This repository contains various projects using the YOLOv8 model for tasks such as object detection, segmentation, tracking, pose estimation, counting, and customer detection. Each project is designed to demonstrate the capabilities of YOLOv8 in real-world applications.
YOLO (You Only Look Once) is a state-of-the-art model for object detection and segmentation. This repository provides implementations of various YOLOv8 functionalities using the Ultralytics YOLO library and OpenCV.
To get started, you need to install the required libraries. You can do this using pip:
pip install ultralytics opencv-pythonEnsure you have Python installed on your machine. This project is compatible with Python 3.7 and above.
This project demonstrates real-time object detection using YOLOv8.
Script: object_detection.py
import cv2
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO('yolov8n.pt')
# Initialize the video capture object
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Error: Could not open video stream.")
exit()
while True:
ret, frame = cap.read()
if not ret:
print("Error: Could not read frame.")
break
# Perform inference on the frame
results = model(frame)
# Annotate the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow('YOLOv8 Detection', annotated_frame)
# Exit on pressing 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()This project demonstrates real-time object segmentation using YOLOv8.
Script: object_segmentation.py
import cv2
from ultralytics import YOLO
# Load the YOLOv8 segmentation model
model = YOLO('yolov8n-seg.pt')
# Initialize the video capture object
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Error: Could not open video stream.")
exit()
while True:
ret, frame = cap.read()
if not ret:
print("Error: Could not read frame.")
break
# Perform inference on the frame
results = model(frame)
# Annotate the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow('YOLOv8 Segmentation', annotated_frame)
# Exit on pressing 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()This project demonstrates real-time object tracking using YOLOv8.
Script: object_tracking.py
import cv2
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO('yolov8n.pt')
# Initialize the video capture object
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Error: Could not open video stream.")
exit()
tracker = cv2.TrackerKCF_create()
while True:
ret, frame = cap.read()
if not ret:
print("Error: Could not read frame.")
break
# Perform inference on the frame
results = model(frame)
# Annotate the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow('YOLOv8 Tracking', annotated_frame)
# Exit on pressing 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()This project demonstrates real-time pose estimation using YOLOv8.
Script: pose_estimation.py
import cv2
from ultralytics import YOLO
# Load the YOLOv8 pose estimation model
model = YOLO('yolov8n-pose.pt')
# Initialize the video capture object
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Error: Could not open video stream.")
exit()
while True:
ret, frame = cap.read()
if not ret:
print("Error: Could not read frame.")
break
# Perform inference on the frame
results = model(frame)
# Annotate the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow('YOLOv8 Pose Estimation', annotated_frame)
# Exit on pressing 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()This project demonstrates real-time object counting using YOLOv8.
Script: object_counting.py
import cv2
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO('yolov8n.pt')
# Initialize the video capture object
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Error: Could not open video stream.")
exit()
while True:
ret, frame = cap.read()
if not ret:
print("Error: Could not read frame.")
break
# Perform inference on the frame
results = model(frame)
# Count the number of detected objects
num_objects = len(results[0].boxes)
# Annotate the frame with the count
annotated_frame = results[0].plot()
cv2.putText(annotated_frame, f'Count: {num_objects}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
# Display the annotated frame
cv2.imshow('YOLOv8 Object Counting', annotated_frame)
# Exit on pressing 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()This project demonstrates real-time customer detection in a retail environment using YOLOv8.
Script: customer_detection.py
import cv2
from ultralytics import YOLO
# Load the YOLOv8 model trained for customer detection
model = YOLO('yolov8n-custom.pt')
# Initialize the video capture object
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Error: Could not open video stream.")
exit()
while True:
ret, frame = cap.read()
if not ret:
print("Error: Could not read frame.")
break
# Perform inference on the frame
results = model(frame)
# Annotate the frame
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow('YOLOv8 Customer Detection', annotated_frame)
# Exit on pressing 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()Contributions are welcome! Please open an issue or submit a pull request if you have any suggestions or improvements.
- Fork the repository.
- Create a new branch:
git checkout -b feature-branch. - Make your changes and commit them:
git commit -m 'Add some feature'. - Push to the branch:
git push origin feature-branch. - Open a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.