Instance Segmentation

Use Python to demonstrate Ultralytics: Instance Segmentation effects on images, videos, and real-time detection.

1. Model Introduction

Instance segmentation goes a step further than object detection, involving identifying individual objects in images and segmenting them from the rest of the image.

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

2. Instance Segmentation: Images

Use yolo26n-seg_ncnn_model to predict images in the ultralytics26 folder.

Enter the code folder:

cd /home/sunrise/ultralytics26/yahboom_demo

Run the code:

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python3 02.segmentation_image.py

Effect Preview

yolo recognition output image location: /home/sunrise/ultralytics26/output/

Example code:

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from ultralytics import YOLO
​
# Load a model
model = YOLO("/home/sunrise/ultralytics26/yolo26n-seg_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics26/yolo26n-seg.pt")
​
# Run batched inference on a list of images
results = model("/home/sunrise/ultralytics26/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/ultralytics26/output/zidane_output.jpg")  # save to disk
​

3. Instance Segmentation: Videos

Use yolo26n-seg_ncnn_model to predict videos in the ultralytics26 folder (not videos that come with ultralytics).

Enter the code folder:

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cd /home/sunrise/ultralytics26/yahboom_demo

Run the code:

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python3 02.segmentation_video.py

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

Example code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/sunrise/ultralytics26/yolo26n-seg_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics26/yolo26n-seg.pt")
​
# Open the video file
video_path = "/home/sunrise/ultralytics26/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/ultralytics26/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 yolo26n-seg_ncnn_model to predict USB camera footage.

Enter the code folder:

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cd /home/sunrise/ultralytics26/yahboom_demo

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

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python3 02.segmentation_camera_usb.py

Effect Preview

yolo recognition output video location: /home/sunrise/ultralytics26/output/

Example code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/sunrise/ultralytics26/yolo26n-seg_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics26/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/ultralytics26/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 yolo26n-seg_ncnn_model to predict CSI camera footage.

Enter the code folder:

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cd /home/sunrise/ultralytics26/yahboom_demo

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

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python3 02.segmentation_camera_csi.py

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

Effect Preview

yolo recognition output video location: /home/sunrise/ultralytics26/output/

Example 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/ultralytics26/yolo26n-seg_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics26/yolo26n-seg.pt")
​
# Set up video output
output_path = "/home/sunrise/ultralytics26/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