YOLOX_deepsort_tracker

yolox+deepsort实现目标跟踪

最新的yolox尝尝鲜~~（yolox正处在频繁更新阶段，因此直接链接yolox仓库作为子模块）

🎉 How to use Detector and Tracker

↳ Detect example

from detector import Detector

detector = Detector(model='yolox-s', ckpt='yolo_s.pth') # instantiate Detector

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

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

Detector uses yolo-x family models to detect targets.

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

↳ Track example

from tracker import Tracker

tracker = Tracker(model='yolox-s', ckpt='yolo_s.pth') # instantiate Tracker

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

while True:
    _, frame = cap.read()	# get new frame
    if frame is None:
       break
    result = tracker.update(frame)	# detect and track targets
    
    cv2.imshow('demo', result['visual'])	# imshow visualized frame
    cv2.waitKey(1)

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

🎨 Install

Clone the repository recursively:

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)
Make sure that you fulfill all the requirements: Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install, run:

pip install -r requirements.txt

⚡ Select a YOLOX family model

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

select the model and checkpoint when using Detector and Tracker

for example:

"""
YOLO family: yolox-s, yolox-m, yolox-l, yolox-x, yolox-tiny, yolox-nano, yolov3
"""
# yolox-s example
detector = Detector(model='yolox-s', ckpt='./yolo_s.pth')
# yolox-m example
detector = Detector(model='yolox-m', ckpt='./yolo_m.pth')

👏 Run demo

Detect on image

python .\demo.py --mode=detect --file=dog.jpg

Track on video

python .\demo.py --mode=track --file=test.mp4

Filter tracked classes

coming soon...

Train your own model