pipeline.py

# Copyright (c) 2022 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.

import os
import yaml
import glob
from collections import defaultdict

import cv2
import numpy as np
import math
import paddle
import sys
import copy
from collections import Sequence
from reid import ReID
from datacollector import DataCollector, Result
from mtmct import mtmct_process

# add deploy path of PadleDetection to sys.path
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
sys.path.insert(0, parent_path)

from python.infer import Detector, DetectorPicoDet
from python.attr_infer import AttrDetector
from python.keypoint_infer import KeyPointDetector
from python.keypoint_postprocess import translate_to_ori_images
from python.action_infer import FallingRecognizer
from python.action_utils import KeyPointBuff, FallingVisualHelper

from pipe_utils import argsparser, print_arguments, merge_cfg, PipeTimer
from pipe_utils import get_test_images, crop_image_with_det, crop_image_with_mot, parse_mot_res, parse_mot_keypoint
from python.preprocess import decode_image
from python.visualize import visualize_box_mask, visualize_attr, visualize_pose, visualize_action

from pptracking.python.mot_sde_infer import SDE_Detector
from pptracking.python.mot.visualize import plot_tracking_dict
from pptracking.python.mot.utils import flow_statistic


class Pipeline(object):
    """
    Pipeline

    Args:
        cfg (dict): config of models in pipeline
        image_file (string|None): the path of image file, default as None
        image_dir (string|None): the path of image directory, if not None, 
            then all the images in directory will be predicted, default as None
        video_file (string|None): the path of video file, default as None
        camera_id (int): the device id of camera to predict, default as -1
        enable_attr (bool): whether use attribute recognition, default as false
        enable_falling (bool): whether use action recognition, default as false
        device (string): the device to predict, options are: CPU/GPU/XPU, 
            default as CPU
        run_mode (string): the mode of prediction, options are: 
            paddle/trt_fp32/trt_fp16, default as paddle
        trt_min_shape (int): min shape for dynamic shape in trt, default as 1
        trt_max_shape (int): max shape for dynamic shape in trt, default as 1280
        trt_opt_shape (int): opt shape for dynamic shape in trt, default as 640
        trt_calib_mode (bool): If the model is produced by TRT offline quantitative
            calibration, trt_calib_mode need to set True. default as False
        cpu_threads (int): cpu threads, default as 1
        enable_mkldnn (bool): whether to open MKLDNN, default as False
        output_dir (string): The path of output, default as 'output'
        draw_center_traj (bool): Whether drawing the trajectory of center, default as False
        secs_interval (int): The seconds interval to count after tracking, default as 10
        do_entrance_counting(bool): Whether counting the numbers of identifiers entering 
            or getting out from the entrance, default as False，only support single class
            counting in MOT.
    """

    def __init__(self,
                 cfg,
                 image_file=None,
                 image_dir=None,
                 video_file=None,
                 video_dir=None,
                 camera_id=-1,
                 enable_attr=False,
                 enable_falling=False,
                 enable_mtmct=False,
                 device='CPU',
                 run_mode='paddle',
                 trt_min_shape=1,
                 trt_max_shape=1280,
                 trt_opt_shape=640,
                 trt_calib_mode=False,
                 cpu_threads=1,
                 enable_mkldnn=False,
                 output_dir='output',
                 draw_center_traj=False,
                 secs_interval=10,
                 do_entrance_counting=False):
        self.multi_camera = False
        self.enable_mtmct = enable_mtmct
        self.is_video = False
        self.output_dir = output_dir
        self.vis_result = cfg['visual']
        self.input = self._parse_input(image_file, image_dir, video_file,
                                       video_dir, camera_id)
        if self.multi_camera:
            self.predictor = []
            for name in self.input:
                predictor_item = PipePredictor(
                    cfg,
                    is_video=True,
                    multi_camera=True,
                    enable_attr=enable_attr,
                    enable_falling=enable_falling,
                    enable_mtmct=enable_mtmct,
                    device=device,
                    run_mode=run_mode,
                    trt_min_shape=trt_min_shape,
                    trt_max_shape=trt_max_shape,
                    trt_opt_shape=trt_opt_shape,
                    cpu_threads=cpu_threads,
                    enable_mkldnn=enable_mkldnn,
                    output_dir=output_dir)
                predictor_item.set_file_name(name)
                self.predictor.append(predictor_item)

