mot_sde_infer.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.

import os
import time
import yaml
import cv2
import numpy as np
from collections import defaultdict

import paddle
from paddle.inference import Config
from paddle.inference import create_predictor

from picodet_postprocess import PicoDetPostProcess
from utils import argsparser, Timer, get_current_memory_mb
from det_infer import Detector, DetectorPicoDet, get_test_images, print_arguments, PredictConfig
from det_infer import load_predictor
from benchmark_utils import PaddleInferBenchmark
from visualize import plot_tracking

from mot.tracker import DeepSORTTracker
from mot.utils import MOTTimer, write_mot_results

# Global dictionary
MOT_SUPPORT_MODELS = {'DeepSORT'}


def bench_log(detector, img_list, model_info, batch_size=1, name=None):
    mems = {
        'cpu_rss_mb': detector.cpu_mem / len(img_list),
        'gpu_rss_mb': detector.gpu_mem / len(img_list),
        'gpu_util': detector.gpu_util * 100 / len(img_list)
    }
    perf_info = detector.det_times.report(average=True)
    data_info = {
        'batch_size': batch_size,
        'shape': "dynamic_shape",
        'data_num': perf_info['img_num']
    }
    log = PaddleInferBenchmark(detector.config, model_info, data_info,
                               perf_info, mems)
    log(name)


def scale_coords(coords, input_shape, im_shape, scale_factor):
    im_shape = im_shape[0]
    ratio = scale_factor[0][0]
    pad_w = (input_shape[1] - int(im_shape[1])) / 2
    pad_h = (input_shape[0] - int(im_shape[0])) / 2
    coords[:, 0::2] -= pad_w
    coords[:, 1::2] -= pad_h
    coords[:, 0:4] /= ratio
    coords[:, :4] = np.clip(coords[:, :4], a_min=0, a_max=coords[:, :4].max())
    return coords.round()


def clip_box(xyxy, input_shape, im_shape, scale_factor):
    im_shape = im_shape[0]
    ratio = scale_factor[0][0]
    img0_shape = [int(im_shape[0] / ratio), int(im_shape[1] / ratio)]
    xyxy[:, 0::2] = np.clip(xyxy[:, 0::2], a_min=0, a_max=img0_shape[1])
    xyxy[:, 1::2] = np.clip(xyxy[:, 1::2], a_min=0, a_max=img0_shape[0])
    w = xyxy[:, 2:3] - xyxy[:, 0:1]
    h = xyxy[:, 3:4] - xyxy[:, 1:2]
    mask = np.logical_and(h > 0, w > 0)
    keep_idx = np.nonzero(mask)
    return xyxy[keep_idx[0]], keep_idx


def preprocess_reid(imgs,
                    w=64,
                    h=192,
                    mean=[0.485, 0.456, 0.406],
                    std=[0.229, 0.224, 0.225]):
    im_batch = []
    for img in imgs:
        img = cv2.resize(img, (w, h))
        img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
        img_mean = np.array(mean).reshape((3, 1, 1))
        img_std = np.array(std).reshape((3, 1, 1))
        img -= img_mean
        img /= img_std
        img = np.expand_dims(img, axis=0)
        im_batch.append(img)
    im_batch = np.concatenate(im_batch, 0)
    return im_batch


class SDE_Detector(Detector):
    """
    Args:
        pred_config (object): config of model, defined by `Config(model_dir)`
        model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
        device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
        run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
        trt_min_shape (int): min shape for dynamic shape in trt
        trt_max_shape (int): max shape for dynamic shape in trt
        trt_opt_shape (int): opt shape for dynamic shape in trt
        trt_calib_mode (bool): If the model is produced by TRT offline quantitative
            calibration, trt_calib_mode need to set True
        cpu_threads (int): cpu threads
        enable_mkldnn (bool): whether to open MKLDNN
    """

