# Copyright (c) 2021 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import cv2
import glob
import re
import paddle
import numpy as np
import os.path as osp
from collections import defaultdict

from ppdet.core.workspace import create
from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
from ppdet.modeling.mot.utils import Detection, get_crops, scale_coords, clip_box
from ppdet.modeling.mot.utils import MOTTimer, load_det_results, write_mot_results, save_vis_results
from ppdet.modeling.mot.tracker import JDETracker, DeepSORTTracker

from ppdet.metrics import Metric, MOTMetric, KITTIMOTMetric
from ppdet.metrics import MCMOTMetric
import ppdet.utils.stats as stats

from .callbacks import Callback, ComposeCallback

from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)

MOT_ARCH = ['DeepSORT', 'JDE', 'FairMOT', 'ByteTrack']
MOT_ARCH_JDE = ['JDE', 'FairMOT']
MOT_ARCH_SDE = ['DeepSORT', 'ByteTrack']
MOT_DATA_TYPE = ['mot', 'mcmot', 'kitti']

__all__ = ['Tracker']


class Tracker(object):
    def __init__(self, cfg, mode='eval'):
        self.cfg = cfg
        assert mode.lower() in ['test', 'eval'], \
                "mode should be 'test' or 'eval'"
        self.mode = mode.lower()
        self.optimizer = None

        # build MOT data loader
        self.dataset = cfg['{}MOTDataset'.format(self.mode.capitalize())]

        # build model
        self.model = create(cfg.architecture)

        self.status = {}
        self.start_epoch = 0

        # initial default callbacks
        self._init_callbacks()

        # initial default metrics
        self._init_metrics()
        self._reset_metrics()

    def _init_callbacks(self):
        self._callbacks = []
        self._compose_callback = None

    def _init_metrics(self):
        if self.mode in ['test']:
            self._metrics = []
            return

        if self.cfg.metric == 'MOT':
            self._metrics = [MOTMetric(), ]
        elif self.cfg.metric == 'MCMOT':
            self._metrics = [MCMOTMetric(self.cfg.num_classes), ]
        elif self.cfg.metric == 'KITTI':
            self._metrics = [KITTIMOTMetric(), ]
        else:
            logger.warning("Metric not support for metric type {}".format(
                self.cfg.metric))
            self._metrics = []

    def _reset_metrics(self):
        for metric in self._metrics:
            metric.reset()

    def register_callbacks(self, callbacks):
        callbacks = [h for h in list(callbacks) if h is not None]
        for c in callbacks:
            assert isinstance(c, Callback), \
                    "metrics shoule be instances of subclass of Metric"
        self._callbacks.extend(callbacks)
        self._compose_callback = ComposeCallback(self._callbacks)

    def register_metrics(self, metrics):
        metrics = [m for m in list(metrics) if m is not None]
        for m in metrics:
            assert isinstance(m, Metric), \
                    "metrics shoule be instances of subclass of Metric"
        self._metrics.extend(metrics)

    def load_weights_jde(self, weights):
        load_weight(self.model, weights, self.optimizer)

    def load_weights_sde(self, det_weights, reid_weights):
        with_detector = self.model.detector is not None
        with_reid = self.model.reid is not None

        if with_detector:
            load_weight(self.model.detector, det_weights)
            if with_reid:
                load_weight(self.model.reid, reid_weights)
        else:
            load_weight(self.model.reid, reid_weights)

    def _eval_seq_jde(self,
                      dataloader,
                      save_dir=None,
                      show_image=False,
                      frame_rate=30,
                      draw_threshold=0):
        if save_dir:
            if not os.path.exists(save_dir): os.makedirs(save_dir)
        tracker = self.model.tracker
        tracker.max_time_lost = int(frame_rate / 30.0 * tracker.track_buffer)

        timer = MOTTimer()
        frame_id = 0
        self.status['mode'] = 'track'
        self.model.eval()
        results = defaultdict(list)  # support single class and multi classes

        for step_id, data in enumerate(dataloader):
            self.status['step_id'] = step_id
            if frame_id % 40 == 0:
                logger.info('Processing frame {} ({:.2f} fps)'.format(
                    frame_id, 1. / max(1e-5, timer.average_time)))
            # forward
            timer.tic()
            pred_dets, pred_embs = self.model(data)

