Python怎么实现人脸识别微笑检测

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一.实验准备

环境搭建

pip install tensorflow==1.2.0pip install keras==2.0.6pip install dlib==19.6.1pip install h6py==2.10

如果是新建虚拟环境，还需安装以下包

pip install opencv_python==4.1.2.30pip install pillowpip install matplotlibpip install h6py

使用genki-4k数据集

可从此处下载

二.图片预处理

打开数据集

我们需要将人脸检测出来并对图片进行裁剪

代码如下：

import dlib         # 人脸识别的库dlibimport numpy as np  # 数据处理的库numpyimport cv2          # 图像处理的库OpenCvimport os # dlib预测器detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor('D:\\shape_predictor_68_face_landmarks.dat') # 读取图像的路径path_read = "C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files"num=0for file_name in os.listdir(path_read):        #aa是图片的全路径    aa=(path_read +"/"+file_name)    #读入的图片的路径中含非英文    img=cv2.imdecode(np.fromfile(aa, dtype=np.uint8), cv2.IMREAD_UNCHANGED)    #获取图片的宽高    img_shape=img.shape    img_height=img_shape[0]    img_width=img_shape[1]       # 用来存储生成的单张人脸的路径    path_save="C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files1"     # dlib检测    dets = detector(img,1)    print("人脸数：", len(dets))    for k, d in enumerate(dets):        if len(dets)>1:            continue        num=num+1        # 计算矩形大小        # (x,y), (宽度width, 高度height)        pos_start = tuple([d.left(), d.top()])        pos_end = tuple([d.right(), d.bottom()])         # 计算矩形框大小        height = d.bottom()-d.top()        width = d.right()-d.left()         # 根据人脸大小生成空的图像        img_blank = np.zeros((height, width, 3), np.uint8)        for i in range(height):            if d.top()+i>=img_height:# 防止越界                continue            for j in range(width):                if d.left()+j>=img_width:# 防止越界                    continue                img_blank[i][j] = img[d.top()+i][d.left()+j]        img_blank = cv2.resize(img_blank, (200, 200), interpolation=cv2.INTER_CUBIC)        cv2.imencode('.jpg', img_blank)[1].tofile(path_save+"\\"+"file"+str(num)+".jpg") # 正确方法

运行效果如下：

共识别出3878张图片。

某些图片没有识别出人脸，所以没有裁剪保存，可以自行添加图片补充。

三.划分数据集

代码：

import os, shutil# 原始数据集路径original_dataset_dir = 'C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files1'# 新的数据集base_dir = 'C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files2'os.mkdir(base_dir)# 训练图像、验证图像、测试图像的目录train_dir = os.path.join(base_dir, 'train')os.mkdir(train_dir)validation_dir = os.path.join(base_dir, 'validation')os.mkdir(validation_dir)test_dir = os.path.join(base_dir, 'test')os.mkdir(test_dir)train_cats_dir = os.path.join(train_dir, 'smile')os.mkdir(train_cats_dir)train_dogs_dir = os.path.join(train_dir, 'unsmile')os.mkdir(train_dogs_dir)validation_cats_dir = os.path.join(validation_dir, 'smile')os.mkdir(validation_cats_dir)validation_dogs_dir = os.path.join(validation_dir, 'unsmile')os.mkdir(validation_dogs_dir)test_cats_dir = os.path.join(test_dir, 'smile')os.mkdir(test_cats_dir)test_dogs_dir = os.path.join(test_dir, 'unsmile')os.mkdir(test_dogs_dir)# 复制1000张笑脸图片到train_c_dirfnames = ['file{}.jpg'.format(i) for i in range(1,900)]for fname in fnames:    src = os.path.join(original_dataset_dir, fname)    dst = os.path.join(train_cats_dir, fname)    shutil.copyfile(src, dst)fnames = ['file{}.jpg'.format(i) for i in range(900, 1350)]for fname in fnames:    src = os.path.join(original_dataset_dir, fname)    dst = os.path.join(validation_cats_dir, fname)    shutil.copyfile(src, dst)    # Copy next 500 cat images to test_cats_dirfnames = ['file{}.jpg'.format(i) for i in range(1350, 1800)]for fname in fnames:    src = os.path.join(original_dataset_dir, fname)    dst = os.path.join(test_cats_dir, fname)    shutil.copyfile(src, dst)    fnames = ['file{}.jpg'.format(i) for i in range(2127,3000)]for fname in fnames:    src = os.path.join(original_dataset_dir, fname)    dst = os.path.join(train_dogs_dir, fname)    shutil.copyfile(src, dst)    # Copy next 500 dog images to validation_dogs_dirfnames = ['file{}.jpg'.format(i) for i in range(3000,3878)]for fname in fnames:    src = os.path.join(original_dataset_dir, fname)    dst = os.path.join(validation_dogs_dir, fname)    shutil.copyfile(src, dst)    # Copy next 500 dog images to test_dogs_dirfnames = ['file{}.jpg'.format(i) for i in range(3000,3878)]for fname in fnames:    src = os.path.join(original_dataset_dir, fname)    dst = os.path.join(test_dogs_dir, fname)    shutil.copyfile(src, dst)