    def __init__(self,
                 pred_config,
                 model_dir,
                 device='CPU',
                 run_mode='fluid',
                 batch_size=1,
                 trt_min_shape=1,
                 trt_max_shape=1088,
                 trt_opt_shape=608,
                 trt_calib_mode=False,
                 cpu_threads=1,
                 enable_mkldnn=False):
        super(SDE_Detector, self).__init__(
            pred_config=pred_config,
            model_dir=model_dir,
            device=device,
            run_mode=run_mode,
            batch_size=batch_size,
            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)
        assert batch_size == 1, "The JDE Detector only supports batch size=1 now"
        self.pred_config = pred_config

    def postprocess(self, boxes, input_shape, im_shape, scale_factor, threshold,
                    scaled):
        over_thres_idx = np.nonzero(boxes[:, 1:2] >= threshold)[0]
        if len(over_thres_idx) == 0:
            pred_dets = np.zeros((1, 6), dtype=np.float32)
            pred_xyxys = np.zeros((1, 4), dtype=np.float32)
            return pred_dets, pred_xyxys
        else:
            boxes = boxes[over_thres_idx]

        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(boxes[:, 2:], input_shape, im_shape,
                                       scale_factor)
        else:
            pred_bboxes = boxes[:, 2:]

        pred_xyxys, keep_idx = clip_box(pred_bboxes, input_shape, im_shape,
                                        scale_factor)
        if len(keep_idx[0]) == 0:
            pred_dets = np.zeros((1, 6), dtype=np.float32)
            pred_xyxys = np.zeros((1, 4), dtype=np.float32)
            return pred_dets, pred_xyxys

        pred_scores = boxes[:, 1:2][keep_idx[0]]
        pred_cls_ids = boxes[:, 0:1][keep_idx[0]]
        pred_tlwhs = np.concatenate(
            (pred_xyxys[:, 0:2], pred_xyxys[:, 2:4] - pred_xyxys[:, 0:2] + 1),
            axis=1)

        pred_dets = np.concatenate(
            (pred_tlwhs, pred_scores, pred_cls_ids), axis=1)

        return pred_dets, pred_xyxys

    def predict(self, image, scaled, threshold=0.5, warmup=0, repeats=1):
        '''
        Args:
            image (np.ndarray): image numpy data
            threshold (float): threshold of predicted box' score
            scaled (bool): whether the coords after detector outputs are scaled,
                default False in jde yolov3, set True in general detector.
        Returns:
            pred_dets (np.ndarray, [N, 6])
        '''
        self.det_times.preprocess_time_s.start()
        inputs = self.preprocess(image)
        self.det_times.preprocess_time_s.end()

        input_names = self.predictor.get_input_names()
        for i in range(len(input_names)):
            input_tensor = self.predictor.get_input_handle(input_names[i])
            input_tensor.copy_from_cpu(inputs[input_names[i]])

        for i in range(warmup):
            self.predictor.run()
            output_names = self.predictor.get_output_names()
            boxes_tensor = self.predictor.get_output_handle(output_names[0])
            boxes = boxes_tensor.copy_to_cpu()

        self.det_times.inference_time_s.start()
        for i in range(repeats):
            self.predictor.run()
            output_names = self.predictor.get_output_names()
            boxes_tensor = self.predictor.get_output_handle(output_names[0])
            boxes = boxes_tensor.copy_to_cpu()
        self.det_times.inference_time_s.end(repeats=repeats)

        self.det_times.postprocess_time_s.start()
        if len(boxes) == 0:
            pred_dets = np.zeros((1, 6), dtype=np.float32)
            pred_xyxys = np.zeros((1, 4), dtype=np.float32)
        else:
            input_shape = inputs['image'].shape[2:]
            im_shape = inputs['im_shape']
            scale_factor = inputs['scale_factor']

            pred_dets, pred_xyxys = self.postprocess(
                boxes, input_shape, im_shape, scale_factor, threshold, scaled)

