• 深度学习模型试跑(十四):Bytetrack(vs2019 训练+trt推理部署)

    目录

    前言

    这里记录了多目标追踪模型Bytetrack在win10/vs2019 上，用自定义数据集训模、tensorrt部署的大致过程。
    我的环境：

    • Visual Studio 2019
    • CUDA 11.5，cudnn 8.2
    • CMake 3.17.1
    • tensorrt:8.4.1.5

    一.模型解读

    可以参考实时目标追踪：ByteTrack算法步骤详解和代码逐行解析，模型和代码关键的地方都有做介绍。需要具体研究的建议去看一下github项目，不了解MOT的可以先看下多目标跟踪MOT数据集格式介绍。

    二.代码

    参考官方

    三.数据准备

    这里参考了官方的数据准备，可以参照我之前写的yolov5 + deepsort准备数据。我这里只用了MOT17，主要我自己的数据集是照着MOT17的格式处理了一遍，下表中的mot对应的就是MOT17数据集，放在/datasets文件夹下。

    1. 配置自己的数据
      在这里插入图片描述
      datasets
      |——————custom_dataset(mot17格式的)
      | └——————train
      | └——————sub_dataset_1(记得后面加FRCNN)
      | └——————det(这个一般没有，可以训练的检测模型生成，我这里用的是yolox，下面是生成脚本)
      | └——————img1
      | └——————gt
      | └——————…
      | └——————sub_dataset_n
      | └——————test
      |——————mot
      | └——————train
      | └——————test
      └——————crowdhuman
      | └——————Crowdhuman_train
      | └——————Crowdhuman_val
      | └——————annotation_train.odgt
      | └——————annotation_val.odgt
      └——————MOT20
      | └——————train
      | └——————test
      └——————Cityscapes
      | └——————images
      | └——————labels_with_ids
      └——————ETHZ
      └——————eth01
      └——————…
      └——————eth07

    生成det下一系列的txt.py(基于YOLOX demo.py 修改出来的，代码修改思路)

    #!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.

import argparse
import os
import time
from loguru import logger

import cv2
import sys
sys.path.append(r'path to yolox home')#改成自己yolox的路径
import torch

from yolox.data.data_augment import ValTransform
from yolox.data.datasets import COCO_CLASSES
from yolox.exp import get_exp
from yolox.utils import fuse_model, get_model_info, postprocess, vis
from yolox.data.datasets import voc_classes
IMAGE_EXT = [".jpg", ".jpeg", ".webp", ".bmp", ".png"]
Dataset_path = r'path to a sub dataset'#改成自己某个子数据集的路径

def make_parser():
    parser = argparse.ArgumentParser("YOLOX Demo!")
    parser.add_argument(
        "--mot_det_gen", default=os.path.join(Dataset_path,'det\det.txt'),
        help="利用yolox制作mot17数据集格式里的det.txt"
    )

    parser.add_argument(
        "--demo", default="image", help="demo type, eg. image, video and webcam"
    )
    parser.add_argument("-expn", "--experiment-name", type=str, default=None)
    parser.add_argument("-n", "--name", type=str, default=None, help="model name")

    parser.add_argument(
        "--path", default=os.path.join(Dataset_path,'img1'), help="path to images or video"
    )
    parser.add_argument("--camid", type=int, default=0, help="webcam demo camera id")
    parser.add_argument(
        "--save_result",
        action="store_true",
        default=True,
        help="whether to save the inference result of image/video",
    )

    # exp file,我用yolox_voc_l的标准把数据训练了一遍，下面两个参数记得改一下
    parser.add_argument(
        "-f",
        "--exp_file",
        default='../exps/example/yolox_voc/yolox_voc_l.py',
        type=str,
        help="please input your experiment description file",
    )
    parser.add_argument("-c", "--ckpt", default='../YOLOX_outputs/yolox_voc_l/best_ckpt.pth', type=str, help="ckpt for eval")
    parser.add_argument(
        "--device",
        default="gpu",
        type=str,
        help="device to run our model, can either be cpu or gpu",
    )
    parser.add_argument("--conf", default=0.8, type=float, help="test conf")
    parser.add_argument("--nms", default=0.3, type=float, help="test nms threshold")
    parser.add_argument("--tsize", default=None, type=int, help="test img size")
    parser.add_argument(
        "--fp16",
        dest="fp16",
        default=False,
        action="store_true",
        help="Adopting mix precision evaluating.",
    )
    parser.add_argument(
        "--legacy",
        dest="legacy",
        default=False,
        action="store_true",
        help="To be compatible with older versions",
    )
    parser.add_argument(
        "--fuse",
        dest="fuse",
        default=False,
        action="store_true",
        help="Fuse conv and bn for testing.",
    )
    parser.add_argument(
        "--trt",
        dest="trt",
        default=False,
        action="store_true",
        help="Using TensorRT model for testing.",
    )
    return parser


def get_image_list(path):
    image_names = []
    for maindir, subdir, file_name_list in os.walk(path):
        for filename in file_name_list:
            apath = os.path.join(maindir, filename)
            ext = os.path.splitext(apath)[1]
            if ext in IMAGE_EXT:
                image_names.append(apath)
    return image_names


class Predictor(object):
    def __init__(
        self,
        model,
        exp,
        cls_names=voc_classes.VOC_CLASSES,
        trt_file=None,
        decoder=None,
        device="cpu",
        fp16=False,
        legacy=False,
    ):
        self.model = model
        self.cls_names = cls_names
        self.decoder = decoder
        self.num_classes = exp.num_classes
        self.confthre = exp.test_conf
        self.nmsthre = exp.nmsthre
        self.test_size = exp.test_size
        self.device = device
        self.fp16 = fp16
        self.preproc = ValTransform(legacy=legacy)
        if trt_file is not None:
            from torch2trt import TRTModule

            model_trt = TRTModule()
            model_trt.load_state_dict(torch.load(trt_file))

            x = torch.ones(1, 3, exp.test_size[0], exp.test_size[1]).cuda()
            self.model(x)
            self.model = model_trt

    def inference(self, img):
        img_info = {"id": 0}
        if isinstance(img, str):
            img_info["file_name"] = os.path.basename(img)
            img = cv2.imread(img)
        else:
            img_info["file_name"] = None

        height, width = img.shape[:2]
        img_info["height"] = height
        img_info["width"] = width
        img_info["raw_img"] = img

        ratio = min(self.test_size[0] / img.shape[0], self.test_size[1] / img.shape[1])
        img_info["ratio"] = ratio

        img, _ = self.preproc(img, None, self.test_size)
        img = torch.from_numpy(img).unsqueeze(0)
        img = img.float()
        if self.device == "gpu":
            img = img.cuda()
            if self.fp16:
                img = img.half()  # to FP16

        with torch.no_grad():
            t0 = time.time()
            outputs = self.model(img)
            if self.decoder is not None:
                outputs = self.decoder(outputs, dtype=outputs.type())
            outputs = postprocess(
                outputs, self.num_classes, self.confthre,
                self.nmsthre, class_agnostic=True
            )
            logger.info("Infer time: {:.4f}s".format(time.time() - t0))
        return outputs, img_info

    def visual(self, output, img_info, cls_conf=0.35):
        ratio = img_info["ratio"]
        img = img_info["raw_img"]
        if output is None:
            return img
        output = output.cpu()

        bboxes = output[:, 0:4]