运行效果如下：

四.CNN提取人脸识别笑脸和非笑脸

1.创建模型

代码：

#创建模型from keras import layersfrom keras import modelsmodel = models.Sequential()model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3)))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(128, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(128, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Flatten())model.add(layers.Dense(512, activation='relu'))model.add(layers.Dense(1, activation='sigmoid'))model.summary()#查看

运行效果：

2.归一化处理

代码：

#归一化from keras import optimizersmodel.compile(loss='binary_crossentropy',              optimizer=optimizers.RMSprop(lr=1e-4),              metrics=['acc'])from keras.preprocessing.image import ImageDataGeneratortrain_datagen = ImageDataGenerator(rescale=1./255)validation_datagen=ImageDataGenerator(rescale=1./255)test_datagen = ImageDataGenerator(rescale=1./255)train_generator = train_datagen.flow_from_directory(        # 目标文件目录        train_dir,        #所有图片的size必须是150x150        target_size=(150, 150),        batch_size=20,        # Since we use binary_crossentropy loss, we need binary labels        class_mode='binary')validation_generator = test_datagen.flow_from_directory(        validation_dir,        target_size=(150, 150),        batch_size=20,        class_mode='binary')test_generator = test_datagen.flow_from_directory(test_dir,                                                   target_size=(150, 150),                                                   batch_size=20,                                                   class_mode='binary')for data_batch, labels_batch in train_generator:    print('data batch shape:', data_batch.shape)    print('labels batch shape:', labels_batch)    break#'smile': 0, 'unsmile': 1

3.数据增强

代码：

#数据增强datagen = ImageDataGenerator(      rotation_range=40,      width_shift_range=0.2,      height_shift_range=0.2,      shear_range=0.2,      zoom_range=0.2,      horizontal_flip=True,      fill_mode='nearest')#数据增强后图片变化import matplotlib.pyplot as plt# This is module with image preprocessing utilitiesfrom keras.preprocessing import imagefnames = [os.path.join(train_smile_dir, fname) for fname in os.listdir(train_smile_dir)]img_path = fnames[3]img = image.load_img(img_path, target_size=(150, 150))x = image.img_to_array(img)x = x.reshape((1,) + x.shape)i = 0for batch in datagen.flow(x, batch_size=1):    plt.figure(i)    imgplot = plt.imshow(image.array_to_img(batch[0]))    i += 1    if i % 4 == 0:        breakplt.show()

