# Copyright (c) OpenMMLab. All rights reserved.
import copy
import tempfile
from os import path as osp

import mmcv
import numpy as np
import torch
from mmcv.utils import print_log

from ..core.bbox import Box3DMode, CameraInstance3DBoxes, points_cam2img
from .builder import DATASETS
from .nuscenes_mono_dataset import NuScenesMonoDataset


@DATASETS.register_module()
class KittiMonoDataset(NuScenesMonoDataset):
    """Monocular 3D detection on KITTI Dataset.

    Args:
        data_root (str): Path of dataset root.
        info_file (str): Path of info file.
        load_interval (int, optional): Interval of loading the dataset. It is
            used to uniformly sample the dataset. Defaults to 1.
        with_velocity (bool, optional): Whether include velocity prediction
            into the experiments. Defaults to False.
        eval_version (str, optional): Configuration version of evaluation.
            Defaults to None.
        version (str, optional): Dataset version. Defaults to None.
        kwargs (dict): Other arguments are the same of NuScenesMonoDataset.
    """

    CLASSES = ('Pedestrian', 'Cyclist', 'Car')

    def __init__(self,
                 data_root,
                 info_file,
                 ann_file,
                 pipeline,
                 load_interval=1,
                 with_velocity=False,
                 eval_version=None,
                 version=None,
                 **kwargs):
        super().__init__(
            data_root=data_root,
            ann_file=ann_file,
            pipeline=pipeline,
            load_interval=load_interval,
            with_velocity=with_velocity,
            eval_version=eval_version,
            version=version,
            **kwargs)
        self.anno_infos = mmcv.load(info_file)
        self.bbox_code_size = 7

    def _parse_ann_info(self, img_info, ann_info):
        """Parse bbox and mask annotation.

        Args:
            ann_info (list[dict]): Annotation info of an image.
            with_mask (bool): Whether to parse mask annotations.

        Returns:
            dict: A dict containing the following keys: bboxes, bboxes_ignore,
                labels, masks, seg_map. "masks" are raw annotations and not
                decoded into binary masks.
        """
        gt_bboxes = []
        gt_labels = []
        gt_bboxes_ignore = []
        gt_masks_ann = []
        gt_bboxes_cam3d = []
        centers2d = []
        depths = []
        for i, ann in enumerate(ann_info):
            if ann.get('ignore', False):
                continue
            x1, y1, w, h = ann['bbox']
            inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0))
            inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0))
            if inter_w * inter_h == 0:
                continue
            if ann['area'] <= 0 or w < 1 or h < 1:
                continue
            if ann['category_id'] not in self.cat_ids:
                continue
            bbox = [x1, y1, x1 + w, y1 + h]
            if ann.get('iscrowd', False):
                gt_bboxes_ignore.append(bbox)
            else:
                gt_bboxes.append(bbox)
                gt_labels.append(self.cat2label[ann['category_id']])
                gt_masks_ann.append(ann.get('segmentation', None))
                # 3D annotations in camera coordinates
                bbox_cam3d = np.array(ann['bbox_cam3d']).reshape(-1, )
                gt_bboxes_cam3d.append(bbox_cam3d)
                # 2.5D annotations in camera coordinates
                center2d = ann['center2d'][:2]
                depth = ann['center2d'][2]
                centers2d.append(center2d)
                depths.append(depth)

        if gt_bboxes:
            gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
            gt_labels = np.array(gt_labels, dtype=np.int64)
        else:
            gt_bboxes = np.zeros((0, 4), dtype=np.float32)
            gt_labels = np.array([], dtype=np.int64)

        if gt_bboxes_cam3d:
            gt_bboxes_cam3d = np.array(gt_bboxes_cam3d, dtype=np.float32)
            centers2d = np.array(centers2d, dtype=np.float32)
            depths = np.array(depths, dtype=np.float32)
        else:
            gt_bboxes_cam3d = np.zeros((0, self.bbox_code_size),
                                       dtype=np.float32)
            centers2d = np.zeros((0, 2), dtype=np.float32)
            depths = np.zeros((0), dtype=np.float32)

        gt_bboxes_cam3d = CameraInstance3DBoxes(
            gt_bboxes_cam3d,
            box_dim=gt_bboxes_cam3d.shape[-1],
            origin=(0.5, 0.5, 0.5))
        gt_labels_3d = copy.deepcopy(gt_labels)

        if gt_bboxes_ignore:
            gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
        else:
            gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)

        seg_map = img_info['filename'].replace('jpg', 'png')

        ann = dict(
            bboxes=gt_bboxes,
            labels=gt_labels,
            gt_bboxes_3d=gt_bboxes_cam3d,
            gt_labels_3d=gt_labels_3d,
            centers2d=centers2d,
            depths=depths,
            bboxes_ignore=gt_bboxes_ignore,
            masks=gt_masks_ann,
            seg_map=seg_map)

        return ann

    def format_results(self,
                       outputs,
                       pklfile_prefix=None,
                       submission_prefix=None):
        """Format the results to pkl file.

        Args:
            outputs (list[dict]): Testing results of the dataset.
            pklfile_prefix (str): 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): 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.

