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import torch
import os
import shutil
import tempfile
import gradio as gr
from PIL import Image
from rembg import remove
import sys
import subprocess
from glob import glob
import requests
def remove_background(input_url):
# Create a temporary folder for downloaded and processed images
temp_dir = tempfile.mkdtemp()
# Download the image from the URL
image_path = os.path.join(temp_dir, 'input_image.png')
try:
image = Image.open(input_url)
image.save(image_path)
except Exception as e:
shutil.rmtree(temp_dir)
return f"Error downloading or saving the image: {str(e)}"
# Run background removal
try:
removed_bg_path = os.path.join(temp_dir, 'output_image_rmbg.png')
img = Image.open(image_path)
result = remove(img)
result.save(removed_bg_path)
except Exception as e:
shutil.rmtree(temp_dir)
return f"Error removing background: {str(e)}"
return removed_bg_path, temp_dir
def run_inference(temp_dir):
# Define the inference configuration
inference_config = "configs/inference-768-6view.yaml"
pretrained_model = "pengHTYX/PSHuman_Unclip_768_6views"
crop_size = 740
seed = 600
num_views = 7
save_mode = "rgb"
try:
# Run the inference command
subprocess.run(
[
"python", "inference.py",
"--config", inference_config,
f"pretrained_model_name_or_path={pretrained_model}",
f"validation_dataset.crop_size={crop_size}",
f"with_smpl=false",
f"validation_dataset.root_dir={temp_dir}",
f"seed={seed}",
f"num_views={num_views}",
f"save_mode={save_mode}"
],
check=True
)
# Collect the output images
output_images = glob(os.path.join(temp_dir, "*.png"))
return output_images
except subprocess.CalledProcessError as e:
return f"Error during inference: {str(e)}"
def process_image(input_url):
# Remove background
result = remove_background(input_url)
if isinstance(result, str) and result.startswith("Error"):
raise gr.Error(f"{result}") # Return the error message if something went wrong
removed_bg_path, temp_dir = result # Unpack only if successful
# Run inference
output_images = run_inference(temp_dir)
if isinstance(output_images, str) and output_images.startswith("Error"):
shutil.rmtree(temp_dir)
raise gr.Error(f"{output_images}") # Return the error message if inference failed
# Prepare outputs for display
results = []
for img_path in output_images:
results.append((img_path, img_path))
shutil.rmtree(temp_dir) # Cleanup temporary folder
return results
def gradio_interface():
with gr.Blocks() as app:
gr.Markdown("# Background Removal and Inference Pipeline")
with gr.Row():
input_image = gr.Image(label="Image input", type="filepath")
submit_button = gr.Button("Process")
output_gallery = gr.Gallery(label="Output Images")
submit_button.click(process_image, inputs=[input_image], outputs=[output_gallery])
return app
# Launch the Gradio app
app = gradio_interface()
app.launch()
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