Image Classification

Image Classification1. Model Introduction2. Image Classification: ImageEffect preview3. Image classification: videoEffect preview4. Image classification: real-time detection4.1. USB cameraEffect preview4.2, CSI cameraEffect previewReferences

Use Python to demonstrate the effect of Ultralytics: Image classification on images, videos, and real-time detection.

1. Model Introduction

Image classification is the simplest of the three tasks and involves classifying the entire image into one of a set of predefined categories.

The output of an image classifier is a single class label and a confidence score. Image classification is very useful when you only need to know which class an image belongs to, without knowing the location or exact shape of the object in that class.

2. Image Classification: Image

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

Enter the code folder:


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cd /home/pi/ultralytics/ultralytics/yahboom_demo

Run the code:


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python3 04.classification_image.py

Effect preview

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

Sample code:


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from ultralytics import YOLO
# Load a model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo11n-cls.pt")
# Run batched inference on a list of images
results = model("/home/pi/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/pi/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:


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cd /home/pi/ultralytics/ultralytics/yahboom_demo

Run the code:


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python3 04.classification_video.py

Effect preview

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

Sample code:


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import cv2
from ultralytics import YOLO
# Load the YOLO model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo11n-cls.pt")
# Open the video file
video_path = "/home/pi/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/pi/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

4.1. USB camera

Use yolo11n-cls.pt to predict the USB camera screen.

Enter the code folder:


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cd /home/pi/ultralytics/ultralytics/yahboom_demo

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


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python3 04.classification_camera_usb.py

Effect preview

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

Sample code:


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import cv2
from ultralytics import YOLO
# Load the YOLO model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo11n-cls.pt")
# Open the cammera
cap = cv2.VideoCapture(0)
# 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/pi/ultralytics/ultralytics/output/04.classification_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 yolo11n-cls.pt to predict the CSI camera image.

Enter the code folder:


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cd /home/pi/ultralytics/ultralytics/yahboom_demo

Run the code: Click the preview image, press the q key to terminate the program!


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python3 04.classification_camera_csi.py

Effect preview

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

Sample code:


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import cv2
from picamera2 import Picamera2
from ultralytics import YOLO
# Initialize the Picamera2
picam2 = Picamera2()
picam2.preview_configuration.main.size = (640, 480)
picam2.preview_configuration.main.format = "RGB888"
picam2.preview_configuration.align()
picam2.configure("preview")
picam2.start()
# Load the YOLO11 model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo11n-cls.pt")
# Set up video output
output_path = "/home/pi/ultralytics/ultralytics/output/04.classification_camera_csi.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, 30, (640, 480))
while True:
    # Capture frame-by-frame
    frame = picam2.capture_array()
    # Run YOLO11 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
picam2.close()
out.release()
cv2.destroyAllWindows()

References

https://docs.ultralytics.com/tasks/classify/