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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')
print(type(webcam_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() |