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
import time
import threading

torch.set_float32_matmul_precision("medium")

device = "cuda" if torch.cuda.is_available() else "cpu"

# Load both BiRefNet models
birefnet = AutoModelForImageSegmentation.from_pretrained(
    "ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to(device)

birefnet_lite = AutoModelForImageSegmentation.from_pretrained(
    "ZhengPeng7/BiRefNet_lite", trust_remote_code=True
)
birefnet_lite.to(device)

transform_image = transforms.Compose(
    [
        transforms.Resize((1024, 1024)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    ]
)


# Function to delete files older than 10 minutes in the temp directory
def cleanup_temp_files():
    while True:
        temp_dir = "temp"
        if os.path.exists(temp_dir):
            for filename in os.listdir(temp_dir):
                filepath = os.path.join(temp_dir, filename)
                if os.path.isfile(filepath):
                    file_age = time.time() - os.path.getmtime(filepath)
                    if file_age > 600:  # 10 minutes in seconds
                        try:
                            os.remove(filepath)
                            print(f"Deleted temporary file: {filepath}")
                        except Exception as e:
                            print(f"Error deleting file {filepath}: {e}")
        time.sleep(60)  # Check every minute


# Start the cleanup thread
cleanup_thread = threading.Thread(target=cleanup_temp_files, daemon=True)
cleanup_thread.start()


@spaces.GPU
def fn(vid, bg_type="Color", bg_image=None, bg_video=None, color="#00FF00", fps=0, video_handling="slow_down", fast_mode=False):
    try:
        # Load the video using moviepy
        video = mp.VideoFileClip(vid)

        # Load original fps if fps value is equal to 0
        if fps == 0:
            fps = video.fps

        # 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 = []
        yield gr.update(visible=True), gr.update(visible=False)

        if bg_type == "Video":
            background_video = mp.VideoFileClip(bg_video)
            if background_video.duration < video.duration:
                if video_handling == "slow_down":
                    background_video = background_video.fx(mp.vfx.speedx, factor=video.duration / background_video.duration)
                else:  # video_handling == "loop"
                    background_video = mp.concatenate_videoclips([background_video] * int(video.duration / background_video.duration + 1))
            background_frames = list(background_video.iter_frames(fps=fps))  # Convert to list
        else:
            background_frames = None

        bg_frame_index = 0  # Initialize background frame index

        for i, frame in enumerate(frames):
            pil_image = Image.fromarray(frame)
            if bg_type == "Color":
                processed_image = process(pil_image, color, fast_mode)
            elif bg_type == "Image":
                processed_image = process(pil_image, bg_image, fast_mode)
            elif bg_type == "Video":
                if video_handling == "slow_down":
                    background_frame = background_frames[bg_frame_index % len(background_frames)]
                    bg_frame_index += 1
                    background_image = Image.fromarray(background_frame)
                    processed_image = process(pil_image, background_image, fast_mode)
                else:  # video_handling == "loop"
                    background_frame = background_frames[bg_frame_index % len(background_frames)]
                    bg_frame_index += 1
                    background_image = Image.fromarray(background_frame)
                    processed_image = process(pil_image, background_image, fast_mode)
            else:
                processed_image = pil_image  # Default to original image if no background is selected

            processed_frames.append(np.array(processed_image))
            yield processed_image, None

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

        yield gr.update(visible=False), gr.update(visible=True)
        # Return the path to the temporary file
        yield processed_image, temp_filepath

    except Exception as e:
        print(f"Error: {e}")
        yield gr.update(visible=False), gr.update(visible=True)
        yield None, f"Error processing video: {e}"


def process(image, bg, fast_mode=False):
    image_size = image.size
    input_images = transform_image(image).unsqueeze(0).to("cuda")

    # Select the model based on fast_mode
    model = birefnet_lite if fast_mode else birefnet

    # Prediction
    with torch.no_grad():
        preds = model(input_images)[-1].sigmoid().cpu()
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    mask = pred_pil.resize(image_size)

    if isinstance(bg, str) and bg.startswith("#"):
        color_rgb = tuple(int(bg[i:i+2], 16) for i in (1, 3, 5))
        background = Image.new("RGBA", image_size, color_rgb + (255,))
    elif isinstance(bg, Image.Image):
        background = bg.convert("RGBA").resize(image_size)
    else:
        background = Image.open(bg).convert("RGBA").resize(image_size)

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

    return image


with gr.Blocks(theme=gr.themes.Ocean()) as demo:
    gr.Markdown("# Video Background Remover & Changer\n### You can replace image background with any color, image or video.\nNOTE: As this Space is running on ZERO GPU it has limit. It can handle approx 200frmaes at once. So, if you have big video than use small chunks or Duplicate this space.")
    with gr.Row():
        in_video = gr.Video(label="Input Video", interactive=True)
        stream_image = gr.Image(label="Streaming Output", visible=False)
        out_video = gr.Video(label="Final Output Video")
    submit_button = gr.Button("Change Background", interactive=True)
    with gr.Row():
        fps_slider = gr.Slider(
            minimum=0,
            maximum=60,
            step=1,
            value=0,
            label="Output FPS (0 will inherit the original fps value)",
            interactive=True
        )
        bg_type = gr.Radio(["Color", "Image", "Video"], label="Background Type", value="Color", interactive=True)
        color_picker = gr.ColorPicker(label="Background Color", value="#00FF00", visible=True, interactive=True)
        bg_image = gr.Image(label="Background Image", type="filepath", visible=False, interactive=True)
        bg_video = gr.Video(label="Background Video", visible=False, interactive=True)
        with gr.Column(visible=False) as video_handling_options:
            video_handling_radio = gr.Radio(["slow_down", "loop"], label="Video Handling", value="slow_down", interactive=True)
        fast_mode_checkbox = gr.Checkbox(label="Fast Mode (Use BiRefNet_lite)", value=False, interactive=True)


    def update_visibility(bg_type):
        if bg_type == "Color":
            return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
        elif bg_type == "Image":
            return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)
        elif bg_type == "Video":
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)
        else:
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)


    bg_type.change(update_visibility, inputs=bg_type, outputs=[color_picker, bg_image, bg_video, video_handling_options])


    examples = gr.Examples(
        [
            ["rickroll-2sec.mp4", "Video", None, "background.mp4"],
            ["rickroll-2sec.mp4", "Image", "images.webp", None],
            ["rickroll-2sec.mp4", "Color", None, None],
        ],
        inputs=[in_video, bg_type, bg_image, bg_video],
        outputs=[stream_image, out_video],
        fn=fn,
        cache_examples=True,
        cache_mode="eager",
    )


    submit_button.click(
        fn,
        inputs=[in_video, bg_type, bg_image, bg_video, color_picker, fps_slider, video_handling_radio, fast_mode_checkbox],
        outputs=[stream_image, out_video],
    )

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