# Copyright (c) OpenMMLab. All rights reserved.
import tempfile
from os import path as osp
from typing import Dict, List, Optional, Sequence, Union

import mmcv
import numpy as np
import torch
from mmcv.utils import print_log
from mmengine import load
from mmengine.evaluator import BaseMetric
from mmengine.logging import MMLogger

from mmdet3d.evaluation import kitti_eval
from mmdet3d.registry import METRICS
from mmdet3d.structures import (Box3DMode, CameraInstance3DBoxes,
                                LiDARInstance3DBoxes, points_cam2img)


@METRICS.register_module()
class KittiMetric(BaseMetric):
    """Kitti evaluation metric.

    Args:
        ann_file (str): Annotation file path.
        metric (str | list[str]): Metrics to be evaluated.
            Default to 'bbox'.
        pcd_limit_range (list): The range of point cloud used to
            filter invalid predicted boxes.
            Default to [0, -40, -3, 70.4, 40, 0.0].
        prefix (str, optional): The prefix that will be added in the metric
            names to disambiguate homonymous metrics of different evaluators.
            If prefix is not provided in the argument, self.default_prefix
            will be used instead. Defaults to None.
        pklfile_prefix (str, optional): The prefix of pkl files, including
            the file path and the prefix of filename, e.g., "a/b/prefix".
            If not specified, a temp file will be created. Default: None.
        default_cam_key (str, optional): The default camera for lidar to
            camear conversion. By default, KITTI: CAM2, Waymo: CAM_FRONT
        submission_prefix (str, optional): The prefix of submission data.
            If not specified, the submission data will not be generated.
            Default: None.
        collect_device (str): Device name used for collecting results
            from different ranks during distributed training. Must be 'cpu' or
            'gpu'. Defaults to 'cpu'.
    """

    def __init__(self,
                 ann_file: str,
                 metric: Union[str, List[str]] = 'bbox',
                 pred_box_type_3d: str = 'LiDAR',
                 pcd_limit_range: List[float] = [0, -40, -3, 70.4, 40, 0.0],
                 prefix: Optional[str] = None,
                 pklfile_prefix: str = None,
                 default_cam_key: str = 'CAM2',
                 submission_prefix: str = None,
                 collect_device: str = 'cpu',
                 file_client_args: dict = dict(backend='disk')):
        self.default_prefix = 'Kitti metric'
        super(KittiMetric, self).__init__(
            collect_device=collect_device, prefix=prefix)
        self.pcd_limit_range = pcd_limit_range
        self.ann_file = ann_file
        self.pklfile_prefix = pklfile_prefix
        self.submission_prefix = submission_prefix
        self.pred_box_type_3d = pred_box_type_3d
        self.file_client_args = file_client_args

        allowed_metrics = ['bbox', 'img_bbox', 'mAP']
        self.metrics = metric if isinstance(metric, list) else [metric]
        for metric in self.metrics:
            if metric not in allowed_metrics:
                raise KeyError("metric should be one of 'bbox', 'img_bbox', "
                               'but got {metric}.')

    def convert_annos_to_kitti_annos(
        self,
        data_annos: list,
        classes: list = [
            'Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck', 'Person_sitting',
            'Tram', 'Misc'
        ]
    ) -> list:
        """Convert loading annotations to Kitti annotations.

        Args:
            data_annos (list[dict]): Annotations loaded from ann_file.
            classes (list[str]): Classes used in the dataset. Default used
                ['Pedestrian', 'Cyclist', 'Car', 'Van', 'Truck',
                'Person_sitting', 'Tram', 'Misc'].

