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
import os
import tempfile
import uuid

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, fps, color):
 # Load the video using moviepy
 video = mp.VideoFileClip(vid)

 # Extract audio from the video
 audio = video.audio

 # Extract frames at the specified FPS
 frames = video.iter_frames(fps=fps)

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

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

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

 # Save the processed video to a temporary file
 temp_dir = "temp"
 os.makedirs(temp_dir, exist_ok=True)
 unique_filename = str(uuid.uuid4()) + ".mp4"
 temp_filepath = os.path.join(temp_dir, unique_filename)
 processed_video.write_videofile(temp_filepath, codec="libx264")

 # Create and save no-background video
 processed_video_no_bg = mp.ImageSequenceClip(processed_frames_no_bg, fps=fps)
 processed_video_no_bg = processed_video_no_bg.set_audio(audio)
 temp_filepath_no_bg = os.path.join(temp_dir, str(uuid.uuid4()) + ".webm")
 processed_video_no_bg.write_videofile(temp_filepath_no_bg, codec="libvpx")

 return temp_filepath_no_bg, temp_filepath


def process(image, color_hex):
 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)

 # Convert hex color to RGB tuple
 color_rgb = tuple(int(color_hex[i:i + 2], 16) for i in (1, 3, 5))

 # Create a background image with the chosen color
 background = Image.new("RGBA", image_size, color_rgb + (255,))

 # Composite the image onto the background using the mask
 image = Image.composite(image, background, mask)

 return image, mask # Return both the processed image and the mask


with gr.Blocks() as demo:
 with gr.Row():
 in_video = gr.Video(label="Input Video")
 no_bg_video = gr.Video(label="No BG Video") # Added for no-background video
 out_video = gr.Video(label="Output Video") # This will be the changed-background video
 submit_button = gr.Button("Change Background")
 with gr.Row():
 fps_slider = gr.Slider(minimum=1, maximum=60, step=1, value=12, label="Output FPS")
 color_picker = gr.ColorPicker(label="Background Color", value="#00FF00")


 submit_button.click(
 fn, inputs=[in_video, fps_slider, color_picker], outputs=[no_bg_video, out_video] 
 )

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