# Copyright (c) 2020 PaddlePaddle Authors. 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 os
import time
import base64
from functools import reduce
from typing import Union

import cv2
import numpy as np
from paddlehub.module.module import moduleinfo, serving

import solov2.processor as P
import solov2.data_feed as D


class Detector(object):
    """
    Args:
        min_subgraph_size (int): number of tensorRT graphs.
        use_gpu (bool): whether use gpu
        threshold (float): threshold to reserve the result for output.
    """

    def __init__(self, min_subgraph_size: int = 60, use_gpu=False, threshold: float = 0.5):

        model_dir = os.path.join(self.directory, 'solov2_r50_fpn_1x')
        self.predictor = D.load_predictor(model_dir, min_subgraph_size=min_subgraph_size, use_gpu=use_gpu)
        self.compose = [
            P.Resize(max_size=1333),
            P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
            P.Permute(),
            P.PadStride(stride=32)
        ]

    def transform(self, im: Union[str, np.ndarray]):
        im, im_info = P.preprocess(im, self.compose)
        inputs = D.create_inputs(im, im_info)
        return inputs, im_info

    def postprocess(self, np_boxes: np.ndarray, np_masks: np.ndarray, im_info: dict, threshold: float = 0.5):
        # postprocess output of predictor
        results = {}
        expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1)
        np_boxes = np_boxes[expect_boxes, :]
        for box in np_boxes:
            print('class_id:{:d}, confidence:{:.4f},'
                  'left_top:[{:.2f},{:.2f}],'
                  ' right_bottom:[{:.2f},{:.2f}]'.format(int(box[0]), box[1], box[2], box[3], box[4], box[5]))
        results['boxes'] = np_boxes
        if np_masks is not None:
            np_masks = np_masks[expect_boxes, :, :, :]
            results['masks'] = np_masks
        return results

    def predict(self, image: Union[str, np.ndarray], threshold: float = 0.5):
        '''
        Args:
            image (str/np.ndarray): path of image/ np.ndarray read by cv2
            threshold (float): threshold of predicted box' score
        Returns:
            results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
                            matix element:[class, score, x_min, y_min, x_max, y_max]
                            MaskRCNN's results include 'masks': np.ndarray:
                            shape:[N, class_num, mask_resolution, mask_resolution]
        '''
        inputs, im_info = self.transform(image)
        np_boxes, np_masks = None, None

        input_names = self.predictor.get_input_names()
        for i in range(len(input_names)):
            input_tensor = self.predictor.get_input_tensor(input_names[i])
            input_tensor.copy_from_cpu(inputs[input_names[i]])

        self.predictor.zero_copy_run()
        output_names = self.predictor.get_output_names()
        boxes_tensor = self.predictor.get_output_tensor(output_names[0])
        np_boxes = boxes_tensor.copy_to_cpu()
        # do not perform postprocess in benchmark mode
        results = []
        if reduce(lambda x, y: x * y, np_boxes.shape) < 6:
            print('[WARNNING] No object detected.')
            results = {'boxes': np.array([])}
        else:
            results = self.postprocess(np_boxes, np_masks, im_info, threshold=threshold)
        return results


@moduleinfo(
    name="solov2",
    type="CV/instance_segmentation",
    author="paddlepaddle",
    author_email="",
    summary="solov2 is a detection model, this module is trained with COCO dataset.",
    version="1.0.0")
class DetectorSOLOv2(Detector):
    """
    Args:
        use_gpu (bool): whether use gpu
        threshold (float): threshold to reserve the result for output.
    """

    def __init__(self, use_gpu: bool = False, threshold: float = 0.5):
        super(DetectorSOLOv2, self).__init__(use_gpu=use_gpu, threshold=threshold)

    def predict(self,
                image: Union[str, np.ndarray],
                threshold: float = 0.5,
                visualization: bool = False,
                save_dir: str = 'solov2_result'):
        '''
        Args:
            image (str/np.ndarray): path of image/ np.ndarray read by cv2
            threshold (float): threshold of predicted box' score
            visualization (bool): Whether to save visualization result.
            save_dir (str): save path.

        '''

        inputs, im_info = self.transform(image)
        np_label, np_score, np_segms = None, None, None

        input_names = self.predictor.get_input_names()
        for i in range(len(input_names)):
            input_tensor = self.predictor.get_input_tensor(input_names[i])
            input_tensor.copy_from_cpu(inputs[input_names[i]])

        self.predictor.zero_copy_run()
        output_names = self.predictor.get_output_names()
        np_label = self.predictor.get_output_tensor(output_names[0]).copy_to_cpu()
        np_score = self.predictor.get_output_tensor(output_names[1]).copy_to_cpu()
        np_segms = self.predictor.get_output_tensor(output_names[2]).copy_to_cpu()
        output = dict(segm=np_segms, label=np_label, score=np_score)

        if visualization:
            if not os.path.exists(save_dir):
                os.makedirs(save_dir)
            image = D.visualize_box_mask(im=image, results=output)
            name = str(time.time()) + '.png'
            save_path = os.path.join(save_dir, name)
            image.save(save_path)
        return output

    @serving
    def serving_method(self, images: list, **kwargs):
        """
        Run as a service.
        """
        images_decode = D.base64_to_cv2(images[0])
        results = self.predict(image=images_decode, **kwargs)
        final = {}
        final['segm'] = base64.b64encode(results['segm']).decode('utf8')
        final['label'] = base64.b64encode(results['label']).decode('utf8')
        final['score'] = base64.b64encode(results['score']).decode('utf8')
        return final