        Returns:
            List[dict]: List of Kitti annotations.
        """
        assert 'instances' in data_annos[0]
        for i, annos in enumerate(data_annos):
            if len(annos['instances']) == 0:
                kitti_annos = {
                    'name': np.array([]),
                    'truncated': np.array([]),
                    'occluded': np.array([]),
                    'alpha': np.array([]),
                    'bbox': np.zeros([0, 4]),
                    'dimensions': np.zeros([0, 3]),
                    'location': np.zeros([0, 3]),
                    'rotation_y': np.array([]),
                    'score': np.array([]),
                }
            else:
                kitti_annos = {
                    'name': [],
                    'truncated': [],
                    'occluded': [],
                    'alpha': [],
                    'bbox': [],
                    'location': [],
                    'dimensions': [],
                    'rotation_y': [],
                    'score': []
                }
                for instance in annos['instances']:
                    labels = instance['bbox_label']
                    if labels == -1:
                        kitti_annos['name'].append('DontCare')
                    else:
                        kitti_annos['name'].append(classes[labels])
                    kitti_annos['truncated'].append(instance['truncated'])
                    kitti_annos['occluded'].append(instance['occluded'])
                    kitti_annos['alpha'].append(instance['alpha'])
                    kitti_annos['bbox'].append(instance['bbox'])
                    kitti_annos['location'].append(instance['bbox_3d'][:3])
                    kitti_annos['dimensions'].append(instance['bbox_3d'][3:6])
                    kitti_annos['rotation_y'].append(instance['bbox_3d'][6])
                    kitti_annos['score'].append(instance['score'])
                for name in kitti_annos:
                    kitti_annos[name] = np.array(kitti_annos[name])
            data_annos[i]['kitti_annos'] = kitti_annos
        return data_annos

    def process(self, data_batch: Sequence[dict],
                predictions: Sequence[dict]) -> None:
        """Process one batch of data samples and predictions.

        The processed results should be stored in ``self.results``,
        which will be used to compute the metrics when all batches
        have been processed.

        Args:
            data_batch (Sequence[dict]): A batch of data
                from the dataloader.
            predictions (Sequence[dict]): A batch of outputs from
                the model.
        """
        assert len(data_batch) == len(predictions)
        for data, pred in zip(data_batch, predictions):
            result = dict()
            for pred_result in pred:
                for attr_name in pred[pred_result]:
                    pred[pred_result][attr_name] = pred[pred_result][
                        attr_name].to(self.collect_device)
                result[pred_result] = pred[pred_result]
            sample_idx = data['data_sample']['sample_idx']
            result['sample_idx'] = sample_idx
        self.results.append(result)

    def compute_metrics(self, results: list) -> Dict[str, float]:
        """Compute the metrics from processed results.

        Args:
            results (list): The processed results of each batch.

        Returns:
            Dict[str, float]: The computed metrics. The keys are the names of
            the metrics, and the values are corresponding results.
        """
        logger: MMLogger = MMLogger.get_current_instance()
        self.classes = self.dataset_meta['CLASSES']

        # load annotations
        pkl_annos = load(
            self.ann_file, file_client_args=self.file_client_args)['data_list']
        self.data_infos = self.convert_annos_to_kitti_annos(pkl_annos)
        result_dict, tmp_dir = self.format_results(
            results,
            pklfile_prefix=self.pklfile_prefix,
            submission_prefix=self.submission_prefix,
            classes=self.classes)

        gt_annos = [
            self.data_infos[result['sample_idx']]['kitti_annos']
            for result in results
        ]

        metric_dict = {}
        for metric in self.metrics:
            ap_dict = self.kitti_evaluate(
                result_dict,
                gt_annos,
                metric=metric,
                logger=logger,
                classes=self.classes)
            for result in ap_dict:
                metric_dict[result] = ap_dict[result]

        if tmp_dir is not None:
            tmp_dir.cleanup()
        return metric_dict

    def kitti_evaluate(self,
                       results_dict: List[dict],
                       gt_annos: List[dict],
                       metric: str = None,
                       classes: List[str] = None,
                       logger: MMLogger = None) -> dict:
        """Evaluation in KITTI protocol.

        Args:
            results_dict (dict): Formatted results of the dataset.
            gt_annos (list[dict]): Contain gt information of each sample.
            metric (str, optional): Metrics to be evaluated.
                Default: None.
            logger (MMLogger, optional): Logger used for printing
                related information during evaluation. Default: None.
            classes (list[String], optional): A list of class name. Defaults
                to None.