        self.det_times.postprocess_time_s.end()
        self.det_times.img_num += 1
        return pred_dets, pred_xyxys


class SDE_DetectorPicoDet(DetectorPicoDet):
    """
    Args:
        pred_config (object): config of model, defined by `Config(model_dir)`
        model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
        device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
        run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
        trt_min_shape (int): min shape for dynamic shape in trt
        trt_max_shape (int): max shape for dynamic shape in trt
        trt_opt_shape (int): opt shape for dynamic shape in trt
        trt_calib_mode (bool): If the model is produced by TRT offline quantitative
            calibration, trt_calib_mode need to set True
        cpu_threads (int): cpu threads
        enable_mkldnn (bool): whether to open MKLDNN
    """

    def __init__(self,
                 pred_config,
                 model_dir,
                 device='CPU',
                 run_mode='fluid',
                 batch_size=1,
                 trt_min_shape=1,
                 trt_max_shape=1088,
                 trt_opt_shape=608,
                 trt_calib_mode=False,
                 cpu_threads=1,
                 enable_mkldnn=False):
        super(SDE_DetectorPicoDet, self).__init__(
            pred_config=pred_config,
            model_dir=model_dir,
            device=device,
            run_mode=run_mode,
            batch_size=batch_size,
            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)
        assert batch_size == 1, "The JDE Detector only supports batch size=1 now"
        self.pred_config = pred_config

    def postprocess_bboxes(self, boxes, input_shape, im_shape, scale_factor,
                           threshold):
        over_thres_idx = np.nonzero(boxes[:, 1:2] >= threshold)[0]
        if len(over_thres_idx) == 0:
            pred_dets = np.zeros((1, 6), dtype=np.float32)
            pred_xyxys = np.zeros((1, 4), dtype=np.float32)
            return pred_dets, pred_xyxys
        else:
            boxes = boxes[over_thres_idx]

        pred_bboxes = boxes[:, 2:]

        pred_xyxys, keep_idx = clip_box(pred_bboxes, input_shape, im_shape,
                                        scale_factor)
        if len(keep_idx[0]) == 0:
            pred_dets = np.zeros((1, 6), dtype=np.float32)
            pred_xyxys = np.zeros((1, 4), dtype=np.float32)
            return pred_dets, pred_xyxys

        pred_scores = boxes[:, 1:2][keep_idx[0]]
        pred_cls_ids = boxes[:, 0:1][keep_idx[0]]
        pred_tlwhs = np.concatenate(
            (pred_xyxys[:, 0:2], pred_xyxys[:, 2:4] - pred_xyxys[:, 0:2] + 1),
            axis=1)

        pred_dets = np.concatenate(
            (pred_tlwhs, pred_scores, pred_cls_ids), axis=1)
        return pred_dets, pred_xyxys

    def predict(self, image, scaled, threshold=0.5, warmup=0, repeats=1):
        '''
        Args:
            image (np.ndarray): image numpy data
            threshold (float): threshold of predicted box' score
            scaled (bool): whether the coords after detector outputs are scaled,
                default False in jde yolov3, set True in general detector.
        Returns:
            pred_dets (np.ndarray, [N, 6])
        '''
        self.det_times.preprocess_time_s.start()
        inputs = self.preprocess(image)
        self.det_times.preprocess_time_s.end()

        input_names = self.predictor.get_input_names()
        for i in range(len(input_names)):
            input_tensor = self.predictor.get_input_handle(input_names[i])
            input_tensor.copy_from_cpu(inputs[input_names[i]])

        np_score_list, np_boxes_list = [], []
        for i in range(warmup):
            self.predictor.run()
            output_names = self.predictor.get_output_names()
            boxes_tensor = self.predictor.get_output_handle(output_names[0])
            boxes = boxes_tensor.copy_to_cpu()