        else:
            self.predictor = PipePredictor(
                cfg,
                self.is_video,
                enable_attr=enable_attr,
                enable_falling=enable_falling,
                enable_mtmct=enable_mtmct,
                device=device,
                run_mode=run_mode,
                trt_min_shape=trt_min_shape,
                trt_max_shape=trt_max_shape,
                trt_opt_shape=trt_opt_shape,
                trt_calib_mode=trt_calib_mode,
                cpu_threads=cpu_threads,
                enable_mkldnn=enable_mkldnn,
                output_dir=output_dir,
                draw_center_traj=draw_center_traj,
                secs_interval=secs_interval,
                do_entrance_counting=do_entrance_counting)
            if self.is_video:
                self.predictor.set_file_name(video_file)

        self.output_dir = output_dir
        self.draw_center_traj = draw_center_traj
        self.secs_interval = secs_interval
        self.do_entrance_counting = do_entrance_counting

    def _parse_input(self, image_file, image_dir, video_file, video_dir,
                     camera_id):

        # parse input as is_video and multi_camera

        if image_file is not None or image_dir is not None:
            input = get_test_images(image_dir, image_file)
            self.is_video = False
            self.multi_camera = False

        elif video_file is not None:
            assert os.path.exists(video_file), "video_file not exists."
            self.multi_camera = False
            input = video_file
            self.is_video = True

        elif video_dir is not None:
            videof = [os.path.join(video_dir, x) for x in os.listdir(video_dir)]
            if len(videof) > 1:
                self.multi_camera = True
                videof.sort()
                input = videof
            else:
                input = videof[0]
            self.is_video = True

        elif camera_id != -1:
            self.multi_camera = False
            input = camera_id
            self.is_video = True

        else:
            raise ValueError(
                "Illegal Input, please set one of ['video_file'，'camera_id'，'image_file', 'image_dir']"
            )

        return input

    def run(self):
        if self.multi_camera:
            multi_res = []
            for predictor, input in zip(self.predictor, self.input):
                predictor.run(input)
                collector_data = predictor.get_result()
                multi_res.append(collector_data)
            if self.enable_mtmct:
                mtmct_process(
                    multi_res,
                    self.input,
                    mtmct_vis=self.vis_result,
                    output_dir=self.output_dir)

        else:
            self.predictor.run(self.input)


class PipePredictor(object):
    """
    Predictor in single camera
    
    The pipeline for image input: 

        1. Detection
        2. Detection -> Attribute

    The pipeline for video input: 

        1. Tracking
        2. Tracking -> Attribute
        3. Tracking -> KeyPoint -> Falling Recognition

    Args:
        cfg (dict): config of models in pipeline
        is_video (bool): whether the input is video, default as False
        multi_camera (bool): whether to use multi camera in pipeline, 
            default as False
        camera_id (int): the device id of camera to predict, default as -1
        enable_attr (bool): whether use attribute recognition, default as false
        enable_falling (bool): whether use action recognition, default as false
        device (string): the device to predict, options are: CPU/GPU/XPU, 
            default as CPU
        run_mode (string): the mode of prediction, options are: 
            paddle/trt_fp32/trt_fp16, default as paddle
        trt_min_shape (int): min shape for dynamic shape in trt, default as 1
        trt_max_shape (int): max shape for dynamic shape in trt, default as 1280
        trt_opt_shape (int): opt shape for dynamic shape in trt, default as 640
        trt_calib_mode (bool): If the model is produced by TRT offline quantitative
            calibration, trt_calib_mode need to set True. default as False
        cpu_threads (int): cpu threads, default as 1
        enable_mkldnn (bool): whether to open MKLDNN, default as False
        output_dir (string): The path of output, default as 'output'
        draw_center_traj (bool): Whether drawing the trajectory of center, default as False
        secs_interval (int): The seconds interval to count after tracking, default as 10
        do_entrance_counting(bool): Whether counting the numbers of identifiers entering 
            or getting out from the entrance, default as False，only support single class
            counting in MOT.
    """

    def __init__(self,
                 cfg,
                 is_video=True,
                 multi_camera=False,
                 enable_attr=False,
                 enable_falling=False,
                 enable_mtmct=False,
                 device='CPU',
                 run_mode='paddle',
                 trt_min_shape=1,
                 trt_max_shape=1280,
                 trt_opt_shape=640,
                 trt_calib_mode=False,
                 cpu_threads=1,
                 enable_mkldnn=False,
                 output_dir='output',
                 draw_center_traj=False,
                 secs_interval=10,
                 do_entrance_counting=False):

