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03.Object detection
Object detection
Object detection1. Enable optimal performance of the motherboard1.1. Enable MAX power mode1.2. Enable Jetson Clocks2. Target prediction: imageEffect preview3. Target prediction: videoEffect preview4. Target prediction: real-time detection4.1. USB cameraEffect preview4.2, CSI cameraEffect previewReferences
Using Python to demonstrate Ultralytics: 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 2
1.2. Enable Jetson Clocks
Enabling Jetson Clocks will ensure that all CPU and GPU cores run at maximum frequency:
xxxxxxxxxxsudo jetson_clocks
2. Target prediction: image
Use yolo11n.pt to predict the images provided by the ultralytics project.
Enter the code folder:
xxxxxxxxxxcd /home/jetson/ultralytics/ultralytics/yahboom_demo
Run the code:
xxxxxxxxxxpython3 01.detection_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.pt")# Run batched inference on a list of imagesresults = model("/home/jetson/ultralytics/ultralytics/assets/bus.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/bus_output.jpg") # save to disk3. Target prediction: video
Use yolo11n.pt to predict videos under the ultralytics project (not the videos that come with ultralytics).
Enter the code folder:
xxxxxxxxxxcd /home/jetson/ultralytics/ultralytics/yahboom_demo
Run the code:
xxxxxxxxxxpython3 01.detection_video.py
Effect preview
Video location of yolo recognition output: /home/jetson/ultralytics/ultralytics/output/
Sample code:
xxxxxxxxxximport cv2from ultralytics import YOLO# Load the YOLO modelmodel = YOLO("/home/jetson/ultralytics/ultralytics/yolo11n.pt")# Open the video filevideo_path = "/home/jetson/ultralytics/ultralytics/videos/people_animals.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/01.people_animals_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", 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 windowcap.release()out.release()cv2.destroyAllWindows()4. Target prediction: real-time detection
4.1. USB camera
Use yolo11n.pt to predict the USB camera image.
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 01.detection_camera_usb.py
Effect preview
Video location of yolo recognition output: /home/jetson/ultralytics/ultralytics/output/
Sample code:
xxxxxxxxxximport cv2from ultralytics import YOLO# Load the YOLO modelmodel = YOLO("/home/jetson/ultralytics/ultralytics/yolo11n.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/01.detection_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", 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 windowcap.release()out.release()cv2.destroyAllWindows()4.2, CSI camera
Use yolo11n.pt to predict the CSI camera image.
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 01.detection_camera_csi.py
Effect preview
Video location of yolo recognition output: /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.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/01.detection_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))