Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,598 Bytes
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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 os
import uuid
from PIL import Image
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(image):
im = load_img(image, output_type="pil")
im = im.convert("RGB")
# Get the segmented image (RGBA)
processed_image = process(im)
# Generate a unique filename for the processed image
unique_id = str(uuid.uuid4())[:8]
output_path = f"output_{unique_id}.jpg"
# Create a white background and properly composite with the RGBA image
white_bg = Image.new("RGB", processed_image.size, (255, 255, 255))
if processed_image.mode == 'RGBA':
# Use the alpha channel as a mask for compositing
white_bg.paste(processed_image, mask=processed_image.split()[3]) # The 4th channel is alpha
white_bg.save(output_path, format="JPEG")
# Return the composited image for display to match what's being downloaded
return white_bg, output_path
else:
rgb_image = processed_image.convert("RGB")
rgb_image.save(output_path, format="JPEG")
return rgb_image, output_path
@spaces.GPU
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)
# Since we're outputting RGB instead of RGBA, create a composite
# We'll keep the image with mask for display purposes
result = image.copy()
result.putalpha(mask)
return result
def process_file(f):
im = load_img(f, output_type="pil")
im = im.convert("RGB")
# Get the segmented image (RGBA)
transparent = process(im)
# Save as JPEG instead of PNG
unique_id = str(uuid.uuid4())[:8]
output_path = f"output_{unique_id}.jpg"
# Create a white background and properly composite with the RGBA image
white_bg = Image.new("RGB", transparent.size, (255, 255, 255))
if transparent.mode == 'RGBA':
# Use the alpha channel as a mask for compositing
white_bg.paste(transparent, mask=transparent.split()[3]) # The 4th channel is alpha
white_bg.save(output_path, format="JPEG")
else:
rgb_image = transparent.convert("RGB")
rgb_image.save(output_path, format="JPEG")
return output_path
# Using a single Blocks API instead of TabbedInterface to avoid compatibility issues
with gr.Blocks(title="Background Removal Tool") as demo:
with gr.Tabs():
with gr.Tab("Image Upload"):
with gr.Row():
image_upload = gr.Image(label="Upload an image")
with gr.Row():
submit_btn = gr.Button("Process Image")
with gr.Row():
output_image = gr.Image(label="Processed Image")
output_file = gr.File(label="Download Processed Image")
submit_btn.click(fn=fn, inputs=image_upload, outputs=[output_image, output_file])
with gr.Tab("URL Input"):
with gr.Row():
url_input = gr.Textbox(label="Paste an image URL")
with gr.Row():
submit_url_btn = gr.Button("Process URL")
with gr.Row():
output_image_url = gr.Image(label="Processed Image")
output_file_url = gr.File(label="Download Processed Image")
submit_url_btn.click(fn=fn, inputs=url_input, outputs=[output_image_url, output_file_url])
with gr.Tab("File Output"):
with gr.Row():
image_file_upload = gr.Image(label="Upload an image", type="filepath")
with gr.Row():
submit_file_btn = gr.Button("Process and Download")
with gr.Row():
output_file_path = gr.File(label="Download JPEG File")
submit_file_btn.click(fn=process_file, inputs=image_file_upload, outputs=output_file_path)
if __name__ == "__main__":
demo.launch(show_error=True) |