tracker.py

# 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 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.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__)

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

    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,
                      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

        timer = MOTTimer()
        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)))

            ori_image = data['ori_image']
            input_shape = data['image'].shape[2:]
            im_shape = data['im_shape']
            scale_factor = data['scale_factor']

            # forward
            timer.tic()
            if not use_detector:
                dets = dets_list[frame_id]
                bbox_tlwh = paddle.to_tensor(dets['bbox'], dtype='float32')
                if bbox_tlwh.shape[0] > 0:
                    # detector outputs: pred_cls_ids, pred_scores, pred_bboxes
                    pred_cls_ids = paddle.to_tensor(
                        dets['cls_id'], dtype='float32').unsqueeze(1)
                    pred_scores = paddle.to_tensor(
                        dets['score'], dtype='float32').unsqueeze(1)
                    pred_bboxes = paddle.concat(
                        (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))
                    frame_id += 1
                    continue
            else:
                outs = self.model.detector(data)
                if outs['bbox_num'] > 0:
                    # 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:
                        # 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:]
                else:
                    logger.warning(
                        'Frame {} has not object, try to modify score threshold.'.
                        format(frame_id))
                    frame_id += 1
                    continue

            pred_xyxys, keep_idx = clip_box(pred_bboxes, input_shape, im_shape,
                                            scale_factor)
            pred_scores = paddle.gather_nd(pred_scores, keep_idx).unsqueeze(1)
            pred_cls_ids = paddle.gather_nd(pred_cls_ids, keep_idx).unsqueeze(1)
            pred_tlwhs = paddle.concat(
                (pred_xyxys[:, 0:2],
                 pred_xyxys[:, 2:4] - pred_xyxys[:, 0:2] + 1),
                axis=1)
            pred_dets = paddle.concat(
                (pred_tlwhs, pred_scores, pred_cls_ids), axis=1)

            tracker = self.model.tracker
            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)

            tracker.predict()
            online_targets = tracker.update(pred_dets, pred_embs)

            online_tlwhs, online_scores, online_ids = [], [], []
            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.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)
            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', 'mcmot', 'kitti'], \
            "data_type should be 'mot', 'mcmot' 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:
            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 ['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,
                        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(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', 'mcmot', 'kitti'], \
            "data_type should be 'mot', 'mcmot' or 'kitti'"
        assert model_type in ['JDE', 'DeepSORT', 'FairMOT'], \
            "model_type should be 'JDE', 'DeepSORT' or 'FairMOT'"

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

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

        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))