Welcome to DOFBOT SE repository

Instance Segmentation

Note: If you are using the factory-installed system image, there is no need to set up the environment from scratch; it is already configured. Simply follow the tutorial to execute the corresponding commands to utilize the features.

1. Instance Segmentation: Images

Use the yolo11n.pt model to perform predictions on the sample images included within the Ultralytics project.

Navigate to the code directory:

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cd ~/ultralytics/ultralytics/yahboom_demo

Run the code:

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python3 02.segmentation_image.py

Preview of Results

Location of the output images generated by YOLO: ~/ultralytics/ultralytics/output/

Sample Code:

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from ultralytics import YOLO
​
# Load a model
model = YOLO("/home/yahboom/ultralytics/ultralytics/yolo11n-seg.pt")  # pretrained yolo11n-seg model
# model = YOLO("/home/yahboom/ultralytics/ultralytics/yolo11n-seg.onnx")
# model = YOLO("/home/yahboom/ultralytics/ultralytics/yolo11n-seg_ncnn_model")
​
# Run batched inference on a list of images
results = model("/home/yahboom/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/yahboom/ultralytics/ultralytics/output/zidane_output.jpg")  # save to disk
​

2. Instance Segmentation: Video

Use yolo11n.pt to perform predictions on a video located within the Ultralytics project directory (specifically, a video other than the default examples included with Ultralytics).

Navigate to the code directory:

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cd /home/yahboom/ultralytics/ultralytics/yahboom_demo

Run the code:

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python3 02.segmentation_video.py

Preview of Results

Location of the output video (generated by YOLO detection): /home/yahboom/ultralytics/ultralytics/output/

Sample Code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/yahboom/ultralytics/ultralytics/yolo11n-seg.pt")
# model = YOLO("/home/yahboom/ultralytics/ultralytics/yolo11n-seg.onnx")
# model = YOLO("/home/yahboom/ultralytics/ultralytics/yolo11n-seg_ncnn_model")
​
# Open the video file
video_path = "/home/yahboom/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/yahboom/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()
​

3. Instance Segmentation: Real-time Detection

3.1. Launching the Camera

Launch the program below according to your specific camera model. Enter the following command in the terminal:

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cd /home/yahboom/ultralytics/ultralytics/yahboom_demo

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

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python3 02.segmentation_camera_usb.py

Preview

Example Code Path: /home/yahboom/ultralytics/ultralytics/yahboom_demo/02.segmentation_camera_usb.py