import gradio as gr import cv2 import numpy as np import insightface from insightface.app import FaceAnalysis import datetime import os from PIL import Image def faceswapper(user_image, result_image, username="test"): output_folder = 'outputs' # Convert PIL images to NumPy arrays for processing guest_img = np.array(user_image) result_img = np.array(result_image) # Convert RGB (PIL) to BGR (OpenCV) guest_img = guest_img[:, :, ::-1] result_img = result_img[:, :, ::-1] # Initialize the FaceAnalysis app app = FaceAnalysis(name='buffalo_l') app.prepare(ctx_id=0, det_size=(640, 640)) # Initialize the face swapper model swapper = insightface.model_zoo.get_model('inswapper_128.onnx', download=False, download_zip=False) # Detect face in the guest image guest_faces = app.get(guest_img) guest_face = guest_faces[0] # Detect faces in the result image faces = app.get(result_img) # Perform face swapping for face in faces: result_img = swapper.get(result_img, face, guest_face, paste_back=True) # Save the result in the specified output folder if not os.path.exists(output_folder): os.makedirs(output_folder) current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") output_path = os.path.join(output_folder, f'{username}_swapped_face_{current_time}.jpg') cv2.imwrite(output_path, result_img) # Convert the final image from BGR to RGB before returning result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB) # Convert back to PIL image result_img_pil = Image.fromarray(result_img) original_size = result_image.size result_img_pil = result_img_pil.resize(original_size, Image.Resampling.LANCZOS) return result_img_pil with gr.Blocks() as demo: with gr.Row(): with gr.Column(): name = gr.Textbox(label="이름(파일저장용)") with gr.Row(): user_image_input = gr.Image(type="pil", label="유저사진(얼굴추출)", width=300, height=300) result_image_input = gr.Image(type="pil", label="결과물 사진", width=300, height=300) swap_btn = gr.Button("Swap Faces") with gr.Column(): output_image = gr.Image(label="합성 후 사진") swap_btn.click(fn=faceswapper, inputs=[user_image_input, result_image_input, name], outputs=output_image) demo.launch(debug=True)