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import os
import numpy as np
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
import cv2
from mtcnn import MTCNN
# Load the trained model
model = load_model('cnn_model.h5')
# Function to detect and crop the face using MTCNN
def detect_and_crop_face(img_path):
img = cv2.imread(img_path)
detector = MTCNN()
results = detector.detect_faces(img)
if results:
bounding_box = results[0]['box']
x, y, width, height = bounding_box
face = img[y:y+height, x:x+width]
face = cv2.resize(face, (128, 128)) # Resize to the target size
return face
else:
# If no face is detected, return the original resized image
return cv2.resize(img, (128, 128))
# Function to preprocess the cropped face
def preprocess_face(face):
img_array = image.img_to_array(face)
img_array = np.expand_dims(img_array, axis=0)
img_array /= 255.0 # Normalize to [0, 1]
return img_array
# Main function to predict if an image is real or fake
def predict_real_or_fake(img_path):
# Detect and preprocess the face in the provided image
face = detect_and_crop_face(img_path)
processed_image = preprocess_face(face)
# Predict the class of the image
prediction = model.predict(processed_image)
# Print the result
result = 'Real' if prediction[0][0] < 0.5 else 'Fake'
print(f"Image: {img_path} - Predicted as: {result}")
# Example usage:
image_path = input("Enter your image path: ") # Replace with your image path
predict_real_or_fake(image_path)
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