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main2.py

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+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()

yolov8m.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c25b0b63b1a433843f06d821a9ac1deb8d5805f74f0f38772c7308c5adc55a5
+size 52117635