Image classification

Using Python to demonstrate Ultralytics: Image classification in images, videos, and real-time detection.

1. Enable optimal performance of the motherboard

Enable Jetson clocks

Enabling Jetson Clocks will ensure that all CPU and GPU cores run at maximum frequency:

sudo jetson_clocks

2. Image classification: Image

Use yolo11n-cls.pt to predict images under the ultralytics project (not ultralytics built-in images).

Enter the code folder:

xxxxxxxxxx
cd /home/jetson/ultralytics/ultralytics/yahboom_demo

Run the code:

xxxxxxxxxx
python3 04.classification_image.py

Effect preview

Yolo recognition output image location: /home/jetson/ultralytics/ultralytics/output/

Sample code:

​x
from ultralytics import YOLO
​
# Load a model
model = YOLO("/home/jetson/ultralytics/ultralytics/yolo11n-cls.pt")
​
# Run batched inference on a list of images
results = model("/home/jetson/ultralytics/ultralytics/assets/dog.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/jetson/ultralytics/ultralytics/output/dog_output.jpg")  # save to disk

3. Image classification: video

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

Enter the code folder:

xxxxxxxxxx
cd /home/jetson/ultralytics/ultralytics/yahboom_demo

Run the code:

xxxxxxxxxx
python3 04.classification_video.py

Effect preview

Video location of yolo recognition output: /home/jetson/ultralytics/ultralytics/output/

Sample code:

xxxxxxxxxx
import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/jetson/ultralytics/ultralytics/yolo11n-cls.pt")
​
# Open the video file
video_path = "/home/jetson/ultralytics/ultralytics/videos/cup.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/jetson/ultralytics/ultralytics/output/04.cup_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. Image classification: real-time detection

Use yolo11n-cls.pt to predict the camera image.

Start the camera.

xxxxxxxxxx
#usb camera
ros2 launch usb_cam camera.launch.py 
#nuwa camera
ros2 launch ascamera hp60c.launch.py

Enter the code folder:

xxxxxxxxxx
cd /home/jetson/ultralytics/ultralytics/yahboom_demo

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

xxxxxxxxxx
python3 04.classification_camera_usb.py

Effect preview

Yolo recognizes the output video location: /home/jetson/ultralytics/ultralytics/output/

Sample code:

x
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image, CompressedImage
from cv_bridge import CvBridge
import cv2
from ultralytics import YOLO
import os
​
​
​
 Pose estimation
Oriented object detection