('\n')
# ######### LR ###########
LR = joblib.load(path_models+"model_LR.m")
ss_LR = ML_ways_sklearn.model_LR()
X_test_LR = ss_LR.transform([positions_lip_test])
y_predict_LR = str(LR.predict(X_test_LR)[0]).replace('0', "no smile").replace('1', "with smile")
print("LR:", y_predict_LR)
# ######### LSVC ###########
LSVC = joblib.load(path_models+"model_LSVC.m")
ss_LSVC = ML_ways_sklearn.model_LSVC()
X_test_LSVC = ss_LSVC.transform([positions_lip_test])
y_predict_LSVC = str(LSVC.predict(X_test_LSVC)[0]).replace('0', "no smile").replace('1', "with smile")
print("LSVC:", y_predict_LSVC)
# ######### MLPC ###########
MLPC = joblib.load(path_models+"model_MLPC.m")
ss_MLPC = ML_ways_sklearn.model_MLPC()
X_test_MLPC = ss_MLPC.transform([positions_lip_test])
y_predict_MLPC = str(MLPC.predict(X_test_MLPC)[0]).replace('0', "no smile").replace('1', "with smile")
print("MLPC:", y_predict_MLPC)
# ######### SGDC ###########
SGDC = joblib.load(path_models+"model_SGDC.m")
ss_SGDC = ML_ways_sklearn.model_SGDC()
X_test_SGDC = ss_SGDC.transform([positions_lip_test])
y_predict_SGDC = str(SGDC.predict(X_test_SGDC)[0]).replace('0', "no smile").replace('1', "with smile")
print("SGDC:", y_predict_SGDC)
img_test = cv2.imread(path_test_img)
img_height = int(img_test.shape[0])
img_width = int(img_test.shape[1])
# show the results on the image
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img_test, "LR: "+y_predict_LR, (int(img_height/10), int(img_width/10)), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
cv2.putText(img_test, "LSVC: "+y_predict_LSVC, (int(img_height/10), int(img_width/10*2)), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
cv2.putText(img_test, "MLPC: "+y_predict_MLPC, (int(img_height/10), int(img_width/10)*3), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
cv2.putText(img_test, "SGDC: "+y_predict_SGDC, (int(img_height/10), int(img_width/10)*4), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
cv2.namedWindow("img", 2)
cv2.imshow("img", img_test)
cv2.waitKey(0)
五、口罩识别
import cv2
detector= cv2.CascadeClassifier('E:/opencv/opencv/build/etc/haarcascades/haarcascade_frontalface_default.xml')
mask_detector=cv2.CascadeClassifier('F:/cascade.xml')
cap = cv2.VideoCapture(0)
while True:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = detector.detectMultiScale(gray, 1.1, 3)
for (x, y, w, h) in faces:
#参数分别为 图片、左上角坐标，右下角坐标，颜色，厚度
face=img[y:y+h,x:x+w] # 裁剪坐标为[y0:y1, x0:x1]
mask_face=mask_detector.detectMultiScale(gray, 1.1, 5)
for (x2,y2,w2,h2) in mask_face:
cv2.rectangle(img, (x2, y2), (x2 + w2, y2 + h2), (0, 0, 255), 2)
cv2.imshow('mask', img)
cv2.waitKey(3)
cap.release()
cv2.destroyAllWindows()
cascade.xml是训练好的分类器,链接：
https://github.com/demon-12/source
opencv/opencv/build/etc/haarcascades/haarcascade_frontalface_default.xml是opencv中自带的人脸识别xml文件，opencv中还有其他的比如笑脸识别，猫脸识别，眼睛识别等等。