如何Python实现人脸微笑检测的功能,具体过程是什�

Admin 2022-06-23 群英技术资�

这篇文章给大家分享的是如何Python实现人脸微笑检测的功能,具体过程是什么。小编觉得挺实用的,因此分享给大家做个参考,文中的介绍得很详细,而要易于理解和学习,有需要的朋友可以参考,接下来就跟随小编一起了解看看吧�

目录
  • 一.实验准备
  • �.图片预处�
  • �.划分数据�
  • �.CNN提取人脸识别笑脸和非笑脸
    • 1.创建模型
    • 2.归一化处�
    • 3.数据增强
    • 4.创建网络
    • 5.单张图片测试
    • 6.摄像头实时测�
  • �.Dlib提取人脸特征识别笑脸和非笑脸

    一.实验准备

    环境搭建

    pip install tensorflow==1.2.0
    pip install keras==2.0.6
    pip install dlib==19.6.1
    pip install h5py==2.10

    如果是新建虚拟环境,还需安装以下�

    pip install opencv_python==4.1.2.30
    pip install pillow
    pip install matplotlib
    pip install h5py
    

    使用genki-4k数据�

    可从此处下载

    �.图片预处�

    打开数据�

    我们需要将人脸检测出来并对图片进行裁�

    代码如下�

    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
    import os
     
    # dlib预测�
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor('D:\\shape_predictor_68_face_landmarks.dat')
     
    # 读取图像的路�
    path_read = "C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files"
    num=0
    for file_name in os.listdir(path_read):
    	#aa是图片的全路�
        aa=(path_read +"/"+file_name)
        #读入的图片的路径中含非英�
        img=cv2.imdecode(np.fromfile(aa, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
        #获取图片的宽�
        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�
        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�
            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�

    �.划分数据�

    代码�

    import os, shutil
    # 原始数据集路�
    original_dataset_dir = 'C:\\Users\\28205\\Documents\\Tencent Files\\2820535964\\FileRecv\\genki4k\\files1'
    
    # 新的数据�
    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_dir
    fnames = ['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_dir
    fnames = ['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_dir
    fnames = ['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_dir
    fnames = ['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�

    �.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 layers
    from keras import models
    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.Dense(512, activation='relu'))
    model.add(layers.Dense(1, activation='sigmoid'))
    model.summary()#查看
    

    运行效果�

    2.归一化处�

    代码�

    #归一�
    from keras import optimizers
    model.compile(loss='binary_crossentropy',
                  optimizer=optimizers.RMSprop(lr=1e-4),
                  metrics=['acc'])
    from keras.preprocessing.image import ImageDataGenerator
    train_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�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�
    import matplotlib.pyplot as plt
    # This is module with image preprocessing utilities
    from keras.preprocessing import image
    fnames = [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 = 0
    for 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:
            break
    plt.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�
    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.h5')
    
    #数据增强过后的训练集与验证集的精确度与损失度的图�
    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�

    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 cv2
    from keras.preprocessing import image
    from keras.models import load_model
    import numpy as np
    #加载模型
    model = load_model('smileAndUnsmile1.h5')
    #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)/255.0
    img_tensor = np.expand_dims(img_tensor, axis=0)
    prediction =model.predict(img_tensor)  
    print(prediction)
    if prediction[0][0]>0.5:
        result='非笑�'
    else:
        result='笑脸'
    print(result)
    

    运行结果�

    6.摄像头实时测�

    代码�

    #检测视频或者摄像头中的人脸
    import cv2
    from keras.preprocessing import image
    from keras.models import load_model
    import numpy as np
    import dlib
    from PIL import Image
    model = load_model('smileAndUnsmile1.h5')
    detector = dlib.get_frontal_face_detector()
    video=cv2.VideoCapture(0)
    font = cv2.FONT_HERSHEY_SIMPLEX
    def 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'):
            break
    video.release()
    cv2.destroyAllWindows()
    

    运行结果�

    �.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 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�
                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�
                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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