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cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f54d9db020da33f99f861d41dc1334ec33adc14991ada4033a4ece790d0904e
+size 312843624

detector2.py

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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)

requirements.txt

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+numpy
+pandas
+tensorflow
+keras>=2.2.0
+keras_applications >= 1.0.7
+opencv-python>=4.1.0
+mtcnn>=0.1.0
+h5py
+efficientnet
+split_folders