# 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.
# adapted from https://github.com/cunjian/pytorch_face_landmark
import os
import sys

from typing import NamedTuple
from pathlib import Path
import numpy as np
import torch

from torchvision import transforms

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

#import mobilefacenet

@register(output_schema=['landmark'])
class Mobilefacenet(NNOperator):
    """
    Mobilefacenet
    """

    def __init__(self, pretrained = True):
        super().__init__()
        sys.path.append(str(Path(__file__).parent))
        from mobilefacenet_impl import MobileFaceNet
        self.model = MobileFaceNet([112, 112], 136)
        if pretrained == True:
            map_location = 'cpu'
            checkpoint = torch.load(
                os.path.dirname(__file__) +'/mobilefacenet_model_best.pth', map_location=map_location)
            self.model.load_state_dict(checkpoint['state_dict'])
        
        normalize = transforms.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        self.tfms = transforms.Compose([transforms.Resize([112,112]),
                                        transforms.ToTensor(),
                                        normalize])

    @arg(1, to_image_color('RGB') )
    def __call__(self, img: Image):
        image = to_pil(img)
        h, w = image.size
        tensor = self._preprocess(image)
        if len(tensor.shape) == 3:
            tensor = torch.unsqueeze(tensor, 0)
        self.model.eval()
        landmark = self.model(tensor)[0][0]
        landmark = landmark.reshape(-1, 2)
        landmark[:, 0] = landmark[:, 0] * w
        landmark[:, 1] = landmark[:, 1] * h
        return np.asarray(landmark.cpu().detach(), dtype=np.int32)

    def _preprocess(self, image):
        return self.tfms(image)

    def _postprocess(self, landmark):
        pass

    def train(self):
        pass