            pred_dets, pred_embs = pred_dets.numpy(), pred_embs.numpy()
            online_targets_dict = self.model.tracker.update(pred_dets,
                                                            pred_embs)
            online_tlwhs = defaultdict(list)
            online_scores = defaultdict(list)
            online_ids = defaultdict(list)
            for cls_id in range(self.cfg.num_classes):
                online_targets = online_targets_dict[cls_id]
                for t in online_targets:
                    tlwh = t.tlwh
                    tid = t.track_id
                    tscore = t.score
                    if tlwh[2] * tlwh[3] <= tracker.min_box_area: continue
                    if tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
                            3] > tracker.vertical_ratio:
                        continue
                    online_tlwhs[cls_id].append(tlwh)
                    online_ids[cls_id].append(tid)
                    online_scores[cls_id].append(tscore)
                # save results
                results[cls_id].append(
                    (frame_id + 1, online_tlwhs[cls_id], online_scores[cls_id],
                     online_ids[cls_id]))

            timer.toc()
            save_vis_results(data, frame_id, online_ids, online_tlwhs,
                             online_scores, timer.average_time, show_image,
                             save_dir, self.cfg.num_classes)
            frame_id += 1

        return results, frame_id, timer.average_time, timer.calls

    def _eval_seq_sde(self,
                      dataloader,
                      save_dir=None,
                      show_image=False,
                      frame_rate=30,
                      seq_name='',
                      scaled=False,
                      det_file='',
                      draw_threshold=0):
        if save_dir:
            if not os.path.exists(save_dir): os.makedirs(save_dir)
        use_detector = False if not self.model.detector else True
        use_reid = False if not self.model.reid else True

        timer = MOTTimer()
        results = defaultdict(list)
        frame_id = 0
        self.status['mode'] = 'track'
        self.model.eval()
        if use_reid:
            self.model.reid.eval()
        if not use_detector:
            dets_list = load_det_results(det_file, len(dataloader))
            logger.info('Finish loading detection results file {}.'.format(
                det_file))

        tracker = self.model.tracker
        for step_id, data in enumerate(dataloader):
            self.status['step_id'] = step_id
            if frame_id % 40 == 0:
                logger.info('Processing frame {} ({:.2f} fps)'.format(
                    frame_id, 1. / max(1e-5, timer.average_time)))

            ori_image = data['ori_image']  # [bs, H, W, 3]
            ori_image_shape = data['ori_image'].shape[1:3]
            # ori_image_shape: [H, W]

            input_shape = data['image'].shape[2:]
            # input_shape: [h, w], before data transforms, set in model config

            im_shape = data['im_shape'][0].numpy()
            # im_shape: [new_h, new_w], after data transforms
            scale_factor = data['scale_factor'][0].numpy()

            empty_detections = False
            # when it has no detected bboxes, will not inference reid model 
            # and if visualize, use original image instead

            # forward
            timer.tic()
            if not use_detector:
                dets = dets_list[frame_id]
                bbox_tlwh = np.array(dets['bbox'], dtype='float32')
                if bbox_tlwh.shape[0] > 0:
                    # detector outputs: pred_cls_ids, pred_scores, pred_bboxes
                    pred_cls_ids = np.array(dets['cls_id'], dtype='float32')
                    pred_scores = np.array(dets['score'], dtype='float32')
                    pred_bboxes = np.concatenate(
                        (bbox_tlwh[:, 0:2],
                         bbox_tlwh[:, 2:4] + bbox_tlwh[:, 0:2]),
                        axis=1)
                else:
                    logger.warning(
                        'Frame {} has not object, try to modify score threshold.'.
                        format(frame_id))
                    empty_detections = True
            else:
                outs = self.model.detector(data)
                outs['bbox'] = outs['bbox'].numpy()
                outs['bbox_num'] = outs['bbox_num'].numpy()

