Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/roboflow/trackers below:

roboflow/trackers: A unified library for object tracking featuring clean room re-implementations of leading multi-object tracking algorithms

trackers is a unified library offering clean room re-implementations of leading multi-object tracking algorithms. Its modular design allows you to easily swap trackers and integrate them with object detectors from various libraries like inference, ultralytics, or transformers.

Pip install the trackers package in a Python>=3.9 environment.

By installing trackers from source, you can explore the most recent features and enhancements that have not yet been officially released. Please note that these updates are still in development and may not be as stable as the latest published release.

pip install git+https://github.com/roboflow/trackers.git

With a modular design, trackers lets you combine object detectors from different libraries with the tracker of your choice. Here's how you can use SORTTracker with various detectors:

import supervision as sv
from trackers import SORTTracker
from inference import get_model

tracker = SORTTracker()
model = get_model(model_id="yolov11m-640")
annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)

def callback(frame, _):
    result = model.infer(frame)[0]
    detections = sv.Detections.from_inference(result)
    detections = tracker.update(detections)
    return annotator.annotate(frame, detections, labels=detections.tracker_id)

sv.process_video(
    source_path="<INPUT_VIDEO_PATH>",
    target_path="<OUTPUT_VIDEO_PATH>",
    callback=callback,
)

import supervision as sv
from trackers import SORTTracker
from ultralytics import YOLO

tracker = SORTTracker()
model = YOLO("yolo11m.pt")
annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)

def callback(frame, _):
    result = model(frame)[0]
    detections = sv.Detections.from_ultralytics(result)
    detections = tracker.update(detections)
    return annotator.annotate(frame, detections, labels=detections.tracker_id)

sv.process_video(
    source_path="<INPUT_VIDEO_PATH>",
    target_path="<OUTPUT_VIDEO_PATH>",
    callback=callback,
)

import torch
import supervision as sv
from trackers import SORTTracker
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor

tracker = SORTTracker()
image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd")
annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)

def callback(frame, _):
    inputs = image_processor(images=frame, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)

    h, w, _ = frame.shape
    results = image_processor.post_process_object_detection(
        outputs,
        target_sizes=torch.tensor([(h, w)]),
        threshold=0.5
    )[0]

    detections = sv.Detections.from_transformers(
        transformers_results=results,
        id2label=model.config.id2label
    )

    detections = tracker.update(detections)
    return annotator.annotate(frame, detections, labels=detections.tracker_id)

sv.process_video(
    source_path="<INPUT_VIDEO_PATH>",
    target_path="<OUTPUT_VIDEO_PATH>",
    callback=callback,
)

The code is released under the Apache 2.0 license.

We welcome all contributions—whether it’s reporting issues, suggesting features, or submitting pull requests. Please read our contributor guidelines to learn about our processes and best practices.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4