diff --git a/OCR YOLO11/license_plate_detector.pt b/OCR YOLO11/license_plate_detector.pt
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index 0000000..a0ebd6b
Binary files /dev/null and b/OCR YOLO11/license_plate_detector.pt differ
diff --git a/OCR YOLO11/yolomain.py b/OCR YOLO11/yolomain.py
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+++ b/OCR YOLO11/yolomain.py	
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+from ultralytics import YOLO
+from ultralytics.utils.downloads import safe_download
+from ultralytics.utils.plotting import Annotator, colors
+from PIL import Image
+import os
+os.chdir("C:/Users/celma/OneDrive - Hanze/School/periode 1.4/IOT/YOLO11/License Plate Recognition.v11i.yolov11")
+
+crop_dir_name = "C:/Users/celma/OneDrive - Hanze/School/periode 1.4/IOT/YOLO11/License Plate Recognition.v11i.yolov11/test/crops"
+if not os.path.exists(crop_dir_name):
+    os.makedirs(crop_dir_name)
+
+model = YOLO("license_plate_detector.pt") # Loads the pre-trained YOLO11 model
+names = model.names
+
+
+results = model.predict("test/images/000c.jpg", show=True, save=True)
+img = Image.open("test/images/000c.jpg")  # Load the original image using PIL
+boxes = results[0].boxes.xyxy.cpu().tolist()
+clss = results[0].boxes.cls.cpu().tolist()
+# annotator = Annotator(results[0], line_width=2, example=names)
+
+#print(results)
+print(boxes)
+
+if boxes is not None:
+    for box, cls in zip(boxes,clss):
+    #   annotator.box_label(box, color=colors(int(cls), True), label=names(int(cls)))
+
+        crop_obj = boxes[int(box[1]):int(box[3]) + int(box[0]):int(box[2])]
+
+cropped_img = img.crop(
+    (int(box[0]), int(box[1]), int(box[2]), int(box[3]))
+)
+
+save_path = os.path.join(crop_dir_name, "cropped_image.jpg")
+cropped_img.save("cropped_image.jpg")
+cropped_img.show()
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