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import gradio as gr
from transformers import pipeline
from PIL import Image, ImageFilter
import numpy as np
# Load the segmentation pipeline
pipe = pipeline("image-segmentation", model="mattmdjaga/segformer_b2_clothes")
# Simplified refine_mask function
def refine_mask(mask):
"""Simplify and smooth the segmentation mask."""
mask_array = np.array(mask)
mask_array = (mask_array > 128).astype(np.uint8) * 255 # Threshold to binary mask
refined_mask = Image.fromarray(mask_array).filter(ImageFilter.GaussianBlur(0.5)) # Smooth edges
return refined_mask
# Function to blur the background
def blur_background(image, blur_radius):
# Perform segmentation
result = pipe(image)
# Extract the background mask
background_mask = None
for entry in result:
if entry["label"] == "Background":
background_mask = refine_mask(entry["mask"]) # Refine the background mask
break
if background_mask is None:
return image # If no background is detected, return the original image
# Convert the image and mask to NumPy arrays
image_np = np.array(image)
background_mask_np = np.array(background_mask)
# Create a blurred version of the entire image
blurred_image = image.filter(ImageFilter.GaussianBlur(radius=blur_radius))
blurred_np = np.array(blurred_image)
# Combine the original image and the blurred background
final_image = np.where(background_mask_np[..., None] == 255, blurred_np, image_np).astype(np.uint8)
# Convert back to PIL image
return Image.fromarray(final_image)
# Example inputs for Gradio
examples = [["1.jpg", 10] ,["2.jpg", 10] ,["3.jpg", 10] ] # Example: Image with a blur intensity of 10
# Gradio interface
interface = gr.Interface(
fn=blur_background,
inputs=[
gr.Image(type="pil"), # Input image
gr.Slider(1, 50, step=1, label="Blur Intensity") # Slider for blur radius
],
outputs=gr.Image(type="pil"), # Output image with blurred background
examples=examples, # Provide examples as a nested list
title="Blur Background with Refined Mask",
description="Upload an image and adjust the slider to control the background blur level. The background edges are smoothed for better blending."
)
# Launch the app
interface.launch()
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