Computer Vision Web UI

Setup

Local environment

Note

• For non-GPU users, please install CPU version of PyTorch first

pip install -i https://download.pytorch.org/whl/cpu torch torchvision

pip install -r requirements.txt

Streamlit (default)	LightningAI (require install)
streamlit run app.py	lightning run app app.py

Run in docker

docker-compose up

Features

• Run locally on Streamlit / LightningAI web UI

  • Model

    • Object detection
    • Object segmentation
    • Pose estimation
    • Image classification

  • On

    • Image
    • Video
    • Webcam

  • With ability to

    • Turn tracking on/off
    • Adjust confidence threshold
    • Filter by class
    • Object motion path
    • Object color classification
    • Trim video

• Draw visual elements interactively

  • Line count (in/out)
  • Polygon zone count

• Customize visual elements

  • Toggle on/off

    • Box
    • Label
    • Mask
    • Area
    • Trail
    • Count
    • FPS

  • Adjust

    • Text size
    • Text color
    • Text padding
    • Text offset
    • Line thickness
    • Mask opacity
    • Trail length

• PRODUCTION READY

  • Save drawed visual elements & settings in JSON
  • Run inference with OpenCV standalone from saved JSON

Note

Camera (/dev/video0) & native run (cv2.imshow()) is not configured to run in docker (you can try to mount your own device)

TODO

Supported models:

• All YOLOv8 models (Detect, Segment, Pose, Classify)
  • With BoT-SORT / ByteTrack object tracking

Object detection:

• RT-DETR
• YOLO-NAS
• YOLOv5
  • new v5u models
  • legacy v5 models
• YOLOv3

Instance Segmentation

• SAM

Experimental Features

- Fisheye undistortion