Python 3 use Dlib face and smile detection sklearn machine learning model

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This link: https://blog.csdn.net/weixin_44517301/article/details/100568061

get_features.py

在这里插入代码片
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
import os           # 读取文件
import csv          # CSV 操作


detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('data/data_dlib_model/shape_predictor_68_face_landmarks.dat')


# 输入图像文件所在路径，返回一个41维数组（包含提取到的40维特征和1维输出标记）
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

    # read img file
    img = cv2.imread(img_rd)
    # 取灰度
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 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, 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


# 读取图像所在的路径
path_images_with_smiles = "data/data_imgs/database/smiles/"
path_images_no_smiles = "data/data_imgs/database/no_smiles/"

# 获取路径下的图像文件
imgs_smiles = os.listdir(path_images_with_smiles)
imgs_no_smiles = os.listdir(path_images_no_smiles)

# 存储提取特征数据的 CSV 的路径
path_csv = "data/data_csvs/"


# write the features into CSV
def write_into_CSV():
    with open(path_csv+"data.csv", "w", newline="") as csvfile:
        writer = csv.writer(csvfile)

        # 处理带笑脸的图像
        print("######## with smiles #########")
        for i in range(len(imgs_smiles)):
            print(path_images_with_smiles+imgs_smiles[i])

            # append "1" means "with smiles"
            features_csv_smiles = get_features(path_images_with_smiles+imgs_smiles[i])
            features_csv_smiles.append(1)
            print("positions of lips:", features_csv_smiles, "\n")

            # 写入CSV
            writer.writerow(features_csv_smiles)

        # 处理不带笑脸的图像
        print("######## no smiles #########")
        for i in range(len(imgs_no_smiles)):
            print(path_images_no_smiles+imgs_no_smiles[i])

            # append "0" means "no smiles"
            features_csv_no_smiles = get_features(path_images_no_smiles + imgs_no_smiles[i])
            features_csv_no_smiles.append(0)
            print("positions of lips:", features_csv_no_smiles, "\n")

            # 写入CSV
            writer.writerow(features_csv_no_smiles)


# 写入CSV
# write_into_CSV()

show_lip.py

# 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 = "data/data_imgs/test_imgs/i064rc-mn.jpg"

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('data/data_dlib_model/shape_predictor_68_face_landmarks.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)

Training: ML_ways_sklearn.py

# 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

from sklearn.externals 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("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 = "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()

Verify: check_smile.py

# 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 = "data/data_imgs/test_imgs/test3.jpg"

# 提取单张40维度特征
positions_lip_test = get_features(path_test_img)

# path of models
path_models = "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)

Open Camera Detection: check_smile_from_camera.py

# use the saved model
from sklearn.externals import joblib

import ML_ways_sklearn

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('data/data_dlib_model/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 = "data/data_models/"

        # #########  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)

        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()

Code has been uploaded GitHub
https://github.com/coneypo/Smile_Detector