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07.Oriented object detection
Oriented object detection
Oriented object detection1. Enable optimal performance of the motherboard1.1. Enable MAX power mode1.2. Enable Jetson clocks2. Oriented object detection: imageEffect preview3. Directed object detection: videoEffect preview4. Directed object detection: real-time detection4.1. USB cameraEffect preview4.2, CSI cameraEffect previewReferences
Use Python to demonstrate the effects of Ultralytics: Oriented Bounding Boxes Object Detection in images, videos, and real-time detection.
1. Enable optimal performance of the motherboard
1.1. Enable MAX power mode
Enabling MAX Power Mode on Jetson will ensure that all CPU and GPU cores are turned on:
sudo nvpmodel -m 0
1.2. Enable Jetson clocks
Enabling Jetson Clocks will ensure that all CPU and GPU cores run at maximum frequency:
xxxxxxxxxxsudo jetson_clocks
2. Oriented object detection: image
Use yolo11n-obb.pt to predict images under the ultralytics project (not ultralytics built-in images).
Enter the code folder:
xxxxxxxxxxcd /home/jetson/ultralytics/ultralytics/yahboom_demo
Run the code:
xxxxxxxxxxpython3 05.obb_image.py
Effect preview
Yolo recognition output image location：/home/jetson/ultralytics/ultralytics/output/
Sample code:
xfrom ultralytics import YOLO# Load a modelmodel = YOLO("/home/jetson/ultralytics/ultralytics/yolo11n-obb.pt")# Run batched inference on a list of imagesresults = model("/home/jetson/ultralytics/ultralytics/assets/car.jpg") # return a list of Results objects# Process results listfor 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/jetson/ultralytics/ultralytics/output/car_output.jpg") # save to disk3. Directed object detection: video
Use yolo11n-obb.pt to predict the video under the ultralytics project (not the video that comes with ultralytics).
Enter the code folder:
xxxxxxxxxxcd /home/jetson/ultralytics/ultralytics/yahboom_demo
Run the code:
xxxxxxxxxxpython3 05.obb_video.py
Effect preview
Yolo identifies the output video position：/home/jetson/ultralytics/ultralytics/output/
Sample code:
xxxxxxxxxximport cv2from ultralytics import YOLO# Load the YOLO modelmodel = YOLO("/home/jetson/ultralytics/ultralytics/yolo11n-obb.pt")# Open the video filevideo_path = "/home/jetson/ultralytics/ultralytics/videos/street.mp4"cap = cv2.VideoCapture(video_path)# Get the video frame size and frame rateframe_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 videooutput_path = "/home/jetson/ultralytics/ultralytics/output/05.street_output.mp4"fourcc = cv2.VideoWriter_fourcc(*'mp4v') # You can use 'XVID' or 'mp4v' depending on your platformout = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))# Loop through the video frameswhile 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 windowcap.release()out.release()cv2.destroyAllWindows()4. Directed object detection: real-time detection
4.1. USB camera
Use yolo11n-obb.pt to predict the USB camera screen.
Enter the code folder:
xxxxxxxxxxcd /home/jetson/ultralytics/ultralytics/yahboom_demo
Run the code: Click the preview screen and press the q key to terminate the program!
xxxxxxxxxxpython3 05.obb_camera_usb.py
Effect preview
Yolo identifies the output video position：/home/jetson/ultralytics/ultralytics/output/
Sample code:
xxxxxxxxxximport cv2from ultralytics import YOLO# Load the YOLO modelmodel = YOLO("/home/jetson/ultralytics/ultralytics/yolo11n-obb.pt")# Open the cammeracap = cv2.VideoCapture(0)# Get the video frame size and frame rateframe_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 videooutput_path = "/home/jetson/ultralytics/ultralytics/output/05.obb_camera_usb.mp4"fourcc = cv2.VideoWriter_fourcc(*'mp4v') # You can use 'XVID' or 'mp4v' depending on your platformout = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))# Loop through the video frameswhile 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 windowcap.release()out.release()cv2.destroyAllWindows()4.2, CSI camera
Use yolo11n-obb.pt to predict the CSI camera image.
Enter the code folder:
xxxxxxxxxxcd /home/jetson/ultralytics/ultralytics/yahboom_demo
Run the code: Click the preview image, press the q key to terminate the program!
xxxxxxxxxxpython3 05.obb_camera_csi.py
Effect preview
Yolo identifies the output video position：/home/jetson/ultralytics/ultralytics/output/
Sample code:
xxxxxxxxxximport cv2from ultralytics import YOLOfrom jetcam.csi_camera import CSICamera# Load the YOLO modelmodel = YOLO("/home/jetson/ultralytics/ultralytics/yolo11n-obb.pt")# Open the camera (CSI Camera)cap = CSICamera(capture_device=0, width=640, height=480)# Get the video frame size and frame rateframe_width = 640frame_height = 480fps = 30# Define the codec and create a VideoWriter object to output the processed videooutput_path = "/home/jetson/ultralytics/ultralytics/output/05.obb_camera_csi.mp4"fourcc = cv2.VideoWriter_fourcc(*'mp4v') # You can use 'XVID' or 'mp4v' depending on your platformout = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))# Loop through the video frameswhile True:# Read a frame from the cameraframe = cap.read()if frame is not None:# Run YOLO inference on the frameresults = model(frame)# Visualize the results on the frameannotated_frame = results[0].plot()# Write the annotated frame to the output video fileout.write(annotated_frame)# Display the annotated framecv2.imshow("YOLO Inference", cv2.resize(annotated_frame, (640, 480)))# Break the loop if 'q' is pressedif cv2.waitKey(1) & 0xFF == ord("q"):breakelse:# Break the loop if no frame is received (camera error or end of stream)print("No frame received, breaking the loop.")break# Release the video capture and writer objects, and close the display windowcap.release()out.release()cv2.destroyAllWindows()