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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+students_database.db filter=lfs diff=lfs merge=lfs -text

db.py

@@ -0,0 +1,76 @@
+import sqlite3
+import pandas as pd
+from PIL import Image
+from helper_fns import pil_to_binary
+
+# Function to add a new student to the database
+def add_student(db, firstname, lastname, matric_no, image_path):
+    # Open the image from the given path
+    image = Image.open(image_path)
+    
+    # Convert the PIL image to binary
+    image_binary = pil_to_binary(image)
+    
+    # Connect to the existing database
+    conn = sqlite3.connect(db)
+    c = conn.cursor()
+    
+    # Insert the new data into the table
+    c.execute("INSERT INTO students (first_name, last_name, matric_no, image) VALUES (?, ?, ?, ?)",
+              (firstname, lastname, matric_no, image_binary))
+    
+    # Save (commit) the changes and close the connection
+    conn.commit()
+    conn.close()
+
+
+def create_db(db_name, df):
+    conn = sqlite3.connect(db_name)
+    c = conn.cursor()
+
+    # Create the table if it doesn't exist
+    c.execute('''CREATE TABLE IF NOT EXISTS students
+             (first_name TEXT, last_name TEXT, matric_no TEXT, image BLOB)''')
+
+    for _, row in df.iterrows():
+        add_student(
+        db_name, 
+        row['first_name'],
+        row['last_name'],
+        row['matric_no'],
+        row['image_path']
+        )
+
+
+def get_student_row(db_path, last_name, matric_no):
+    """
+    Retrieve a row from the database based on the last name and matric number.
+    
+    Parameters:
+    db_path (str): Path to the SQLite database file.
+    last_name (str): The last name of the student.
+    matric_no (str): The matriculation number of the student.
+    
+    Returns:
+    tuple: The row matching the last name and matric number, or None if not found.
+    """
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+    
+    query = """
+    SELECT * FROM students
+    WHERE last_name = ? AND matric_no = ?
+    """
+    cursor.execute(query, (last_name, matric_no))
+    row = cursor.fetchone()
+
+    conn.close()
+    if row:
+        columns = [col[0] for col in cursor.description]
+        return dict(zip(columns, row))
+    else:
+        return None
+
+
+if __name__ == '__main__':
+    create_db('students_database.db', pd.read_csv('students_df.csv'))

helper_fns.py

@@ -0,0 +1,54 @@
+import io
+import cv2
+import time
+from PIL import Image
+
+
+# Function to convert PIL image to binary
+def pil_to_binary(img):
+    with io.BytesIO() as output:
+        img.save(output, format="PNG")
+        return output.getvalue()
+
+
+def binary_to_pil(binary):
+    return Image.open(io.BytesIO(binary))
+
+
+def crop_image(image_array, x, y, w, h):
+    return image_array[y: y + h, x: x + w]
+
+
+def extract_faces(image_one, image_two, results):
+    image_one_results = results['facial_areas']['img1']
+    cropped_image_one = crop_image(image_one, 
+                                    image_one_results['x'],
+                                    image_one_results['y'],
+                                    image_one_results['w'],
+                                    image_one_results['h'],)
+    
+    image_two_results = results['facial_areas']['img2']
+    cropped_image_two = crop_image(image_two, 
+                                    image_two_results['x'],
+                                    image_two_results['y'],
+                                    image_two_results['w'],
+                                    image_two_results['h'],)
+    
+    return Image.fromarray(cropped_image_one).resize((480, 480)), Image.fromarray(cropped_image_two).resize((480, 480))
+
+def read_image_from_webcam():
+    #initialize webcam
+    cap = cv2.VideoCapture(0)
+
+    if not cap.isOpened():
+        print(f'Error could not open webcam')
+        return None
+    else:
+        print(f'Webcam opened successfully!')
+        #wait for 5 seconds
+        time.sleep(5)
+        _, frame = cap.read()
+        #Image.fromarray
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        cap.release()
+        return  rgb_frame

main.py

@@ -0,0 +1,65 @@
+import os
+import warnings
+
+os.environ['TF_ENABLE_ONEDNN_OPTS']=str(0)
+warnings.filterwarnings('ignore')
+
+#import pandas as pd
+import numpy as np
+
+from deepface import DeepFace
+from db import create_db, get_student_row
+from helper_fns import  binary_to_pil, extract_faces
+
+
+
+
+def verify_student(db_path, last_name, matric_no, input_image):
+    data = get_student_row(db_path, last_name, matric_no)
+    
+    if data is not None:
+        binary_image = data['image']
+    else: 
+        raise ValueError('No student having last name {last_name} and matric no: {matric_no} exists in this database.')
+    actual_image =np.array(binary_to_pil(binary_image))
+
+    webcam_image = input_image
+    if webcam_image is None:
+        raise ValueError(f'No image')
+    else:
+        print('Received image')
+    results = DeepFace.verify(webcam_image,
+                            actual_image,
+                            model_name='Facenet',
+                            detector_backend="retinaface",
+                            distance_metric="cosine",
+                            enforce_detection=True,
+                            anti_spoofing=True,
+                            align=True,
+                            normalization="Facenet")
+    result = results['verified']
+    if results['verified'] == True:
+        result = f"Verification check complete! Successfully verified student: {data['last_name']} {data['first_name']} with matriculation number {data['matric_no']}"
+    else:
+        result = f"Verification check complete! You are not student {data['last_name']} {data['first_name']} with matriculation number {data['matric_no']}"
+        
+    cropped_input_image, cropped_returned_image = extract_faces(webcam_image, actual_image, results)
+    return result, cropped_input_image, cropped_returned_image, actual_image
+
+
+def main(last_name, matric_no, input_image):
+
+    #df = pd.read_csv('students_df.csv')
+    db_path = 'students_database.db'
+    #create_db(db_path, df)
+        
+    result, cropped_input_image, cropped_returned_image, actual_image = verify_student(db_path,
+                                                                                        last_name,
+                                                                                        matric_no,
+                                                                                        input_image)
+
+    #os.remove('students_database.db')
+    return result, cropped_input_image, cropped_returned_image, actual_image
+
+if __name__ ==  '__main__':
+    main()

students_database.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a7bcbb8dd37f20a6e74dd5d4263487170db4e4af64707510d1cf32093257159
+size 18714624