app.py

import gradio as gr
from loadimg import load_img
import spaces
from transformers import AutoModelForImageSegmentation
import torch
from torchvision import transforms
import moviepy.editor as mp
from pydub import AudioSegment
from PIL import Image
import numpy as np


torch.set_float32_matmul_precision(["high", "highest"][0])

birefnet = AutoModelForImageSegmentation.from_pretrained(
 "ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to("cuda")
transform_image = transforms.Compose(
 [
 transforms.Resize((1024, 1024)),
 transforms.ToTensor(),
 transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
 ]
)

@spaces.GPU
def fn(vid):
 # Load the video using moviepy
 video = mp.VideoFileClip(vid)

 # Extract audio from the video
 audio = video.audio

 # Extract frames at 12 fps
 frames = video.iter_frames(fps=12)

 # Process each frame for background removal
 processed_frames = []
 for frame in frames:
 pil_image = Image.fromarray(frame)
 processed_image = process(pil_image)
 processed_frames.append(np.array(processed_image))

 # Create a new video from the processed frames
 processed_video = mp.ImageSequenceClip(processed_frames, fps=12)

 # Add the original audio back to the processed video
 processed_video = processed_video.set_audio(audio)

 # Return the processed video
 return processed_video



def process(image):
 image_size = image.size
 input_images = transform_image(image).unsqueeze(0).to("cuda")
 # Prediction
 with torch.no_grad():
 preds = birefnet(input_images)[-1].sigmoid().cpu()
 pred = preds[0].squeeze()
 pred_pil = transforms.ToPILImage()(pred)
 mask = pred_pil.resize(image_size)

 # Create a green screen image
 green_screen = Image.new("RGBA", image_size, (0, 255, 0, 255))

 # Composite the image onto the green screen using the mask
 image = Image.composite(image, green_screen, mask)

 return image


def process_file(f):
 name_path = f.rsplit(".", 1)[0] + ".png"
 im = load_img(f, output_type="pil")
 im = im.convert("RGB")
 transparent = process(im)
 transparent.save(name_path)
 return name_path


in_video = gr.Video(label="birefnet")
out_video = gr.Video()


demo = gr.Interface(
 fn, inputs=in_video, outputs=out_video, api_name="video"
)


if __name__ == "__main__":
 demo.launch(show_error=True)