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app.py

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+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import easyocr
+import numpy as np
+from PIL import Image
+
+# Load YOLO model
+model = YOLO('yolo11n-custom.pt')
+model.fuse()
+
+# Load EasyOCR
+reader = easyocr.Reader(['en'])
+
+def detect_license_plate(image):
+    results = model.predict(image, conf=0.15, iou=0.3, classes=[0])
+    plate_texts = []
+    img_array = np.array(image)
+    img = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    img_height, img_width, _ = img.shape
+
+    for result in results:
+        for bbox in result.boxes.xyxy:
+            x1, y1, x2, y2 = map(int, bbox.tolist())
+            plate = img[int(y1):int(y2), int(x1):int(x2)]
+            scale = 2
+            height, width = plate.shape[:2]
+            plate = cv2.resize(plate, (width * scale, height * scale), interpolation=cv2.INTER_CUBIC)
+            lab = cv2.cvtColor(plate, cv2.COLOR_RGB2LAB)
+            l, a, b = cv2.split(lab)
+            clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+            l = clahe.apply(l)
+            plate = cv2.merge((l, a, b))
+            plate = cv2.cvtColor(plate, cv2.COLOR_LAB2RGB)
+
+            text = reader.readtext(plate, detail=0, allowlist="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-")
+            text = " ".join(text).upper()
+
+            text_scale = max(1, width / 250)
+            thickness = max(2, width // 200)
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), thickness)
+            (text_width, text_height), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, text_scale, thickness)
+            text_x = x1 + (width - text_width) // 2
+            text_y = y1 - 10 if y1 > 50 else y2 + text_height + 20
+            text_box_y1 = text_y - text_height - 5
+            text_box_y2 = text_y + 5
+            cv2.rectangle(img, (text_x - 8, text_box_y1 - 3), (text_x + text_width + 8, text_box_y2 + 3), (0, 0, 0), -1)
+            cv2.rectangle(img, (text_x - 5, text_box_y1), (text_x + text_width + 5, text_box_y2), (255, 255, 255), -1)
+            cv2.putText(img, text, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, text_scale, (0, 0, 0), thickness)
+
+            plate_texts.append(text)
+
+    result_img = Image.fromarray(img)
+    return result_img, "\n".join(plate_texts) if plate_texts else "No license plates detected."
+
+# Gradio UI
+iface = gr.Interface(
+    fn=detect_license_plate,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Image(type="pil", label="Detected Image"),
+        gr.Textbox(label="Detected License Plates")
+    ],
+    title="🚘 License Plate Detector",
+    description="Upload an image with license plates to detect them using YOLO and EasyOCR."
+)
+
+iface.launch()

requirements.txt

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+ultralytics==8.1.25       # for YOLO (ensure this matches your model version)
+opencv-python
+easyocr
+numpy
+pillow
+gradio

yolo11n-custom.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5ebaab2bc7c2036d97706cd1992389998de338961d0485879d0d128755c52e2
+size 5477537