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import gradio as gr
import torch
from model.models import UNet
from scripts.test_functions import process_image, process_video
window_size = 512
stride = 256
steps = 18
frame_count = 0
def get_model():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
unet_model = UNet().to(device)
unet_model.load_state_dict(torch.load("model/best_unet_model.pth", map_location=device))
unet_model.eval()
return unet_model
unet_model = get_model()
def block_img(image, source_age, target_age):
from PIL import Image as PILImage
import numpy as np
if isinstance(image, str):
image = PILImage.open(image).convert('RGB')
elif isinstance(image, np.ndarray) and image.dtype == object:
image = image.astype(np.uint8)
return process_image(unet_model, image, video=False, source_age=source_age,
target_age=target_age, window_size=window_size, stride=stride)
def block_img_vid(image, source_age):
from PIL import Image as PILImage
import numpy as np
if isinstance(image, str):
image = PILImage.open(image).convert('RGB')
elif isinstance(image, np.ndarray) and image.dtype == object:
image = image.astype(np.uint8)
return process_image(unet_model, image, video=True, source_age=source_age,
target_age=0, window_size=window_size, stride=stride, steps=steps)
def block_vid(video_path, source_age, target_age):
return process_video(unet_model, video_path, source_age, target_age,
window_size=window_size, stride=stride, frame_count=frame_count)
demo_img = gr.Interface(
fn=block_img,
inputs=[
gr.Image(type="pil"),
gr.Slider(10, 90, value=20, step=1, label="Current age", info="Choose your current age"),
gr.Slider(10, 90, value=80, step=1, label="Target age", info="Choose the age you want to become")
],
outputs="image",
examples=[
['assets/gradio_example_images/1.png', 20, 80],
['assets/gradio_example_images/2.png', 75, 40],
['assets/gradio_example_images/3.png', 30, 70],
['assets/gradio_example_images/4.png', 22, 60],
['assets/gradio_example_images/5.png', 28, 75],
['assets/gradio_example_images/6.png', 35, 15]
],
description="Input an image of a person and age them from the source age to the target age."
)
demo_img_vid = gr.Interface(
fn=block_img_vid,
inputs=[
gr.Image(type="pil"),
gr.Slider(10, 90, value=20, step=1, label="Current age", info="Choose your current age"),
],
outputs=gr.Video(),
examples=[
['assets/gradio_example_images/1.png', 20],
['assets/gradio_example_images/2.png', 75],
['assets/gradio_example_images/3.png', 30],
['assets/gradio_example_images/4.png', 22],
['assets/gradio_example_images/5.png', 28],
['assets/gradio_example_images/6.png', 35]
],
description="Input an image of a person and a video will be returned of the person at different ages."
)
demo_vid = gr.Interface(
fn=block_vid,
inputs=[
gr.Video(),
gr.Slider(10, 90, value=20, step=1, label="Current age", info="Choose your current age"),
gr.Slider(10, 90, value=80, step=1, label="Target age", info="Choose the age you want to become")
],
outputs=gr.Video(),
examples=[
['assets/gradio_example_images/orig.mp4', 35, 60],
],
description="Input a video of a person, and it will be aged frame-by-frame."
)
demo = gr.TabbedInterface([demo_img, demo_img_vid, demo_vid],
tab_names=['Image inference demo', 'Image animation demo', 'Video inference demo'],
title="Face Re-Aging Demo",
)
if __name__ == "__main__":
demo.launch() |