Update app.py

app.py

@@ -1,6 +1,6 @@
 from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
 import gradio as gr
-from PIL import Image, ImageFilter
+from PIL import Image
 import torch
 import matplotlib.pyplot as plt
 import torch
@@ -53,10 +53,7 @@ def extract_image(img, pos_prompts, neg_prompts, threshold, alpha_value=0.5, blu
     neg_mask = np.any(np.stack(negative_masks), axis=0)
     final_mask = pos_mask & ~neg_mask
 
-    # apply Gaussian blur for feathering
-    final_mask_img = Image.fromarray((final_mask * 255).astype(np.uint8), "L")
-    final_mask_img = final_mask_img.filter(ImageFilter.GaussianBlur(radius=blur_radius))
-    final_mask = np.array(final_mask_img) / 255
+    final_mask = Image.fromarray(final_mask.astype(np.uint8) * 255, "L")
     final_mask = final_mask > threshold
 
     # blend the original image and the mask using the alpha value
@@ -76,7 +73,6 @@ def extract_image(img, pos_prompts, neg_prompts, threshold, alpha_value=0.5, blu
 
 
 
-
 title = "Interactive demo: zero-shot image segmentation with CLIPSeg"
 description = "Demo for using CLIPSeg, a CLIP-based model for zero- and one-shot image segmentation. To use it, simply upload an image and add a text to mask (identify in the image), or use one of the examples below and click 'submit'. Results will show up in a few seconds."
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10003'>CLIPSeg: Image Segmentation Using Text and Image Prompts</a> | <a href='https://huggingface.co/docs/transformers/main/en/model_doc/clipseg'>HuggingFace docs</a></p>"