# Load YOLOv9 model

model = YOLO('yolov9c.pt')

model.to(device)

# Initialize video capture (0 for default camera)

cap = cv2.VideoCapture(0)

# Check if the video capture device is opened

if not cap.isOpened():

print("Error: Could not open video capture device")

return

while True:

# Capture frame-by-frame

ret, frame = cap.read()

if not ret:

print("Error: Could not read frame")

break

# Use YOLOv9 model to make predictions

results = model(frame)

# Process the results

for result in results:

# Loop through each detected object

for box in result.boxes:

# Get coordinates and class label

x1, y1, x2, y2 = box.xyxy[0]

label_id = int(box.cls[0].item())

confidence = box.conf[0].item()

# Get the class label from YOLO model

class_label = model.names[label_id]

# Create the label text

label_text = f"{class_label}: {confidence:.2f}"

# Draw bounding box on the frame

cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)

# Draw the label text on the frame above the bounding box

cv2.putText(frame, label_text, (int(x1), int(y1) - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

# Display the frame with bounding boxes and labels

cv2.imshow('Real-Time Object Detection', frame)

# Exit the loop if the user presses 'q'

if cv2.waitKey(1) & 0xFF == ord('q'):

break

# Release the video capture device and close the window

cap.release()