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Running
on
Zero
Running
on
Zero
| 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]), | |
| ] | |
| ) | |
| 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 = [] | |
| for frame in frames: | |
| pil_image = Image.fromarray(frame) | |
| processed_image = process(pil_image, color) | |
| 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 None, 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 | |
| def process_file(f, color="#00FF00"): | |
| name_path = f.rsplit(".", 1)[0] + ".png" | |
| im = load_img(f, output_type="pil") | |
| im = im.convert("RGB") | |
| transparent = process(im, color) | |
| transparent.save(name_path) | |
| return name_path | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| in_video = gr.Video(label="Input Video") | |
| stream_image = gr.Image(label="Streaming Output", visible=False) | |
| out_video = gr.Video(label="Final Output 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=lambda: gr.update(visible=True), inputs=None, outputs=[stream_image] | |
| ).then( | |
| fn=lambda: gr.update(visible=False), inputs=None, outputs=[out_video], | |
| ).then( | |
| fn, inputs=[in_video, fps_slider, color_picker], outputs=[stream_image, out_video] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(show_error=True) |