        # preprocessing: resize
        bboxes /= ratio

        cls = output[:, 6]
        scores = output[:, 4] * output[:, 5]

        vis_res = vis(img, bboxes, scores, cls, cls_conf, self.cls_names)
        return vis_res

    def visual_motdet(self, output, img_info, cls_conf=0.35):
        ratio = img_info["ratio"]
        img = img_info["raw_img"]
        if output is None:
            return img
        output = output.cpu()

        bboxes = output[:, 0:4]

        # preprocessing: resize
        bboxes /= ratio

        cls = output[:, 6]
        scores = output[:, 4] * output[:, 5]

        vis_res = [img, bboxes, scores, cls, cls_conf, self.cls_names]
        return vis_res

def image_demo(predictor, vis_folder, path, current_time, save_result, motdet_txt):
    if os.path.isdir(path):
        files = get_image_list(path)
    else:
        files = [path]
    files.sort()
    note = open(motdet_txt, mode='w')
    print(motdet_txt)
    for file_num,image_name in enumerate(files):
        outputs, img_info = predictor.inference(image_name)
        result_image = predictor.visual(outputs[0], img_info, predictor.confthre)
        result_paras = predictor.visual_motdet(outputs[0], img_info, predictor.confthre)
        if save_result:
            save_folder = os.path.join(
                vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
            )
            os.makedirs(save_folder, exist_ok=True)
            save_file_name = os.path.join(save_folder, os.path.basename(image_name))
            logger.info("Saving detection result in {}".format(save_file_name))
            #cv2.imwrite(save_file_name, result_image)
            boxes = result_paras[1]
            scores = result_paras[2]
            for i in range(len(boxes)):
                box = boxes[i]
                score = float(scores[i])
                x0 = int(box[0])
                y0 = int(box[1])
                w0 = int(box[2]) - x0
                h0 = int(box[3]) - y0
                #print(x0,y0,w0,h0,score)
                motdet_line = str(file_num)+',-1,'+str(x0)+ ','+str(y0)+ ','+str(w0)+ ','+str(h0)+ ','+str(score)+',-1,-1,-1'
                note.write(motdet_line+' \n')  # \n 换行符
            # cv2.namedWindow("yolox", cv2.WINDOW_NORMAL)
            # cv2.imshow("yolox", result_image)
        ch = cv2.waitKey(0)
        if ch == 27 or ch == ord("q") or ch == ord("Q"):
            break


def imageflow_demo(predictor, vis_folder, current_time, args):
    cap = cv2.VideoCapture(args.path if args.demo == "video" else args.camid)
    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float
    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float
    fps = cap.get(cv2.CAP_PROP_FPS)
    if args.save_result:
        save_folder = os.path.join(
            vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
        )
        os.makedirs(save_folder, exist_ok=True)
        if args.demo == "video":
            save_path = os.path.join(save_folder, os.path.basename(args.path))
        else:
            save_path = os.path.join(save_folder, "camera.mp4")
        logger.info(f"video save_path is {save_path}")
        vid_writer = cv2.VideoWriter(
            save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
        )
    while True:
        ret_val, frame = cap.read()
        if ret_val:
            outputs, img_info = predictor.inference(frame)
            result_frame = predictor.visual(outputs[0], img_info, predictor.confthre)
            if args.save_result:
                vid_writer.write(result_frame)
            else:
                cv2.namedWindow("yolox", cv2.WINDOW_NORMAL)
                cv2.imshow("yolox", result_frame)
            ch = cv2.waitKey(1)
            if ch == 27 or ch == ord("q") or ch == ord("Q"):
                break
        else:
            break


def main(exp, args):
    if not args.experiment_name:
        args.experiment_name = exp.exp_name

    file_name = os.path.join(exp.output_dir, args.experiment_name)
    os.makedirs(file_name, exist_ok=True)

    vis_folder = None
    if args.save_result:
        vis_folder = os.path.join(file_name, "vis_res")
        os.makedirs(vis_folder, exist_ok=True)

    if args.trt:
        args.device = "gpu"

    logger.info("Args: {}".format(args))

    if args.conf is not None:
        exp.test_conf = args.conf
    if args.nms is not None:
        exp.nmsthre = args.nms
    if args.tsize is not None:
        exp.test_size = (args.tsize, args.tsize)

    model = exp.get_model()
    logger.info("Model Summary: {}".format(get_model_info(model, exp.test_size)))

    if args.device == "gpu":
        model.cuda()
        if args.fp16:
            model.half()  # to FP16
    model.eval()

    if not args.trt:
        if args.ckpt is None:
            ckpt_file = os.path.join(file_name, "best_ckpt.pth")
        else:
            ckpt_file = args.ckpt
        logger.info("loading checkpoint")
        ckpt = torch.load(ckpt_file, map_location="cpu")
        # load the model state dict
        model.load_state_dict(ckpt["model"])
        logger.info("loaded checkpoint done.")

    if args.fuse:
        logger.info("\tFusing model...")
        model = fuse_model(model)

    if args.trt:
        assert not args.fuse, "TensorRT model is not support model fusing!"
        trt_file = os.path.join(file_name, "model_trt.pth")
        assert os.path.exists(
            trt_file
        ), "TensorRT model is not found!\n Run python3 tools/trt.py first!"
        model.head.decode_in_inference = False
        decoder = model.head.decode_outputs
        logger.info("Using TensorRT to inference")
    else:
        trt_file = None
        decoder = None

    predictor = Predictor(
        model, exp, voc_classes.VOC_CLASSES, trt_file, decoder,
        args.device, args.fp16, args.legacy,
    )
    current_time = time.localtime()
    if args.demo == "image":
        image_demo(predictor, vis_folder, args.path, current_time, args.save_result, args.mot_det_gen)
    elif args.demo == "video" or args.demo == "webcam":
        imageflow_demo(predictor, vis_folder, current_time, args)


if __name__ == "__main__":
    args = make_parser().parse_args()
    exp = get_exp(args.exp_file, args.name)

    main(exp, args)


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    2.将自定义数据转换为coco的数据集格式

    cd <ByteTrack_HOME>
python tools/convert_mot17_to_coco.py	#注意把第8行	DATA_PATH = 'datasets/mot'	改为自己的数据集路径

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    3.如果还有其它格式的数据集，可以参考官方将数据进行混合

    cd <ByteTrack_HOME>
python tools/mix_data_ablation.py
python tools/mix_data_test_mot17.py
python tools/mix_data_test_mot20.py

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    四.模型训练

    可参考官方教程，我这里直接把train.py直接放到了项目根目录下。

    1. train.py:
    from loguru import logger

import torch
import torch.backends.cudnn as cudnn

from yolox.core import Trainer, launch
from yolox.exp import get_exp

import argparse
import random
import warnings


def make_parser():
    parser = argparse.ArgumentParser("YOLOX train parser")
    parser.add_argument("-expn", "--experiment-name", type=str, default=None)
    parser.add_argument("-n", "--name", type=str, default=None, help="model name")

