Computer vision YOLO11 model

Test_video.py

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+import os
+import pandas as pd
+from ultralytics import YOLO
+import cv2
+
+video_path = r'D:\AIM\lemon\test.mp4'
+video_path_out = r'D:\AIM\lemon\test_out.mp4'
+
+cap = cv2.VideoCapture(video_path)
+ret, frame = cap.read()
+H, W, _ = frame.shape
+out = cv2.VideoWriter(video_path_out, cv2.VideoWriter_fourcc(*'MP4V'), int(cap.get(cv2.CAP_PROP_FPS)), (W, H))
+
+model_path = os.path.join('.', 'runs', 'detect', 'train', 'weights', 'last.pt')
+
+# Load a model
+model = YOLO(r"D:\AIM\lemon\YOLO-Training\YOLOv11_finetune\weights\best.pt")  # load a custom model
+
+threshold = 0.5
+classifications = []
+
+while ret:
+
+    results = model(frame)[0]
+
+    for result in results.boxes.data.tolist():
+        x1, y1, x2, y2, score, class_id = result
+
+        if score > threshold:
+            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
+            cv2.putText(frame, results.names[int(class_id)].upper(), (int(x1), int(y1 - 10)),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA)
+            
+            # percentage = 
+            
+            # if results.names[int(class_id)] in classifications:
+                
+            # else:
+            classifications.append(results.names[int(class_id)])
+
+    out.write(frame)
+    ret, frame = cap.read()
+
+# Create a new DataFrame from the classifications
+new_df = pd.DataFrame(classifications)
+
+# Write the new DataFrame to an Excel file
+new_df.to_excel('output_classifications.xlsx', index=False)
+
+cap.release()
+out.release()
+cv2.destroyAllWindows()