        self.det_times.inference_time_s.start()
        for i in range(repeats):
            self.predictor.run()
            np_score_list.clear()
            np_boxes_list.clear()
            output_names = self.predictor.get_output_names()
            num_outs = int(len(output_names) / 2)
            for out_idx in range(num_outs):
                np_score_list.append(
                    self.predictor.get_output_handle(output_names[out_idx])
                    .copy_to_cpu())
                np_boxes_list.append(
                    self.predictor.get_output_handle(output_names[
                        out_idx + num_outs]).copy_to_cpu())

        self.det_times.inference_time_s.end(repeats=repeats)
        self.det_times.img_num += 1
        self.det_times.postprocess_time_s.start()
        self.postprocess = PicoDetPostProcess(
            inputs['image'].shape[2:],
            inputs['im_shape'],
            inputs['scale_factor'],
            strides=self.pred_config.fpn_stride,
            nms_threshold=self.pred_config.nms['nms_threshold'])
        boxes, boxes_num = self.postprocess(np_score_list, np_boxes_list)

        if len(boxes) == 0:
            pred_dets = np.zeros((1, 6), dtype=np.float32)
            pred_xyxys = np.zeros((1, 4), dtype=np.float32)
        else:
            input_shape = inputs['image'].shape[2:]
            im_shape = inputs['im_shape']
            scale_factor = inputs['scale_factor']
            pred_dets, pred_xyxys = self.postprocess_bboxes(
                boxes, input_shape, im_shape, scale_factor, threshold)

        return pred_dets, pred_xyxys


class SDE_ReID(object):
    def __init__(self,
                 pred_config,
                 model_dir,
                 device='CPU',
                 run_mode='fluid',
                 batch_size=50,
                 trt_min_shape=1,
                 trt_max_shape=1088,
                 trt_opt_shape=608,
                 trt_calib_mode=False,
                 cpu_threads=1,
                 enable_mkldnn=False):
        self.pred_config = pred_config
        self.predictor, self.config = load_predictor(
            model_dir,
            run_mode=run_mode,
            batch_size=batch_size,
            min_subgraph_size=self.pred_config.min_subgraph_size,
            device=device,
            use_dynamic_shape=self.pred_config.use_dynamic_shape,
            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)
        self.det_times = Timer()
        self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0
        self.batch_size = batch_size
        assert pred_config.tracker, "Tracking model should have tracker"
        pt = pred_config.tracker
        max_age = pt['max_age'] if 'max_age' in pt else 30
        max_iou_distance = pt[
            'max_iou_distance'] if 'max_iou_distance' in pt else 0.7
        self.tracker = DeepSORTTracker(
            max_age=max_age, max_iou_distance=max_iou_distance)

    def get_crops(self, xyxy, ori_img):
        w, h = self.tracker.input_size
        self.det_times.preprocess_time_s.start()
        crops = []
        xyxy = xyxy.astype(np.int64)
        ori_img = ori_img.transpose(1, 0, 2)  # [h,w,3]->[w,h,3]
        for i, bbox in enumerate(xyxy):
            crop = ori_img[bbox[0]:bbox[2], bbox[1]:bbox[3], :]
            crops.append(crop)
        crops = preprocess_reid(crops, w, h)
        self.det_times.preprocess_time_s.end()

        return crops

    def preprocess(self, crops):
        # to keep fast speed, only use topk crops
        crops = crops[:self.batch_size]
        inputs = {}
        inputs['crops'] = np.array(crops).astype('float32')
        return inputs

    def postprocess(self, pred_dets, pred_embs):
        tracker = self.tracker
        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 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)

        return online_tlwhs, online_scores, online_ids

    def predict(self, crops, pred_dets, warmup=0, repeats=1):
        self.det_times.preprocess_time_s.start()
        inputs = self.preprocess(crops)
        self.det_times.preprocess_time_s.end()

        input_names = self.predictor.get_input_names()
        for i in range(len(input_names)):
            input_tensor = self.predictor.get_input_handle(input_names[i])
            input_tensor.copy_from_cpu(inputs[input_names[i]])

