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=True):
 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 = 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

 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))
 elapsed_time = time.time() - start_time
 yield processed_image, None, f"Processing frame {i+1}... 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
 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")

 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_image, 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 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=True, 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],
 outputs=[stream_image, out_video, time_textbox],
 )

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