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Instance Segmentation and Tracking using Ultralytics YOLO11 🚀

What is Instance Segmentation?

Ultralytics YOLO11 instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike semantic segmentation, it uniquely labels and precisely delineates each object, crucial for tasks like object detection and medical imaging.

Ultralytics paketinde iki tür örnek segmentasyon takibi mevcuttur:

  • Sınıf Nesneleri ile Örnek Segmentasyonu: Net bir görsel ayrım için her sınıf nesnesine benzersiz bir renk atanır.

  • Nesne İzleri ile Örnek Segmentasyonu: Her iz farklı bir renkle temsil edilir, bu da kolay tanımlama ve izlemeyi kolaylaştırır.



İzle: Instance Segmentation with Object Tracking using Ultralytics YOLO11

Örnekler

Örnek Segmentasyonu Örnek Segmentasyonu + Nesne Takibi
Ultralytics Örnek Segmentasyonu Ultralytics Nesne Takibi ile Örnek Segmentasyonu
Ultralytics Örnek Segmentasyonu 😍 Ultralytics Nesne Takibi ile Örnek Segmentasyonu 🔥

Örnek Segmentasyonu ve İzleme

import cv2

from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors

model = YOLO("yolo11n-seg.pt")  # segmentation model
names = model.model.names
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

out = cv2.VideoWriter("instance-segmentation.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

while True:
    ret, im0 = cap.read()
    if not ret:
        print("Video frame is empty or video processing has been successfully completed.")
        break

    results = model.predict(im0)
    annotator = Annotator(im0, line_width=2)

    if results[0].masks is not None:
        clss = results[0].boxes.cls.cpu().tolist()
        masks = results[0].masks.xy
        for mask, cls in zip(masks, clss):
            color = colors(int(cls), True)
            txt_color = annotator.get_txt_color(color)
            annotator.seg_bbox(mask=mask, mask_color=color, label=names[int(cls)], txt_color=txt_color)

    out.write(im0)
    cv2.imshow("instance-segmentation", im0)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

out.release()
cap.release()
cv2.destroyAllWindows()
from collections import defaultdict

import cv2

from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors

track_history = defaultdict(lambda: [])

model = YOLO("yolo11n-seg.pt")  # segmentation model
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

out = cv2.VideoWriter("instance-segmentation-object-tracking.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

while True:
    ret, im0 = cap.read()
    if not ret:
        print("Video frame is empty or video processing has been successfully completed.")
        break

    annotator = Annotator(im0, line_width=2)

    results = model.track(im0, persist=True)

    if results[0].boxes.id is not None and results[0].masks is not None:
        masks = results[0].masks.xy
        track_ids = results[0].boxes.id.int().cpu().tolist()

        for mask, track_id in zip(masks, track_ids):
            color = colors(int(track_id), True)
            txt_color = annotator.get_txt_color(color)
            annotator.seg_bbox(mask=mask, mask_color=color, label=str(track_id), txt_color=txt_color)

    out.write(im0)
    cv2.imshow("instance-segmentation-object-tracking", im0)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

out.release()
cap.release()
cv2.destroyAllWindows()

seg_bbox Argümanlar

İsim Tip Varsayılan Açıklama
mask array None Segmentasyon maskesi koordinatları
mask_color RGB (255, 0, 255) Her bölümlenmiş kutu için maske rengi
label str None Bölümlere ayrılmış nesne için etiket
txt_color RGB None Segmente edilen ve izlenen nesne için etiket rengi

Not

Sorularınız için Ultralytics Sorun Bölümüne veya aşağıda belirtilen tartışma bölümüne sorularınızı göndermekten çekinmeyin.

SSS

How do I perform instance segmentation using Ultralytics YOLO11?

To perform instance segmentation using Ultralytics YOLO11, initialize the YOLO model with a segmentation version of YOLO11 and process video frames through it. Here's a simplified code example:

Örnek

import cv2

from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors

model = YOLO("yolo11n-seg.pt")  # segmentation model
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

out = cv2.VideoWriter("instance-segmentation.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

while True:
    ret, im0 = cap.read()
    if not ret:
        break

    results = model.predict(im0)
    annotator = Annotator(im0, line_width=2)

    if results[0].masks is not None:
        clss = results[0].boxes.cls.cpu().tolist()
        masks = results[0].masks.xy
        for mask, cls in zip(masks, clss):
            annotator.seg_bbox(mask=mask, mask_color=colors(int(cls), True), det_label=model.model.names[int(cls)])

    out.write(im0)
    cv2.imshow("instance-segmentation", im0)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

out.release()
cap.release()
cv2.destroyAllWindows()

Learn more about instance segmentation in the Ultralytics YOLO11 guide.

What is the difference between instance segmentation and object tracking in Ultralytics YOLO11?

Instance segmentation identifies and outlines individual objects within an image, giving each object a unique label and mask. Object tracking extends this by assigning consistent labels to objects across video frames, facilitating continuous tracking of the same objects over time. Learn more about the distinctions in the Ultralytics YOLO11 documentation.

Why should I use Ultralytics YOLO11 for instance segmentation and tracking over other models like Mask R-CNN or Faster R-CNN?

Ultralytics YOLO11 offers real-time performance, superior accuracy, and ease of use compared to other models like Mask R-CNN or Faster R-CNN. YOLO11 provides a seamless integration with Ultralytics HUB, allowing users to manage models, datasets, and training pipelines efficiently. Discover more about the benefits of YOLO11 in the Ultralytics blog.

How can I implement object tracking using Ultralytics YOLO11?

Nesne izlemeyi uygulamak için model.track yöntemini kullanın ve her nesnenin kimliğinin çerçeveler arasında tutarlı bir şekilde atandığından emin olun. Aşağıda basit bir örnek verilmiştir:

Örnek

from collections import defaultdict

import cv2

from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors

track_history = defaultdict(lambda: [])

model = YOLO("yolo11n-seg.pt")  # segmentation model
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

out = cv2.VideoWriter("instance-segmentation-object-tracking.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

while True:
    ret, im0 = cap.read()
    if not ret:
        break

    annotator = Annotator(im0, line_width=2)
    results = model.track(im0, persist=True)

    if results[0].boxes.id is not None and results[0].masks is not None:
        masks = results[0].masks.xy
        track_ids = results[0].boxes.id.int().cpu().tolist()

        for mask, track_id in zip(masks, track_ids):
            annotator.seg_bbox(mask=mask, mask_color=colors(track_id, True), track_label=str(track_id))

    out.write(im0)
    cv2.imshow("instance-segmentation-object-tracking", im0)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

out.release()
cap.release()
cv2.destroyAllWindows()

Örnek Segmentasyonu ve İzleme bölümünde daha fazlasını bulabilirsiniz.

Are there any datasets provided by Ultralytics suitable for training YOLO11 models for instance segmentation and tracking?

Yes, Ultralytics offers several datasets suitable for training YOLO11 models, including segmentation and tracking datasets. Dataset examples, structures, and instructions for use can be found in the Ultralytics Datasets documentation.


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