        Returns:
            dict[str, float]: Results of each evaluation metric.
        """
        ap_dict = dict()
        for name in results_dict:
            if name == 'pred_instances' or metric == 'img_bbox':
                eval_types = ['bbox']
            else:
                eval_types = ['bbox', 'bev', '3d']
            ap_result_str, ap_dict_ = kitti_eval(
                gt_annos, results_dict[name], classes, eval_types=eval_types)
            for ap_type, ap in ap_dict_.items():
                ap_dict[f'{name}/{ap_type}'] = float('{:.4f}'.format(ap))

            print_log(f'Results of {name}:\n' + ap_result_str, logger=logger)

        return ap_dict

    def format_results(self,
                       results: List[dict],
                       pklfile_prefix: str = None,
                       submission_prefix: str = None,
                       classes: List[str] = None):
        """Format the results to pkl file.

        Args:
            results (list[dict]): Testing results of the
                dataset.
            pklfile_prefix (str, optional): The prefix of pkl files. It
                includes the file path and the prefix of filename, e.g.,
                "a/b/prefix". If not specified, a temp file will be created.
                Default: None.
            submission_prefix (str, optional): The prefix of submitted files.
                It includes the file path and the prefix of filename, e.g.,
                "a/b/prefix". If not specified, a temp file will be created.
                Default: None.
            classes (list[String], optional): A list of class name. Defaults
                to None.

        Returns:
            tuple: (result_dict, tmp_dir), result_dict is a dict containing
                the formatted result, tmp_dir is the temporal directory created
                for saving json files when jsonfile_prefix is not specified.
        """
        if pklfile_prefix is None:
            tmp_dir = tempfile.TemporaryDirectory()
            pklfile_prefix = osp.join(tmp_dir.name, 'results')
        else:
            tmp_dir = None
        result_dict = dict()
        sample_id_list = [result['sample_idx'] for result in results]
        for name in results[0]:
            if submission_prefix is not None:
                submission_prefix_ = osp.join(submission_prefix, name)
            else:
                submission_prefix_ = None
            if pklfile_prefix is not None:
                pklfile_prefix_ = osp.join(pklfile_prefix, name) + '.pkl'
            else:
                pklfile_prefix_ = None
            if 'pred_instances' in name and '3d' in name and name[0] != '_':
                net_outputs = [result[name] for result in results]
                result_list_ = self.bbox2result_kitti(net_outputs,
                                                      sample_id_list, classes,
                                                      pklfile_prefix_,
                                                      submission_prefix_)
                result_dict[name] = result_list_
            elif name == 'pred_instances' and name[0] != '_':
                net_outputs = [info[name] for info in results]
                result_list_ = self.bbox2result_kitti2d(
                    net_outputs, sample_id_list, classes, pklfile_prefix_,
                    submission_prefix_)
                result_dict[name] = result_list_
        return result_dict, tmp_dir

    def bbox2result_kitti(self,
                          net_outputs: list,
                          sample_id_list: list,
                          class_names: list,
                          pklfile_prefix: str = None,
                          submission_prefix: str = None):
        """Convert 3D detection results to kitti format for evaluation and test
        submission.

        Args:
            net_outputs (list[dict]): List of array storing the
                inferenced bounding boxes and scores.
            sample_id_list (list[int]): List of input sample id.
            class_names (list[String]): A list of class names.
            pklfile_prefix (str, optional): The prefix of pkl file.
                Defaults to None.
            submission_prefix (str, optional): The prefix of submission file.
                Defaults to None.

        Returns:
            list[dict]: A list of dictionaries with the kitti format.
        """
        assert len(net_outputs) == len(self.data_infos), \
            'invalid list length of network outputs'
        if submission_prefix is not None:
            mmcv.mkdir_or_exist(submission_prefix)

