infer_bytetrack

infer_bytetrack

About

1.0.0
MIT

Infer ByteTrack for object tracking

Task: Object tracking
multiple
object
tracking
kalman

Multiple object tracking algorithm for object detection using ByteTrack.

Example git

🚀 Use with Ikomia API

1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.

pip install ikomia

2. Create your workflow

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
import cv2

# Init your workflow
wf = Workflow()

# Add object detection algorithm
detector = wf.add_task(name="infer_yolo_v7", auto_connect=True)

# Add ByteTrack tracking algorithm
tracking = wf.add_task(name="infer_bytetrack", auto_connect=True)

stream = cv2.VideoCapture(0)
while True:
    # Read image from stream
    ret, frame = stream.read()

    # Test if streaming is OK
    if not ret:
        continue

    # Run the workflow on current frame
    wf.run_on(array=frame)

    # Get results
    image_out = tracking.get_output(0)
    obj_detect_out = tracking.get_output(1)

    # Display
    img_res = cv2.cvtColor(image_out.get_image_with_graphics(obj_detect_out), cv2.COLOR_BGR2RGB)
    display(img_res, title="ByteTrack", viewer="opencv")

    # Press 'q' to quit the streaming process
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# After the loop release the stream object
stream.release()
# Destroy all windows
cv2.destroyAllWindows()

☀️ Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

  • If you haven't started using Ikomia Studio yet, download and install it from this page.
  • For additional guidance on getting started with Ikomia Studio, check out this blog post.

📝 Set algorithm parameters

  • categories (str) - Default 'all': Categories of objects you want to track. Use a comma separated string to set multiple categories (ex: "dog,person,car").
  • conf_thres (float) - Default '0.25': Object detection confidence threshold
  • conf_thres_match (float) - Default '0.7': Threshold for considering an assignment valid.
  • track_buffer (int) - Default '30': Buffer size.
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_bytetrack", auto_connect=True)

algo.set_parameters({
    "param1": "value1",
    "param2": "value2",
    ...
})

🔍 Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the documentation.

from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add detection algorithm
detector = wf.add_task(name="infer_yolo_v7", auto_connect=True)

# Add algorithm
track = wf.add_task(name="infer_bytetrack", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/main/examples/img/img_work.jpg")

# Iterate over outputs
for output in track.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()

Developer

  • Ikomia
    Ikomia

License

A short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.

PermissionsConditionsLimitations

Commercial use

License and copyright notice

Liability

Modification

Warranty

Distribution

Private use

This is not legal advice: this description is for informational purposes only and does not constitute the license itself. Provided by choosealicense.com.