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
from concurrent.futures import ThreadPoolExecutor

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 process a single frame
def process_frame(frame, bg_type, bg, fast_mode, bg_frame_index, background_frames, color):
    try:
        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, fast_mode)
        elif bg_type == "Video":
            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
        return np.array(processed_image), bg_frame_index
    except Exception as e:
        print(f"Error processing frame: {e}")
        return frame, bg_frame_index

@spaces.GPU
def fn(vid, bg_type="Color", bg_image=None, bg_video=None, color="#00FF00", fps=0, video_handling="slow_down", fast_mode=True, max_workers=5):
    try:
        start_time = time.time()  # Start the timer

        # 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 = list(video.iter_frames(fps=fps))

        # Process each frame for background removal
        processed_frames = []
        yield gr.update(visible=True), gr.update(visible=False), f"Processing started... Elapsed time: 0 seconds"

        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

        # Use ThreadPoolExecutor for parallel processing with specified max_workers
        with ThreadPoolExecutor(max_workers=max_workers) as executor: 
            futures = [executor.submit(process_frame, frames[i], bg_type, bg_image, fast_mode, bg_frame_index, background_frames, color) for i in range(len(frames))]
            for future in futures:
                result, bg_frame_index = future.result()
                processed_frames.append(result)
                elapsed_time = time.time() - start_time
                yield result, None, f"Processing frame {len(processed_frames)}... Elapsed time: {elapsed_time:.2f} seconds"

        # 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 using tempfile
        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_file:
            temp_filepath = temp_file.name
            processed_video.write_videofile(temp_filepath, codec="libx264")

        elapsed_time = time.time() - start_time
        yield gr.update(visible=False), gr.update(visible=True), f"Processing complete! Elapsed time: {elapsed_time:.2f} seconds"

        # Return the path to the temporary file
        yield processed_frames[-1], temp_filepath, f"Processing complete! Elapsed time: {elapsed_time:.2f} seconds"

    except Exception as e:
        print(f"Error: {e}")
        elapsed_time = time.time() - start_time
        yield gr.update(visible=False), gr.update(visible=True), f"Error processing video: {e}. Elapsed time: {elapsed_time:.2f} seconds"
        yield None, f"Error processing video: {e}", f"Error processing video: {e}. Elapsed time: {elapsed_time:.2f} seconds"

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 200 frames at once. So, if you have a 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=True, interactive=True)
        max_workers_slider = gr.Slider( minimum=1, maximum=32, step=1, value=5, label="Max Workers", info="Determines how many Franes to process parallel", interactive=True
        )
    time_textbox = gr.Textbox(label="Time Elapsed", interactive=False)  # Add time textbox

    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, time_textbox],
        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, max_workers_slider],
        outputs=[stream_image, out_video, time_textbox],
    )

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