Object Detection

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

1. Model Introduction

Object detection is a task that involves identifying the location and class of objects in an image or video stream.

The output of an object detector is a set of bounding boxes surrounding objects in the image, along with class labels and confidence scores for each box. If you need to identify objects of interest in a scene but do not need to know their exact location or precise shape, object detection is a good choice.

2. Object Prediction: Image

Use the yolo26n_ncnn_model to predict images in the ultralytics folder.

Navigate to the code folder:

cd /home/sunrise/ultralytics/ultralytics/yahboom_demo_26

Run the code:

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python3 01.detection_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_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n.pt")
​
# Run batched inference on a list of images
results = model("/home/sunrise/ultralytics/ultralytics/assets/bus.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/bus_output.jpg")  # save to disk

3. Object Prediction: Video

Use the yolo26n_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 01.detection_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_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n.pt")
​
# Open the video file
video_path = "/home/sunrise/ultralytics/ultralytics/videos/people_animals.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/01.people_animals_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. Object Prediction: Real-time Detection

4.1. USB Camera

Use the yolo26n_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 01.detection_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_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n.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/01.detection_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_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 01.detection_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 YOLO model
model = YOLO("/home/sunrise/ultralytics/ultralytics/yolo26n_ncnn_model")
# model = YOLO("/home/pi/ultralytics26/yolo26n.pt")
​
# Set up video output
output_path = "/home/sunrise/ultralytics/ultralytics/output/01.detection_camera_csi.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, 30, (640, 480))
​
while True:
    # Capture frame-by-frame
    ret, frame = g_camera.read()
    frame  =  cv2.resize(frame,(640,480))
​
    # 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
g_camera.release()
out.release()
cv2.destroyAllWindows()

References

Object Detection - Ultralytics YOLO Documentation