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app.py

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+import cv2
+import numpy as np
+from ultralytics import YOLO
+import easyocr
+import gradio as gr
+import json
+
+
+number_plate_model = YOLO("best.pt", task='detection')
+reader = easyocr.Reader(['en'])
+
+def detect_number_plate(image):
+    results = number_plate_model(image)
+    for result in results:
+        result = json.loads(result.tojson()) 
+        
+        x1, y1, x2, y2 = map(int, result[0]['box'].values())
+        # print(x1, y1, x2, y2)
+        
+        number_plate = image[y1:y2, x1:x2]
+        number_plate = cv2.cvtColor(number_plate, cv2.COLOR_BGR2RGB)
+        
+        number_plate_text = reader.readtext(number_plate)
+        if len(number_plate_text) > 0:
+            number_plate_text = number_plate_text[0][-2]
+        else:
+            number_plate_text = "No text detected"
+        
+        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.putText(image, number_plate_text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
+    
+    return image
+
+# Define Gradio interface
+image = gr.Image(height=640, width=640, type="numpy")
+label = gr.Image(height=640, width=640, type="numpy")
+
+gr.Interface(fn=detect_number_plate, inputs=image, outputs=label).launch(debug=True, share=False)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ac7f9183c233032df6b41cf786b14c69915f3d2f46620f06d0d6c7cc80559672
+size 6226339

last.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73c870c92dc41ce25b5dc06f0b53f7d8adcf85032e76de6de13ac4cdc6faa8ec
+size 6226467

readme.md

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+# Number Plate Detection using YOLO and Gradio
+
+This repository contains code and resources for number plate detection using YOLO (You Only Look Once) object detection algorithm and Gradio for building a user-friendly interface.
+
+## Table of Contents
+- [Introduction](#introduction)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Introduction
+Number plate detection is an important task in various applications such as traffic surveillance, parking management, and law enforcement. This project utilizes the YOLO algorithm, a state-of-the-art object detection model, to detect number plates in images or videos. Gradio, a Python library, is used to create a simple and interactive user interface for easy testing and deployment.
+
+## Installation
+To use this project, follow these steps:
+
+1. Clone the repository: `git clone https://github.com/yuval728/number-plate-detection.git`
+2. Install the required dependencies: `pip install -r requirements.txt`
+
+## Usage
+1. Run the `app/app.py` script.
+2. Open your web browser and navigate to `http://localhost:7860`.
+3. Upload an image or video file to detect number plates.
+4. View the detected number plates and their bounding boxes.
+
+## Contributing
+Contributions are welcome! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request.
+
+## License
+This project is licensed under the [MIT License](LICENSE).

requirements.txt

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+numpy
+opencv-python
+ultralytics
+gradio
+easyocr