YOLO (You Only Look Once) is a real-time object detection system that is widely used in various applications such as self-driving cars, surveillance systems, and facial recognition software. YOLO V8 is the latest version of YOLO, released in 2022.
Here are some key features of YOLO V8:
Improved accuracy: YOLO V8 has improved object detection accuracy compared to its predecessors, especially for objects with complex shapes and sizes.
Real-time performance: YOLO V8 is designed for real-time object detection and can process images and videos at high frame rates.
**Multi-scale features:**YOLO V8 uses multi-scale features to detect objects of different sizes and shapes.
Improved bounding box regression: YOLO V8 has improved bounding box regression, which helps to more accurately detect the location and size of objects.
New algorithms: YOLO V8 includes several new algorithms, such as spatial pyramid pooling and a new loss function, that improve object detection accuracy and speed.
Support for multiple platforms: YOLO V8 can be run on a variety of platforms, including Windows, Linux, and Android.
If you're interested in using YOLO V8 for a specific project, you can find more information and resources on the YOLO website, including documentation, tutorials, and sample code.
import cv2
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO('yolov8l.engine')
# Open the video file
video_path = "path/to/your/video/file.mp4"
cap = cv2.VideoCapture(0)
# Loop through the video frames
while cap.isOpened():
# Read a frame from the video
success, frame = cap.read()
if success:
# Run YOLOv8 tracking on the frame, persisting tracks between frames
results = model.track(frame, persist=True)
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow("YOLOv8 Tracking", 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 object and close the display window
cap.release()
cv2.destroyAllWindows()
