Python基于keras训练如何实现微笑识别

这篇文章主要介绍Python基于keras训练如何实现微笑识别，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！

一、数据预处理

实验数据来自genki4k

提取含有完整人脸的图片

def init_file():    num = 0    bar = tqdm(os.listdir(read_path))    for file_name in bar:        bar.desc = "预处理图片: "        # a图片的全路径        img_path = (read_path + "/" + file_name)        # 读入的图片的路径中含非英文        img = cv2.imdecode(np.fromfile(img_path, 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        # dlib检测        dets = detector(img, 1)        for k, d in enumerate(dets):            if len(dets) > 1:                continue            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)            # 保存图片            cv2.imencode('.jpg', img_blank)[1].tofile(save_path + "/" + "file" + str(num) + ".jpg")    logging.info("一共", len(os.listdir(read_path)), "个样本")    logging.info("有效样本", num)

二、训练模型

创建模型

# 创建网络def create_model():    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'])    return model

训练模型

# 训练模型def train_model(model):    # 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=60,        epochs=12,        validation_data=validation_generator,        validation_steps=30)    # 保存模型    save_path = "../output/model"    if not os.path.exists(save_path):        os.makedirs(save_path)    model.save(save_path + "/smileDetect.h6")    return history

训练结果

准确率

丢失率

训练过程

三、预测

通过读取摄像头内容进行预测

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

效果

四、源代码

pretreatment.py

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 osimport shutilfrom tqdm import tqdmimport logging# dlib预测器detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor('../resources/shape_predictor_68_face_landmarks.dat')# 原图片路径read_path = "../resources/genki4k/files"# 提取人脸存储路径save_path = "../output/genki4k/files"if not os.path.exists(save_path):    os.makedirs(save_path)# 新的数据集data_dir = '../resources/data'if not os.path.exists(data_dir):    os.makedirs(data_dir)# 训练集train_dir = data_dir + "/train"if not os.path.exists(train_dir):    os.makedirs(train_dir)# 验证集validation_dir = os.path.join(data_dir, 'validation')if not os.path.exists(validation_dir):    os.makedirs(validation_dir)# 测试集test_dir = os.path.join(data_dir, 'test')if not os.path.exists(test_dir):    os.makedirs(test_dir)# 初始化训练数据def init_data(file_list):    # 如果不存在文件夹则新建    for file_path in file_list:        if not os.path.exists(file_path):            os.makedirs(file_path)        # 存在则清空里面所有数据        else:            for i in os.listdir(file_path):                path = os.path.join(file_path, i)                if os.path.isfile(path):                    os.remove(path)def init_file():    num = 0    bar = tqdm(os.listdir(read_path))    for file_name in bar:        bar.desc = "预处理图片: "        # a图片的全路径        img_path = (read_path + "/" + file_name)        # 读入的图片的路径中含非英文        img = cv2.imdecode(np.fromfile(img_path, 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        # dlib检测        dets = detector(img, 1)        for k, d in enumerate(dets):            if len(dets) > 1:                continue            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)            # 保存图片            cv2.imencode('.jpg', img_blank)[1].tofile(save_path + "/" + "file" + str(num) + ".jpg")    logging.info("一共", len(os.listdir(read_path)), "个样本")    logging.info("有效样本", num)# 划分数据集def divide_data(file_path, message, begin, end):    files = ['file{}.jpg'.format(i) for i in range(begin, end)]    bar = tqdm(files)    bar.desc = message    for file in bar:        src = os.path.join(save_path, file)        dst = os.path.join(file_path, file)        shutil.copyfile(src, dst)if __name__ == "__main__":    init_file()    positive_train_dir = os.path.join(train_dir, 'smile')    negative_train_dir = os.path.join(train_dir, 'unSmile')    positive_validation_dir = os.path.join(validation_dir, 'smile')    negative_validation_dir = os.path.join(validation_dir, 'unSmile')    positive_test_dir = os.path.join(test_dir, 'smile')    negative_test_dir = os.path.join(test_dir, 'unSmile')    file_list = [positive_train_dir, positive_validation_dir, positive_test_dir,                 negative_train_dir, negative_validation_dir, negative_test_dir]    init_data(file_list)    divide_data(positive_train_dir, "划分训练集正样本", 1, 1001)    divide_data(negative_train_dir, "划分训练集负样本", 2200, 3200)    divide_data(positive_validation_dir, "划分验证集正样本", 1000, 1500)    divide_data(negative_validation_dir, "划分验证集负样本", 3000, 3500)    divide_data(positive_test_dir, "划分测试集正样本", 1500, 2000)    divide_data(negative_test_dir, "划分测试集负样本", 2800, 3500)

train.py

import osfrom keras import layersfrom keras import modelsfrom tensorflow import optimizersimport matplotlib.pyplot as pltfrom keras.preprocessing.image import ImageDataGeneratortrain_dir = "../resources/data/train"validation_dir = "../resources/data/validation"# 创建网络def create_model():    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'])    return model# 训练模型def train_model(model):    # 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=60,        epochs=12,        validation_data=validation_generator,        validation_steps=30)    # 保存模型    save_path = "../output/model"    if not os.path.exists(save_path):        os.makedirs(save_path)    model.save(save_path + "/smileDetect.h6")    return history# 展示训练结果def show_results(history):    # 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']    # 绘制结果    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()if __name__ == "__main__":    model = create_model()    history = train_model(model)    show_results(history)

predict.py

import osfrom keras import layersfrom keras import modelsfrom tensorflow import optimizersimport matplotlib.pyplot as pltfrom keras.preprocessing.image import ImageDataGeneratortrain_dir = "../resources/data/train"validation_dir = "../resources/data/validation"# 创建网络# 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('../output/model/smileDetect.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()

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