Computer vision YOLO11 model

Test_logic.py

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+import os
+import pandas as pd
+from ultralytics import YOLO
+import cv2
+
+# Input and output video paths
+video_path = r'D:\AIM\pecan\GH014359.mp4'
+video_path_out = r'D:\AIM\pecan\GH014359_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))
+
+# Load the YOLO model
+model = YOLO(r"D:\AIM\pecan\runs\detect\train2\weights\best.pt")  # Load a custom model
+
+threshold = 0.5
+detected_cracked = False  # Initialize a flag for detecting cracked pecans
+
+while ret:
+    # Perform detection on the current frame
+    results = model(frame)[0]
+
+    for result in results.boxes.data.tolist():
+        x1, y1, x2, y2, score, class_id = result
+
+        if score > threshold:
+            # Draw bounding boxes and labels
+            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
+            label = results.names[int(class_id)].upper()
+            cv2.putText(frame, f"{label} {score:.2f}", (int(x1), int(y1 - 10)),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA)
+
+            # Check for the "cracked" label
+            if label == "CRACKED":
+                detected_cracked = True
+
+    # Write the processed frame to the output video
+    out.write(frame)
+    ret, frame = cap.read()
+
+# Determine the final label based on detections
+final_label = "CRACKED" if detected_cracked else "GOOD"
+
+# Print the final label
+print(f"Final Label: {final_label}")
+
+# Release video resources
+cap.release()
+out.release()
+cv2.destroyAllWindows()