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825a2e4 | 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 | import cv2
from ultralytics import YOLO
import tensorflow as tf
# Load a larger YOLOv8 model for better accuracy
model = YOLO('yolov8m.pt') # You can use 'yolov8m.pt', 'yolov8l.pt', etc., for more accuracy
# Initialize the video capture
cap = cv2.VideoCapture(0)
with tf.device('/device:GPU:0'): # Ensuring we use the GPU if available
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Perform inference on the frame
results = model(frame)
# Extract detection results
for result in results:
boxes = result.boxes.xyxy.cpu().numpy() # Bounding boxes
scores = result.boxes.conf.cpu().numpy() # Confidence scores
classes = result.boxes.cls.cpu().numpy() # Class IDs
# Draw bounding boxes and labels on the frame
for i in range(len(boxes)):
box = boxes[i]
score = scores[i]
class_id = int(classes[i])
label = model.names[class_id]
if score > 0.5: # Adjusted threshold for more confident detections
# Extract box coordinates
start_x, start_y, end_x, end_y = map(int, box[:4])
# Draw the bounding box
cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
# Draw the label and score
label_text = f"{label}: {score:.2f}"
cv2.putText(frame, label_text, (start_x, start_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# Display the frame
cv2.imshow('YOLOv8 Object Detection', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release resources
cap.release()
cv2.destroyAllWindows()
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