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import gradio as gr
import cv2
import torch
import dlib
import numpy as np
from imutils import face_utils
from torchvision import models, transforms
from tempfile import NamedTemporaryFile
import shutil
# Load face detector and landmark predictor
face_detector = dlib.get_frontal_face_detector()
PREDICTOR_PATH = "./shape_predictor_81_face_landmarks.dat"
face_predictor = dlib.shape_predictor(PREDICTOR_PATH)
import torch
import torchvision.models as models
# Load pretrained ResNet-34 model
resnet34 = models.resnet34(weights=models.ResNet34_Weights.IMAGENET1K_V1)
resnet34.fc = torch.nn.Linear(resnet34.fc.in_features, 2)
ckpt_path = "./resnet34.pkl"
# Save model state dict
torch.save(resnet34.state_dict(), ckpt_path)
print(f"✅ Model saved at {ckpt_path}")
# Load deepfake detection model
model = models.resnet34()
model.fc = torch.nn.Linear(model.fc.in_features, 2)
model.load_state_dict(torch.load(ckpt_path, map_location="cpu"))
model.eval()
# Define transformation for face images
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def process_video(video_path: str):
cap = cv2.VideoCapture(video_path)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
output_path = video_path.replace(".mp4", "_processed.mp4")
output_video = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
faces = face_detector(rgb_frame, 1)
for face in faces:
landmarks = face_utils.shape_to_np(face_predictor(rgb_frame, face))
x_min, y_min = np.min(landmarks, axis=0)
x_max, y_max = np.max(landmarks, axis=0)
face_crop = rgb_frame[y_min:y_max, x_min:x_max]
if face_crop.size == 0:
continue
face_tensor = transform(face_crop).unsqueeze(0)
with torch.no_grad():
output = torch.softmax(model(face_tensor), dim=1)
fake_confidence = output[0, 1].item() * 100 # Fake confidence as a percentage
label = "Fake" if fake_confidence > 50 else "Real"
color = (0, 0, 255) if label == "Fake" else (0, 255, 0)
# Annotating confidence score with label
label_text = f"{label} ({fake_confidence:.2f}%)"
cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, 2)
cv2.putText(frame, label_text, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
output_video.write(frame)
cap.release()
output_video.release()
return output_path
def gradio_interface(video_file):
if video_file is None:
return "Error: No video uploaded."
# Create a temporary file and copy the uploaded video content
with NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
temp_file_path = temp_file.name
# Read the uploaded video file using its path
with open(video_file, "rb") as uploaded_file:
temp_file.write(uploaded_file.read())
output_path = process_video(temp_file_path)
return output_path
# Gradio UI
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.Video(label="Upload Video"),
outputs=gr.Video(label="Processed Video"),
title="Deepfake Detection",
description="Upload a video to detect deepfakes. The model will process faces and classify them as real or fake."
)
if __name__ == "__main__":
iface.launch()
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