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.
В пакете Ultralytics доступны два типа отслеживания сегментации инстансов:
Сегментация экземпляра с помощью объектов класса: Каждому объекту класса присваивается уникальный цвет для четкого визуального разделения.
Сегментация экземпляров с помощью треков объектов: Каждый трек представлен отдельным цветом, что облегчает его идентификацию и отслеживание.
Смотри: Instance Segmentation with Object Tracking using Ultralytics YOLO11
Образцы
| Сегментация экземпляров | Сегментация объектов + отслеживание объектов |
|---|---|
 |  |
| Ultralytics Сегментация экземпляра 😍. | Ultralytics Сегментация экземпляров с помощью отслеживания объектов 🔥. |
Сегментация и отслеживание экземпляров
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 Аргументы
| Имя | Тип | По умолчанию | Описание |
|---|---|---|---|
mask | array | None | Координаты маски сегментации |
mask_color | RGB | (255, 0, 255) | Цвет маски для каждой сегментированной коробки |
label | str | None | Метка для сегментированного объекта |
txt_color | RGB | None | Цвет метки для сегментированного и отслеживаемого объекта |
Примечание
По всем вопросам не стесняйся писать в разделе вопросовUltralytics или в разделе обсуждений, который указан ниже.
ВОПРОСЫ И ОТВЕТЫ
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:
Пример
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?
Чтобы реализовать слежение за объектами, используй model.track метод и убедись, что ID каждого объекта последовательно присваивается во всех кадрах. Ниже приведен простой пример:
Пример
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()
Узнай больше в разделе Instance Segmentation and Tracking.
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.