                if len(outs['bbox']) > 0 and empty_detections == False:
                    # detector outputs: pred_cls_ids, pred_scores, pred_bboxes
                    pred_cls_ids = outs['bbox'][:, 0:1]
                    pred_scores = outs['bbox'][:, 1:2]
                    if not scaled:
                        # Note: scaled=False only in JDE YOLOv3 or other detectors
                        # with LetterBoxResize and JDEBBoxPostProcess.
                        #
                        # 'scaled' means whether the coords after detector outputs
                        # have been scaled back to the original image, set True 
                        # in general detector, set False in JDE YOLOv3.
                        pred_bboxes = scale_coords(outs['bbox'][:, 2:],
                                                   input_shape, im_shape,
                                                   scale_factor)
                    else:
                        pred_bboxes = outs['bbox'][:, 2:]
                    pred_dets_old = np.concatenate(
                        (pred_cls_ids, pred_scores, pred_bboxes), axis=1)
                else:
                    logger.warning(
                        'Frame {} has not detected object, try to modify score threshold.'.
                        format(frame_id))
                    empty_detections = True

            if not empty_detections:
                pred_xyxys, keep_idx = clip_box(pred_bboxes, ori_image_shape)
                if len(keep_idx[0]) == 0:
                    logger.warning(
                        'Frame {} has not detected object left after clip_box.'.
                        format(frame_id))
                    empty_detections = True

            if empty_detections:
                timer.toc()
                # if visualize, use original image instead
                online_ids, online_tlwhs, online_scores = None, None, None
                save_vis_results(data, frame_id, online_ids, online_tlwhs,
                                 online_scores, timer.average_time, show_image,
                                 save_dir, self.cfg.num_classes)
                frame_id += 1
                # thus will not inference reid model
                continue

            pred_cls_ids = pred_cls_ids[keep_idx[0]]
            pred_scores = pred_scores[keep_idx[0]]
            pred_dets = np.concatenate(
                (pred_cls_ids, pred_scores, pred_xyxys), axis=1)

            if use_reid:
                crops = get_crops(
                    pred_xyxys,
                    ori_image,
                    w=tracker.input_size[0],
                    h=tracker.input_size[1])
                crops = paddle.to_tensor(crops)

                data.update({'crops': crops})
                pred_embs = self.model(data)['embeddings'].numpy()
            else:
                pred_embs = None

            if isinstance(tracker, DeepSORTTracker):
                online_tlwhs, online_scores, online_ids = [], [], []
                tracker.predict()
                online_targets = tracker.update(pred_dets, pred_embs)
                for t in online_targets:
                    if not t.is_confirmed() or t.time_since_update > 1:
                        continue
                    tlwh = t.to_tlwh()
                    tscore = t.score
                    tid = t.track_id
                    if tscore < draw_threshold: continue
                    if tlwh[2] * tlwh[3] <= tracker.min_box_area: continue
                    if tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
                            3] > tracker.vertical_ratio:
                        continue
                    online_tlwhs.append(tlwh)
                    online_scores.append(tscore)
                    online_ids.append(tid)
                timer.toc()

                # save results
                results[0].append(
                    (frame_id + 1, online_tlwhs, online_scores, online_ids))
                save_vis_results(data, frame_id, online_ids, online_tlwhs,
                                online_scores, timer.average_time, show_image,
                                save_dir, self.cfg.num_classes)

            elif isinstance(tracker, JDETracker):
                # trick hyperparams only used for MOTChallenge (MOT17, MOT20) Test-set
                tracker.track_buffer, tracker.conf_thres = get_trick_hyperparams(
                    seq_name, tracker.track_buffer, tracker.conf_thres)

                online_targets_dict = tracker.update(pred_dets_old, pred_embs)
                online_tlwhs = defaultdict(list)
                online_scores = defaultdict(list)
                online_ids = defaultdict(list)
                for cls_id in range(self.cfg.num_classes):
                    online_targets = online_targets_dict[cls_id]
                    for t in online_targets:
                        tlwh = t.tlwh
                        tid = t.track_id
                        tscore = t.score
                        if tlwh[2] * tlwh[3] <= tracker.min_box_area: continue
                        if tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
                                3] > tracker.vertical_ratio:
                            continue
                        online_tlwhs[cls_id].append(tlwh)
                        online_ids[cls_id].append(tid)
                        online_scores[cls_id].append(tscore)
                    # save results
                    results[cls_id].append(
                        (frame_id + 1, online_tlwhs[cls_id], online_scores[cls_id],
                        online_ids[cls_id]))
                timer.toc()
                save_vis_results(data, frame_id, online_ids, online_tlwhs,
                                online_scores, timer.average_time, show_image,
                                save_dir, self.cfg.num_classes)

