人脸识别SVM

一.实验准备

下载实验所需包

pip install scikit-image

pip install playsound

pip install pandas

pip install sklearn

二、Dlib提取人脸特征

# 从人脸图像文件中提取人脸特征存入 CSV
# Features extraction from images and save into features_all.csv

# return_128d_features()          获取某张图像的128D特征
# compute_the_mean()              计算128D特征均值

from cv2 import cv2 as cv2
import os
import dlib
from skimage import io
import csv
import numpy as np

# 要读取人脸图像文件的路径
path_images_from_camera = "D:/myworkspace/JupyterNotebook/Smile/files2/test/"

# Dlib 正向人脸检测器
detector = dlib.get_frontal_face_detector()

# Dlib 人脸预测器
predictor = dlib.shape_predictor("D:/shape_predictor_68_face_landmarks.dat")

# Dlib 人脸识别模型
# Face recognition model, the object maps human faces into 128D vectors
face_rec = dlib.face_recognition_model_v1("D:/dlib_face_recognition_resnet_model_v1.dat")


# 返回单张图像的 128D 特征
def return_128d_features(path_img):
    img_rd = io.imread(path_img)
    img_gray = cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)
    faces = detector(img_gray, 1)

    print("%-40s %-20s" % ("检测到人脸的图像 / image with faces detected:", path_img), '\n')

    # 因为有可能截下来的人脸再去检测，检测不出来人脸了
    # 所以要确保是 检测到人脸的人脸图像 拿去算特征
    if len(faces) != 0:
        shape = predictor(img_gray, faces[0])
        face_descriptor = face_rec.compute_face_descriptor(img_gray, shape)
    else:
        face_descriptor = 0
        print("no face")

    return face_descriptor


# 将文件夹中照片特征提取出来, 写入 CSV
def return_features_mean_personX(path_faces_personX):
    features_list_personX = []
    photos_list = os.listdir(path_faces_personX)
    if photos_list:
        for i in range(len(photos_list)):
            # 调用return_128d_features()得到128d特征
            print("%-40s %-20s" % ("正在读的人脸图像 / image to read:", path_faces_personX + "/" + photos_list[i]))
            features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])
            #  print(features_128d)
            # 遇到没有检测出人脸的图片跳过
            if features_128d == 0:
                i += 1
            else:
                features_list_personX.append(features_128d)
                i1=str(i+1)
                add="D:/myworkspace/JupyterNotebook/Smile/feature/face_feature"+i1+".csv"
                print(add)
                with open(add, "w", newline="") as csvfile:
                    writer1 = csv.writer(csvfile)
                    writer1.writerow(features_128d)
    else:
        print("文件夹内图像文件为空 / Warning: No images in " + path_faces_personX + '/', '\n')

    # 计算 128D 特征的均值
    # N x 128D -> 1 x 128D
    if features_list_personX:
        features_mean_personX = np.array(features_list_personX).mean(axis=0)
    else:
        features_mean_personX = '0'

    return features_mean_personX


# 读取某人所有的人脸图像的数据
people = os.listdir(path_images_from_camera)
people.sort()

with open("D:/myworkspace/JupyterNotebook/Smile/feature/features2_all.csv", "w", newline="") as csvfile:
    writer = csv.writer(csvfile)
    for person in people:
        print("##### " + person + " #####")
        # Get the mean/average features of face/personX, it will be a list with a length of 128D
        features_mean_personX = return_features_mean_personX(path_images_from_camera + person)
        writer.writerow(features_mean_personX)
        print("特征均值 / The mean of features:", list(features_mean_personX))
        print('\n')
    print("所有录入人脸数据存入 / Save all the features of faces registered into: D:/myworkspace/JupyterNotebook/Smile/feature/features2_all.csv")

2、微笑识别

# pandas 读取 CSV
import pandas as pd

# 分割数据
from sklearn.model_selection import train_test_split

# 用于数据预加工标准化
from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression     # 线性模型中的 Logistic 回归模型
from sklearn.neural_network import MLPClassifier        # 神经网络模型中的多层网络模型
from sklearn.svm import LinearSVC                       # SVM 模型中的线性 SVC 模型
from sklearn.linear_model import SGDClassifier          # 线性模型中的随机梯度下降模型

import joblib


# 从 csv 读取数据
def pre_data():
    # 41 维表头
    column_names = []
    for i in range(0, 40):
        column_names.append("feature_" + str(i + 1))
    column_names.append("output")

    # read csv
    rd_csv = pd.read_csv("D:/myworkspace/JupyterNotebook/Smile/data/data_csvs/data.csv", names=column_names)

    # 输出 csv 文件的维度
    # print("shape:", rd_csv.shape)

    X_train, X_test, y_train, y_test = train_test_split(

        # input 0-40
        # output 41
        rd_csv[column_names[0:40]],
        rd_csv[column_names[40]],

        # 25% for testing, 75% for training
        test_size=0.25,
        random_state=33)

    return X_train, X_test, y_train, y_test


path_models = "D:/myworkspace/JupyterNotebook/Smile/data/data_models/"


# LR, logistic regression, 逻辑斯特回归分类（线性模型）
def model_LR():
    # get data
    X_train_LR, X_test_LR, y_train_LR, y_test_LR = pre_data()

    # 数据预加工
    # 标准化数据，保证每个维度的特征数据方差为1，均值为0。使得预测结果不会被某些维度过大的特征值而主导
    ss_LR = StandardScaler()
    X_train_LR = ss_LR.fit_transform(X_train_LR)
    X_test_LR = ss_LR.transform(X_test_LR)

