【CV】使用Keras和迁移学习从人脸图像中预测体重指数BMI

import osimport cv2import dlibfrom matplotlib import pyplot as pltimport numpy as npimport config
detector = dlib.get_frontal_face_detector()

def crop_faces():    bad_crop_count = 0    if not os.path.exists(config.CROPPED_IMGS_DIR):        os.makedirs(config.CROPPED_IMGS_DIR)    print 'Cropping faces and saving to %s' % config.CROPPED_IMGS_DIR    good_cropped_images = []    good_cropped_img_file_names = []    detected_cropped_images = []    original_images_detected = []    for file_name in sorted(os.listdir(config.ORIGINAL_IMGS_DIR)):        np_img = cv2.imread(os.path.join(config.ORIGINAL_IMGS_DIR,file_name))        detected = detector(np_img, 1)        img_h, img_w, _ = np.shape(np_img)        original_images_detected.append(np_img)
        if len(detected) != 1:            bad_crop_count += 1            continue
        d = detected[0]        x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()        xw1 = int(x1 - config.MARGIN * w)        yw1 = int(y1 - config.MARGIN * h)        xw2 = int(x2 + config.MARGIN * w)        yw2 = int(y2 + config.MARGIN * h)        cropped_img = crop_image_to_dimensions(np_img, xw1, yw1, xw2, yw2)        norm_file_path = '%s/%s' % (config.CROPPED_IMGS_DIR, file_name)        cv2.imwrite(norm_file_path, cropped_img)
        good_cropped_img_file_names.append(file_name)
    # save info of good cropped images    with open(config.ORIGINAL_IMGS_INFO_FILE, 'r') as f:        column_headers = f.read().splitlines()[0]        all_imgs_info = f.read().splitlines()[1:]    cropped_imgs_info = [l for l in all_imgs_info if l.split(',')[-1] in good_cropped_img_file_names]
    with open(config.CROPPED_IMGS_INFO_FILE, 'w') as f:        f.write('%s\n' % column_headers)        for l in cropped_imgs_info:            f.write('%s\n' % l)
    print 'Cropped %d images and saved in %s - info in %s' % (len(original_images_detected), config.CROPPED_IMGS_DIR, config.CROPPED_IMGS_INFO_FILE)    print 'Error detecting face in %d images - info in Data/unnormalized.txt' % bad_crop_count    return good_cropped_images


# image cropping function taken from:# https://stackoverflow.com/questions/15589517/how-to-crop-an-image-in-opencv-using-pythondef crop_image_to_dimensions(img, x1, y1, x2, y2):    if x1 < 0 or y1 < 0 or x2 > img.shape[1] or y2 > img.shape[0]:        img, x1, x2, y1, y2 = pad_img_to_fit_bbox(img, x1, x2, y1, y2)    return img[y1:y2, x1:x2, :]
def pad_img_to_fit_bbox(img, x1, x2, y1, y2):    img = cv2.copyMakeBorder(img, - min(0, y1), max(y2 - img.shape[0], 0),                             -min(0, x1), max(x2 - img.shape[1], 0), cv2.BORDER_REPLICATE)    y2 += -min(0, y1)    y1 += -min(0, y1)    x2 += -min(0, x1)    x1 += -min(0, x1)    return img, x1, x2, y1, y2
if __name__ == '__main__':    crop_faces()

from keras.preprocessing.image import ImageDataGeneratorimport pandas as pdimport Augmentorfrom PIL import Imageimport randomimport numpy as npimport matplotlib.pyplot as pltimport mathimport config

def plot_imgs_from_generator(generator, number_imgs_to_show=9):    print ('Plotting images...')    n_rows_cols = int(math.ceil(math.sqrt(number_imgs_to_show)))    plot_index = 1    x_batch, _ = next(generator)    while plot_index <= number_imgs_to_show:        plt.subplot(n_rows_cols, n_rows_cols, plot_index)        plt.imshow(x_batch[plot_index-1])        plot_index += 1    plt.show()

