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import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from mtcnn.mtcnn import MTCNN
import cv2
# We still need config for the image size, so this import is fine
try:
from . import config
except ImportError:
import config
# Suppress TensorFlow logs
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.get_logger().setLevel('ERROR')
# Initialize MTCNN detector globally (for efficiency)
print("Initializing MTCNN face detector...")
DETECTOR = MTCNN()
print("MTCNN initialized successfully.")
def detect_and_crop_face(image_path):
"""Detects and crops the largest face using MTCNN"""
img = cv2.imread(image_path)
if img is None:
print(f"Error: Could not read image from {image_path}")
return None
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
print("Detecting faces in the image...")
faces = DETECTOR.detect_faces(img_rgb)
if not faces:
print("Warning: No faces detected!")
return None
if len(faces) > 1:
print(f"Found {len(faces)} faces. Using the largest one.")
faces = sorted(faces, key=lambda f: f['box'][2] * f['box'][3], reverse=True)
face = faces[0]
x, y, w, h = face['box']
x, y = abs(x), abs(y)
# CRITICAL FIX: No padding (matches training exactly)
face_crop = img[y:y+h, x:x+w]
if face_crop.size == 0:
print("Error: Cropped face is empty")
return None
print(f"Face detected and cropped: {w}x{h} pixels")
return face_crop
def preprocess_face(face_crop):
"""Preprocesses cropped face for model input"""
resized_face = cv2.resize(face_crop, (config.TARGET_IMAGE_SIZE, config.TARGET_IMAGE_SIZE))
resized_face_rgb = cv2.cvtColor(resized_face, cv2.COLOR_BGR2RGB)
normalized_face = resized_face_rgb.astype(np.float32) / 255.0
img_batch = np.expand_dims(normalized_face, axis=0)
return img_batch
def preprocess_image(img_path):
"""Complete preprocessing: detect, crop, and prepare"""
face_crop = detect_and_crop_face(img_path)
if face_crop is None:
return None
return preprocess_face(face_crop)
def get_image_prediction(image_path, model):
print(f"\nProcessing image: {image_path}")
if not os.path.exists(image_path):
return {
"prediction": None,
"confidence": 0.0,
"raw_score": 0.0,
"error": f"File not found: {image_path}"
}
processed_image = preprocess_image(image_path)
if processed_image is None:
return {
"prediction": None,
"confidence": 0.0,
"raw_score": 0.0,
"error": "Failed to detect face in the image"
}
print("Running model prediction...")
prediction_prob = model.predict(processed_image, verbose=0)[0][0]
if prediction_prob > 0.5:
label = 'REAL'
confidence = prediction_prob * 100
else:
label = 'FAKE'
confidence = (1 - prediction_prob) * 100
print(f"Prediction: {label} (Confidence: {confidence:.2f}%)")
return {
"prediction": label,
"confidence": float(confidence),
"raw_score": float(prediction_prob),
"error": None
}
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