Welcome to ROSMASTER M3 repository

Object Detection

Note: Using the Docker container in the factory image does not require re-setting up the environment. The environment is already set up. Simply follow the previous tutorial to enter Docker and run the corresponding function commands.

1. Object Prediction: Image

Use yolo11n.pt to predict images provided 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

Preview

Yolo recognition output image location:

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

Sample code:

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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")  

2. Target Prediction: Video

Use yolo11n.pt to predict videos in the Ultralytics project (not included with Ultralytics).

Go to 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

Preview

Yolo recognition output video location: /root/ultralytics/ultralytics/output/

Sample Code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/root/ultralytics/ultralytics/yolo11n.pt")
​
# Open the video file
video_path = "/root/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 = "/root/ultralytics/ultralytics/output/01.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", annotated_frame)
​
        # 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. Target Prediction: Real-Time Detection

3.1. Starting the Camera

Start the following program based on your camera model and enter，

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#USB camera
ros2 launch usb_cam camera.launch.py
#nuwa camera
ros2 launch ascamera hp60c.launch.py

Open another terminal and navigate to the code folder:

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cd /root/ultralytics/ultralytics/yahboom_demo

Run the code: Click the preview screen and press q to terminate the program!

 CLI use
Instance segmentation