Welcome to DOFBOT-Pi repository

Object Detection

Note: If using the docker container from the factory image, you don't need to rebuild the environment. The environment is already set up, you just need to enter the docker according to the previous tutorial and run the corresponding function commands to use it.

1. Object Detection: Image

Use yolo11n.pt to predict images that come with the ultralytics project.

Enter the code folder:

cd /root/ultralytics/ultralytics/yahboom_demo

Run the code:

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python3 01.detection_image.py

Effect Preview

yolo recognition output image location:

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/root/ultralytics/ultralytics/output/bus_output.jpg

Example code:

​x
from ultralytics import YOLO
​
# Load a model
model = YOLO("/root/ultralytics/ultralytics/yolo11n.pt")
​
# Run batched inference on a list of images
results = model("/root/ultralytics/ultralytics/assets/bus.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="/root/ultralytics/ultralytics/output/bus_output.jpg")  # save to disk

2. Object Detection: Video

Use yolo11n.pt to predict videos in the ultralytics project (not videos that come with ultralytics).

Enter the code folder:

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cd /root/ultralytics/ultralytics/yahboom_demo

Run the code:

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python3 01.detection_video.py
 CLI Usage
Instance Segmentation