    # distributed
    parser.add_argument(
        "--dist-backend", default="gloo", type=str, help="distributed backend"
    )
    parser.add_argument(
        "--dist-url",
        default=None,
        type=str,
        help="url used to set up distributed training",
    )
    parser.add_argument("-b", "--batch-size", type=int, default=64, help="batch size")
    parser.add_argument(
        "-d", "--devices", default=1, type=int, help="device for training"
    )
    parser.add_argument(
        "--local_rank", default=0, type=int, help="local rank for dist training"
    )
    parser.add_argument(
        "-f",
        "--exp_file",
        default=None,
        type=str,
        help="plz input your expriment description file",
    )
    parser.add_argument(
        "--resume", default=False, action="store_true", help="resume training"
    )
    parser.add_argument("-c", "--ckpt", default=None, type=str, help="checkpoint file")
    parser.add_argument(
        "-e",
        "--start_epoch",
        default=None,
        type=int,
        help="resume training start epoch",
    )
    parser.add_argument(
        "--num_machines", default=1, type=int, help="num of node for training"
    )
    parser.add_argument(
        "--machine_rank", default=0, type=int, help="node rank for multi-node training"
    )
    parser.add_argument(
        "--fp16",
        dest="fp16",
        default=False,
        action="store_true",
        help="Adopting mix precision training.",
    )
    parser.add_argument(
        "-o",
        "--occupy",
        dest="occupy",
        default=False,
        action="store_true",
        help="occupy GPU memory first for training.",
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )
    return parser


@logger.catch
def main(exp, args):
    if exp.seed is not None:
        random.seed(exp.seed)
        torch.manual_seed(exp.seed)
        cudnn.deterministic = True
        warnings.warn(
            "You have chosen to seed training. This will turn on the CUDNN deterministic setting, "
            "which can slow down your training considerably! You may see unexpected behavior "
            "when restarting from checkpoints."
        )

    # set environment variables for distributed training
    cudnn.benchmark = False

    trainer = Trainer(exp, args)
    trainer.train()


if __name__ == "__main__":
    args = make_parser().parse_args()
    exp = get_exp(args.exp_file, args.name)
    exp.merge(args.opts)

    if not args.experiment_name:
        args.experiment_name = exp.exp_name

    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices
    assert num_gpu <= torch.cuda.device_count()

    launch(
        main,
        num_gpu,
        args.num_machines,
        args.machine_rank,
        backend=args.dist_backend,
        dist_url=args.dist_url,
        args=(exp, args),
    )

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    1. 训练:
      #开始训练：
python train.py -f exps/example/mot/yolox_x_ablation.py -d 1 -b 2 -c pretrained/yolox_x.pth
#恢复训练：


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    2. 预测: (记得注释掉./yolox/data/datasets/data_augment.py line203~208)
      运行tools/demo_track.py,要修改的地方可以看里面的注释
    from loguru import logger
import cv2
import torch

from yolox.data.data_augment import preproc
from yolox.exp import get_exp
from yolox.utils import fuse_model, get_model_info, postprocess, vis
from yolox.utils.visualize import plot_tracking
from yolox.tracker.byte_tracker import BYTETracker
from yolox.tracking_utils.timer import Timer

import argparse
import os
import time

IMAGE_EXT = [".jpg", ".jpeg", ".webp", ".bmp", ".png"]
Dataset_path =r'path to a sub dataset'#改成自己某个子数据集的路径


def make_parser():
    parser = argparse.ArgumentParser("ByteTrack Demo!")
    parser.add_argument(
        "--demo", default="video", help="demo type, eg. image, video and webcam"
    )
    parser.add_argument("-expn", "--experiment-name", type=str, default=None)
    parser.add_argument("-n", "--name", type=str, default='yolox-x', help="model name")

    parser.add_argument(
        #"--path", default=os.path.join(Dataset_path, 'img1'), help="path to images or video"
        "--path", default="../videos/CRE-BAG2-run03.mp4", help="path to images or video,改成自己要预测的数据集"
    )
    parser.add_argument("--camid", type=int, default=0, help="webcam demo camera id")
    parser.add_argument(
        "--save_result",
        action="store_true",
        help="whether to save the inference result of image/video",
        default=True,
    )

    # exp file
    parser.add_argument(
        "-f",
        "--exp_file",
        default='../exps/example/mot/yolox_x_ablation.py',
        type=str,
        help="pls input your expriment description file",
    )
    parser.add_argument("-c", "--ckpt", default='../YOLOX_outputs/yolox_x_ablation/best_ckpt.pth.tar', type=str, help="ckpt for eval,改成自己的训模后的模型")
    parser.add_argument(
        "--device",
        default="gpu",
        type=str,
        help="device to run our model, can either be cpu or gpu",
    )
    parser.add_argument("--conf", default=None, type=float, help="test conf")
    parser.add_argument("--nms", default=None, type=float, help="test nms threshold")
    parser.add_argument("--tsize", default=None, type=int, help="test img size")
    parser.add_argument(
        "--fp16",
        dest="fp16",
        default=True,
        action="store_true",
        help="Adopting mix precision evaluating.",
    )
    parser.add_argument(
        "--fuse",
        dest="fuse",
        default=False,
        action="store_true",
        help="Fuse conv and bn for testing.",
    )
    parser.add_argument(
        "--trt",
        dest="trt",
        default=False,
        action="store_true",
        help="Using TensorRT model for testing.",
    )
    # tracking args
    parser.add_argument("--track_thresh", type=float, default=0.5, help="tracking confidence threshold")
    parser.add_argument("--track_buffer", type=int, default=30, help="the frames for keep lost tracks")
    parser.add_argument("--match_thresh", type=int, default=0.8, help="matching threshold for tracking")
    parser.add_argument('--min-box-area', type=float, default=10, help='filter out tiny boxes')
    parser.add_argument("--mot20", dest="mot20", default=False, action="store_true", help="test mot20.")
    return parser


def get_image_list(path):
    image_names = []
    for maindir, subdir, file_name_list in os.walk(path):
        for filename in file_name_list:
            apath = os.path.join(maindir, filename)
            ext = os.path.splitext(apath)[1]
            if ext in IMAGE_EXT:
                image_names.append(apath)
    return image_names


def write_results(filename, results):
    save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\n'
    with open(filename, 'w') as f:
        for frame_id, tlwhs, track_ids, scores in results:
            for tlwh, track_id, score in zip(tlwhs, track_ids, scores):
                if track_id < 0:
                    continue
                x1, y1, w, h = tlwh
                line = save_format.format(frame=frame_id, id=track_id, x1=round(x1, 1), y1=round(y1, 1), w=round(w, 1),
                                          h=round(h, 1), s=round(score, 2))
                f.write(line)
    logger.info('save results to {}'.format(filename))


class Predictor(object):
    def __init__(
            self,
            model,
            exp,
            trt_file=None,
            decoder=None,
            device="cpu",
            fp16=False
    ):
        self.model = model
        self.decoder = decoder
        self.num_classes = exp.num_classes
        self.confthre = exp.test_conf
        self.nmsthre = exp.nmsthre
        self.test_size = exp.test_size
        self.device = device
        self.fp16 = fp16
        if trt_file is not None:
            from torch2trt import TRTModule

            model_trt = TRTModule()
            model_trt.load_state_dict(torch.load(trt_file))

