Update processor.py

processor.py

@@ -56,6 +56,9 @@ model = YOLO('yolov8_Medium.pt')  # Update the path as needed
 # Define lane area coordinates (example coordinates)
 red_lane = np.array([[2,1583],[1,1131],[1828,1141],[1912,1580]], np.int32)
 
+# Path for Arabic font
+font_path = "alfont_com_arial-1.ttf"
+
 # Dictionary to track violations per license plate
 violations_dict = {}
 
@@ -198,7 +201,7 @@ def process_image(image_path, font_path, violation_image_path='violation.jpg'):
 
 def process_video(video_path):
     # Paths for saving violation images
-    violation_image_path = '/kaggle/working/violation.jpg'
+    violation_image_path = 'violation.jpg'
 
     # Track emails already sent to avoid duplicate emails
     sent_emails = {}
@@ -216,7 +219,7 @@ def process_video(video_path):
 
     # Define codec and output video settings
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    output_video_path = '/kaggle/working/output_violation.mp4'
+    output_video_path = 'output_violation.mp4'
     fps = cap.get(cv2.CAP_PROP_FPS)
     width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))  # Frame width
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))  # Frame height
@@ -287,7 +290,7 @@ def process_video(video_path):
                             # Perform OCR on the license plate
                             cv2.imwrite(violation_image_path, frame)
                             license_plate_pil = Image.fromarray(cv2.cvtColor(license_crop, cv2.COLOR_BGR2RGB))
-                            temp_image_path = '/kaggle/working/license_plate.png'
+                            temp_image_path = 'license_plate.png'
                             license_plate_pil.save(temp_image_path)
                             license_plate_text = model_ocr.chat(processor, temp_image_path, ocr_type='ocr')
                             filtered_text = filter_license_plate_text(license_plate_text)