# Copyright 2022 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.

import sys
import os
from typing import NamedTuple, List

from PIL import Image
import torch
from torchvision import transforms
from pathlib import Path
import numpy

import towhee
from towhee.operator import Operator
from towhee.types.image_utils import to_pil
from towhee._types import Image

from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

@register(output_schema=['bboxes', 'scores'])
class Retinaface(Operator):
    """
    Retinaface
    """
    def __init__(self, framework: str = 'pytorch') -> None:
        super().__init__()
        sys.path.append(str(Path(__file__).parent))
        from retinaface_impl import Model
        self.model = Model()

    @arg(1, to_image_color('RGB') )
    def __call__(self, image: 'towhee._types.Image'):
        img = torch.FloatTensor(numpy.asarray(to_pil(image)))
        bboxes, keypoints = self.model(img)
        return bboxes[:,:4],  bboxes[:,4]