# YOLOX_deepsort_tracker

**Repository Path**: Alpha9527/YOLOX_deepsort_tracker

## Basic Information

- **Project Name**: YOLOX_deepsort_tracker
- **Description**: using yolox+deepsort for object-tracking
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 2
- **Created**: 2021-10-28
- **Last Updated**: 2023-04-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# YOLOX_deepsort_tracker

<div align="center">
<p>
<img src="utils/img2.gif" width="400"/> <img src="utils/img1.gif" width="400"/> 
</p>
<br>
<div>

</div>

</div>

## :tada: How to use

### &#8627; Tracker example

```python
from tracker import Tracker

tracker = Tracker()    # instantiate Tracker

cap = cv2.VideoCapture('test.mp4')  # open one video

while True:
    _, im = cap.read() # read frame from video
    if im is None:
       break
    
    img_visual, bbox = tracker.update(img)  # feed one frame and get result
    
    cv2.imshow('demo', image)	# imshow
    cv2.waitKey(1)
    if cv2.getWindowProperty('demo', cv2.WND_PROP_AUTOSIZE) < 1:
        break

cap.release()
cv2.destroyAllWindows()
```

Tracker uses YOLOX as detector to get each target's boundingbox, and use deepsort to get every bbox's ID.

### &#8627; Select specific category

If you just  want to track only some specific categories, you can set by param *filter_classes*.

For example:

```python
tracker = Tracker(filter_classes=['car','person']) 
```

## &#8627; Detector example

If you don't need tracking and just want to use YOLOX for object-detection, you can use the class **Detector** to inference easliy .

For example:

```python
from detector import Detector
import cv2
detector = Detector() # instantiate Detector

img = cv2.imread('YOLOX/assets/dog.jpg') 	# load image
result = detector.detect(img) 	# detect targets

img_visual = result['visual'] 	 # visualized image
cv2.imshow('detect', img_visual) # imshow
cv2.waitKey(0)
```

You can also get more information like *raw_img/boudingbox/score/class_id* from the result of detector.

## :art: Install

1. Clone the repository recursively:

   ```bash
   git clone --recurse-submodules https://github.com/pmj110119/YOLOX_deepsort_tracker.git
   ```

   If you already cloned and forgot to use `--recurse-submodules` you can run `git submodule update --init`(clone最新的YOLOX仓库)

2. Make sure that you fulfill all the requirements: Python 3.8 or later with all [requirements.txt](https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch/blob/master/requirements.txt) dependencies installed, including torch>=1.7. To install, run:

   `pip install -r requirements.txt`


## :zap: Select a YOLOX family model

1. train your own model or just download pretrained models from https://github.com/Megvii-BaseDetection/YOLOX

   | Model                                       | size | mAP<sup>test<br>0.5:0.95 | Speed V100<br>(ms) | Params<br>(M) | FLOPs<br>(G) |                           weights                            |
   | ------------------------------------------- | :--: | :----------------------: | :----------------: | :-----------: | :----------: | :----------------------------------------------------------: |
   | [YOLOX-s](./exps/default/yolox_s.py)        | 640  |           39.6           |        9.8         |      9.0      |     26.8     | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EW62gmO2vnNNs5npxjzunVwB9p307qqygaCkXdTO88BLUg?e=NMTQYw)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_s.pth) |
   | [YOLOX-m](./exps/default/yolox_m.py)        | 640  |           46.4           |        12.3        |     25.3      |     73.8     | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/ERMTP7VFqrVBrXKMU7Vl4TcBQs0SUeCT7kvc-JdIbej4tQ?e=1MDo9y)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_m.pth) |
   | [YOLOX-l](./exps/default/yolox_l.py)        | 640  |           50.0           |        14.5        |     54.2      |    155.6     | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EWA8w_IEOzBKvuueBqfaZh0BeoG5sVzR-XYbOJO4YlOkRw?e=wHWOBE)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_l.pth) |
   | [YOLOX-x](./exps/default/yolox_x.py)        | 640  |         **51.2**         |        17.3        |     99.1      |    281.9     | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EdgVPHBziOVBtGAXHfeHI5kBza0q9yyueMGdT0wXZfI1rQ?e=tABO5u)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_x.pth) |
   | [YOLOX-Darknet53](./exps/default/yolov3.py) | 640  |           47.4           |        11.1        |     63.7      |    185.3     | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EZ-MV1r_fMFPkPrNjvbJEMoBLOLAnXH-XKEB77w8LhXL6Q?e=mf6wOc)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_darknet53.pth) |

   Download **yolox_s.pth** to the folder **weights** , which is the default model path of **Tracker**.

2. You can also use other yolox models as detector,. For example:

   ```python
   """
   YOLO family: yolox-s, yolox-m, yolox-l, yolox-x, yolox-tiny, yolox-nano, yolov3
   """
   # yolox-s example
   detector = Tracker(model='yolox-s', ckpt='./yolox_s.pth')
   # yolox-m example
   detector = Tracker(model='yolox-m', ckpt='./yolox_m.pth')
   ```

##  :rose: Run demo

```python
python demo.py --path=test.mp4
```