        Returns:
            tuple: (result_files, tmp_dir), result_files is a dict containing
                the json filepaths, 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

        if not isinstance(outputs[0], dict):
            result_files = self.bbox2result_kitti2d(outputs, self.CLASSES,
                                                    pklfile_prefix,
                                                    submission_prefix)
        elif 'pts_bbox' in outputs[0] or 'img_bbox' in outputs[0] or \
                'img_bbox2d' in outputs[0]:
            result_files = dict()
            for name in outputs[0]:
                results_ = [out[name] for out in outputs]
                pklfile_prefix_ = pklfile_prefix + name
                if submission_prefix is not None:
                    submission_prefix_ = submission_prefix + name
                else:
                    submission_prefix_ = None
                if '2d' in name:
                    result_files_ = self.bbox2result_kitti2d(
                        results_, self.CLASSES, pklfile_prefix_,
                        submission_prefix_)
                else:
                    result_files_ = self.bbox2result_kitti(
                        results_, self.CLASSES, pklfile_prefix_,
                        submission_prefix_)
                result_files[name] = result_files_
        else:
            result_files = self.bbox2result_kitti(outputs, self.CLASSES,
                                                  pklfile_prefix,
                                                  submission_prefix)
        return result_files, tmp_dir

    def evaluate(self,
                 results,
                 metric=None,
                 logger=None,
                 pklfile_prefix=None,
                 submission_prefix=None,
                 show=False,
                 out_dir=None,
                 pipeline=None):
        """Evaluation in KITTI protocol.

        Args:
            results (list[dict]): Testing results of the dataset.
            metric (str | list[str], optional): Metrics to be evaluated.
                Defaults to None.
            logger (logging.Logger | str, optional): Logger used for printing
                related information during evaluation. Default: 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.
            submission_prefix (str, optional): The prefix of submission data.
                If not specified, the submission data will not be generated.
            show (bool, optional): Whether to visualize.
                Default: False.
            out_dir (str, optional): Path to save the visualization results.
                Default: None.
            pipeline (list[dict], optional): raw data loading for showing.
                Default: None.

        Returns:
            dict[str, float]: Results of each evaluation metric.
        """
        result_files, tmp_dir = self.format_results(results, pklfile_prefix)
        from mmdet3d.core.evaluation import kitti_eval
        gt_annos = [info['annos'] for info in self.anno_infos]

        if isinstance(result_files, dict):
            ap_dict = dict()
            for name, result_files_ in result_files.items():
                eval_types = ['bbox', 'bev', '3d']
                if '2d' in name:
                    eval_types = ['bbox']
                ap_result_str, ap_dict_ = kitti_eval(
                    gt_annos,
                    result_files_,
                    self.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)

        else:
            if metric == 'img_bbox2d':
                ap_result_str, ap_dict = kitti_eval(
                    gt_annos, result_files, self.CLASSES, eval_types=['bbox'])
            else:
                ap_result_str, ap_dict = kitti_eval(gt_annos, result_files,
                                                    self.CLASSES)
            print_log('\n' + ap_result_str, logger=logger)

        if tmp_dir is not None:
            tmp_dir.cleanup()
        if show or out_dir:
            self.show(results, out_dir, show=show, pipeline=pipeline)
        return ap_dict

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

        Args:
            net_outputs (list[np.ndarray]): List of array storing the
                inferenced bounding boxes and scores.
            class_names (list[String]): A list of class names.
            pklfile_prefix (str): The prefix of pkl file.
            submission_prefix (str): The prefix of submission file.

        Returns:
            list[dict]: A list of dictionaries with the kitti format.
        """
        assert len(net_outputs) == len(self.anno_infos)
        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 = []
            info = self.anno_infos[idx]
            sample_idx = info['image']['image_idx']
            image_shape = info['image']['image_shape'][:2]

            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']

                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)
                    anno['alpha'].append(-np.arctan2(box[0], box[2]) + 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_idx'] = 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'
            mmcv.dump(det_annos, out)
            print('Result is saved to %s' % out)

        return det_annos

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

        Args:
            net_outputs (list[np.ndarray]): List of array storing the
                inferenced bounding boxes and scores.
            class_names (list[String]): A list of class names.
            pklfile_prefix (str): The prefix of pkl file.
            submission_prefix (str): The prefix of submission file.

        Returns:
            list[dict]: A list of dictionaries have the kitti format
        """
        assert len(net_outputs) == len(self.anno_infos)

        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 = self.anno_infos[i]['image']['image_idx']

            num_example = 0
            for label in range(len(bboxes_per_sample)):
                bbox = bboxes_per_sample[label]
                for i in range(bbox.shape[0]):
                    anno['name'].append(class_names[int(label)])
                    anno['truncated'].append(0.0)
                    anno['occluded'].append(0)
                    anno['alpha'].append(-10)
                    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(bbox[i, 4])
                    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_idx'] = 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'
            mmcv.dump(det_annos, out)
            print('Result is saved to %s' % 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.anno_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, info):
        """Convert the predicted boxes into valid ones.

        Args:
            box_dict (dict): Box dictionaries to be converted.
                - boxes_3d (:obj:`CameraInstance3DBoxes`): 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.
                - scores (np.ndarray): Scores of boxes.
                - label_preds (np.ndarray): Class label predictions.
                - sample_idx (int): Sample index.
        """
        box_preds = box_dict['boxes_3d']
        scores = box_dict['scores_3d']
        labels = box_dict['labels_3d']
        sample_idx = info['image']['image_idx']

        if len(box_preds) == 0:
            return dict(
                bbox=np.zeros([0, 4]),
                box3d_camera=np.zeros([0, 7]),
                scores=np.zeros([0]),
                label_preds=np.zeros([0, 4]),
                sample_idx=sample_idx)

        rect = info['calib']['R0_rect'].astype(np.float32)
        Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
        P2 = info['calib']['P2'].astype(np.float32)
        img_shape = info['image']['image_shape']
        P2 = box_preds.tensor.new_tensor(P2)

        box_preds_camera = box_preds
        box_preds_lidar = box_preds.convert_to(Box3DMode.LIDAR,
                                               np.linalg.inv(rect @ Trv2c))

        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
        valid_inds = valid_cam_inds

        if valid_inds.sum() > 0:
            return dict(
                bbox=box_2d_preds[valid_inds, :].numpy(),
                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]),
                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)
