app.py

import argparse

import gradio as gr

from benchmark.app_image import ImageSwap
from benchmark.app_video import VideoSwap
from configs.train_config import TrainConfig
from models.model import HifiFace


class ConfigPath:
    face_detector_weights = "/checkpoints/face_detector/face_detector_scrfd_10g_bnkps.onnx"
    model_path = ""
    model_idx = 80000
    ffmpeg_device = "cuda"
    device = "cuda"


def main():
    cfg = ConfigPath()
    parser = argparse.ArgumentParser(
        prog="benchmark", description="What the program does", epilog="Text at the bottom of help"
    )
    parser.add_argument("-m", "--model_path", default="/checkpoints/hififace_pretrained/standard_model")
    parser.add_argument("-i", "--model_idx", default="320000")
    parser.add_argument("-f", "--ffmpeg_device", default="cpu")
    parser.add_argument("-d", "--device", default="cpu")

    args = parser.parse_args()

    cfg.model_path = args.model_path
    cfg.model_idx = int(args.model_idx)
    cfg.ffmpeg_device = args.ffmpeg_device
    cfg.device = args.device
    opt = TrainConfig()
    checkpoint = (cfg.model_path, cfg.model_idx)
    model_path_1 = "/checkpoints/hififace_pretrained/with_gaze_and_mouth"
    checkpoint1 = ("/checkpoints/hififace_pretrained/with_gaze_and_mouth", "190000")
    model = HifiFace(opt.identity_extractor_config, is_training=False, device=cfg.device, load_checkpoint=checkpoint)

    model1 = HifiFace(opt.identity_extractor_config, is_training=False, device=cfg.device, load_checkpoint=checkpoint1)
    image_infer = ImageSwap(cfg, model)
    image_infer1 = ImageSwap(cfg, model1)
    def inference_image(source_face, target_face, shape_rate, id_rate, iterations):
        return image_infer.inference(source_face, target_face, shape_rate, id_rate, int(iterations))

    def inference_image1(source_face, target_face, shape_rate, id_rate, iterations):
        return image_infer1.inference(source_face, target_face, shape_rate, id_rate, int(iterations))

    model_name = cfg.model_path.split("/")[-1] + ":" + f"{cfg.model_idx}"
    model_name1 = model_path_1.split("/")[-1] + ":" + "190000"
    with gr.Blocks(title="FaceSwap") as demo:
        gr.Markdown(
            f"""
        ### standard model: {model_name} \n
        ### model with eye and mouth hm loss: {model_name1}
        """
        )
        with gr.Tab("Image swap with standard model"):
            with gr.Row():
                source_image = gr.Image(shape=None, label="source image")
                target_image = gr.Image(shape=None, label="target image")
            with gr.Row():
                with gr.Column():
                    structure_sim = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=1.0,
                        step=0.1,
                        label="3d similarity",
                    )
                    id_sim = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=1.0,
                        step=0.1,
                        label="id similarity",
                    )
                    iters = gr.Slider(
                        minimum=1,
                        maximum=10,
                        value=1,
                        step=1,
                        label="iters",
                    )
                    image_btn = gr.Button("image swap")
                output_image = gr.Image(shape=None, label="Result")

            image_btn.click(
                fn=inference_image,
                inputs=[source_image, target_image, structure_sim, id_sim, iters],
                outputs=output_image,
            )

        with gr.Tab("Image swap with eye&mouth hm loss model"):
            with gr.Row():
                source_image = gr.Image(shape=None, label="source image")
                target_image = gr.Image(shape=None, label="target image")
            with gr.Row():
                with gr.Column():
                    structure_sim = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=1.0,
                        step=0.1,
                        label="3d similarity",
                    )
                    id_sim = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=1.0,
                        step=0.1,
                        label="id similarity",
                    )
                    iters = gr.Slider(
                        minimum=1,
                        maximum=10,
                        value=1,
                        step=1,
                        label="iters",
                    )
                    image_btn = gr.Button("image swap")
                output_image = gr.Image(shape=None, label="Result")

            image_btn.click(
                fn=inference_image1,
                inputs=[source_image, target_image, structure_sim, id_sim, iters],
                outputs=output_image,
            )
        demo.launch(server_name="0.0.0.0", server_port=7860)


if __name__ == "__main__":
    main()