Oriented Bounding Boxes Object Detection

Demonstrating Ultralytics Oriented Bounding Boxes Object Detection effects on images, videos, and real-time detection using Python.

1. Model Introduction

Oriented bounding box object detection, also called oriented object detection, goes a step further than standard object detection by introducing an additional angle to more accurately locate objects in an image.

The output of an oriented object detector is a set of rotated bounding boxes that precisely enclose objects in the image, along with class labels and confidence scores for each bounding box. Oriented bounding boxes are especially useful when objects appear at different angles, such as in aerial imagery, where traditional axis-aligned bounding boxes may include unnecessary background.

In simple terms, oriented object detection can use rotated boxes to precisely enclose rotated objects, thereby reducing unnecessary background areas and improving detection accuracy.

2. Oriented Bounding Boxes Object Detection: Image

Use the yolo26n-obb_ncnn_model to predict images in the ultralytics folder (non-ultralytics built-in images).

Navigate to the code folder:

cd /home/sunrise/ultralytics/ultralytics/yahboom_demo_26

Run the code:

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python3 05.obb_image.py

Preview

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

Sample code:

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from ultralytics import YOLO
​
# Load a model
model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-obb_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-obb.pt")
​
# Run batched inference on a list of images
results = model("/home/sunrise/ultralytics/ultralytics/assets/car.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/car_output.jpg")  # save to disk
​

3. Oriented Bounding Boxes Object Detection: Video

Use the yolo26n-obb_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 05.obb_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-obb_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-obb.pt")
​
# Open the video file
video_path = "/home/sunrise/ultralytics/ultralytics/videos/street.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/05.street_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. Oriented Bounding Boxes Object Detection: Real-time Detection

4.1. USB Camera

Use the yolo26n-obb_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 05.obb_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-obb_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-obb.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/05.obb_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-obb_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 05.obb_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 YOLO11 model
model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-obb_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n-obb.pt")
​
# Set up video output
output_path = "/home/sunrise/ultralytics/ultralytics/output/05.obb_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 YOLO 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

Oriented Bounding Boxes Object Detection - Ultralytics YOLO Documentation