            frame_id += 1

        return results, frame_id, timer.average_time, timer.calls

    def mot_evaluate(self,
                     data_root,
                     seqs,
                     output_dir,
                     data_type='mot',
                     model_type='JDE',
                     save_images=False,
                     save_videos=False,
                     show_image=False,
                     scaled=False,
                     det_results_dir=''):
        if not os.path.exists(output_dir): os.makedirs(output_dir)
        result_root = os.path.join(output_dir, 'mot_results')
        if not os.path.exists(result_root): os.makedirs(result_root)
        assert data_type in MOT_DATA_TYPE, \
            "data_type should be 'mot', 'mcmot' or 'kitti'"
        assert model_type in MOT_ARCH, \
            "model_type should be 'JDE', 'DeepSORT', 'FairMOT' or 'ByteTrack'"

        # run tracking
        n_frame = 0
        timer_avgs, timer_calls = [], []
        for seq in seqs:
            infer_dir = os.path.join(data_root, seq)
            if not os.path.exists(infer_dir) or not os.path.isdir(infer_dir):
                logger.warning("Seq {} error, {} has no images.".format(
                    seq, infer_dir))
                continue
            if os.path.exists(os.path.join(infer_dir, 'img1')):
                infer_dir = os.path.join(infer_dir, 'img1')

            frame_rate = 30
            seqinfo = os.path.join(data_root, seq, 'seqinfo.ini')
            if os.path.exists(seqinfo):
                meta_info = open(seqinfo).read()
                frame_rate = int(meta_info[meta_info.find('frameRate') + 10:
                                           meta_info.find('\nseqLength')])

            save_dir = os.path.join(output_dir, 'mot_outputs',
                                    seq) if save_images or save_videos else None
            logger.info('start seq: {}'.format(seq))

            self.dataset.set_images(self.get_infer_images(infer_dir))
            dataloader = create('EvalMOTReader')(self.dataset, 0)

            result_filename = os.path.join(result_root, '{}.txt'.format(seq))

            with paddle.no_grad():
                if model_type in MOT_ARCH_JDE:
                    results, nf, ta, tc = self._eval_seq_jde(
                        dataloader,
                        save_dir=save_dir,
                        show_image=show_image,
                        frame_rate=frame_rate)
                elif model_type in MOT_ARCH_SDE:
                    results, nf, ta, tc = self._eval_seq_sde(
                        dataloader,
                        save_dir=save_dir,
                        show_image=show_image,
                        frame_rate=frame_rate,
                        seq_name=seq,
                        scaled=scaled,
                        det_file=os.path.join(det_results_dir,
                                              '{}.txt'.format(seq)))
                else:
                    raise ValueError(model_type)

            write_mot_results(result_filename, results, data_type,
                              self.cfg.num_classes)
            n_frame += nf
            timer_avgs.append(ta)
            timer_calls.append(tc)

            if save_videos:
                output_video_path = os.path.join(save_dir, '..',
                                                 '{}_vis.mp4'.format(seq))
                cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg {}'.format(
                    save_dir, output_video_path)
                os.system(cmd_str)
                logger.info('Save video in {}.'.format(output_video_path))

            logger.info('Evaluate seq: {}'.format(seq))
            # update metrics
            for metric in self._metrics:
                metric.update(data_root, seq, data_type, result_root,
                              result_filename)

        timer_avgs = np.asarray(timer_avgs)
        timer_calls = np.asarray(timer_calls)
        all_time = np.dot(timer_avgs, timer_calls)
        avg_time = all_time / np.sum(timer_calls)
        logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(
            all_time, 1.0 / avg_time))

        # accumulate metric to log out
        for metric in self._metrics:
            metric.accumulate()
            metric.log()
        # reset metric states for metric may performed multiple times
        self._reset_metrics()

    def get_infer_images(self, infer_dir):
        assert infer_dir is None or os.path.isdir(infer_dir), \
            "{} is not a directory".format(infer_dir)
        images = set()
        assert os.path.isdir(infer_dir), \
            "infer_dir {} is not a directory".format(infer_dir)
        exts = ['jpg', 'jpeg', 'png', 'bmp']
        exts += [ext.upper() for ext in exts]
        for ext in exts:
            images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
        images = list(images)
        images.sort()
        assert len(images) > 0, "no image found in {}".format(infer_dir)
        logger.info("Found {} inference images in total.".format(len(images)))
        return images