            x = torch.ones(1, 3, exp.test_size[0], exp.test_size[1]).cuda()
            self.model(x)
            self.model = model_trt
        self.rgb_means = (0.485, 0.456, 0.406)
        self.std = (0.229, 0.224, 0.225)

    def inference(self, img, timer):
        img_info = {"id": 0}
        if isinstance(img, str):
            img_info["file_name"] = os.path.basename(img)
            img = cv2.imread(img)
        else:
            img_info["file_name"] = None

        height, width = img.shape[:2]
        img_info["height"] = height
        img_info["width"] = width
        img_info["raw_img"] = img

        img, ratio = preproc(img, self.test_size, self.rgb_means, self.std)
        img_info["ratio"] = ratio
        img = torch.from_numpy(img).unsqueeze(0)
        img = img.float()
        if self.device == "gpu":
            img = img.cuda()
            if self.fp16:
                img = img.half()  # to FP16

        with torch.no_grad():
            timer.tic()
            outputs = self.model(img)
            if self.decoder is not None:
                outputs = self.decoder(outputs, dtype=outputs.type())
            outputs = postprocess(
                outputs, self.num_classes, self.confthre, self.nmsthre
            )
            # logger.info("Infer time: {:.4f}s".format(time.time() - t0))
        return outputs, img_info


def image_demo(predictor, vis_folder, path, current_time, save_result):
    if os.path.isdir(path):
        files = get_image_list(path)
    else:
        files = [path]
    files.sort()
    tracker = BYTETracker(args, frame_rate=30)
    timer = Timer()
    frame_id = 0
    results = []
    for image_name in files:
        if frame_id % 20 == 0:
            logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))
        outputs, img_info = predictor.inference(image_name, timer)
        online_targets = tracker.update(outputs[0], [img_info['height'], img_info['width']], exp.test_size)
        online_tlwhs = []
        online_ids = []
        online_scores = []
        for t in online_targets:
            tlwh = t.tlwh
            tid = t.track_id
            vertical = tlwh[2] / tlwh[3] > 1.6
            if tlwh[2] * tlwh[3] > args.min_box_area and not vertical:
                online_tlwhs.append(tlwh)
                online_ids.append(tid)
                online_scores.append(t.score)
        timer.toc()
        # save results
        results.append((frame_id + 1, online_tlwhs, online_ids, online_scores))
        online_im = plot_tracking(img_info['raw_img'], online_tlwhs, online_ids, frame_id=frame_id + 1,
                                  fps=1. / timer.average_time)

        # result_image = predictor.visual(outputs[0], img_info, predictor.confthre)
        if save_result:
            save_folder = os.path.join(
                vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
            )
            os.makedirs(save_folder, exist_ok=True)
            save_file_name = os.path.join(save_folder, os.path.basename(image_name))
            # cv2.imshow('demo', online_im)
            cv2.imwrite(save_file_name, online_im)
        ch = cv2.waitKey(0)
        frame_id += 1
        if ch == 27 or ch == ord("q") or ch == ord("Q"):
            break
    # write_results(result_filename, results)


def imageflow_demo(predictor, vis_folder, current_time, args):
    cap = cv2.VideoCapture(args.path if args.demo == "video" else args.camid)
    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float
    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float
    fps = cap.get(cv2.CAP_PROP_FPS)
    save_folder = os.path.join(
        vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
    )
    os.makedirs(save_folder, exist_ok=True)
    if args.demo == "video":
        save_path = os.path.join(save_folder, args.path.split("/")[-1])
    else:
        save_path = os.path.join(save_folder, "camera.mp4")
    logger.info(f"video save_path is {save_path}")
    vid_writer = cv2.VideoWriter(
        save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
    )
    tracker = BYTETracker(args, frame_rate=30)
    timer = Timer()
    frame_id = 0
    results = []
    frame_id_tmp = 0
    print(os.path.join(Dataset_path, 'det', 'det.txt'))
    det_txt = open(os.path.join(Dataset_path, 'det', 'det.txt'), mode='w')
    while True:
        if frame_id % 20 == 0:
            logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))
        ret_val, frame = cap.read()
        if ret_val:

            outputs, img_info = predictor.inference(frame, timer)

            # #限制目标检测的x1y1x2y2尺寸要超出边界
            # outputs_tmp = outputs[0]
            # outputs_tmp[outputs_tmp[:, 0] <= 0, 0] = 0
            # outputs_tmp[outputs_tmp[:, 2] <= 0, 2] = 0
            # outputs_tmp[outputs_tmp[:, 1] >= torch.from_numpy(np.array(exp.test_size[0])).cuda().float(), 1] = torch.from_numpy(np.array(exp.test_size[0]-1)).cuda().float()
            # outputs_tmp[outputs_tmp[:, 3] >= torch.from_numpy(np.array(exp.test_size[1])).cuda().float(), 3] = torch.from_numpy(np.array(exp.test_size[1]-1)).cuda().float()

            box_id_tmp = 0
            for tmp_i in outputs[0]:
                box_id_tmp += 1
                tmp_np = tmp_i.cpu().numpy()
                # print(str(frame_id_tmp) ,  str(box_id_tmp) ,tmp_np[:4] , tmp_np[2:4]-tmp_np[0:2] , tmp_np[4]*tmp_np[5])  #打印信息
            frame_id_tmp += 1

            # 限制因为细胞在边缘，检测框长宽比异常，经过卡尔曼预测之后框尺寸错误的情况；
            bbox_ratio_thresh = 2.0
            edge = 20
            bbox_i = 0
            draw_output_results = outputs[0].cpu().numpy()
            for draw_box_pos in draw_output_results:
                scores = draw_box_pos[4] * draw_box_pos[5]
                bboxes = draw_box_pos[:4]  # x1y1x2y2
                bbox_x = draw_box_pos[0]
                bbox_y = draw_box_pos[1]
                bbox_w = draw_box_pos[2] - draw_box_pos[0]
                bbox_h = draw_box_pos[3] - draw_box_pos[1]
                if (bbox_w > bbox_h):
                    bbox_ratio = bbox_w / bbox_h
                    fix_size = bbox_w
                else:
                    bbox_ratio = bbox_h / bbox_w
                    fix_size = bbox_h
                if (bbox_ratio < bbox_ratio_thresh):
                    pass  # 尺寸正常，不进行操作
                else:
                    # 尺寸异常，进行修正
                    draw_box_pos[2] = draw_box_pos[0] + fix_size
                    draw_box_pos[3] = draw_box_pos[1] + fix_size
                    outputs_copy = outputs[0][bbox_i]
                    outputs_copy[2] = torch.tensor(draw_box_pos[0] + fix_size)
                    outputs_copy[3] = torch.tensor(draw_box_pos[1] + fix_size)
                bbox_i += 1

            if outputs[0] is not None:
                online_targets = tracker.update(outputs[0], [img_info['height'], img_info['width']], exp.test_size)

