REalTime-X-Ray-security-Scanning-System

X-ray machine AI Model to detect objects

The project involves:

Key Requirements:

• Build/adapt AI model for real-time X-ray security scanning (both video and image processing)
• Utilize pre-existing X-ray security datasets and models (I do not have one currently)
• Implement object detection and classification
• Develop REST API for labeled object output
• Optimize for real-time video stream analysis
• Provide model fine-tuning capabilities for new object classes

Required Skills/Experience:

• Proven experience with X-ray image processing and security scanning systems
• Strong background in computer vision and deep learning
• Experience with real-time video processing
• REST API development expertise
• Portfolio must include similar X-ray-based projects

Please include in your proposal:

1. Examples of previous X-ray imaging projects
2. Suggested approach for real-time processing
3. Estimated timeline for initial implementation ================================ structured Python code example that outlines how to build an AI model for real-time X-ray security scanning, along with a REST API for object detection and classification. This is a high-level framework that demonstrates key components and concepts, but please note that actual implementation will require extensive training data, model selection, and optimizations. Project Structure

python

xray_security/ ├── app/ │ ├── main.py │ ├── model.py │ ├── api.py │ └── utils.py ├── requirements.txt └── data/ └── dataset.zip # Placeholder for X-ray datasets

Step 1: Install Required Libraries

Create a requirements.txt file for dependencies:

plaintext

Flask Flask-RESTful opencv-python tensorflow numpy Pillow

Install the libraries:

bash

pip install -r requirements.txt

Step 2: Model Development (model.py)

In this section, we define a function to create or load a pre-trained object detection model. You might want to use a model like YOLO, Faster R-CNN, or SSD for this purpose.

python

import tensorflow as tf

class XRayModel: def init(self, model_path=None): self.model = self.load_model(model_path)

def load_model(self, model_path):
    if model_path:
        model = tf.keras.models.load_model(model_path)
    else:
        # Load a pre-trained model, for example, MobileNet or YOLO
        model = tf.keras.applications.MobileNetV2(weights='imagenet')
    return model

def predict(self, image):
    # Pre-process image for model
    image = tf.image.resize(image, (224, 224))  # Resize to fit model
    image = tf.expand_dims(image, axis=0)  # Add batch dimension
    predictions = self.model.predict(image)
    return predictions

Step 3: API Development (api.py)

Create a REST API to interact with the model.

python

from flask import Flask, request, jsonify from flask_restful import Api, Resource from model import XRayModel import numpy as np from PIL import Image import io

app = Flask(name) api = Api(app)

xray_model = XRayModel() # Initialize the model

class XRayScan(Resource): def post(self): if 'file' not in request.files: return {'message': 'No file uploaded'}, 400

    file = request.files['file']
    image = Image.open(io.BytesIO(file.read()))
    image_array = np.array(image)

    predictions = xray_model.predict(image_array)

    return jsonify({'predictions': predictions.tolist()})

api.add_resource(XRayScan, '/scan')

if name == 'main': app.run(debug=True)

Step 4: Real-Time Video Processing (main.py)

Implement real-time video processing using OpenCV.

python

import cv2 import requests import numpy as np

def process_video_stream(): cap = cv2.VideoCapture(0) # Use the appropriate source for your camera

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Prepare the image for the API call
    _, img_encoded = cv2.imencode('.jpg', frame)
    files = {'file': img_encoded.tostring()}

    response = requests.post('http://localhost:5000/scan', files=files)
    predictions = response.json()

    # Display predictions on the video frame
    # Example: Draw bounding boxes (you'll need coordinates from predictions)
    # cv2.rectangle(frame, start_point, end_point, color, thickness)

    cv2.imshow('X-ray Video Stream', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

if name == 'main': process_video_stream()

Step 5: Running the Application

Start the Flask API: Run main.py to start the REST API server:

bash

python app/api.py

Start Video Processing: In another terminal, run main.py to start processing video:

bash

python app/main.py

Considerations for Real-Time Processing

Model Optimization: Use TensorFlow Lite or ONNX for optimizing the model for faster inference.
Batch Processing: Consider processing multiple frames in batches to improve efficiency.
Edge Computing: If latency is a concern, deploy the model on an edge device.
Streaming Protocol: Use WebRTC or RTSP for better real-time streaming capabilities.

Conclusion

This code provides a foundational structure for a real-time X-ray security scanning system. You'll need to refine the model, handle more detailed predictions, and ensure compliance with security and data privacy standards (like HIPAA). Be sure to also implement error handling, logging, and extensive testing for production readiness.