    # 初始化 LogisticRegression
    LR = LogisticRegression()

    # 调用 LogisticRegression 中的 fit() 来训练模型参数
    LR.fit(X_train_LR, y_train_LR)

    # save LR model
    joblib.dump(LR, path_models + "model_LR.m")

    # 评分函数
    score_LR = LR.score(X_test_LR, y_test_LR)
    print("The accurary of LR:", score_LR)

    # print(type(ss_LR))
    return (ss_LR)


model_LR()


# MLPC, Multi-layer Perceptron Classifier, 多层感知机分类（神经网络）
def model_MLPC():
    # get data
    X_train_MLPC, X_test_MLPC, y_train_MLPC, y_test_MLPC = pre_data()

    # 数据预加工
    ss_MLPC = StandardScaler()
    X_train_MLPC = ss_MLPC.fit_transform(X_train_MLPC)
    X_test_MLPC = ss_MLPC.transform(X_test_MLPC)

    # 初始化 MLPC
    MLPC = MLPClassifier(hidden_layer_sizes=(13, 13, 13), max_iter=500)

    # 调用 MLPC 中的 fit() 来训练模型参数
    MLPC.fit(X_train_MLPC, y_train_MLPC)

    # save MLPC model
    joblib.dump(MLPC, path_models + "model_MLPC.m")

    # 评分函数
    score_MLPC = MLPC.score(X_test_MLPC, y_test_MLPC)
    print("The accurary of MLPC:", score_MLPC)

    return (ss_MLPC)


model_MLPC()


# Linear SVC， Linear Supported Vector Classifier, 线性支持向量分类(SVM支持向量机)
def model_LSVC():
    # get data
    X_train_LSVC, X_test_LSVC, y_train_LSVC, y_test_LSVC = pre_data()

    # 数据预加工
    ss_LSVC = StandardScaler()
    X_train_LSVC = ss_LSVC.fit_transform(X_train_LSVC)
    X_test_LSVC = ss_LSVC.transform(X_test_LSVC)

    # 初始化 LSVC
    LSVC = LinearSVC()

    # 调用 LSVC 中的 fit() 来训练模型参数
    LSVC.fit(X_train_LSVC, y_train_LSVC)

    # save LSVC model
    joblib.dump(LSVC, path_models + "model_LSVC.m")

    # 评分函数
    score_LSVC = LSVC.score(X_test_LSVC, y_test_LSVC)
    print("The accurary of LSVC:", score_LSVC)

    return ss_LSVC


model_LSVC()


# SGDC, Stochastic Gradient Decent Classifier, 随机梯度下降法求解(线性模型)
def model_SGDC():
    # get data
    X_train_SGDC, X_test_SGDC, y_train_SGDC, y_test_SGDC = pre_data()

    # 数据预加工
    ss_SGDC = StandardScaler()
    X_train_SGDC = ss_SGDC.fit_transform(X_train_SGDC)
    X_test_SGDC = ss_SGDC.transform(X_test_SGDC)

    # 初始化 SGDC
    SGDC = SGDClassifier(max_iter=5)

    # 调用 SGDC 中的 fit() 来训练模型参数
    SGDC.fit(X_train_SGDC, y_train_SGDC)

    # save SGDC model
    joblib.dump(SGDC, path_models + "model_SGDC.m")

    # 评分函数
    score_SGDC = SGDC.score(X_test_SGDC, y_test_SGDC)
    print("The accurary of SGDC:", score_SGDC)

    return ss_SGDC

model_SGDC()

4、相机检测模型

# use the saved model
import joblib

import smile_test1

import dlib         # 人脸处理的库 Dlib
import numpy as np  # 数据处理的库 numpy
import cv2          # 图像处理的库 OpenCv


detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('D:/shape_predictor_68_face_landmarks.dat')

# OpenCv 调用摄像头
cap = cv2.VideoCapture(0)

# 设置视频参数
cap.set(3, 480)


def get_features(img_rd):

    # 输入:  img_rd:      图像文件
    # 输出:  positions_lip_arr:  feature point 49 to feature point 68, 20 feature points / 40D in all

    # 取灰度
    img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)

    # 计算68点坐标
    positions_68_arr = []
    faces = detector(img_gray, 0)
    landmarks = np.matrix([[p.x, p.y] for p in predictor(img_rd, faces[0]).parts()])

    for idx, point in enumerate(landmarks):
        # 68点的坐标
        pos = (point[0, 0], point[0, 1])
        positions_68_arr.append(pos)

    positions_lip_arr = []
    # 将点 49-68 写入 CSV
    # 即 positions_68_arr[48]-positions_68_arr[67]
    for i in range(48, 68):
        positions_lip_arr.append(positions_68_arr[i][0])
        positions_lip_arr.append(positions_68_arr[i][1])

    return positions_lip_arr


while cap.isOpened():
    # 480 height * 640 width
    flag, img_rd = cap.read()
    kk = cv2.waitKey(1)

    img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)

    # 人脸数 faces
    faces = detector(img_gray, 0)
    # 检测到人脸
    if len(faces) != 0:
        # 提取单张40维度特征
        positions_lip_test = get_features(img_rd)

        # path of models
        path_models = "D:/myworkspace/JupyterNotebook/Smile/data/data_models/"

        # #########  LR  ###########
        LR = joblib.load(path_models+"model_LR.m")
        ss_LR = smile_test1.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 = smile_test1.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 = smile_test1.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 = smile_test1.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)

        print('\n')

        # 按下 'q' 键退出
        if kk == ord('q'):
            break

    # 窗口显示
    # cv2.namedWindow("camera", 0) # 如果需要摄像头窗口大小可调
    cv2.imshow("camera", img_rd)

# 释放摄像头
cap.release()

# 删除建立的窗口
cv2.destroyAllWindows()

三、参考博客

https://blog.csdn.net/qq_41133375/article/details/107141898