                for tmp_i in range(0, len(online_targets)):
                    # print(str(frame_id_tmp-1), str(tmp_i), online_targets[tmp_i].tlbr , online_targets[tmp_i].tlwh)
                    # print(str(frame_id_tmp - 1), str(tmp_i), online_targets[tmp_i].tlbr[0], online_targets[tmp_i].tlbr[1],online_targets[tmp_i].tlbr[2],online_targets[tmp_i].tlbr[3] , online_targets[tmp_i].track_id)
                    # 输出格式:
                    single_obj = str(frame_id_tmp - 1) + ',' + '-1' + ',' + str(
                        format(online_targets[tmp_i].tlbr[0], '.3f')) + ',' + str(
                        format(online_targets[tmp_i].tlbr[1], '.3f')) + ',' + str(
                        format(online_targets[tmp_i].tlbr[2], '.3f')) + ',' + str(
                        format(online_targets[tmp_i].tlbr[3], '.3f')) + ',' + str(
                        online_targets[tmp_i].score) + ',-1,-1,-1\n'
                    det_txt.write(single_obj)  # \n 换行符
                    # print(str(frame_id_tmp - 1) + ',' + '-1' + ',' +
                    #       str(format(online_targets[tmp_i].tlbr[0], '.3f')) + ',' +
                    #       str(format(online_targets[tmp_i].tlbr[1], '.3f')) + ',' +
                    #       str(format(online_targets[tmp_i].tlbr[2], '.3f')) + ',' +
                    #       str(format(online_targets[tmp_i].tlbr[3], '.3f')) + ',' +
                    #       str(online_targets[tmp_i].score) + ',-1,-1,-1')

            online_tlwhs = []
            online_ids = []
            online_scores = []
            for t in online_targets:
                tlwh = t.tlwh
                tid = t.track_id
                vertical = tlwh[2] / tlwh[3] > 1.6
                if tlwh[2] * tlwh[3] > args.min_box_area and not vertical:
                    online_tlwhs.append(tlwh)
                    online_ids.append(tid)
                    online_scores.append(t.score)
            timer.toc()
            results.append((frame_id + 1, online_tlwhs, online_ids, online_scores))
            online_im = plot_tracking(img_info['raw_img'], online_tlwhs, online_ids, frame_id=frame_id + 1,
                                      fps=1. / timer.average_time)
            if args.save_result:
                cv2.imshow('online_im', online_im)
                vid_writer.write(online_im)
            ch = cv2.waitKey(1)
            if ch == 27 or ch == ord("q") or ch == ord("Q"):
                break
        else:
            break
        frame_id += 1
    det_txt.close()

def main(exp, args):
    torch.cuda.set_device('cuda:0')
    if not args.experiment_name:
        args.experiment_name = exp.exp_name

    file_name = os.path.join(exp.output_dir, args.experiment_name)
    os.makedirs(file_name, exist_ok=True)

    # if args.save_result:
    vis_folder = os.path.join(file_name, "track_vis")
    os.makedirs(vis_folder, exist_ok=True)

    if args.trt:
        args.device = "gpu"

    logger.info("Args: {}".format(args))

    if args.conf is not None:
        exp.test_conf = args.conf
    if args.nms is not None:
        exp.nmsthre = args.nms
    if args.tsize is not None:
        exp.test_size = (args.tsize, args.tsize)

    model = exp.get_model()
    logger.info("Model Summary: {}".format(get_model_info(model, exp.test_size)))

    if args.device == "gpu":
        model.cuda()
    model.eval()

    if not args.trt:
        if args.ckpt is None:
            ckpt_file = os.path.join(file_name, "best_ckpt.pth.tar")
        else:
            ckpt_file = args.ckpt
        logger.info("loading checkpoint")
        ckpt = torch.load(ckpt_file, map_location="cpu")
        # load the model state dict
        model.load_state_dict(ckpt["model"])
        logger.info("loaded checkpoint done.")

    if args.fuse:
        logger.info("\tFusing model...")
        model = fuse_model(model)

    if args.fp16:
        model = model.half()  # to FP16

    if args.trt:
        assert not args.fuse, "TensorRT model is not support model fusing!"
        trt_file = os.path.join(file_name, "model_trt.pth")
        assert os.path.exists(
            trt_file
        ), "TensorRT model is not found!\n Run python3 tools/trt.py first!"
        model.head.decode_in_inference = False
        decoder = model.head.decode_outputs
        logger.info("Using TensorRT to inference")
    else:
        trt_file = None
        decoder = None

    predictor = Predictor(model, exp, trt_file, decoder, args.device, args.fp16)
    current_time = time.localtime()
    if args.demo == "image":
        image_demo(predictor, vis_folder, args.path, current_time, args.save_result)
    elif args.demo == "video" or args.demo == "webcam":
        imageflow_demo(predictor, vis_folder, current_time, args)


if __name__ == "__main__":
    args = make_parser().parse_args()
    print(args)
    exp = get_exp(args.exp_file, args.name)

    main(exp, args)


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    1. 导出预测模型(torch 👉 onnx)

      python export_onnx.py --output-name bytetrack_x.onnx -f exps/example/mot/yolox_x_ablation.py -c YOLOX_outputs/yolox_x_ablation/best_ckpt.pth.tar


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    2. 转trt模型(onnx 👉 trt)

      cp path_to_model/bytetrack_x.onnx tensorrt_home/bin	(拷贝onnx)
cd tensorrt_home/bin
trtexec.exe --onnx=bytetrack_x.onnx --saveEngine=bytetrack_x.engine --buildOnly


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    五.VS2019运行C++预测

    主要参照官方给的Deploy里的2、3条

    1. 将转好的trt模型拷贝到.\YOLOX_outputs\yolox_s_mix_det下，修改并替换CmakeLists，用CMake工具生成vs2019工程，在release模式下编译。

    cmakelist.txt (自行修改标记了 # 的地方)

    cmake_minimum_required(VERSION 3.0)

project(bytetrack)

list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
set(CMAKE_CXX_FLAGS "-std=c++0x")
set(OpenCV_DIR D:\\software\\opencv\\opencv460\\build) #修改
find_package(OpenCV REQUIRED)
add_definitions(-std=c++11)
add_definitions(-DAPI_EXPORTS)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Release)
set(CUDA_BIN_PATH C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6)  #修改
set(TRT_DIR "D:\\lbq\\TensorRT-8.4.1.5")  #修改
set(TRT_INCLUDE_DIRS ${TRT_DIR}\\include)
set(TRT_LIB_DIRS ${TRT_DIR}\\lib)
set(Eigen3_PATH D:\\lbq\\eigen\\eigen-3.4.0) #修改,在网上下载完成后无需编译直接调用
set(Dirent_INCLUDE_DIRS "D:\\lbq\\dirent\\include") #修改,在网上下载完成后无需编译直接调用

find_package(CUDA REQUIRED)
set(CUDA_NVCC_PLAGS ${CUDA_NVCC_PLAGS};-std=c++11;-g;-G;-gencode;arch=compute_86;code=sm_86)

if(WIN32)
enable_language(CUDA)
endif(WIN32)

# include_directories(${PROJECT_SOURCE_DIR}/deepsort/include)
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different

# tensorrt
link_directories(${TRT_LIB_DIRS})
link_directories(${OpenCV_LIB_DIRS})

include_directories(
    ${CUDA_INCLUDE_DIRS}
    ${OpenCV_INCLUDE_DIRS}
	${TRT_INCLUDE_DIRS}
	${Eigen3_PATH}
	${Dirent_INCLUDE_DIRS}
	${PROJECT_SOURCE_DIR}/include
)
link_directories(${PROJECT_SOURCE_DIR}/include)

file(GLOB My_Source_Files ${PROJECT_SOURCE_DIR}/src/*.cpp)
add_executable(bytetrack ${My_Source_Files})
target_link_libraries(bytetrack nvinfer)
target_link_libraries(bytetrack cudart)
target_link_libraries(bytetrack ${OpenCV_LIBS})

add_definitions(-O2 -pthread)