        for i in range(warmup):
            self.predictor.run()
            output_names = self.predictor.get_output_names()
            feature_tensor = self.predictor.get_output_handle(output_names[0])
            pred_embs = feature_tensor.copy_to_cpu()

        self.det_times.inference_time_s.start()
        for i in range(repeats):
            self.predictor.run()
            output_names = self.predictor.get_output_names()
            feature_tensor = self.predictor.get_output_handle(output_names[0])
            pred_embs = feature_tensor.copy_to_cpu()
        self.det_times.inference_time_s.end(repeats=repeats)

        self.det_times.postprocess_time_s.start()
        online_tlwhs, online_scores, online_ids = self.postprocess(pred_dets,
                                                                   pred_embs)
        self.det_times.postprocess_time_s.end()
        self.det_times.img_num += 1

        return online_tlwhs, online_scores, online_ids


def predict_image(detector, reid_model, image_list):
    image_list.sort()
    for i, img_file in enumerate(image_list):
        frame = cv2.imread(img_file)
        if FLAGS.run_benchmark:
            pred_dets, pred_xyxys = detector.predict(
                [frame], FLAGS.scaled, FLAGS.threshold, warmup=10, repeats=10)
            cm, gm, gu = get_current_memory_mb()
            detector.cpu_mem += cm
            detector.gpu_mem += gm
            detector.gpu_util += gu
            print('Test iter {}, file name:{}'.format(i, img_file))
        else:
            pred_dets, pred_xyxys = detector.predict([frame], FLAGS.scaled,
                                                     FLAGS.threshold)

        if len(pred_dets) == 1 and np.sum(pred_dets) == 0:
            print('Frame {} has no object, try to modify score threshold.'.
                  format(i))
            online_im = frame
        else:
            # reid process
            crops = reid_model.get_crops(pred_xyxys, frame)

            if FLAGS.run_benchmark:
                online_tlwhs, online_scores, online_ids = reid_model.predict(
                    crops, pred_dets, warmup=10, repeats=10)
            else:
                online_tlwhs, online_scores, online_ids = reid_model.predict(
                    crops, pred_dets)
                online_im = plot_tracking(
                    frame, online_tlwhs, online_ids, online_scores, frame_id=i)

        if FLAGS.save_images:
            if not os.path.exists(FLAGS.output_dir):
                os.makedirs(FLAGS.output_dir)
            img_name = os.path.split(img_file)[-1]
            out_path = os.path.join(FLAGS.output_dir, img_name)
            cv2.imwrite(out_path, online_im)
            print("save result to: " + out_path)


def predict_video(detector, reid_model, camera_id):
    if camera_id != -1:
        capture = cv2.VideoCapture(camera_id)
        video_name = 'mot_output.mp4'
    else:
        capture = cv2.VideoCapture(FLAGS.video_file)
        video_name = os.path.split(FLAGS.video_file)[-1]
    # 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("fps: %d, frame_count: %d" % (fps, frame_count))

    if not os.path.exists(FLAGS.output_dir):
        os.makedirs(FLAGS.output_dir)
    out_path = os.path.join(FLAGS.output_dir, video_name)
    if not FLAGS.save_images:
        video_format = 'mp4v'
        fourcc = cv2.VideoWriter_fourcc(*video_format)
        writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
    frame_id = 0
    timer = MOTTimer()
    results = defaultdict(list)
    while (1):
        ret, frame = capture.read()
        if not ret:
            break
        timer.tic()
        pred_dets, pred_xyxys = detector.predict([frame], FLAGS.scaled,
                                                 FLAGS.threshold)

