# Copyright (c) Alibaba, Inc. and its affiliates.
from typing import Any, Dict, Union

import cv2
import numpy as np
import PIL
import torch

from modelscope.metainfo import Pipelines
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Model, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import LoadImage
from modelscope.utils.constant import Tasks
from modelscope.utils.cv.image_utils import depth_to_color
from modelscope.utils.logger import get_logger

logger = get_logger()


@PIPELINES.register_module(
    Tasks.panorama_depth_estimation,
    module_name=Pipelines.panorama_depth_estimation_s2net)
class PanoramaDepthEstimationPipeline(Pipeline):
    """ This pipeline will estimation the depth panoramic image from one rgb panoramic image.
        The input panoramic image should be equirectanlar, in the size of 512x1024.

    Examples:

    >>> import cv2
    >>> from modelscope.outputs import OutputKeys
    >>> from modelscope.pipelines import pipeline
    >>> from modelscope.utils.constant import Tasks

    >>> task = 'panorama-depth-estimation'
    >>> model_id = 'damo/cv_s2net_image-depth-estimation'

    >>> input_location = 'data/test/images/panorama_depth_estimation.jpg'
    >>> estimator = pipeline(Tasks.panorama_depth_estimation, model=model_id)
    >>> result = estimator(input_location)
    >>> depth_vis = result[OutputKeys.DEPTHS_COLOR]
    >>> cv2.imwrite('result.jpg', depth_vis)
    """

    def __init__(self, model: str, **kwargs):
        """
        use `model` to create a panorama depth estimation pipeline for prediction
        Args:
            model: model id on modelscope hub.
        """
        super().__init__(model=model, **kwargs)

        logger.info('depth estimation model, pipeline init')

    def preprocess(self, input: Input) -> Dict[str, Any]:
        img = LoadImage.convert_to_ndarray(input)
        H, W = 512, 1024
        img = cv2.resize(img, dsize=(W, H), interpolation=cv2.INTER_CUBIC)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        rgb = self.model.to_tensor(img.copy())
        rgb = self.model.normalize(rgb)[None, ...]
        return rgb

    def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
        results = self.model.forward(input)
        return results

    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        results = self.model.postprocess(inputs)
        depths = results[OutputKeys.DEPTHS]
        if isinstance(depths, torch.Tensor):
            depths = depths.detach().cpu().squeeze().numpy()
        depths_color = depth_to_color(depths)
        outputs = {
            OutputKeys.DEPTHS: depths,
            OutputKeys.DEPTHS_COLOR: depths_color
        }
        return outputs