app.py

import gradio as gr
import numpy as np
import cv2
from PIL import Image
import insightface
from insightface.app import FaceAnalysis
import torch
from torch import nn
import torchvision 

# Gender model
gender_model = torchvision.models.resnet50(weights=None)
num_features = gender_model.fc.in_features
gender_model.fc = nn.Linear(num_features, 2)

def load_face_analysis_model():
    app = FaceAnalysis(name='buffalo_l')
    app.prepare(ctx_id=0, det_size=(640, 640))
    return app

def detect_faces(img):
    app = load_face_analysis_model()
    faces = app.get(img)
    return faces

def detect_gender(img_list, 
                  model, model_file='resnet50.pth', device='cpu'):
    model.load_state_dict(torch.load(model_file, map_location=device))
    model.to(device)
    model.eval()
    results = []    
    transform = torchvision.transforms.Compose([
        torchvision.transforms.Resize((224, 224)),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    class_names = ['Female', 'Male']
    
    for img in img_list:
        image = Image.fromarray(img).convert('RGB')
        image = transform(image).unsqueeze(0).to(device)
        with torch.no_grad():
            outputs = model(image)
            _, predicted = torch.max(outputs, 1)
            res = class_names[predicted.item()]
            results.append(res)
    return results    

def swap_faces(target_image, source_image):
    if target_image is None or source_image is None:
        return None
    
    # Convert to OpenCV BGR
    target_img = cv2.cvtColor(np.array(target_image), cv2.COLOR_RGB2BGR)
    source_img = cv2.cvtColor(np.array(source_image), cv2.COLOR_RGB2BGR)
    res = target_img.copy()
    
    # Detect faces
    faces_target = detect_faces(target_img)
    faces_source = detect_faces(source_img)
    
    if len(faces_target) == 0 or len(faces_source) == 0:
        return target_image  # yüz bulunmazsa orijinali döndür
    
    # Load swapper
    swapper = insightface.model_zoo.get_model('inswapper_128.onnx',
                                              download=False,
                                              download_zip=False)
    
    # İlk source yüzünü alıyoruz
    source_face = faces_source[0]
    
    for face in faces_target:
        res = swapper.get(res, face, source_face, paste_back=True)
    
    res = cv2.cvtColor(res, cv2.COLOR_BGR2RGB)
    return res

# Gradio arayüzü
with gr.Blocks() as demo:
    gr.Markdown("### Face Swapper Uygulaması")
    
    with gr.Row():
        with gr.Column():
            target_input = gr.Image(label="Hedef Resim", type="pil")
            source_input = gr.Image(label="Kaynak Resim", type="pil")
            process_button = gr.Button("Face Swap Yap", variant="primary")
        
        with gr.Column():
            output_image = gr.Image(label="İşlem Sonucu")
    
    process_button.click(
        fn=swap_faces, 
        inputs=[target_input, source_input], 
        outputs=[output_image]
    )

demo.launch()