Deconvnet-keras.py 文件源码

def main():
    parser = argparser()
    args = parser.parse_args()
    image_path = args.image
    layer_name = args.layer_name
    feature_to_visualize = args.feature
    visualize_mode = args.mode

    model = vgg16.VGG16(weights = 'imagenet', include_top = True)
    layer_dict = dict([(layer.name, layer) for layer in model.layers])
    if not layer_dict.has_key(layer_name):
        print('Wrong layer name')
        sys.exit()

    # Load data and preprocess
    img = Image.open(image_path)
    img = img.resize(224, 224)
    img_array = np.array(img)
    img_array = np.transpose(img_array, (2, 0, 1))
    img_array = img_array[np.newaxis, :]
    img_array = img_array.astype(np.float)
    img_array = imagenet_utils.preprocess_input(img_array)

    deconv = visualize(model, img_array, 
            layer_name, feature_to_visualize, visualize_mode)

    # postprocess and save image
    deconv = np.transpose(deconv, (1, 2, 0))
    deconv = deconv - deconv.min()
    deconv *= 1.0 / (deconv.max() + 1e-8)
    deconv = deconv[:, :, ::-1]
    uint8_deconv = (deconv * 255).astype(np.uint8)
    img = Image.fromarray(uint8_deconv, 'RGB')
    img.save('results/{}_{}_{}.png'.format(layer_name, feature_to_visualize, visualize_mode))