        if enable_attr and not cfg.get('ATTR', False):
            ValueError(
                'enable_attr is set to True, please set ATTR in config file')
        if enable_falling and (not cfg.get('FALLING', False) or
                               not cfg.get('KPT', False)):
            ValueError(
                'enable_falling is set to True, please set KPT and FALLING in config file'
            )

        self.with_attr = cfg.get('ATTR', False) and enable_attr
        self.with_falling = cfg.get('FALLING', False) and enable_falling
        self.with_mtmct = cfg.get('REID', False) and enable_mtmct
        if self.with_attr:
            print('Attribute Recognition enabled')
        if self.with_falling:
            print('Falling Recognition enabled')
        if enable_mtmct:
            if not self.with_mtmct:
                print(
                    'Warning!!! MTMCT enabled, but cannot find REID config in [infer_cfg.yml], please check!'
                )
            else:
                print("MTMCT enabled")

        self.modebase = {
            "framebased": False,
            "videobased": False,
            "idbased": False,
            "skeletonbased": False
        }
        self.is_video = is_video
        self.multi_camera = multi_camera
        self.cfg = cfg
        self.output_dir = output_dir
        self.draw_center_traj = draw_center_traj
        self.secs_interval = secs_interval
        self.do_entrance_counting = do_entrance_counting

        self.warmup_frame = self.cfg['warmup_frame']
        self.pipeline_res = Result()
        self.pipe_timer = PipeTimer()
        self.file_name = None
        self.collector = DataCollector()

        if not is_video:
            det_cfg = self.cfg['DET']
            model_dir = det_cfg['model_dir']
            batch_size = det_cfg['batch_size']
            self.det_predictor = Detector(
                model_dir, device, run_mode, batch_size, trt_min_shape,
                trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
                enable_mkldnn)
            if self.with_attr:
                attr_cfg = self.cfg['ATTR']
                model_dir = attr_cfg['model_dir']
                batch_size = attr_cfg['batch_size']
                basemode = attr_cfg['basemode']
                self.modebase[basemode] = True
                self.attr_predictor = AttrDetector(
                    model_dir, device, run_mode, batch_size, trt_min_shape,
                    trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
                    enable_mkldnn)

        else:
            mot_cfg = self.cfg['MOT']
            model_dir = mot_cfg['model_dir']
            tracker_config = mot_cfg['tracker_config']
            batch_size = mot_cfg['batch_size']
            basemode = mot_cfg['basemode']
            self.modebase[basemode] = True
            self.mot_predictor = SDE_Detector(
                model_dir,
                tracker_config,
                device,
                run_mode,
                batch_size,
                trt_min_shape,
                trt_max_shape,
                trt_opt_shape,
                trt_calib_mode,
                cpu_threads,
                enable_mkldnn,
                draw_center_traj=draw_center_traj,
                secs_interval=secs_interval,
                do_entrance_counting=do_entrance_counting)
            if self.with_attr:
                attr_cfg = self.cfg['ATTR']
                model_dir = attr_cfg['model_dir']
                batch_size = attr_cfg['batch_size']
                basemode = attr_cfg['basemode']
                self.modebase[basemode] = True
                self.attr_predictor = AttrDetector(
                    model_dir, device, run_mode, batch_size, trt_min_shape,
                    trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
                    enable_mkldnn)
            if self.with_falling:
                falling_cfg = self.cfg['FALLING']
                falling_model_dir = falling_cfg['model_dir']
                falling_batch_size = falling_cfg['batch_size']
                falling_frames = falling_cfg['max_frames']
                display_frames = falling_cfg['display_frames']
                self.coord_size = falling_cfg['coord_size']
                basemode = falling_cfg['basemode']
                self.modebase[basemode] = True

                self.falling_predictor = FallingRecognizer(
                    falling_model_dir,
                    device,
                    run_mode,
                    falling_batch_size,
                    trt_min_shape,
                    trt_max_shape,
                    trt_opt_shape,
                    trt_calib_mode,
                    cpu_threads,
                    enable_mkldnn,
                    window_size=falling_frames)
                self.falling_visual_helper = FallingVisualHelper(display_frames)

                if self.modebase["skeletonbased"]:
                    kpt_cfg = self.cfg['KPT']
                    kpt_model_dir = kpt_cfg['model_dir']
                    kpt_batch_size = kpt_cfg['batch_size']
                    self.kpt_predictor = KeyPointDetector(
                        kpt_model_dir,
                        device,
                        run_mode,
                        kpt_batch_size,
                        trt_min_shape,
                        trt_max_shape,
                        trt_opt_shape,
                        trt_calib_mode,
                        cpu_threads,
                        enable_mkldnn,
                        use_dark=False)
                    self.kpt_buff = KeyPointBuff(falling_frames)

