# 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 paddle
import numpy as np

from ppdet.core.workspace import create
from ppdet.utils.checkpoint import load_weight

from ppdet.modeling.mot.utils import Timer, load_det_results
from ppdet.modeling.mot import visualization as mot_vis

from ppdet.metrics import Metric, MOTMetric

from .callbacks import Callback, ComposeCallback

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

__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(), ]
        else:
            logger.warn("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):
        if self.model.detector:
            load_weight(self.model.detector, det_weights, self.optimizer)
        load_weight(self.model.reid, reid_weights, self.optimizer)

    def _eval_seq_jde(self,
                      dataloader,
                      save_dir=None,
                      show_image=False,
                      frame_rate=30):
        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 = Timer()
        results = []
        frame_id = 0
        self.status['mode'] = 'track'
        self.model.eval()
        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()
            online_targets = self.model(data)

            online_tlwhs, online_ids = [], []
            for t in online_targets:
                tlwh = t.tlwh
                tid = t.track_id
                vertical = tlwh[2] / tlwh[3] > 1.6
                if tlwh[2] * tlwh[3] > tracker.min_box_area and not vertical:
                    online_tlwhs.append(tlwh)
                    online_ids.append(tid)
            timer.toc()

            # save results
            results.append((frame_id + 1, online_tlwhs, online_ids))
            self.save_results(data, frame_id, online_ids, online_tlwhs,
                              timer.average_time, show_image, save_dir)
            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,
                      det_file=''):
        if save_dir:
            if not os.path.exists(save_dir): os.makedirs(save_dir)
        tracker = self.model.tracker
        use_detector = False if not self.model.detector else True

        timer = Timer()
        results = []
        frame_id = 0
        self.status['mode'] = 'track'
        self.model.eval()
        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))

        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)))

            timer.tic()
            if not use_detector:
                timer.tic()
                dets = dets_list[frame_id]
                bbox_tlwh = paddle.to_tensor(dets['bbox'], dtype='float32')
                pred_scores = paddle.to_tensor(dets['score'], dtype='float32')
                if bbox_tlwh.shape[0] > 0:
                    pred_bboxes = paddle.concat(
                        (bbox_tlwh[:, 0:2],
                         bbox_tlwh[:, 2:4] + bbox_tlwh[:, 0:2]),
                        axis=1)
                else:
                    pred_bboxes = []
                    pred_scores = []
                data.update({
                    'pred_bboxes': pred_bboxes,
                    'pred_scores': pred_scores
                })

            # forward
            timer.tic()
            online_targets = self.model(data)

            online_tlwhs = []
            online_ids = []
            for track in online_targets:
                if not track.is_confirmed() or track.time_since_update > 1:
                    continue
                tlwh = track.to_tlwh()
                track_id = track.track_id
                online_tlwhs.append(tlwh)
                online_ids.append(track_id)
            timer.toc()

            # save results
            results.append((frame_id + 1, online_tlwhs, online_ids))
            self.save_results(data, frame_id, online_ids, online_tlwhs,
                              timer.average_time, show_image, save_dir)
            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,
                     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', 'kitti'], \
            "data_type should be 'mot' or 'kitti'"
        assert model_type in ['JDE', 'DeepSORT', 'FairMOT'], \
            "model_type should be 'JDE', 'DeepSORT' or 'FairMOT'"

        # run tracking
        n_frame = 0
        timer_avgs, timer_calls = [], []
        for seq in seqs:
            save_dir = os.path.join(output_dir, 'mot_outputs',
                                    seq) if save_images or save_videos else None
            logger.info('start seq: {}'.format(seq))

            infer_dir = os.path.join(data_root, seq, 'img1')
            images = self.get_infer_images(infer_dir)
            self.dataset.set_images(images)

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

            result_filename = os.path.join(result_root, '{}.txt'.format(seq))
            meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read()
            frame_rate = int(meta_info[meta_info.find('frameRate') + 10:
                                       meta_info.find('\nseqLength')])

            if model_type in ['JDE', 'FairMOT']:
                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 ['DeepSORT']:
                results, nf, ta, tc = self._eval_seq_sde(
                    dataloader,
                    save_dir=save_dir,
                    show_image=show_image,
                    frame_rate=frame_rate,
                    det_file=os.path.join(det_results_dir,
                                          '{}.txt'.format(seq)))
            else:
                raise ValueError(model_type)

            self.write_mot_results(result_filename, results, data_type)
            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(self,
                    video_file,
                    output_dir,
                    data_type='mot',
                    model_type='JDE',
                    save_images=False,
                    save_videos=True,
                    show_image=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', 'kitti'], \
            "data_type should be 'mot' or 'kitti'"
        assert model_type in ['JDE', 'DeepSORT', 'FairMOT'], \
            "model_type should be 'JDE', 'DeepSORT' or 'FairMOT'"

        # run tracking
        seq = video_file.split('/')[-1].split('.')[0]
        save_dir = os.path.join(output_dir, 'mot_outputs',
                                seq) if save_images or save_videos else None
        logger.info('Starting tracking {}'.format(video_file))

        self.dataset.set_video(video_file)
        dataloader = create('TestMOTReader')(self.dataset, 0)
        result_filename = os.path.join(result_root, '{}.txt'.format(seq))
        frame_rate = self.dataset.frame_rate

        if model_type in ['JDE', 'FairMOT']:
            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 ['DeepSORT']:
            results, nf, ta, tc = self._eval_seq_sde(
                dataloader,
                save_dir=save_dir,
                show_image=show_image,
                frame_rate=frame_rate,
                det_file=os.path.join(det_results_dir, '{}.txt'.format(seq)))
        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))

    def write_mot_results(self, filename, results, data_type='mot'):
        if data_type in ['mot', 'mcmot', 'lab']:
            save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
        elif data_type == 'kitti':
            save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
        else:
            raise ValueError(data_type)

        with open(filename, 'w') as f:
            for frame_id, tlwhs, track_ids in results:
                if data_type == 'kitti':
                    frame_id -= 1
                for tlwh, track_id in zip(tlwhs, track_ids):
                    if track_id < 0:
                        continue
                    x1, y1, w, h = tlwh
                    x2, y2 = x1 + w, y1 + h
                    line = save_format.format(
                        frame=frame_id,
                        id=track_id,
                        x1=x1,
                        y1=y1,
                        x2=x2,
                        y2=y2,
                        w=w,
                        h=h)
                    f.write(line)
        logger.info('MOT results save in {}'.format(filename))

    def save_results(self, data, frame_id, online_ids, online_tlwhs,
                     average_time, show_image, save_dir):
        if show_image or save_dir is not None:
            assert 'ori_image' in data
            img0 = data['ori_image'].numpy()[0]
            online_im = mot_vis.plot_tracking(
                img0,
                online_tlwhs,
                online_ids,
                frame_id=frame_id,
                fps=1. / average_time)
        if show_image:
            cv2.imshow('online_im', online_im)
        if save_dir is not None:
            cv2.imwrite(
                os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)),
                online_im)