运行效果：

4.创建网络

代码：

#创建网络model = models.Sequential()model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3)))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(128, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(128, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Flatten())model.add(layers.Dropout(0.5))model.add(layers.Dense(512, activation='relu'))model.add(layers.Dense(1, activation='sigmoid'))model.compile(loss='binary_crossentropy',              optimizer=optimizers.RMSprop(lr=1e-4),              metrics=['acc'])#归一化处理train_datagen = ImageDataGenerator(    rescale=1./255,    rotation_range=40,    width_shift_range=0.2,    height_shift_range=0.2,    shear_range=0.2,    zoom_range=0.2,    horizontal_flip=True,)test_datagen = ImageDataGenerator(rescale=1./255)train_generator = train_datagen.flow_from_directory(        # This is the target directory        train_dir,        # All images will be resized to 150x150        target_size=(150, 150),        batch_size=32,        # Since we use binary_crossentropy loss, we need binary labels        class_mode='binary')validation_generator = test_datagen.flow_from_directory(        validation_dir,        target_size=(150, 150),        batch_size=32,        class_mode='binary')history = model.fit_generator(      train_generator,      steps_per_epoch=100,      epochs=60,        validation_data=validation_generator,      validation_steps=50)model.save('smileAndUnsmile1.h6')#数据增强过后的训练集与验证集的精确度与损失度的图形acc = history.history['acc']val_acc = history.history['val_acc']loss = history.history['loss']val_loss = history.history['val_loss']epochs = range(len(acc))plt.plot(epochs, acc, 'bo', label='Training acc')plt.plot(epochs, val_acc, 'b', label='Validation acc')plt.title('Training and validation accuracy')plt.legend()plt.figure()plt.plot(epochs, loss, 'bo', label='Training loss')plt.plot(epochs, val_loss, 'b', label='Validation loss')plt.title('Training and validation loss')plt.legend()plt.show()

运行结果：

速度较慢，要等很久

5.单张图片测试

代码：

# 单张图片进行判断  是笑脸还是非笑脸import cv2from keras.preprocessing import imagefrom keras.models import load_modelimport numpy as np#加载模型model = load_model('smileAndUnsmile1.h6')#本地图片路径img_path='test.jpg'img = image.load_img(img_path, target_size=(150, 150))img_tensor = image.img_to_array(img)/255.0img_tensor = np.expand_dims(img_tensor, axis=0)prediction =model.predict(img_tensor)  print(prediction)if prediction[0][0]>0.5:    result='非笑脸'else:    result='笑脸'print(result)

运行结果：

6.摄像头实时测试

代码：

#检测视频或者摄像头中的人脸import cv2from keras.preprocessing import imagefrom keras.models import load_modelimport numpy as npimport dlibfrom PIL import Imagemodel = load_model('smileAndUnsmile1.h6')detector = dlib.get_frontal_face_detector()video=cv2.VideoCapture(0)font = cv2.FONT_HERSHEY_SIMPLEXdef rec(img):    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)    dets=detector(gray,1)    if dets is not None:        for face in dets:            left=face.left()            top=face.top()            right=face.right()            bottom=face.bottom()            cv2.rectangle(img,(left,top),(right,bottom),(0,255,0),2)            img1=cv2.resize(img[top:bottom,left:right],dsize=(150,150))            img1=cv2.cvtColor(img1,cv2.COLOR_BGR2RGB)            img1 = np.array(img1)/255.            img_tensor = img1.reshape(-1,150,150,3)            prediction =model.predict(img_tensor)                if prediction[0][0]>0.5:                result='unsmile'            else:                result='smile'            cv2.putText(img, result, (left,top), font, 2, (0, 255, 0), 2, cv2.LINE_AA)        cv2.imshow('Video', img)while video.isOpened():    res, img_rd = video.read()    if not res:        break    rec(img_rd)    if cv2.waitKey(1) & 0xFF == ord('q'):        breakvideo.release()cv2.destroyAllWindows()

运行结果：

五.Dlib提取人脸特征识别笑脸和非笑脸

代码：

import cv2                     #  图像处理的库 OpenCvimport dlib                    # 人脸识别的库 dlibimport numpy as np             # 数据处理的库 numpyclass 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):                                # 对每个人脸都标出68个特征点                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  # 眉毛距离占比                        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()

运行结果：

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