Instance Segmentation

Demonstrating Ultralytics Instance Segmentation effects on images, videos, and real-time detection using Python.

1. Model Introduction

Instance segmentation goes a step further than object detection; it involves identifying individual objects in an image and segmenting them from the rest of the image.

The output of an instance segmentation model is a set of masks or contours outlining each object in the image, along with class labels and confidence scores for each object. Instance segmentation is very useful when you need to know not only where objects are in an image, but also their specific shapes.

2. Instance Segmentation: Image

Use the yolo26n-seg_ncnn_model to predict images in the ultralytics folder.

Navigate to the code folder:

cd /home/sunrise/ultralytics/ultralytics/yahboom_demo_26

Run the code:

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python3 02.segmentation_image.py

Preview

yolo recognition output image location: /home/sunrise/ultralytics/ultralytics/output/

Sample code:

​x
from ultralytics import YOLO
​
# Load a model
model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-seg_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-seg.pt")
​
# Run batched inference on a list of images
results = model("/home/sunrise/ultralytics/ultralytics/assets/zidane.jpg")  # return a list of Results objects
​
# Process results list
for result in results:
    # boxes = result.boxes  # Boxes object for bounding box outputs
    masks = result.masks  # Masks object for segmentation masks outputs
    # keypoints = result.keypoints  # Keypoints object for pose outputs
    # probs = result.probs  # Probs object for classification outputs
    # obb = result.obb  # Oriented boxes object for OBB outputs
    result.show()  # display to screen
    result.save(filename="/home/sunrise/ultralytics/ultralytics/output/zidane_output.jpg")  # save to disk
​

3. Instance Segmentation: Video

Use the yolo26n-seg_ncnn_model to predict videos in the ultralytics folder (non-ultralytics built-in videos).

Navigate to the code folder:

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cd /home/sunrise/ultralytics/ultralytics/yahboom_demo_26

Run the code:

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python3 02.segmentation_video.py

Preview

yolo recognition output video location: /home/sunrise/ultralytics/ultralytics/output/

Sample code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-seg_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-seg.pt")
​
# Open the video file
video_path = "/home/sunrise/ultralytics/ultralytics/videos/people_animals.mp4"
cap = cv2.VideoCapture(video_path)
​
# Get the video frame size and frame rate
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
​
# Define the codec and create a VideoWriter object to output the processed video
output_path = "/home/sunrise/ultralytics/ultralytics/output/02.people_animals_output.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can use 'XVID' or 'mp4v' depending on your platform
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
​
# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()
​
    if success:
        # Run YOLO inference on the frame
        results = model(frame)
​
        # Visualize the results on the frame
        annotated_frame = results[0].plot()
​
        # Write the annotated frame to the output video file
        out.write(annotated_frame)
​
        # Display the annotated frame
        cv2.imshow("YOLO Inference", cv2.resize(annotated_frame, (640, 480)))
​
        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break
# Release the video capture and writer objects, and close the display window
cap.release()
out.release()
cv2.destroyAllWindows()
​

4. Instance Segmentation: Real-time Detection

4.1. USB Camera

Use the yolo26n-seg_ncnn_model to predict USB camera feed.

Navigate to the code folder:

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cd /home/sunrise/ultralytics/ultralytics/yahboom_demo_26

Run the code: Click on the preview window, press the q key to terminate the program!

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python3 02.segmentation_camera_usb.py

Preview

yolo recognition output video location: /home/sunrise/ultralytics/ultralytics/output/

Sample code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-seg_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-seg.pt")
​
​
# Open the cammera
cap = cv2.VideoCapture(0)
cap.set(6, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
​
# Get the video frame size and frame rate
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
​
# Define the codec and create a VideoWriter object to output the processed video
output_path = "/home/sunrise/ultralytics/ultralytics/output/02.segmentation_camera_usb.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can use 'XVID' or 'mp4v' depending on your platform
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
​
# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()
​
    if success:
        # Run YOLO inference on the frame
        results = model(frame)
​
        # Visualize the results on the frame
        annotated_frame = results[0].plot()
​
        # Write the annotated frame to the output video file
        out.write(annotated_frame)
​
        # Display the annotated frame
        cv2.imshow("YOLO Inference", cv2.resize(annotated_frame, (640, 480)))
​
        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break
# Release the video capture and writer objects, and close the display window
cap.release()
out.release()
cv2.destroyAllWindows()
​

4.2. CSI Camera

Use the yolo26n-seg_ncnn_model to predict CSI camera feed.

Navigate to the code folder:

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cd /home/sunrise/ultralytics/ultralytics/yahboom_demo_26

Run the code: Click on the preview window, press the q key to terminate the program!

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python3 02.segmentation_camera_csi.py

When this prompt appears in the terminal, press 2, then Enter.

Preview

yolo recognition output video location: /home/sunrise/ultralytics/ultralytics/output/

Sample code:

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import cv2
from SunriseRobotLib import Mipi_Camera
​
from ultralytics import YOLO
​
width=640
height=480
g_camera = Mipi_Camera(width, height, debug=False)
​
# Load the YOLO model
model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-seg_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-seg.pt")
​
# Set up video output
output_path = "/home/sunrise/ultralytics/ultralytics/output/02.segmentation_camera_csi.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, 30, (640, 480))
​
# Loop through the video frames
while True:
    # Capture frame-by-frame
    ret, frame = g_camera.read()
    frame  =  cv2.resize(frame,(640,480))
​
    # Run YOLO11 inference on the frame
    results = model(frame)
​
    # Visualize the results on the frame
    annotated_frame = results[0].plot()
​
    # Write the frame to the video file
    out.write(annotated_frame)
​
    # Display the resulting frame
    cv2.imshow("Camera", annotated_frame)
​
    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) == ord("q"):
        break
​
# Release resources and close windows
g_camera.release()
out.release()
cv2.destroyAllWindows()
​

References

Instance Segmentation - Ultralytics YOLO Documentation