fixed video crash issue

main.py

@@ -31,7 +31,7 @@ CAMERA_SOURCE = 1 #Webcam / Camera Source REMEMBER WINDOWS HAS WEBCAM ACCESS ISS
 # Load YOLO models
 motorcycle_model = YOLO("models/yolov8n.pt")  # Pretrained COCO model (motorcycles)
 helmet_model = YOLO("models/helmetYoloV8_25epochs.pt")  # Custom helmet detection model
-plate_model = YOLO("models/license_plate_detector.pt")  # Custom license plate detection model
+plate_model = YOLO("models/license_plate_detector1.pt")  # Custom license plate detection model
 
 # Initialize OCR
 ocr = PaddleOCR(use_angle_cls=True, lang='en')  # Angle correction enabled
@@ -39,7 +39,7 @@ ocr = PaddleOCR(use_angle_cls=True, lang='en')  # Angle correction enabled
 # Define Colors
 COLOR_MOTORCYCLE = (0, 255, 0)  # Green
 COLOR_NO_HELMET = (0, 0, 255)  # Red
-COLOR_LICENSE_PLATE = (0, 0, 255)  # Red
+COLOR_LICENSE_PLATE = (255, 0, 0)  # Blue
 
 #Report Filepath
 EXCEL_FILE = "report.xlsx"
@@ -173,7 +173,7 @@ class RealTimeProcessor(QThread):
                 break
 
             # Process the frame
-            processed_frame, _ = self.parent.process_image(frame, is_video=True)
+            processed_frame, license_plate_text = self.parent.process_image(frame, is_video=True)
 
             # Emit signal to update UI
             self.frame_processed.emit(processed_frame)
@@ -579,6 +579,7 @@ class HelmetDetectionApp(QWidget):
         # Format the Excel file
         format_excel(EXCEL_FILE)
 
+
     def process_image(self, image_path, is_video=False, output_folder="output/"):
 
         # 🖼️ Handle video frames directly
@@ -681,12 +682,21 @@ class HelmetDetectionApp(QWidget):
                     # Convert BGR to RGB (PaddleOCR expects RGB)
                     plate_roi_rgb = cv2.cvtColor(plate_roi_denoised, cv2.COLOR_GRAY2RGB)
 
+
                     # Perform OCR with PaddleOCR
                     result = ocr.ocr(plate_roi_rgb, cls=True)
                     print("Raw Result:", result)
 
-                    # Extract text from PaddleOCR result
-                    if result and len(result[0]) > 0:
+                    # Perform OCR with PaddleOCR
+                    result = ocr.ocr(plate_roi_rgb, cls=True)
+                    print("Raw OCR Result:", result)
+
+                    # Ensure result is valid
+                    if result is None or not isinstance(result, list) or len(result) == 0:
+                        print("⚠️ OCR result is None or empty!")
+                        license_plate_text = "N/A"  # Set default value
+                    elif isinstance(result[0], list) and len(result[0]) > 0:
+                        print("✅ OCR detected text.")
                         license_plate_text = " ".join([entry[1][0] for entry in result[0]])  # Extract text
                         print("🚗 Detected Plate:", license_plate_text)
 
@@ -697,7 +707,9 @@ class HelmetDetectionApp(QWidget):
                         cv2.rectangle(image, (mx1 + px1, my1 + py1), (mx1 + px2, my1 + py2), COLOR_LICENSE_PLATE, 2)
                         cv2.putText(image, f"Plate: {license_plate_text}", (mx1 + px1, my1 + py1 - 10),
                                     cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_LICENSE_PLATE, 2)
-
+                    else:
+                        print("⚠️ OCR result does not contain expected text format!")
+                        license_plate_text = "N/A"  # Set default value
 
         # 📁 Save processed image for single-image mode
         os.makedirs(output_folder, exist_ok=True)