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import gradio as gr
import numpy as np
import cv2
import onnxruntime
from insightface.app import FaceAnalysis
from pathlib import Path
# Initialize Face Analysis
face_analyzer = FaceAnalysis(name="buffalo_l")
face_analyzer.prepare(ctx_id=0, det_size=(640, 640))
# Load Face Swapper Model
MODEL_PATH = Path("models/inswapper_128.onnx")
if not MODEL_PATH.exists():
raise FileNotFoundError("Model file inswapper_128.onnx not found.")
session = onnxruntime.InferenceSession(str(MODEL_PATH))
def swap_faces(source_img, target_img):
"""Perform face swapping using the ONNX model."""
try:
# Convert images to correct format
source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
# Detect faces
source_faces = face_analyzer.get(source_img)
target_faces = face_analyzer.get(target_img)
if not source_faces or not target_faces:
return "No faces detected in one or both images."
if len(source_faces) > 1 or len(target_faces) > 1:
return "Multiple faces detected; only one face per image is supported."
source_face = source_faces[0]
target_face = target_faces[0]
# Prepare input data for ONNX model
input_data = {
"target_image": target_img,
"target_face": target_face.embedding,
"source_face": source_face.embedding
}
# Run the ONNX model
result = session.run(None, input_data)[0]
# Convert result to image format
result_img = np.clip(result * 255, 0, 255).astype(np.uint8)
result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
return result_img
except Exception as e:
return f"Face swap failed: {e}"
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# Face Swap Tool 🚀")
with gr.Row():
input_source = gr.Image(label="Source Face", type="pil")
input_target = gr.Image(label="Target Image", type="pil")
btn_swap = gr.Button("Swap Faces")
output_image = gr.Image(label="Swapped Face")
btn_swap.click(swap_faces, inputs=[input_source, input_target], outputs=output_image)
# Launch Gradio App
demo.launch()
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