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    1. 将主程序代码bytetrack.cpp用我写的替换，注意其中我的文件调用是用了绝对路径，第444行和479行。
    #include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "logging.h"
#include "BYTETracker.h"

#define CUDA_CHECK(callstr)\
    {\
        cudaError_t error_code = callstr;\
        if (error_code != cudaSuccess) {\
            std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__;\
            assert(0);\
        }\
    }

#define CHECK(status) \
    do\
    {\
        auto ret = (status);\
        if (ret != 0)\
        {\
            cerr << "Cuda failure: " << ret << endl;\
            abort();\
        }\
    } while (0)

#define DEVICE 0  // GPU id
#define NMS_THRESH 0.7
#define BBOX_CONF_THRESH 0.1

using namespace nvinfer1;

std::string keys =
"{ help  h     | | Print help message. }"
"{ @alias      | | An alias name of model to extract preprocessing parameters from models.yml file. }"
"{ zoo         | models.yml | An optional path to file with preprocessing parameters }"
"{ device      |  0 | camera device number. }"
"{ input i     | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
"{ classes     | | Optional path to a text file with names of classes to label detected objects. }"
"{ thr         | .5 | Confidence threshold. }"
"{ nms         | .4 | Non-maximum suppression threshold. }"
"{ backend     |  0 | Choose one of computation backends: "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA }"
"{ target      | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }"
"{ async       | 0 | Number of asynchronous forwards at the same time. "
"Choose 0 for synchronous mode }";
// stuff we know about the network and the input/output blobs

// s
//static const int INPUT_W = 1088;
//static const int INPUT_H = 608;

// others(x/l/m)
static const int INPUT_W = 1440;
static const int INPUT_H = 800;

const char* INPUT_BLOB_NAME = "images";
const char* OUTPUT_BLOB_NAME = "output";
static Logger gLogger;

Mat static_resize(Mat& img) {
    float r = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));
    // r = std::min(r, 1.0f);
    int unpad_w = r * img.cols;
    int unpad_h = r * img.rows;
    Mat re(unpad_h, unpad_w, CV_8UC3);
    resize(img, re, re.size());
    Mat out(INPUT_H, INPUT_W, CV_8UC3, Scalar(114, 114, 114));
    re.copyTo(out(Rect(0, 0, re.cols, re.rows)));
    return out;
}

struct GridAndStride
{
    int grid0;
    int grid1;
    int stride;
};

static void generate_grids_and_stride(const int target_w, const int target_h, vector<int>& strides, vector<GridAndStride>& grid_strides)
{
    for (auto stride : strides)
    {
        int num_grid_w = target_w / stride;
        int num_grid_h = target_h / stride;
        for (int g1 = 0; g1 < num_grid_h; g1++)
        {
            for (int g0 = 0; g0 < num_grid_w; g0++)
            {
                GridAndStride a{ g0, g1, stride };
                grid_strides.push_back(a);
            }
        }
    }
}

static inline float intersection_area(const Object& a, const Object& b)
{
    Rect_<float> inter = a.rect & b.rect;
    return inter.area();
}

static void qsort_descent_inplace(vector<Object>& faceobjects, int left, int right)
{
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;

    while (i <= j)
    {
        while (faceobjects[i].prob > p)
            i++;

        while (faceobjects[j].prob < p)
            j--;

        if (i <= j)
        {
            // swap
            swap(faceobjects[i], faceobjects[j]);

            i++;
            j--;
        }
    }

    #pragma omp parallel sections
    {
        #pragma omp section
        {
            if (left < j) qsort_descent_inplace(faceobjects, left, j);
        }
        #pragma omp section
        {
            if (i < right) qsort_descent_inplace(faceobjects, i, right);
        }
    }
}

static void qsort_descent_inplace(vector<Object>& objects)
{
    if (objects.empty())
        return;

    qsort_descent_inplace(objects, 0, objects.size() - 1);
}

static void nms_sorted_bboxes(const vector<Object>& faceobjects, vector<int>& picked, float nms_threshold)
{
    picked.clear();

    const int n = faceobjects.size();

    vector<float> areas(n);
    for (int i = 0; i < n; i++)
    {
        areas[i] = faceobjects[i].rect.area();
    }

    for (int i = 0; i < n; i++)
    {
        const Object& a = faceobjects[i];

        int keep = 1;
        for (int j = 0; j < (int)picked.size(); j++)
        {
            const Object& b = faceobjects[picked[j]];

            // intersection over union
            float inter_area = intersection_area(a, b);
            float union_area = areas[i] + areas[picked[j]] - inter_area;
            // float IoU = inter_area / union_area
            if (inter_area / union_area > nms_threshold)
                keep = 0;
        }

        if (keep)
            picked.push_back(i);
    }
}


static void generate_yolox_proposals(vector<GridAndStride> grid_strides, float* feat_blob, float prob_threshold, vector<Object>& objects)
{
    const int num_class = 1;

    const int num_anchors = grid_strides.size();

    for (int anchor_idx = 0; anchor_idx < num_anchors; anchor_idx++)
    {
        const int grid0 = grid_strides[anchor_idx].grid0;
        const int grid1 = grid_strides[anchor_idx].grid1;
        const int stride = grid_strides[anchor_idx].stride;

        const int basic_pos = anchor_idx * (num_class + 5);

        // yolox/models/yolo_head.py decode logic
        float x_center = (feat_blob[basic_pos+0] + grid0) * stride;
        float y_center = (feat_blob[basic_pos+1] + grid1) * stride;
        float w = exp(feat_blob[basic_pos+2]) * stride;
        float h = exp(feat_blob[basic_pos+3]) * stride;
        float x0 = x_center - w * 0.5f;
        float y0 = y_center - h * 0.5f;

        float box_objectness = feat_blob[basic_pos+4];
        for (int class_idx = 0; class_idx < num_class; class_idx++)
        {
            float box_cls_score = feat_blob[basic_pos + 5 + class_idx];
            float box_prob = box_objectness * box_cls_score;
            if (box_prob > prob_threshold)
            {
                Object obj;
                obj.rect.x = x0;
                obj.rect.y = y0;
                obj.rect.width = w;
                obj.rect.height = h;
                obj.label = class_idx;
                obj.prob = box_prob;

                objects.push_back(obj);
            }

        } // class loop

    } // point anchor loop
}

float* blobFromImage(Mat& img){
    cvtColor(img, img, COLOR_BGR2RGB);
    //std::cout << "img.total() " << img.total() * 3 << std::endl;
    float* blob = new float[img.total()*3];
    int channels = 3;
    int img_h = img.rows;
    int img_w = img.cols;
    vector<float> mean = {0.485, 0.456, 0.406};
    vector<float> std = {0.229, 0.224, 0.225};
    for (size_t c = 0; c < channels; c++) 
    {
        for (size_t  h = 0; h < img_h; h++) 
        {
            for (size_t w = 0; w < img_w; w++) 
            {
                //blob[c * img_w * img_h + h * img_w + w] = (((float)img.at(h, w)[c]) / 255.0f - mean[c]) / std[c];
                blob[c * img_w * img_h + h * img_w + w] = (float)img.at<Vec3b>(h, w)[c];
            }
        }
    }
    return blob;
}


static void decode_outputs(float* prob, vector<Object>& objects, float scale, const int img_w, const int img_h) {
        vector<Object> proposals;
        vector<int> strides = {8, 16, 32};
        vector<GridAndStride> grid_strides;
        generate_grids_and_stride(INPUT_W, INPUT_H, strides, grid_strides);
        generate_yolox_proposals(grid_strides, prob,  BBOX_CONF_THRESH, proposals);
        //std::cout << "num of boxes before nms: " << proposals.size() << std::endl;

        qsort_descent_inplace(proposals);

        vector<int> picked;
        nms_sorted_bboxes(proposals, picked, NMS_THRESH);


        int count = picked.size();