        if self.with_mtmct:
            reid_cfg = self.cfg['REID']
            model_dir = reid_cfg['model_dir']
            batch_size = reid_cfg['batch_size']
            self.reid_predictor = ReID(model_dir, device, run_mode, batch_size,
                                       trt_min_shape, trt_max_shape,
                                       trt_opt_shape, trt_calib_mode,
                                       cpu_threads, enable_mkldnn)

    def set_file_name(self, path):
        if path is not None:
            self.file_name = os.path.split(path)[-1]
        else:
            # use camera id
            self.file_name = None

    def get_result(self):
        return self.collector.get_res()

    def run(self, input):
        if self.is_video:
            self.predict_video(input)
        else:
            self.predict_image(input)
        self.pipe_timer.info()

    def predict_image(self, input):
        # det
        # det -> attr
        batch_loop_cnt = math.ceil(
            float(len(input)) / self.det_predictor.batch_size)
        for i in range(batch_loop_cnt):
            start_index = i * self.det_predictor.batch_size
            end_index = min((i + 1) * self.det_predictor.batch_size, len(input))
            batch_file = input[start_index:end_index]
            batch_input = [decode_image(f, {})[0] for f in batch_file]

            if i > self.warmup_frame:
                self.pipe_timer.total_time.start()
                self.pipe_timer.module_time['det'].start()
            # det output format: class, score, xmin, ymin, xmax, ymax
            det_res = self.det_predictor.predict_image(
                batch_input, visual=False)
            det_res = self.det_predictor.filter_box(det_res,
                                                    self.cfg['crop_thresh'])
            if i > self.warmup_frame:
                self.pipe_timer.module_time['det'].end()
            self.pipeline_res.update(det_res, 'det')

            if self.with_attr:
                crop_inputs = crop_image_with_det(batch_input, det_res)
                attr_res_list = []

                if i > self.warmup_frame:
                    self.pipe_timer.module_time['attr'].start()

                for crop_input in crop_inputs:
                    attr_res = self.attr_predictor.predict_image(
                        crop_input, visual=False)
                    attr_res_list.extend(attr_res['output'])

                if i > self.warmup_frame:
                    self.pipe_timer.module_time['attr'].end()

                attr_res = {'output': attr_res_list}
                self.pipeline_res.update(attr_res, 'attr')

            self.pipe_timer.img_num += len(batch_input)
            if i > self.warmup_frame:
                self.pipe_timer.total_time.end()

            if self.cfg['visual']:
                self.visualize_image(batch_file, batch_input, self.pipeline_res)

    def predict_video(self, video_file):
        # mot
        # mot -> attr
        # mot -> pose -> action
        capture = cv2.VideoCapture(video_file)
        video_out_name = 'output.mp4' if self.file_name is None else self.file_name

        # Get Video info : resolution, fps, frame count
        width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
        fps = int(capture.get(cv2.CAP_PROP_FPS))
        frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
        print("video fps: %d, frame_count: %d" % (fps, frame_count))

        if not os.path.exists(self.output_dir):
            os.makedirs(self.output_dir)
        out_path = os.path.join(self.output_dir, video_out_name)
        fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
        writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
        frame_id = 0

        entrance, records, center_traj = None, None, None
        if self.draw_center_traj:
            center_traj = [{}]
        id_set = set()
        interval_id_set = set()
        in_id_list = list()
        out_id_list = list()
        prev_center = dict()
        records = list()
        entrance = [0, height / 2., width, height / 2.]
        video_fps = fps

        while (1):
            if frame_id % 10 == 0:
                print('frame id: ', frame_id)
            ret, frame = capture.read()
            if not ret:
                break

            if self.modebase["idbased"] or self.modebase["skeletonbased"]:
                if frame_id > self.warmup_frame:
                    self.pipe_timer.total_time.start()
                    self.pipe_timer.module_time['mot'].start()
                res = self.mot_predictor.predict_image(
                    [copy.deepcopy(frame)], visual=False)

                if frame_id > self.warmup_frame:
                    self.pipe_timer.module_time['mot'].end()