        det_annos = []
        print('\nConverting prediction to KITTI format')
        for idx, pred_dicts in enumerate(
                mmcv.track_iter_progress(net_outputs)):
            annos = []
            sample_idx = sample_id_list[idx]
            info = self.data_infos[sample_idx]
            # Here default used 'CAM2' to compute metric. If you want to
            # use another camera, please modify it.
            image_shape = (info['images'][self.default_cam_key]['height'],
                           info['images'][self.default_cam_key]['width'])
            box_dict = self.convert_valid_bboxes(pred_dicts, info)
            anno = {
                'name': [],
                'truncated': [],
                'occluded': [],
                'alpha': [],
                'bbox': [],
                'dimensions': [],
                'location': [],
                'rotation_y': [],
                'score': []
            }
            if len(box_dict['bbox']) > 0:
                box_2d_preds = box_dict['bbox']
                box_preds = box_dict['box3d_camera']
                scores = box_dict['scores']
                box_preds_lidar = box_dict['box3d_lidar']
                label_preds = box_dict['label_preds']
                pred_box_type_3d = box_dict['pred_box_type_3d']

                for box, box_lidar, bbox, score, label in zip(
                        box_preds, box_preds_lidar, box_2d_preds, scores,
                        label_preds):
                    bbox[2:] = np.minimum(bbox[2:], image_shape[::-1])
                    bbox[:2] = np.maximum(bbox[:2], [0, 0])
                    anno['name'].append(class_names[int(label)])
                    anno['truncated'].append(0.0)
                    anno['occluded'].append(0)
                    if pred_box_type_3d == CameraInstance3DBoxes:
                        anno['alpha'].append(-np.arctan2(box[0], box[2]) +
                                             box[6])
                    elif pred_box_type_3d == LiDARInstance3DBoxes:
                        anno['alpha'].append(
                            -np.arctan2(-box_lidar[1], box_lidar[0]) + box[6])
                    anno['bbox'].append(bbox)
                    anno['dimensions'].append(box[3:6])
                    anno['location'].append(box[:3])
                    anno['rotation_y'].append(box[6])
                    anno['score'].append(score)

                anno = {k: np.stack(v) for k, v in anno.items()}
                annos.append(anno)
            else:
                anno = {
                    'name': np.array([]),
                    'truncated': np.array([]),
                    'occluded': np.array([]),
                    'alpha': np.array([]),
                    'bbox': np.zeros([0, 4]),
                    'dimensions': np.zeros([0, 3]),
                    'location': np.zeros([0, 3]),
                    'rotation_y': np.array([]),
                    'score': np.array([]),
                }
                annos.append(anno)

            if submission_prefix is not None:
                curr_file = f'{submission_prefix}/{sample_idx:06d}.txt'
                with open(curr_file, 'w') as f:
                    bbox = anno['bbox']
                    loc = anno['location']
                    dims = anno['dimensions']  # lhw -> hwl

                    for idx in range(len(bbox)):
                        print(
                            '{} -1 -1 {:.4f} {:.4f} {:.4f} {:.4f} '
                            '{:.4f} {:.4f} {:.4f} '
                            '{:.4f} {:.4f} {:.4f} {:.4f} {:.4f} {:.4f}'.format(
                                anno['name'][idx], anno['alpha'][idx],
                                bbox[idx][0], bbox[idx][1], bbox[idx][2],
                                bbox[idx][3], dims[idx][1], dims[idx][2],
                                dims[idx][0], loc[idx][0], loc[idx][1],
                                loc[idx][2], anno['rotation_y'][idx],
                                anno['score'][idx]),
                            file=f)

            annos[-1]['sample_id'] = np.array(
                [sample_idx] * len(annos[-1]['score']), dtype=np.int64)

            det_annos += annos

        if pklfile_prefix is not None:
            if not pklfile_prefix.endswith(('.pkl', '.pickle')):
                out = f'{pklfile_prefix}.pkl'
            else:
                out = pklfile_prefix
            mmcv.dump(det_annos, out)
            print(f'Result is saved to {out}.')

        return det_annos

    def bbox2result_kitti2d(self,
                            net_outputs: list,
                            sample_id_list,
                            class_names: list,
                            pklfile_prefix: str = None,
                            submission_prefix: str = None):
        """Convert 2D detection results to kitti format for evaluation and test
        submission.

