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https://github.com/pmj110119/YOLOX_deepsort_tracker
using yolox+deepsort for object-tracking
https://github.com/pmj110119/YOLOX_deepsort_tracker
deep-sort deepsort object-detection object-tracker pytorch tracker yolo yolo-x yolov3 yolov5 yolox
Last synced: 12 days ago
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using yolox+deepsort for object-tracking
- Host: GitHub
- URL: https://github.com/pmj110119/YOLOX_deepsort_tracker
- Owner: pmj110119
- Created: 2021-07-30T15:52:18.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2024-03-16T07:06:59.000Z (3 months ago)
- Last Synced: 2024-03-16T08:24:00.544Z (3 months ago)
- Topics: deep-sort, deepsort, object-detection, object-tracker, pytorch, tracker, yolo, yolo-x, yolov3, yolov5, yolox
- Language: Python
- Homepage:
- Size: 59.6 MB
- Stars: 278
- Watchers: 4
- Forks: 58
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
Lists
- awesome-yolo-object-detection - pmj110119/YOLOX_deepsort_tracker - tracking. (Applications)
README
# YOLOX_deepsort_tracker
## :tada: How to use
### ↳ Tracker example
```python
from tracker import Tracker
tracker = Tracker() # instantiate Tracker
cap = cv2.VideoCapture('test.mp4') # open one video
while True:
_, img = cap.read() # read frame from video
if img is None:
break
img_visual, bbox = tracker.update(img) # feed one frame and get result
cv2.imshow('demo', img_visual) # 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.
### ↳ 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'])
```
## ↳ 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 | mAPtest
0.5:0.95 | Speed V100
(ms) | Params
(M) | FLOPs
(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
```