                # mot output format: id, class, score, xmin, ymin, xmax, ymax
                mot_res = parse_mot_res(res)

                # flow_statistic only support single class MOT
                boxes, scores, ids = res[0]  # batch size = 1 in MOT
                mot_result = (frame_id + 1, boxes[0], scores[0],
                              ids[0])  # single class
                statistic = flow_statistic(
                    mot_result, self.secs_interval, self.do_entrance_counting,
                    video_fps, entrance, id_set, interval_id_set, in_id_list,
                    out_id_list, prev_center, records)
                records = statistic['records']

                # nothing detected
                if len(mot_res['boxes']) == 0:
                    frame_id += 1
                    if frame_id > self.warmup_frame:
                        self.pipe_timer.img_num += 1
                        self.pipe_timer.total_time.end()
                    if self.cfg['visual']:
                        _, _, fps = self.pipe_timer.get_total_time()
                        im = self.visualize_video(frame, mot_res, frame_id, fps,
                                                  entrance, records,
                                                  center_traj)  # visualize
                        writer.write(im)
                        if self.file_name is None:  # use camera_id
                            cv2.imshow('PPHuman', im)
                            if cv2.waitKey(1) & 0xFF == ord('q'):
                                break

                    continue

                self.pipeline_res.update(mot_res, 'mot')
                if self.with_attr or self.with_falling:
                    crop_input, new_bboxes, ori_bboxes = crop_image_with_mot(
                        frame, mot_res)

                if self.with_attr:
                    if frame_id > self.warmup_frame:
                        self.pipe_timer.module_time['attr'].start()
                    attr_res = self.attr_predictor.predict_image(
                        crop_input, visual=False)
                    if frame_id > self.warmup_frame:
                        self.pipe_timer.module_time['attr'].end()
                    self.pipeline_res.update(attr_res, 'attr')

                if self.with_falling:
                    if self.modebase["skeletonbased"]:
                        if frame_id > self.warmup_frame:
                            self.pipe_timer.module_time['kpt'].start()
                        kpt_pred = self.kpt_predictor.predict_image(
                            crop_input, visual=False)
                        keypoint_vector, score_vector = translate_to_ori_images(
                            kpt_pred, np.array(new_bboxes))
                        kpt_res = {}
                        kpt_res['keypoint'] = [
                            keypoint_vector.tolist(), score_vector.tolist()
                        ] if len(keypoint_vector) > 0 else [[], []]
                        kpt_res['bbox'] = ori_bboxes
                        if frame_id > self.warmup_frame:
                            self.pipe_timer.module_time['kpt'].end()

                        self.pipeline_res.update(kpt_res, 'kpt')

                        self.kpt_buff.update(kpt_res,
                                             mot_res)  # collect kpt output
                    state = self.kpt_buff.get_state(
                    )  # whether frame num is enough or lost tracker

                    falling_res = {}
                    if state:
                        if frame_id > self.warmup_frame:
                            self.pipe_timer.module_time['falling'].start()
                        collected_keypoint = self.kpt_buff.get_collected_keypoint(
                        )  # reoragnize kpt output with ID
                        falling_input = parse_mot_keypoint(collected_keypoint,
                                                           self.coord_size)
                        falling_res = self.falling_predictor.predict_skeleton_with_mot(
                            falling_input)
                        if frame_id > self.warmup_frame:
                            self.pipe_timer.module_time['falling'].end()
                        self.pipeline_res.update(falling_res, 'falling')

                    if self.cfg['visual']:
                        self.falling_visual_helper.update(falling_res)

                if self.with_mtmct and frame_id % 10 == 0:
                    crop_input, img_qualities, rects = self.reid_predictor.crop_image_with_mot(
                        frame, mot_res)
                    if frame_id > self.warmup_frame:
                        self.pipe_timer.module_time['reid'].start()
                    reid_res = self.reid_predictor.predict_batch(crop_input)

                    if frame_id > self.warmup_frame:
                        self.pipe_timer.module_time['reid'].end()

                    reid_res_dict = {
                        'features': reid_res,
                        "qualities": img_qualities,
                        "rects": rects
                    }
                    self.pipeline_res.update(reid_res_dict, 'reid')
                else:
                    self.pipeline_res.clear('reid')

            if self.modebase["videobased"]:
                pass

            if self.modebase["framebased"]:
                pass

            self.collector.append(frame_id, self.pipeline_res)

            if frame_id > self.warmup_frame:
                self.pipe_timer.img_num += 1
                self.pipe_timer.total_time.end()
            frame_id += 1

