File size: 1,788 Bytes
b489ffd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import numpy as np
import cv2
from tensorflow.keras.models import load_model
# ✅ LOAD MODEL
model = load_model("custom_cnn_model.h5")
# ✅ LOAD FACE DETECTOR
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 no face → use full frame
if len(faces) == 0:
return frame
x, y, w, h = faces[0]
face = frame[y:y+h, x:x+w]
# ✅ Safety check
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):
# 🔥 Step 1: detect face or fallback
face_or_frame = detect_face(frame)
# 🔥 Step 2: preprocess
img = preprocess_frame(face_or_frame)
# 🔥 Step 3: predict
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)
} |