# 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.
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# limitations under the License.

from typing import NamedTuple, Union

import torch
from PIL import Image
from torchvision import transforms

from towhee.operator import Operator


class TransformImage(Operator):
    """
    Use PyTorch to transform an image (resize, crop, normalize, etc...)

    Args:
        size (`int`):
            Image size to use. A resize to `size x size` followed by center crop and
            image normalization will be done.
    """
    def __init__(self, size: int) -> None:
        super().__init__()
        # user defined transform
        self.tfms = transforms.Compose(
            [
                transforms.Resize(size),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
            ]
        )

    def __call__(self, img_path: Union[Image.Image, torch.Tensor, str]) -> NamedTuple('Outputs', [('img_transformed', torch.Tensor)]):
        """
        Call it when use this class.
        
        Args:
            img(`Union[mage.Image, torch.Tensor, str]`):
                The image data to be normalized, you can try one of the 
                four formats: Image.Image, torch.Tensor and str.
        Returns:
            (`torch.Tensor`)
                The normalized image tensor.
        """
        if isinstance(img_path, str):
            img_tensor = Image.open(img_path)
        if isinstance(img_path, Image.Image):
            img_tensor = img_path.convert('RGB')
        Outputs = NamedTuple('Outputs', [('img_transformed', torch.Tensor)])
        return Outputs(self.tfms(img_tensor).unsqueeze(0))