最新版本yolov5+deepsort目标检测和追踪，支持5.0版本可训练自己数据集

Last update: Dec 30, 2022

Related tags

Deep Learning Yolov5-Deepsort

Overview

本文禁止转载!

本文地址：https://blog.csdn.net/weixin_44936889/article/details/112002152

项目简介：

使用YOLOv5+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中。

代码地址（欢迎star）：

https://github.com/Sharpiless/yolov5-deepsort/

最终效果：

YOLOv5检测器：

class Detector(baseDet):

    def __init__(self):
        super(Detector, self).__init__()
        self.init_model()
        self.build_config()

    def init_model(self):

        self.weights = 'weights/yolov5m.pt'
        self.device = '0' if torch.cuda.is_available() else 'cpu'
        self.device = select_device(self.device)
        model = attempt_load(self.weights, map_location=self.device)
        model.to(self.device).eval()
        model.half()
        # torch.save(model, 'test.pt')
        self.m = model
        self.names = model.module.names if hasattr(
            model, 'module') else model.names

    def preprocess(self, img):

        img0 = img.copy()
        img = letterbox(img, new_shape=self.img_size)[0]
        img = img[:, :, ::-1].transpose(2, 0, 1)
        img = np.ascontiguousarray(img)
        img = torch.from_numpy(img).to(self.device)
        img = img.half()  # 半精度
        img /= 255.0  # 图像归一化
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        return img0, img

    def detect(self, im):

        im0, img = self.preprocess(im)

        pred = self.m(img, augment=False)[0]
        pred = pred.float()
        pred = non_max_suppression(pred, self.threshold, 0.4)

        pred_boxes = []
        for det in pred:

            if det is not None and len(det):
                det[:, :4] = scale_coords(
                    img.shape[2:], det[:, :4], im0.shape).round()

                for *x, conf, cls_id in det:
                    lbl = self.names[int(cls_id)]
                    if not lbl in ['person', 'car', 'truck']:
                        continue
                    x1, y1 = int(x[0]), int(x[1])
                    x2, y2 = int(x[2]), int(x[3])
                    pred_boxes.append(
                        (x1, y1, x2, y2, lbl, conf))

        return im, pred_boxes

调用 self.detect 方法返回图像和预测结果

DeepSort追踪器：

deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
                    max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
                    nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
                    max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
                    use_cuda=True)

调用 self.update 方法更新追踪结果

运行demo：

python demo.py

训练自己的模型：

参考我的另一篇博客：

【小白CV】手把手教你用YOLOv5训练自己的数据集（从Windows环境配置到模型部署）

训练好后放到 weights 文件夹下

调用接口：

创建检测器：

from AIDetector_pytorch import Detector

det = Detector()

调用检测接口：

result = det.feedCap(im)

其中 im 为 BGR 图像

返回的 result 是字典，result['frame'] 返回可视化后的图像

联系作者：

B站：https://space.bilibili.com/470550823

CSDN：https://blog.csdn.net/weixin_44936889

AI Studio：https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156

Github：https://github.com/Sharpiless

遵循 GNU General Public License v3.0 协议，标明目标检测部分来源：https://github.com/ultralytics/yolov5/

最新版本yolov5+deepsort目标检测和追踪，支持5.0版本可训练自己数据集

Related tags

Overview

本文禁止转载!

项目简介：

YOLOv5检测器：

DeepSort追踪器：

运行demo：

训练自己的模型：

调用接口：

创建检测器：

调用检测接口：

联系作者：

Owner

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models

Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

Config files for my GitHub profile.

Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)

Tutorials and implementations for "Self-normalizing networks"

A modular PyTorch library for optical flow estimation using neural networks

[ICCV'21] Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

Code from the paper "High-Performance Brain-to-Text Communication via Handwriting"

[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception

CS5242_2021 - Neural Networks and Deep Learning, NUS CS5242, 2021

[NeurIPS 2020] Blind Video Temporal Consistency via Deep Video Prior

Official PaddlePaddle implementation of Paint Transformer

NeuPy is a Tensorflow based python library for prototyping and building neural networks

(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"

Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX.

git git《Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking》(CVPR 2021) GitHub:git2] 《Masksembles for Uncertainty Estimation》(CVPR 2021) GitHub:git3]

PyTorch implementation of "A Simple Baseline for Low-Budget Active Learning".

Code for STFT Transformer used in BirdCLEF 2021 competition.

PRTR: Pose Recognition with Cascade Transformers