Update app.py

app.py

@@ -1,38 +1,40 @@
 import gradio as gr
-import cv2
 import numpy as np
+import cv2
 from PIL import Image
 
-def refine_edges(image: Image.Image):
-    # Convert PIL Image to NumPy array
-    image_np = np.array(image)
-
-    # Check if image has alpha channel
-    if image_np.shape[2] != 4:
-        return image  # Return original if no alpha channel
+def smooth_edges(image: Image.Image):
+    # Convert to numpy array
+    img_np = np.array(image.convert("RGB"))
 
-    # Separate color and alpha channels
-    b, g, r, a = cv2.split(image_np)
+    # Create a grayscale version to detect edges
+    gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY)
 
-    # Apply Gaussian blur to alpha channel
-    a_blurred = cv2.GaussianBlur(a, (5, 5), 0)
+    # Detect edges using Canny
+    edges = cv2.Canny(gray, threshold1=50, threshold2=150)
 
-    # Apply morphological operations to smooth edges
+    # Dilate edges a bit to make the mask thicker
     kernel = np.ones((3, 3), np.uint8)
-    a_morph = cv2.morphologyEx(a_blurred, cv2.MORPH_OPEN, kernel)
+    dilated = cv2.dilate(edges, kernel, iterations=1)
+
+    # Create a mask with blur
+    mask = cv2.GaussianBlur(dilated, (11, 11), 0)
+
+    # Convert mask to 3-channel
+    mask_3c = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB) / 255.0
 
-    # Merge channels back
-    result = cv2.merge((b, g, r, a_morph))
+    # Apply soft blending of edges
+    softened = (img_np * (1 - mask_3c) + cv2.GaussianBlur(img_np, (15, 15), 0) * mask_3c).astype(np.uint8)
 
-    # Convert back to PIL Image
-    return Image.fromarray(result)
+    return Image.fromarray(softened)
 
+# Interface
 iface = gr.Interface(
-    fn=refine_edges,
-    inputs=gr.Image(type="pil", image_mode="RGBA", label="Upload Transparent PNG"),
-    outputs=gr.Image(type="pil", image_mode="RGBA", label="Refined Image"),
-    title="Edge Smoother",
-    description="Upload a transparent PNG image to refine its edges, especially around hair and fur."
+    fn=smooth_edges,
+    inputs=gr.Image(type="pil", label="Upload Image (even non-transparent!)"),
+    outputs=gr.Image(type="pil", label="Softened Edges"),
+    title="Edge Smoother v2",
+    description="Smooth the edges of your subject — works for normal and background-removed images."
 )
 
 if __name__ == "__main__":