        Args:
            net_outputs (list[dict]): List of array storing the
                inferenced bounding boxes and scores.
            sample_id_list (list[int]): List of input sample id.
            class_names (list[String]): A list of class names.
            pklfile_prefix (str, optional): The prefix of pkl file.
                Defaults to None.
            submission_prefix (str, optional): The prefix of submission file.
                Defaults to None.

        Returns:
            list[dict]: A list of dictionaries have the kitti format
        """
        assert len(net_outputs) == len(self.data_infos), \
            'invalid list length of network outputs'
        det_annos = []
        print('\nConverting prediction to KITTI format')
        for i, bboxes_per_sample in enumerate(
                mmcv.track_iter_progress(net_outputs)):
            annos = []
            anno = dict(
                name=[],
                truncated=[],
                occluded=[],
                alpha=[],
                bbox=[],
                dimensions=[],
                location=[],
                rotation_y=[],
                score=[])
            sample_idx = sample_id_list[i]

            num_example = 0
            bbox = bboxes_per_sample['bboxes']
            for i in range(bbox.shape[0]):
                anno['name'].append(class_names[int(
                    bboxes_per_sample['labels'][i])])
                anno['truncated'].append(0.0)
                anno['occluded'].append(0)
                anno['alpha'].append(0.0)
                anno['bbox'].append(bbox[i, :4])
                # set dimensions (height, width, length) to zero
                anno['dimensions'].append(
                    np.zeros(shape=[3], dtype=np.float32))
                # set the 3D translation to (-1000, -1000, -1000)
                anno['location'].append(
                    np.ones(shape=[3], dtype=np.float32) * (-1000.0))
                anno['rotation_y'].append(0.0)
                anno['score'].append(bboxes_per_sample['scores'][i])
                num_example += 1

            if num_example == 0:
                annos.append(
                    dict(
                        name=np.array([]),
                        truncated=np.array([]),
                        occluded=np.array([]),
                        alpha=np.array([]),
                        bbox=np.zeros([0, 4]),
                        dimensions=np.zeros([0, 3]),
                        location=np.zeros([0, 3]),
                        rotation_y=np.array([]),
                        score=np.array([]),
                    ))
            else:
                anno = {k: np.stack(v) for k, v in anno.items()}
                annos.append(anno)

            annos[-1]['sample_id'] = np.array(
                [sample_idx] * num_example, dtype=np.int64)
            det_annos += annos

        if pklfile_prefix is not None:
            if not pklfile_prefix.endswith(('.pkl', '.pickle')):
                out = f'{pklfile_prefix}.pkl'
            else:
                out = pklfile_prefix
            mmcv.dump(det_annos, out)
            print(f'Result is saved to {out}.')

        if submission_prefix is not None:
            # save file in submission format
            mmcv.mkdir_or_exist(submission_prefix)
            print(f'Saving KITTI submission to {submission_prefix}')
            for i, anno in enumerate(det_annos):
                sample_idx = self.data_infos[i]['image']['image_idx']
                cur_det_file = f'{submission_prefix}/{sample_idx:06d}.txt'
                with open(cur_det_file, 'w') as f:
                    bbox = anno['bbox']
                    loc = anno['location']
                    dims = anno['dimensions'][::-1]  # lhw -> hwl
                    for idx in range(len(bbox)):
                        print(
                            '{} -1 -1 {:4f} {:4f} {:4f} {:4f} {:4f} {:4f} '
                            '{:4f} {:4f} {:4f} {:4f} {:4f} {:4f} {:4f}'.format(
                                anno['name'][idx],
                                anno['alpha'][idx],
                                *bbox[idx],  # 4 float
                                *dims[idx],  # 3 float
                                *loc[idx],  # 3 float
                                anno['rotation_y'][idx],
                                anno['score'][idx]),
                            file=f,
                        )
            print(f'Result is saved to {submission_prefix}')

        return det_annos

    def convert_valid_bboxes(self, box_dict: dict, info: dict):
        """Convert the predicted boxes into valid ones.

        Args:
            box_dict (dict): Box dictionaries to be converted.

                - boxes_3d (:obj:`LiDARInstance3DBoxes`): 3D bounding boxes.
                - scores_3d (torch.Tensor): Scores of boxes.
                - labels_3d (torch.Tensor): Class labels of boxes.
            info (dict): Data info.