    def mot_predict_seq(self,
                        video_file,
                        frame_rate,
                        image_dir,
                        output_dir,
                        data_type='mot',
                        model_type='JDE',
                        save_images=False,
                        save_videos=True,
                        show_image=False,
                        scaled=False,
                        det_results_dir='',
                        draw_threshold=0.5):
        assert video_file is not None or image_dir is not None, \
            "--video_file or --image_dir should be set."
        assert video_file is None or os.path.isfile(video_file), \
                "{} is not a file".format(video_file)
        assert image_dir is None or os.path.isdir(image_dir), \
                "{} is not a directory".format(image_dir)

        if not os.path.exists(output_dir): os.makedirs(output_dir)
        result_root = os.path.join(output_dir, 'mot_results')
        if not os.path.exists(result_root): os.makedirs(result_root)
        assert data_type in MOT_DATA_TYPE, \
            "data_type should be 'mot', 'mcmot' or 'kitti'"
        assert model_type in MOT_ARCH, \
            "model_type should be 'JDE', 'DeepSORT', 'FairMOT' or 'ByteTrack'"

        # run tracking        
        if video_file:
            seq = video_file.split('/')[-1].split('.')[0]
            self.dataset.set_video(video_file, frame_rate)
            logger.info('Starting tracking video {}'.format(video_file))
        elif image_dir:
            seq = image_dir.split('/')[-1].split('.')[0]
            if os.path.exists(os.path.join(image_dir, 'img1')):
                image_dir = os.path.join(image_dir, 'img1')
            images = [
                '{}/{}'.format(image_dir, x) for x in os.listdir(image_dir)
            ]
            images.sort()
            self.dataset.set_images(images)
            logger.info('Starting tracking folder {}, found {} images'.format(
                image_dir, len(images)))
        else:
            raise ValueError('--video_file or --image_dir should be set.')

        save_dir = os.path.join(output_dir, 'mot_outputs',
                                seq) if save_images or save_videos else None

        dataloader = create('TestMOTReader')(self.dataset, 0)
        result_filename = os.path.join(result_root, '{}.txt'.format(seq))
        if frame_rate == -1:
            frame_rate = self.dataset.frame_rate

        with paddle.no_grad():
            if model_type in MOT_ARCH_JDE:
                results, nf, ta, tc = self._eval_seq_jde(
                    dataloader,
                    save_dir=save_dir,
                    show_image=show_image,
                    frame_rate=frame_rate,
                    draw_threshold=draw_threshold)
            elif model_type in MOT_ARCH_SDE:
                results, nf, ta, tc = self._eval_seq_sde(
                    dataloader,
                    save_dir=save_dir,
                    show_image=show_image,
                    frame_rate=frame_rate,
                    seq_name=seq,
                    scaled=scaled,
                    det_file=os.path.join(det_results_dir,
                                          '{}.txt'.format(seq)),
                    draw_threshold=draw_threshold)
            else:
                raise ValueError(model_type)

        if save_videos:
            output_video_path = os.path.join(save_dir, '..',
                                             '{}_vis.mp4'.format(seq))
            cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg {}'.format(
                save_dir, output_video_path)
            os.system(cmd_str)
            logger.info('Save video in {}'.format(output_video_path))

        write_mot_results(result_filename, results, data_type,
                          self.cfg.num_classes)

def get_trick_hyperparams(video_name, ori_buffer, ori_thresh):
    if video_name[:3] != 'MOT':
        # only used for MOTChallenge (MOT17, MOT20) Test-set
        return ori_buffer, ori_thresh

    video_name = video_name[:8]
    if 'MOT17-05' in video_name:
        track_buffer = 14
    elif 'MOT17-13' in video_name:
        track_buffer = 25
    else:
        track_buffer = ori_buffer

    if 'MOT17-01' in video_name:
        track_thresh = 0.65
    elif 'MOT17-06' in video_name:
        track_thresh = 0.65
    elif 'MOT17-12' in video_name:
        track_thresh = 0.7
    elif 'MOT17-14' in video_name:
        track_thresh = 0.67
    else:
        track_thresh = ori_thresh

    if 'MOT20-06' in video_name or 'MOT20-08' in video_name:
        track_thresh = 0.3
    else:
        track_thresh = ori_thresh
    
    return track_buffer, ori_thresh