app.py

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)