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 from concurrent.futures import ThreadPoolExecutor torch.set_float32_matmul_precision("highest") birefnet = AutoModelForImageSegmentation.from_pretrained( "ZhengPeng7/BiRefNet", trust_remote_code=True ).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]), ] ) BATCH_SIZE = 3 executor = ThreadPoolExecutor(max_workers=4) @spaces.GPU def fn(vid, bg_type="Color", bg_image=None, bg_video=None, color="#00FF00", fps=0, video_handling="slow_down"): try: video = mp.VideoFileClip(vid) try: audio = video.audio except AttributeError: audio = None if fps == 0: fps = video.fps frames = video.iter_frames(fps=fps) 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 and video_handling == "slow_down": slow_down_factor = video.duration / background_video.duration else: slow_down_factor = 1 background_frames = list(background_video.iter_frames(fps=fps)) else: background_frames = None slow_down_factor = None bg_frame_index = 0 frame_batch = [] for i, frame in enumerate(frames): frame_batch.append(frame) if len(frame_batch) == BATCH_SIZE or i == int(video.fps * video.duration) - 1: pil_images = [Image.fromarray(f) for f in frame_batch] if bg_type == "Video": processed_images = list(executor.map(process, pil_images, [get_background_image(bg_type, bg_image, background_frames, bg_frame_index + j, video_handling, slow_down_factor) for j in range(len(pil_images))])) bg_frame_index += len(frame_batch) elif bg_type == "Color": processed_images = list(executor.map(process, pil_images, [color] * len(pil_images))) elif bg_type == "Image": processed_images = list(executor.map(process, pil_images, [bg_image] * len(pil_images))) else: processed_images = pil_images for processed_image in processed_images: processed_frames.append(np.array(processed_image)) yield processed_image, None frame_batch = [] processed_video = mp.ImageSequenceClip(processed_frames, fps=fps) if audio: processed_video = processed_video.set_audio(audio) 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", logger=None) yield gr.update(visible=False), gr.update(visible=True) 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): 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) 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: 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) 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], outputs=[stream_image, out_video], ) if __name__ == "__main__": demo.launch(show_error=True)