使用Ultralytics YOLOv8 🚀 进行实例分割和跟踪
什么是实例分割?
Ultralytics YOLOv8实例分割包括识别和勾勒图像中的单个物体,提供对空间分布的详细了解。与语义分割不同的是,它对每个物体进行唯一标记和精确划分,这对物体检测和医学成像等任务至关重要。
Ultralytics 软件包中有两种类型的实例分割跟踪:
-
使用类对象进行实例分割:每个类对象都有一种独特的颜色,以便进行清晰的视觉区分。
-
使用对象轨迹进行实例分割:每个轨迹都用不同的颜色表示,便于识别和跟踪。
观看: 使用对象跟踪技术进行实例分割Ultralytics YOLOv8
样品
实例分割 | 实例分割 + 物体跟踪 |
---|---|
Ultralytics 实例分割 😍 | Ultralytics 利用对象跟踪进行实例分割 🔥 |
实例分割和跟踪
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
model = YOLO("yolov8n-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):
annotator.seg_bbox(mask=mask,
mask_color=colors(int(cls), True),
det_label=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()
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
from collections import defaultdict
track_history = defaultdict(lambda: [])
model = YOLO("yolov8n-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):
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()
seg_bbox
论据
名称 | 类型 | 默认值 | 说明 |
---|---|---|---|
mask |
array |
None |
分割掩码坐标 |
mask_color |
tuple |
(255, 0, 255) |
每个分割框的屏蔽颜色 |
det_label |
str |
None |
分段对象的标签 |
track_label |
str |
None |
被分割和跟踪物体的标签 |
备注
如有任何疑问,请随时在Ultralytics 问题板块或下面提到的讨论板块发布您的问题。