extract_cnn_vgg16_keras.py 文件源码

def extract_feat(img_path):
    # weights: 'imagenet'
    # pooling: 'max' or 'avg'
    # input_shape: (width, height, 3), width and height should >= 48

    input_shape = (224, 224, 3)
    model = VGG16(weights = 'imagenet', input_shape = (input_shape[0], input_shape[1], input_shape[2]), pooling = 'max', include_top = False)

    img = image.load_img(img_path, target_size=(input_shape[0], input_shape[1]))
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = preprocess_input(img)
    feat = model.predict(img)
    norm_feat = feat[0]/LA.norm(feat[0])
    return norm_feat