            if self.cfg['visual']:
                _, _, fps = self.pipe_timer.get_total_time()
                im = self.visualize_video(frame, self.pipeline_res, frame_id,
                                          fps, entrance, records,
                                          center_traj)  # visualize
                writer.write(im)
                if self.file_name is None:  # use camera_id
                    cv2.imshow('PPHuman', im)
                    if cv2.waitKey(1) & 0xFF == ord('q'):
                        break

        writer.release()
        print('save result to {}'.format(out_path))

    def visualize_video(self,
                        image,
                        result,
                        frame_id,
                        fps,
                        entrance=None,
                        records=None,
                        center_traj=None):
        mot_res = copy.deepcopy(result.get('mot'))
        if mot_res is not None:
            ids = mot_res['boxes'][:, 0]
            scores = mot_res['boxes'][:, 2]
            boxes = mot_res['boxes'][:, 3:]
            boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
            boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
        else:
            boxes = np.zeros([0, 4])
            ids = np.zeros([0])
            scores = np.zeros([0])

        # single class, still need to be defaultdict type for ploting
        num_classes = 1
        online_tlwhs = defaultdict(list)
        online_scores = defaultdict(list)
        online_ids = defaultdict(list)
        online_tlwhs[0] = boxes
        online_scores[0] = scores
        online_ids[0] = ids

        image = plot_tracking_dict(
            image,
            num_classes,
            online_tlwhs,
            online_ids,
            online_scores,
            frame_id=frame_id,
            fps=fps,
            do_entrance_counting=self.do_entrance_counting,
            entrance=entrance,
            records=records,
            center_traj=center_traj)

        attr_res = result.get('attr')
        if attr_res is not None:
            boxes = mot_res['boxes'][:, 1:]
            attr_res = attr_res['output']
            image = visualize_attr(image, attr_res, boxes)
            image = np.array(image)

        kpt_res = result.get('kpt')
        if kpt_res is not None:
            image = visualize_pose(
                image,
                kpt_res,
                visual_thresh=self.cfg['kpt_thresh'],
                returnimg=True)

        falling_res = result.get('falling')
        if falling_res is not None:
            image = visualize_action(image, mot_res['boxes'],
                                     self.falling_visual_helper, "Falling")

        return image

    def visualize_image(self, im_files, images, result):
        start_idx, boxes_num_i = 0, 0
        det_res = result.get('det')
        attr_res = result.get('attr')
        for i, (im_file, im) in enumerate(zip(im_files, images)):
            if det_res is not None:
                det_res_i = {}
                boxes_num_i = det_res['boxes_num'][i]
                det_res_i['boxes'] = det_res['boxes'][start_idx:start_idx +
                                                      boxes_num_i, :]
                im = visualize_box_mask(
                    im,
                    det_res_i,
                    labels=['person'],
                    threshold=self.cfg['crop_thresh'])
                im = np.ascontiguousarray(np.copy(im))
                im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
            if attr_res is not None:
                attr_res_i = attr_res['output'][start_idx:start_idx +
                                                boxes_num_i]
                im = visualize_attr(im, attr_res_i, det_res_i['boxes'])
            img_name = os.path.split(im_file)[-1]
            if not os.path.exists(self.output_dir):
                os.makedirs(self.output_dir)
            out_path = os.path.join(self.output_dir, img_name)
            cv2.imwrite(out_path, im)
            print("save result to: " + out_path)
            start_idx += boxes_num_i


def main():
    cfg = merge_cfg(FLAGS)
    print_arguments(cfg)
    pipeline = Pipeline(
        cfg, FLAGS.image_file, FLAGS.image_dir, FLAGS.video_file,
        FLAGS.video_dir, FLAGS.camera_id, FLAGS.enable_attr,
        FLAGS.enable_falling, FLAGS.enable_mtmct, FLAGS.device, FLAGS.run_mode,
        FLAGS.trt_min_shape, FLAGS.trt_max_shape, FLAGS.trt_opt_shape,
        FLAGS.trt_calib_mode, FLAGS.cpu_threads, FLAGS.enable_mkldnn,
        FLAGS.output_dir, FLAGS.draw_center_traj, FLAGS.secs_interval,
        FLAGS.do_entrance_counting)

    pipeline.run()


if __name__ == '__main__':
    paddle.enable_static()
    parser = argsparser()
    FLAGS = parser.parse_args()
    FLAGS.device = FLAGS.device.upper()
    assert FLAGS.device in ['CPU', 'GPU', 'XPU'
                            ], "device should be CPU, GPU or XPU"

    main()