| import numpy as np
|
| import cv2
|
| from tensorflow.keras.models import load_model
|
|
|
|
|
| model = load_model("custom_cnn_model.h5")
|
|
|
|
|
| face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
|
|
|
|
|
| def detect_face(frame):
|
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
|
| faces = face_cascade.detectMultiScale(
|
| gray,
|
| scaleFactor=1.1,
|
| minNeighbors=3,
|
| minSize=(30, 30)
|
| )
|
|
|
| print("Faces detected:", len(faces))
|
|
|
|
|
| if len(faces) == 0:
|
| return frame
|
|
|
| x, y, w, h = faces[0]
|
| face = frame[y:y+h, x:x+w]
|
|
|
|
|
| if face is None or face.size == 0:
|
| return frame
|
|
|
| return face
|
|
|
|
|
| def preprocess_frame(frame):
|
| img = cv2.resize(frame, (128, 128))
|
| img = img / 255.0
|
| img = np.reshape(img, (1, 128, 128, 3))
|
| return img
|
|
|
|
|
| def predict_frame(frame):
|
|
|
| face_or_frame = detect_face(frame)
|
|
|
|
|
| img = preprocess_frame(face_or_frame)
|
|
|
|
|
| prediction = model.predict(img, verbose=0)[0][0]
|
|
|
| if prediction > 0.5:
|
| return "REAL", float(1 - prediction)
|
| else:
|
| return "FAKE", float(prediction)
|
|
|
|
|
| def aggregate_results(results):
|
| fake_scores = [s for l, s in results if l == "FAKE"]
|
| real_scores = [s for l, s in results if l == "REAL"]
|
|
|
| if len(fake_scores) > len(real_scores):
|
| return {
|
| "result": "FAKE",
|
| "confidence": sum(fake_scores) / len(results)
|
| }
|
| else:
|
| return {
|
| "result": "REAL",
|
| "confidence": sum(real_scores) / len(results)
|
| } |
