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app.py

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+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()

inswapper_128.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e4a3f08c753cb72d04e10aa0f7dbe3deebbf39567d4ead6dce08e98aa49e16af
+size 554253681

requirements.txt

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+onnxruntime
+insightface
+numpy
+opencv-python
+torch
+torchvision
+gradio

resnet50.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:372dcdf7c4650ce475025018dc663eae35da8dbcfa3a1b0b4ae85a49785dcc3d
+size 94366454