# Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import NamedTuple

import numpy as np
import torch
import cv2
from facenet_pytorch import InceptionResnetV1

from towhee import register
from towhee.operator import NNOperator
from towhee.types.image_utils import to_pil
from towhee._types import Image
from towhee.types import arg, to_image_color

@register(output_schema=['vec'])
class Inceptionresnetv1(NNOperator):
    """
    comment placeholder
    """
    def __init__(self, image_size = 160):
        self.image_size = image_size
        self._model = InceptionResnetV1(pretrained='vggface2')
        self._model.eval()

    @arg(1, to_image_color('RGB') )
    def __call__(self, img: Image) -> np.ndarray:
        img = self.preprocess(img)
        embs = self._model(torch.FloatTensor(img).permute(0,3,1,2)).detach().numpy()
        return embs

    def preprocess(self, img: Image):
        #img shape expected to be [n, h, w, c=3] or [h, w, c=3].
        if len(img.shape) == 3:
            img = cv2.resize(img, (self.image_size, self.image_size))
            img = np.expand_dims(img, 0)
        elif len(img.shape) == 4:
            pass
        else:
            raise ValueError('unknown tensor shape, need to be [n, h, w, c=3] or [h, w, c=3].')
        img = self._fixed_image_standardization(img)
        return img

    def _fixed_image_standardization(self, image_tensor):
        processed_tensor = (image_tensor - 127.5) / 128.0
        return processed_tensor

    def train(self):
        """
        For training model
        """
        pass