# 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, List
from PIL import Image
import torch
from torchvision import transforms
import sys
import towhee
from pathlib import Path
import numpy

from towhee.operator import Operator
from towhee.utils.pil_utils import to_pil
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
import os

class RetinafaceFaceDetection(Operator):
    """
    Embedding extractor using efficientnet.
    Args:
        model_name (`string`):
            Model name.
        weights_path (`string`):
            Path to local weights.
    """

    def __init__(self, need_crop = True, framework: str = 'pytorch') -> None:
        super().__init__()
        if framework == 'pytorch':
            import importlib.util
            path = os.path.join(str(Path(__file__).parent), 'pytorch', 'model.py')
            opname = os.path.basename(str(Path(__file__))).split('.')[0]
            spec = importlib.util.spec_from_file_location(opname, path)
            module = importlib.util.module_from_spec(spec)
            spec.loader.exec_module(module)
        self.need_crop = need_crop
        self.model = module.Model()

    def __call__(self, image: 'towhee.types.Image') -> List[NamedTuple('Outputs', [('boxes', numpy.ndarray),
                                                                            ('keypoints', numpy.ndarray),
                                                                           ('cropped_imgs', numpy.ndarray)])]:
        Outputs = NamedTuple('Outputs', [('boxes', numpy.ndarray), ('keypoints', numpy.ndarray), ('cropped_imgs', numpy.ndarray)])
        img = torch.FloatTensor(numpy.asarray(to_pil(image)))
        bboxes, keypoints = self.model(img)
        croppeds = []
        if self.need_crop is True:
            h, w, _ = img.shape
            for bbox in bboxes:
                x1, y1, x2, y2, _ = bbox
                x1 = max(int(x1), 0)
                y1 = max(int(y1), 0)
                x2 = min(int(x2), w)
                y2 = min(int(y2), h)
                croppeds.append(img[y1:y2, x1:x2, :].numpy())
        outputs = []

        for i in range(len(croppeds)):
            output = Outputs(bboxes[i], keypoints[i,:], croppeds[i])
            outputs.append(output)
        return outputs