infer_bytetrack

Multiple object tracking algorithm for object detection using ByteTrack.

🚀 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()