        //std::cout << "num of boxes: " << count << std::endl;

        objects.resize(count);
        cout << "Total count: " << count << endl;
        for (int i = 0; i < count; i++)
        {
            objects[i] = proposals[picked[i]];

            // adjust offset to original unpadded
            float x0 = (objects[i].rect.x) / scale;
            float y0 = (objects[i].rect.y) / scale;
            float x1 = (objects[i].rect.x + objects[i].rect.width) / scale;
            float y1 = (objects[i].rect.y + objects[i].rect.height) / scale;

            // clip
            // x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
            // y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
            // x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
            // y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);

            objects[i].rect.x = x0;
            objects[i].rect.y = y0;
            objects[i].rect.width = x1 - x0;
            objects[i].rect.height = y1 - y0;
        }
}

const float color_list[80][3] =
{
    {0.000, 0.447, 0.741},
    {0.850, 0.325, 0.098},
    {0.929, 0.694, 0.125},
    {0.494, 0.184, 0.556},
    {0.466, 0.674, 0.188},
    {0.301, 0.745, 0.933},
    {0.635, 0.078, 0.184},
    {0.300, 0.300, 0.300},
    {0.600, 0.600, 0.600},
    {1.000, 0.000, 0.000},
    {1.000, 0.500, 0.000},
    {0.749, 0.749, 0.000},
    {0.000, 1.000, 0.000},
    {0.000, 0.000, 1.000},
    {0.667, 0.000, 1.000},
    {0.333, 0.333, 0.000},
    {0.333, 0.667, 0.000},
    {0.333, 1.000, 0.000},
    {0.667, 0.333, 0.000},
    {0.667, 0.667, 0.000},
    {0.667, 1.000, 0.000},
    {1.000, 0.333, 0.000},
    {1.000, 0.667, 0.000},
    {1.000, 1.000, 0.000},
    {0.000, 0.333, 0.500},
    {0.000, 0.667, 0.500},
    {0.000, 1.000, 0.500},
    {0.333, 0.000, 0.500},
    {0.333, 0.333, 0.500},
    {0.333, 0.667, 0.500},
    {0.333, 1.000, 0.500},
    {0.667, 0.000, 0.500},
    {0.667, 0.333, 0.500},
    {0.667, 0.667, 0.500},
    {0.667, 1.000, 0.500},
    {1.000, 0.000, 0.500},
    {1.000, 0.333, 0.500},
    {1.000, 0.667, 0.500},
    {1.000, 1.000, 0.500},
    {0.000, 0.333, 1.000},
    {0.000, 0.667, 1.000},
    {0.000, 1.000, 1.000},
    {0.333, 0.000, 1.000},
    {0.333, 0.333, 1.000},
    {0.333, 0.667, 1.000},
    {0.333, 1.000, 1.000},
    {0.667, 0.000, 1.000},
    {0.667, 0.333, 1.000},
    {0.667, 0.667, 1.000},
    {0.667, 1.000, 1.000},
    {1.000, 0.000, 1.000},
    {1.000, 0.333, 1.000},
    {1.000, 0.667, 1.000},
    {0.333, 0.000, 0.000},
    {0.500, 0.000, 0.000},
    {0.667, 0.000, 0.000},
    {0.833, 0.000, 0.000},
    {1.000, 0.000, 0.000},
    {0.000, 0.167, 0.000},
    {0.000, 0.333, 0.000},
    {0.000, 0.500, 0.000},
    {0.000, 0.667, 0.000},
    {0.000, 0.833, 0.000},
    {0.000, 1.000, 0.000},
    {0.000, 0.000, 0.167},
    {0.000, 0.000, 0.333},
    {0.000, 0.000, 0.500},
    {0.000, 0.000, 0.667},
    {0.000, 0.000, 0.833},
    {0.000, 0.000, 1.000},
    {0.000, 0.000, 0.000},
    {0.143, 0.143, 0.143},
    {0.286, 0.286, 0.286},
    {0.429, 0.429, 0.429},
    {0.571, 0.571, 0.571},
    {0.714, 0.714, 0.714},
    {0.857, 0.857, 0.857},
    {0.000, 0.447, 0.741},
    {0.314, 0.717, 0.741},
    {0.50, 0.5, 0}
};

void doInference(IExecutionContext& context, float* input, float* output, const int output_size, Size input_shape) {
    const ICudaEngine& engine = context.getEngine();

    // Pointers to input and output device buffers to pass to engine.
    // Engine requires exactly IEngine::getNbBindings() number of buffers.
    assert(engine.getNbBindings() == 2);
    void* buffers[2];

    // In order to bind the buffers, we need to know the names of the input and output tensors.
    // Note that indices are guaranteed to be less than IEngine::getNbBindings()
    const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);

    assert(engine.getBindingDataType(inputIndex) == nvinfer1::DataType::kFLOAT);
    const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
    assert(engine.getBindingDataType(outputIndex) == nvinfer1::DataType::kFLOAT);

    int mBatchSize = engine.getMaxBatchSize();

    // Create GPU buffers on device
    CUDA_CHECK(cudaMalloc(&buffers[inputIndex], 3 * input_shape.height * input_shape.width * sizeof(float)));
    CUDA_CHECK(cudaMalloc(&buffers[outputIndex], output_size*sizeof(float)));

    // Create stream
    cudaStream_t stream;
    CUDA_CHECK(cudaStreamCreate(&stream));

    // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
    CUDA_CHECK(cudaMemcpyAsync(buffers[inputIndex], input, 3 * input_shape.height * input_shape.width * sizeof(float), cudaMemcpyHostToDevice, stream));
    context.enqueue(1, buffers, stream, nullptr);
    CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], output_size * sizeof(float), cudaMemcpyDeviceToHost, stream));
    cudaStreamSynchronize(stream);

    // Release stream and buffers
    cudaStreamDestroy(stream);
    CUDA_CHECK(cudaFree(buffers[inputIndex]));
    CUDA_CHECK(cudaFree(buffers[outputIndex]));
}

int main() {
    cudaSetDevice(DEVICE);
    
