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face-verify

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kadabengaran commited on 4 days ago

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847ca61

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1 Parent(s): 57482e4

Create app.py

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+from fastapi import FastAPI, File, UploadFile, Form, HTTPException
+from fastapi.responses import JSONResponse, HTMLResponse
+import gradio as gr
+from deepface import DeepFace
+import os
+from threading import Thread
+import asyncio
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+# FastAPI instance
+app = FastAPI()
+# Gradio Interface Function
+def face_verification_uii(img1, img2, dist="cosine", model="Facenet", detector="ssd"):
+    """
+    Gradio function for face verification
+    """
+    try:
+        result = DeepFace.verify(
+            img1_path=img1,
+            img2_path=img2,
+            distance_metric=dist,
+            model_name=model,
+            detector_backend=detector,
+            enforce_detection=False,
+        )
+        return {
+            "verified": result["verified"],
+            "distance": result["distance"],
+            "threshold": result["threshold"],
+            "model": result["model"],
+            "detector_backend": result["detector_backend"],
+            "similarity_metric": result["similarity_metric"],
+        }
+    except Exception as e:
+        return {"error": str(e)}
+# FastAPI Endpoint
+@app.get("/", response_class=HTMLResponse)
+async def gradio_ui():
+    html_content = """
+    <html>
+        <head>
+            <title>Gradio UI</title>
+        </head>
+        <body>
+            <iframe src="http://localhost:7861" width="100%" height="100%" frameborder="0"></iframe>
+        </body>
+    </html>
+    """
+    return HTMLResponse(content=html_content)
+@app.post("/face_verification")
+async def face_verification(
+    img1: UploadFile = File(...),
+    img2: UploadFile = File(...),
+    dist: str = Form("cosine"),
+    model: str = Form("Facenet"),
+    detector: str = Form("ssd")
+):
+    """
+    Endpoint to verify if two images belong to the same person.
+    """
+    try:
+        # Ensure uploads directory exists
+        if not os.path.exists("uploads"):
+            os.makedirs("uploads")
+        # Save uploaded images to disk
+        img1_path = os.path.join("uploads", img1.filename)
+        img2_path = os.path.join("uploads", img2.filename)
+        with open(img1_path, "wb") as f:
+            f.write(await img1.read())
+        with open(img2_path, "wb") as f:
+            f.write(await img2.read())
+        # Run DeepFace verification
+        result = DeepFace.verify(
+            img1_path=img1_path,
+            img2_path=img2_path,
+            distance_metric=dist,
+            model_name=model,
+            detector_backend=detector,
+            enforce_detection=False,
+        )
+        # Delete uploaded images after processing
+        os.remove(img1_path)
+        os.remove(img2_path)
+        # Return verification results
+        return {
+            "verified": result["verified"],
+            "distance": result["distance"],
+            "threshold": result["threshold"],
+            "model": result["model"],
+            "detector_backend": result["detector_backend"],
+            "similarity_metric": result["similarity_metric"]
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+def run_gradio_ui():
+    """
+    Function to run Gradio in a separate thread
+    """
+    # Create and set an event loop for this thread
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    def face_verification_ui(img1, img2, dist, model, detector):
+        result = face_verification_uii(img1, img2, dist, model, detector)
+        return result
+    with gr.Blocks() as demo:
+        img1 = gr.Image(type="filepath", label="Image 1")
+        img2 = gr.Image(type="filepath", label="Image 2")
+        dist = gr.Dropdown(choices=["cosine", "euclidean", "euclidean_l2"], label="Distance Metric", value="cosine")
+        model = gr.Dropdown(choices=["VGG-Face", "Facenet", "Facenet512", "ArcFace"], label="Model", value="Facenet")
+        detector = gr.Dropdown(choices=["opencv", "ssd", "mtcnn", "retinaface", "mediapipe"], label="Detector", value="ssd")
+        btn = gr.Button("Verify")
+        output = gr.Textbox()
+        btn.click(face_verification_ui, inputs=[img1, img2, dist, model, detector], outputs=output)
+    demo.launch(server_name="0.0.0.0", server_port=7861, show_api=False)
+# FastAPI Startup Event
+# FastAPI Startup Event
+@app.on_event("startup")
+def startup_event():
+    """
+    Start Gradio UI in a separate thread
+    """
+    thread = Thread(target=run_gradio_ui)
+    thread.start()
+# Running Both Servers
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860, reload=True)