Werkend ANPR systeem

ANPR.py

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+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import easyocr
+
+car_model = YOLO("yolov8n.pt")
+plate_model = YOLO("license_plate_detector.pt")
+reader = easyocr.Reader(["nl"])
+
+img = Image.open("test.jpg")
+
+results = car_model.predict(source=img)
+
+for r in results:
+    for box in r.boxes:
+        cls_id = int(box.cls[0])
+        cls_name = r.names[cls_id]
+        if cls_name == "car":
+            conf = float(box.conf[0])
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            print(f"{cls_name} ({conf:.2f}) at [{x1},{y1},{x2},{y2}]")
+
+            # Crop Image
+            cropped_img = img.crop((x1, y1, x2, y2))
+            cropped_img.save("car_crop_pillow.jpg")
+            results2 = plate_model.predict(source=cropped_img)
+            for x in results2:
+                for box2 in x.boxes:
+                    cls_id = int(box2.cls[0])
+                    cls_name = x.names[cls_id]
+                    if cls_name == "License_Plate":
+                        conf = float(box2.conf[0])
+                        x1, y1, x2, y2 = map(int, box2.xyxy[0])
+                        print(
+                            f"{cls_name} ({conf:.2f}) at [{x1},{y1},{x2},{y2}]"
+                        )
+                        lp_img = cropped_img.crop((x1, y1, x2, y2))
+                        lp_img.save("license_plate.jpg")
+                        lp_np = np.array(lp_img)
+                        result3 = reader.readtext(lp_np)
+                        print(result3)