        if len(pred_dets) == 1 and np.sum(pred_dets) == 0:
            print('Frame {} has no object, try to modify score threshold.'.
                  format(frame_id))
            timer.toc()
            im = frame
        else:
            # reid process
            crops = reid_model.get_crops(pred_xyxys, frame)
            online_tlwhs, online_scores, online_ids = reid_model.predict(
                crops, pred_dets)
            results[0].append(
                (frame_id + 1, online_tlwhs, online_scores, online_ids))
            timer.toc()

            fps = 1. / timer.duration
            im = plot_tracking(
                frame,
                online_tlwhs,
                online_ids,
                online_scores,
                frame_id=frame_id,
                fps=fps)

        if FLAGS.save_images:
            save_dir = os.path.join(FLAGS.output_dir, video_name.split('.')[-2])
            if not os.path.exists(save_dir):
                os.makedirs(save_dir)
            cv2.imwrite(
                os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im)
        else:
            writer.write(im)

        frame_id += 1
        print('detect frame:%d, fps: %f' % (frame_id, fps))

        if camera_id != -1:
            cv2.imshow('Tracking Detection', im)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

    if FLAGS.save_mot_txts:
        result_filename = os.path.join(FLAGS.output_dir,
                                       video_name.split('.')[-2] + '.txt')
        write_mot_results(result_filename, results)

    if FLAGS.save_images:
        save_dir = os.path.join(FLAGS.output_dir, video_name.split('.')[-2])
        cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg {}'.format(save_dir,
                                                              out_path)
        os.system(cmd_str)
        print('Save video in {}.'.format(out_path))
    else:
        writer.release()


def main():
    pred_config = PredictConfig(FLAGS.model_dir)
    detector_func = 'SDE_Detector'
    if pred_config.arch == 'PicoDet':
        detector_func = 'SDE_DetectorPicoDet'

    detector = eval(detector_func)(pred_config,
                                   FLAGS.model_dir,
                                   device=FLAGS.device,
                                   run_mode=FLAGS.run_mode,
                                   batch_size=FLAGS.batch_size,
                                   trt_min_shape=FLAGS.trt_min_shape,
                                   trt_max_shape=FLAGS.trt_max_shape,
                                   trt_opt_shape=FLAGS.trt_opt_shape,
                                   trt_calib_mode=FLAGS.trt_calib_mode,
                                   cpu_threads=FLAGS.cpu_threads,
                                   enable_mkldnn=FLAGS.enable_mkldnn)

    pred_config = PredictConfig(FLAGS.reid_model_dir)
    reid_model = SDE_ReID(
        pred_config,
        FLAGS.reid_model_dir,
        device=FLAGS.device,
        run_mode=FLAGS.run_mode,
        batch_size=FLAGS.reid_batch_size,
        trt_min_shape=FLAGS.trt_min_shape,
        trt_max_shape=FLAGS.trt_max_shape,
        trt_opt_shape=FLAGS.trt_opt_shape,
        trt_calib_mode=FLAGS.trt_calib_mode,
        cpu_threads=FLAGS.cpu_threads,
        enable_mkldnn=FLAGS.enable_mkldnn)

    # predict from video file or camera video stream
    if FLAGS.video_file is not None or FLAGS.camera_id != -1:
        predict_video(detector, reid_model, FLAGS.camera_id)
    else:
        # predict from image
        img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
        predict_image(detector, reid_model, img_list)

        if not FLAGS.run_benchmark:
            detector.det_times.info(average=True)
            reid_model.det_times.info(average=True)
        else:
            mode = FLAGS.run_mode
            det_model_dir = FLAGS.model_dir
            det_model_info = {
                'model_name': det_model_dir.strip('/').split('/')[-1],
                'precision': mode.split('_')[-1]
            }
            bench_log(detector, img_list, det_model_info, name='Det')

            reid_model_dir = FLAGS.reid_model_dir
            reid_model_info = {
                'model_name': reid_model_dir.strip('/').split('/')[-1],
                'precision': mode.split('_')[-1]
            }
            bench_log(reid_model, img_list, reid_model_info, name='ReID')


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

    main()