Update app.py

app.py

@@ -38,9 +38,6 @@ def segment_dress(image_np):
     mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)  # Close small gaps
     mask = cv2.dilate(mask, kernel, iterations=2)  # Expand the detected dress area
     
-    # Apply Gaussian blur to smooth mask edges
-    mask = cv2.GaussianBlur(mask, (5, 5), 0)
-    
     return mask
 
 def change_dress_color(image_path, color):
@@ -65,20 +62,23 @@ def change_dress_color(image_path, color):
     }
     new_color_bgr = np.array(color_map.get(color, (0, 0, 255)), dtype=np.uint8)  # Default to Red
     
-    # Convert image to HSV for a more natural recoloring
-    img_hsv = cv2.cvtColor(img_np, cv2.COLOR_RGB2HSV)
-    target_hue = cv2.cvtColor(np.uint8([[new_color_bgr]]), cv2.COLOR_BGR2HSV)[0][0][0]  # Extract Hue
-    
-    # Replace Hue where the mask is active
-    img_hsv[..., 0] = np.where(mask == 255, target_hue, img_hsv[..., 0])
+    # Convert image to LAB color space for better blending
+    img_lab = cv2.cvtColor(img_np, cv2.COLOR_RGB2LAB)
+    new_color_lab = cv2.cvtColor(np.uint8([[new_color_bgr]]), cv2.COLOR_BGR2LAB)[0][0]
     
+    # Preserve texture by only modifying the A & B channels
+    img_lab[..., 1] = np.where(mask == 255, new_color_lab[1], img_lab[..., 1])  # Modify A-channel
+    img_lab[..., 2] = np.where(mask == 255, new_color_lab[2], img_lab[..., 2])  # Modify B-channel
+
     # Convert back to RGB
-    img_recolored = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2RGB)
-    
-    img_recolored = cv2.seamlessClone(img_recolored, img_np, mask, (img_np.shape[1]//2, img_np.shape[0]//2), cv2.MIXED_CLONE)
+    img_recolored = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB)
+
+    # Apply Poisson blending for realistic color application
+    img_recolored = cv2.seamlessClone(img_recolored, img_np, mask, (img_np.shape[1]//2, img_np.shape[0]//2), cv2.NORMAL_CLONE)
 
     return Image.fromarray(img_recolored)
 
+# Gradio Interface
 demo = gr.Interface(
     fn=change_dress_color,
     inputs=[