Object Detection

This section demonstrates Ultralytics Object Detection on images, videos, and real-time detection using Python.

1、Model Introduction

Object detection is a task that involves identifying the location and category of objects in images or video streams.

The output of an object detector is a set of bounding boxes surrounding objects in the image, along with a class label and confidence score for each box. Object detection is a good choice if you need to identify objects of interest in a scene but don't need to know the exact location or precise shape of the objects.

2、Object Prediction: Images

Use yolo26n_ncnn_model to predict images in the ultralytics26 folder.

Navigate to the code folder:

cd /home/sunrise/ultralytics26/yahboom_demo

Run the code:

xxxxxxxxxx
python3 01.detection_image.py

Preview

Output image location from YOLO detection: /home/sunrise/ultralytics26/output/

Example code:

​x
from ultralytics import YOLO

# Load a model
model = YOLO("/home/sunrise/ultralytics26/yolo26n_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics26/yolo26n.pt")

# Run batched inference on a list of images
results = model("/home/sunrise/ultralytics26/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/ultralytics26/output/bus_output.jpg")  # save to disk

3、Object Prediction: Video

Use yolo26n_ncnn_model to predict videos in the ultralytics26 folder (not Ultralytics built-in videos).

Navigate to the code folder:

xxxxxxxxxx
cd /home/sunrise/ultralytics26/yahboom_demo

Run the code:

xxxxxxxxxx
python3 01.detection_video.py

Preview

Output video location from YOLO detection: /home/sunrise/ultralytics26/output/

Example code:

xxxxxxxxxx
import cv2
from ultralytics import YOLO

# Load the YOLO model
model = YOLO("/home/sunrise/ultralytics26/yolo26n_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics26/yolo26n.pt")

# Open the video file
video_path = "/home/sunrise/ultralytics26/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/ultralytics26/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 yolo26n_ncnn_model to predict from USB camera feed.

Navigate to the code folder:

xxxxxxxxxx
cd /home/sunrise/ultralytics26/yahboom_demo

Run the code: Click the preview window and press 'q' to terminate the program!

xxxxxxxxxx
python3 01.detection_camera_usb.py

Preview

Output video location from YOLO detection: /home/sunrise/ultralytics26/output/

Example code:

xxxxxxxxxx
import cv2
from ultralytics import YOLO

# Load the YOLO model
model = YOLO("/home/sunrise/ultralytics26/yolo26n_ncnn_model")
# model = YOLO("/home/sunrise/ultralytics26/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/ultralytics26/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()

References

Object Detection - Ultralytics YOLO Docs