    // create a model using the API directly and serialize it to a stream
    char *trtModelStream{nullptr};
    size_t size{0};

    const string engine_file_path = "D:\\lbq\\TensorRT-8.4.1.5\\bin\\bytetrack_x_cell_1.engine";//engine所在的绝对路径
    ifstream file(engine_file_path, ios::binary);
    if (file.good()) 
    {
        file.seekg(0, file.end);
        size = file.tellg();
        file.seekg(0, file.beg);
        trtModelStream = new char[size];
        assert(trtModelStream);
        file.read(trtModelStream, size);
        file.close();
    }

    //CommandLineParser parser(argc, argv, keys);
    //parser = CommandLineParser(argc, argv, keys);
    //if (TRUE) {
    //    const string engine_file_path = "D:\\lbq\\TensorRT-8.4.1.5\\bin\\bytetrack_x.engine";
    //    ifstream file(engine_file_path, ios::binary);
    //    if (file.good()) {
    //        file.seekg(0, file.end);
    //        size = file.tellg();
    //        file.seekg(0, file.beg);
    //        trtModelStream = new char[size];
    //        assert(trtModelStream);
    //        file.read(trtModelStream, size);
    //        file.close();
    //    }
    //} else {
    //    cerr << "arguments not right!" << endl;
    //    cerr << "run 'python3 tools/trt.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar' to serialize model first!" << std::endl;
    //    cerr << "Then use the following command:" << endl;
    //    cerr << "cd demo/TensorRT/cpp/build" << endl;
    //    cerr << "./bytetrack ../../../../YOLOX_outputs/yolox_s_mix_det/model_trt.engine -i ../../../../videos/palace.mp4  // deserialize file and run inference" << std::endl;
    //    return -1;
    //}
    const string input_video_path = "D:\\lbq\\code\\2_tracking\\ByteTrack\\videos\\test.mp4";//测试视频

    IRuntime* runtime = createInferRuntime(gLogger);
    assert(runtime != nullptr);
    ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
    assert(engine != nullptr); 
    IExecutionContext* context = engine->createExecutionContext();
    assert(context != nullptr);
    delete[] trtModelStream;
    auto out_dims = engine->getBindingDimensions(1);
    auto output_size = 1;
    for(int j=0;j<out_dims.nbDims;j++) {
        output_size *= out_dims.d[j];
    }
    static float* prob = new float[output_size];

    VideoCapture cap(input_video_path);
	if (!cap.isOpened())
		return 0;

	int img_w = cap.get(CAP_PROP_FRAME_WIDTH);
	int img_h = cap.get(CAP_PROP_FRAME_HEIGHT);
    int fps = cap.get(CAP_PROP_FPS);
    long nFrame = static_cast<long>(cap.get(CAP_PROP_FRAME_COUNT));
    cout << "Total frames: " << nFrame << endl;

    VideoWriter writer("demo.mp4", VideoWriter::fourcc('m', 'p', '4', 'v'), fps, Size(img_w, img_h));

    Mat img;
    BYTETracker tracker(fps, 30);
    int num_frames = 0;
    int total_ms = 0;
	while (true)
    {
        if(!cap.read(img))
            break;
        num_frames ++;
        if (num_frames % 20 == 0)
        {
            cout << "Processing frame " << num_frames << " (" << num_frames * 1000000 / total_ms << " fps)" << endl;
        }
		if (img.empty())
			break;
        Mat pr_img = static_resize(img);
        
        float* blob;
        blob = blobFromImage(pr_img);

        float scale = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));
        
        // run inference
        auto start = chrono::system_clock::now();

        //doInference(*context, blob, prob, output_size, pr_img.size());
        //const ICudaEngine& engine = context.getEngine();

        // Pointers to input and output device buffers to pass to engine.
        // Engine requires exactly IEngine::getNbBindings() number of buffers.
        assert(engine->getNbBindings() == 2);
        void* buffers[2];

        // In order to bind the buffers, we need to know the names of the input and output tensors.
        // Note that indices are guaranteed to be less than IEngine::getNbBindings()
        const int inputIndex = engine->getBindingIndex(INPUT_BLOB_NAME);

        assert(engine.getBindingDataType(inputIndex) == nvinfer1::DataType::kFLOAT);
        const int outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME);
        assert(engine.getBindingDataType(outputIndex) == nvinfer1::DataType::kFLOAT);

        // int mBatchSize = engine->getMaxBatchSize();

        // Create GPU buffers on device
        CUDA_CHECK(cudaMalloc(&buffers[inputIndex], 3 * pr_img.size().height * pr_img.size().width * sizeof(float)));
        CUDA_CHECK(cudaMalloc(&buffers[outputIndex], output_size * sizeof(float)));

        // Create stream
        cudaStream_t stream;
        CUDA_CHECK(cudaStreamCreate(&stream));
        cout << pr_img.size() << endl;
        // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
        CUDA_CHECK(cudaMemcpyAsync(buffers[inputIndex], blob, 3 * pr_img.size().height * pr_img.size().width * sizeof(float), cudaMemcpyHostToDevice, stream));
        //context->enqueue(1, buffers, stream, nullptr);
        context->enqueueV2(buffers, stream, nullptr);
        CUDA_CHECK(cudaMemcpyAsync(prob, buffers[outputIndex], output_size * sizeof(float), cudaMemcpyDeviceToHost, stream));
        cudaStreamSynchronize(stream);

        // Release stream and buffers
        cudaStreamDestroy(stream);
        CUDA_CHECK(cudaFree(buffers[inputIndex]));
        CUDA_CHECK(cudaFree(buffers[outputIndex]));

        vector<Object> objects;
        decode_outputs(prob, objects, scale, img_w, img_h);

        vector<STrack> output_stracks = tracker.update(objects);
        auto end = chrono::system_clock::now();
        total_ms = total_ms + chrono::duration_cast<chrono::microseconds>(end - start).count();

        for (int i = 0; i < output_stracks.size(); i++)
		{
			vector<float> tlwh = output_stracks[i].tlwh;
			bool vertical = tlwh[2] / tlwh[3] > 1.6;
			if (tlwh[2] * tlwh[3] > 20 && !vertical)
			{
				Scalar s = tracker.get_color(output_stracks[i].track_id);
				putText(img, format("%d", output_stracks[i].track_id), Point(tlwh[0], tlwh[1] - 5), 
                        0, 0.6, Scalar(0, 0, 255), 2, LINE_AA);
                rectangle(img, Rect(tlwh[0], tlwh[1], tlwh[2], tlwh[3]), s, 2);
			}
		}
        putText(img, format("frame: %d fps: %d num: %d", num_frames, num_frames * 1000000 / total_ms, output_stracks.size()), 
                Point(0, 30), 0, 0.6, Scalar(0, 0, 255), 2, LINE_AA);
        cv::imshow("bytetrack_inference", img);
        writer.write(img);
        cout << "Current frame latency: " << chrono::duration_cast<chrono::microseconds>(end - start).count() / 1000 << "ms" << endl;
        delete blob;
        char c = waitKey(1);
        if (c > 0)
        {
            break;
        }
    }
    cap.release();
    cout << "FPS: " << num_frames * 1000000 / total_ms << endl;
    // destroy the engine
    context->destroy();
    engine->destroy();
    runtime->destroy();
    return 0;
}


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    3.运行

    请添加图片描述

    请添加图片描述

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  • 原文地址：https://blog.csdn.net/qq_33642342/article/details/125674422