File size: 1,628 Bytes
014de59 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 4 13:35:09 2024
@author: ysnrfd
"""
import cv2
from ultralytics import YOLO
# Load the YOLOv8n model (nano) for ultra-fast inference
model = YOLO('yolov10n.pt') # Replace with the path to your YOLOv8n model
# Open a connection to the camera
cap = cv2.VideoCapture(0)
# Check if the camera opened successfully
if not cap.isOpened():
print("Error: Could not open camera.")
exit()
# Set the camera resolution (lower resolution for speed)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 512)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 512)
while True:
# Capture frame-by-frame
ret, frame = cap.read()
if not ret:
print("Error: Failed to capture image")
break
# Perform object detection with YOLOv8n
results = model(frame, imgsz=512, stream=True) # Adjust img size if necessary
# Draw bounding boxes and labels on the frame
for result in results:
boxes = result.boxes.data.cpu().numpy()
for box in boxes:
x1, y1, x2, y2, score, class_id = map(int, box)
label = f"{model.names[class_id]}: {score:.2f}"
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 1) # Thin box for speed
cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1) # Thin text for speed
# Display the resulting frame
cv2.imshow('YOLOv8n Real-Time Detection', frame)
# Break the loop on 'q' key press
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the capture and close all windows
cap.release()
cv2.destroyAllWindows()
|