7-7 Instance Segmentation
Learning Objectives
Instance segmentation is a more fine-grained task in computer vision than object detection. It not only identifies what an object is and where it is, but also determines the precise shape of each object.
For a single image, the model outputs:
1. Object Category (What)
For example: person, car, dog
2. Instance Mask
Each object has a pixel-level binary mask
→ accurately outlines the shape of the object
We will use Python and a pre-trained model to perform instance segmentation.
Perform instance segmentation on an image and print the results.
from ultralytics import YOLO
model = YOLO("yolo11n-seg.pt")
results = model.predict("https://ultralytics.com/images/bus.jpg")
for result in results:
print(f'mask: {result.masks.data}') # Mask data (Number of objects x Height x Width)
The results are as follows: you will obtain a mask array with dimensions number of objects × height × width, where 0 indicates that the pixel does not belong to the object, and 1 indicates that the pixel belongs to the object.
Perform instance segmentation using a webcam and visualize the results.
import cv2
from ultralytics import YOLO
model = YOLO("yolo11n-seg.pt")
video_path = 0
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
success, frame = cap.read()
if success:
results = model.predict(frame)
annotated_frame = results[0].plot()
cv2.imshow("YOLO Inference", annotated_frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
else:
break
cap.release()
cv2.destroyAllWindows()
The results are as follows.