        Returns:
            dict: Valid predicted boxes.

                - bbox (np.ndarray): 2D bounding boxes.
                - box3d_camera (np.ndarray): 3D bounding boxes in
                    camera coordinate.
                - box3d_lidar (np.ndarray): 3D bounding boxes in
                    LiDAR coordinate.
                - scores (np.ndarray): Scores of boxes.
                - label_preds (np.ndarray): Class label predictions.
                - sample_idx (int): Sample index.
        """
        # TODO: refactor this function
        box_preds = box_dict['bboxes_3d']
        scores = box_dict['scores_3d']
        labels = box_dict['labels_3d']
        sample_idx = info['sample_id']
        box_preds.limit_yaw(offset=0.5, period=np.pi * 2)

        if len(box_preds) == 0:
            return dict(
                bbox=np.zeros([0, 4]),
                box3d_camera=np.zeros([0, 7]),
                box3d_lidar=np.zeros([0, 7]),
                scores=np.zeros([0]),
                label_preds=np.zeros([0, 4]),
                sample_idx=sample_idx)
        # Here default used 'CAM2' to compute metric. If you want to
        # use another camera, please modify it.
        lidar2cam = np.array(
            info['images'][self.default_cam_key]['lidar2cam']).astype(
                np.float32)
        P2 = np.array(info['images'][self.default_cam_key]['cam2img']).astype(
            np.float32)
        img_shape = (info['images'][self.default_cam_key]['height'],
                     info['images'][self.default_cam_key]['width'])
        P2 = box_preds.tensor.new_tensor(P2)

        if isinstance(box_preds, LiDARInstance3DBoxes):
            box_preds_camera = box_preds.convert_to(Box3DMode.CAM, lidar2cam)
            box_preds_lidar = box_preds
        elif isinstance(box_preds, CameraInstance3DBoxes):
            box_preds_camera = box_preds
            box_preds_lidar = box_preds.convert_to(Box3DMode.LIDAR,
                                                   np.linalg.inv(lidar2cam))

        box_corners = box_preds_camera.corners
        box_corners_in_image = points_cam2img(box_corners, P2)
        # box_corners_in_image: [N, 8, 2]
        minxy = torch.min(box_corners_in_image, dim=1)[0]
        maxxy = torch.max(box_corners_in_image, dim=1)[0]
        box_2d_preds = torch.cat([minxy, maxxy], dim=1)
        # Post-processing
        # check box_preds_camera
        image_shape = box_preds.tensor.new_tensor(img_shape)
        valid_cam_inds = ((box_2d_preds[:, 0] < image_shape[1]) &
                          (box_2d_preds[:, 1] < image_shape[0]) &
                          (box_2d_preds[:, 2] > 0) & (box_2d_preds[:, 3] > 0))
        # check box_preds_lidar
        if isinstance(box_preds, LiDARInstance3DBoxes):
            limit_range = box_preds.tensor.new_tensor(self.pcd_limit_range)
            valid_pcd_inds = ((box_preds_lidar.center > limit_range[:3]) &
                              (box_preds_lidar.center < limit_range[3:]))
            valid_inds = valid_cam_inds & valid_pcd_inds.all(-1)
        else:
            valid_inds = valid_cam_inds

        if valid_inds.sum() > 0:
            return dict(
                bbox=box_2d_preds[valid_inds, :].numpy(),
                pred_box_type_3d=type(box_preds),
                box3d_camera=box_preds_camera[valid_inds].tensor.numpy(),
                box3d_lidar=box_preds_lidar[valid_inds].tensor.numpy(),
                scores=scores[valid_inds].numpy(),
                label_preds=labels[valid_inds].numpy(),
                sample_idx=sample_idx)
        else:
            return dict(
                bbox=np.zeros([0, 4]),
                pred_box_type_3d=type(box_preds),
                box3d_camera=np.zeros([0, 7]),
                box3d_lidar=np.zeros([0, 7]),
                scores=np.zeros([0]),
                label_preds=np.zeros([0, 4]),
                sample_idx=sample_idx)
