Requirements

.gitignore

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+# .gitignore
+__pychache__
+.idea
+venv

.ipynb_checkpoints/app-checkpoint.py

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+import os
+import cv2
+from io import BytesIO
+from flask import Response, send_file
+from fpdf import FPDF
+import numpy as np
+import matplotlib.pyplot as plt
+from flask import Flask, request, render_template, redirect, url_for, flash, send_from_directory
+from werkzeug.utils import secure_filename
+from m1.image_processing import process_images, auto_crop_and_correct
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'static/uploads'
+app.config['TEMP_FOLDER'] = 'static/temp'
+app.secret_key = 'your_secret_key'
+
+# Ensure the UPLOAD_FOLDER exists
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+os.makedirs(app.config['TEMP_FOLDER'], exist_ok=True)
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        ref_image = request.files['ref_image']
+        scanned_image = request.files['scanned_image']
+
+        if ref_image and scanned_image:
+            # Save uploaded images to the UPLOAD_FOLDER
+            ref_filename = os.path.join(app.config['UPLOAD_FOLDER'], secure_filename(ref_image.filename))
+            scanned_filename = os.path.join(app.config['UPLOAD_FOLDER'], secure_filename(scanned_image.filename))
+            ref_image.save(ref_filename)
+            scanned_image.save(scanned_filename)
+
+            # Read the uploaded images
+            ref_image_cv2 = cv2.imread(ref_filename)
+            scanned_image_cv2 = cv2.imread(scanned_filename)
+
+            # Auto Crop and Correct the scanned image
+            ref_image_cv2, scanned_image_cv2 = auto_crop_and_correct(ref_image_cv2, scanned_image_cv2)
+
+            # Process the images using the machine learning code
+            ref_image_cv2, scanned_image_aligned_cv2 = process_images(ref_image_cv2, scanned_image_cv2)
+
+            # Generate a unique filename for the processed image
+            processed_image_filename = os.path.join(app.config['UPLOAD_FOLDER'], 'processed_image.jpg')
+            cv2.imwrite(processed_image_filename, scanned_image_aligned_cv2)
+
+            return render_template('result.html', ref_image_filename=secure_filename(ref_image.filename),
+                                   processed_image='uploads/processed_image.jpg')
+
+    return render_template('index.html')
+
+@app.route('/download_image', methods=['GET'])
+def download_image():
+    # Generate a PDF version of the processed image
+    processed_image_path = os.path.join(app.config['UPLOAD_FOLDER'], 'processed_image.jpg')
+
+    # Convert the image to PDF (you may need to install the fpdf library)
+    from fpdf import FPDF
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.image(processed_image_path, x=10, y=10, w=190)
+
+    # Create a unique temporary filename for the PDF
+    temp_pdf_filename = os.path.join(app.config['TEMP_FOLDER'], 'processed_image.pdf')
+
+    # Save the PDF to the temporary file
+    pdf.output(temp_pdf_filename)
+
+    # Send the PDF as a downloadable file
+    return send_from_directory(app.config['TEMP_FOLDER'], 'processed_image.pdf', as_attachment=True)
+
+if __name__ == '__main__':
+    app.run(debug=True)