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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
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):
# Load the video using moviepy
video = mp.VideoFileClip(vid)
# Extract audio from the video
audio = video.audio
# Extract frames at 12 fps
frames = video.iter_frames(fps=12)
# Process each frame for background removal
processed_frames = []
for frame in frames:
pil_image = Image.fromarray(frame)
processed_image = process(pil_image)
processed_frames.append(np.array(processed_image))
# Create a new video from the processed frames
processed_video = mp.ImageSequenceClip(processed_frames, fps=12)
# Add the original audio back to the processed video
processed_video = processed_video.set_audio(audio)
# Return the processed video
return processed_video
def process(image):
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)
# Create a green screen image
green_screen = Image.new("RGBA", image_size, (0, 255, 0, 255))
# Composite the image onto the green screen using the mask
image = Image.composite(image, green_screen, mask)
return image
def process_file(f):
name_path = f.rsplit(".", 1)[0] + ".png"
im = load_img(f, output_type="pil")
im = im.convert("RGB")
transparent = process(im)
transparent.save(name_path)
return name_path
in_video = gr.Video(label="birefnet")
out_video = gr.Video()
demo = gr.Interface(
fn, inputs=in_video, outputs=out_video, api_name="video"
)
if __name__ == "__main__":
demo.launch(show_error=True) |