def augment_image(np_img):    p = Augmentor.Pipeline()    p.rotate(probability=1, max_left_rotation=5, max_right_rotation=5)    p.flip_left_right(probability=0.5)    p.random_distortion(probability=0.25, grid_width=2, grid_height=2, magnitude=8)    p.random_color(probability=1, min_factor=0.8, max_factor=1.2)    p.random_contrast(probability=.5, min_factor=0.8, max_factor=1.2)    p.random_brightness(probability=1, min_factor=0.5, max_factor=1.5)
    image = [Image.fromarray(np_img.astype('uint8'))]    for operation in p.operations:        r = round(random.uniform(0, 1), 1)        if r <= operation.probability:            image = operation.perform_operation(image)    image = [np.array(i).astype('float64') for i in image]    return image[0]
image_processor = ImageDataGenerator(    rescale=1./255,    preprocessing_function=augment_image)
# subtract validation size from training datawith open(config.CROPPED_IMGS_INFO_FILE) as f:    for i, _ in enumerate(f):        pass    training_n = i - config.VALIDATION_SIZE
train_df=pd.read_csv(config.CROPPED_IMGS_INFO_FILE, nrows=training_n)
train_generator=image_processor.flow_from_dataframe(    dataframe=train_df,    directory=config.CROPPED_IMGS_DIR,    x_col='name',    y_col='bmi',    class_mode='other',    color_mode='rgb',    target_size=(config.RESNET50_DEFAULT_IMG_WIDTH,config.RESNET50_DEFAULT_IMG_WIDTH),    batch_size=config.TRAIN_BATCH_SIZE)

from tensorflow.python.keras.models import Modelfrom tensorflow.python.keras.applications import ResNet50from tensorflow.python.keras.layers import Denseimport config
def get_age_model():    # adapted from https://github.com/yu4u/age-gender-estimation/blob/master/age_estimation/model.py    age_model = ResNet50(        include_top=False,        weights='imagenet',        input_shape=(config.RESNET50_DEFAULT_IMG_WIDTH, config.RESNET50_DEFAULT_IMG_WIDTH, 3),        pooling='avg')
    prediction = Dense(units=101,                       kernel_initializer='he_normal',                       use_bias=False,                       activation='softmax',                       name='pred_age')(age_model.output)
    age_model = Model(inputs=age_model.input, outputs=prediction)    age_model.load_weights(config.AGE_TRAINED_WEIGHTS_FILE)    print 'Loaded weights from age classifier'    return age_model

def get_model():    base_model = get_age_model()    last_hidden_layer = base_model.get_layer(index=-2)
    base_model = Model(        inputs=base_model.input,        outputs=last_hidden_layer.output)    prediction = Dense(1, kernel_initializer='normal')(base_model.output)
    model = Model(inputs=base_model.input, outputs=prediction)    return model

import cv2import numpy as npfrom tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoardfrom train_generator import train_generator, plot_imgs_from_generatorfrom mae_callback import MAECallbackimport config


batches_per_epoch=train_generator.n //train_generator.batch_size

def train_top_layer(model):
    print 'Training top layer...'
    for l in model.layers[:-1]:        l.trainable = False
    model.compile(        loss='mean_absolute_error',        optimizer='adam')
    mae_callback = MAECallback()
    early_stopping_callback = EarlyStopping(        monitor='val_mae',        mode='min',        verbose=1,        patience=1)
    model_checkpoint_callback = ModelCheckpoint(        'saved_models/top_layer_trained_weights.{epoch:02d}-{val_mae:.2f}.h5',        monitor='val_mae',        mode='min',        verbose=1,        save_best_only=True)
    tensorboard_callback = TensorBoard(        log_dir=config.TOP_LAYER_LOG_DIR,        batch_size=train_generator.batch_size)
    model.fit_generator(        generator=train_generator,        steps_per_epoch=batches_per_epoch,        epochs=20,        callbacks=[            mae_callback,            early_stopping_callback,            model_checkpoint_callback,            tensorboard_callback])

def train_all_layers(model):
    print 'Training all layers...'
    for l in model.layers:        l.trainable = True
    mae_callback = MAECallback()
    early_stopping_callback = EarlyStopping(        monitor='val_mae',        mode='min',        verbose=1,        patience=10)
    model_checkpoint_callback = ModelCheckpoint(        'saved_models/all_layers_trained_weights.{epoch:02d}-{val_mae:.2f}.h5',        monitor='val_mae',        mode='min',        verbose=1,        save_best_only=True)
    tensorboard_callback = TensorBoard(        log_dir=config.ALL_LAYERS_LOG_DIR,        batch_size=train_generator.batch_size)
    model.compile(        loss='mean_absolute_error',        optimizer='adam')
    model.fit_generator(        generator=train_generator,        steps_per_epoch=batches_per_epoch,        epochs=100,        callbacks=[            mae_callback,            early_stopping_callback,            model_checkpoint_callback,            tensorboard_callback])