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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]),
]
)
@spaces.GPU
def fn(vid, fps=12, color="#00FF00"):
# 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 = []
yield gr.update(visible=True), gr.update(visible=False)
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 processed_image, 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
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")
examples = gr.Examples(["rickroll-2sec.mp4"], inputs=in_video, outputs=[stream_image, out_video], fn=fn, cache_examples=True, cache_mode="eager")
submit_button.click(
fn, inputs=[in_video, fps_slider, color_picker], outputs=[stream_image, out_video]
)
if __name__ == "__main__":
demo.launch(show_error=True) |