Update app.py

app.py

@@ -34,17 +34,20 @@ def segment_dress(image_np):
     mask = cv2.resize(mask, (image_np.shape[1], image_np.shape[0]), interpolation=cv2.INTER_NEAREST)
     
     # Apply morphological operations for better segmentation
-    kernel = np.ones((7, 7), np.uint8)
+    kernel = np.ones((5, 5), np.uint8)
     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 for smoother transitions
-    mask = cv2.GaussianBlur(mask, (15, 15), 5)
+    mask = cv2.GaussianBlur(mask, (21, 21), 10)  # Smooth edges for natural blending
     
     return mask
 
+def get_ambient_light(img_np):
+    """Estimate ambient lighting from non-dress areas for realistic blending."""
+    lab = cv2.cvtColor(img_np, cv2.COLOR_RGB2LAB)
+    L_channel = lab[:, :, 0]  # Lightness channel
+    return np.median(L_channel)  # Median light level in the image
+
 def change_dress_color(image_path, color):
-    """Change the dress color naturally while keeping textures."""
+    """Change the dress color naturally while keeping textures and adjusting to lighting."""
     if image_path is None:
         return None
 
@@ -59,7 +62,7 @@ def change_dress_color(image_path, color):
     color_map = {
         "Red": (0, 0, 255), "Blue": (255, 0, 0), "Green": (0, 255, 0), "Yellow": (0, 255, 255),
         "Purple": (128, 0, 128), "Orange": (0, 165, 255), "Cyan": (255, 255, 0), "Magenta": (255, 0, 255),
-        "White": (255, 255, 255)
+        "White": (255, 255, 255), "Black": (0, 0, 0)
     }
     new_color_bgr = np.array(color_map.get(color, (0, 0, 255)), dtype=np.uint8)  # Default to Red
     
@@ -67,7 +70,11 @@ def change_dress_color(image_path, color):
     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 modifying A & B channels with blend factor
+    # Adjust color to match ambient lighting
+    ambient_light = get_ambient_light(img_np)
+    img_lab[..., 0] = np.clip(img_lab[..., 0] * (ambient_light / 128), 0, 255)  # Normalize lighting
+    
+    # Preserve texture by modifying only A & B channels
     blend_factor = 0.6  # Controls intensity of color change
     img_lab[..., 1] = np.where(mask > 128, img_lab[..., 1] * (1 - blend_factor) + new_color_lab[1] * blend_factor, img_lab[..., 1])
     img_lab[..., 2] = np.where(mask > 128, img_lab[..., 2] * (1 - blend_factor) + new_color_lab[2] * blend_factor, img_lab[..., 2])
@@ -75,6 +82,9 @@ def change_dress_color(image_path, color):
     # Convert back to RGB
     img_recolored = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB)
     
+    # Use Poisson blending for seamless integration with the environment
+    img_recolored = cv2.seamlessClone(img_recolored, img_np, mask, (img_np.shape[1]//2, img_np.shape[0]//2), cv2.MIXED_CLONE)
+
     return Image.fromarray(img_recolored)
 
 # Gradio Interface
@@ -82,7 +92,7 @@ demo = gr.Interface(
     fn=change_dress_color,
     inputs=[
         gr.Image(type="filepath", label="Upload Dress Image"),
-        gr.Radio(["Red", "Blue", "Green", "Yellow", "Purple", "Orange", "Cyan", "Magenta", "White"], label="Choose New Dress Color")
+        gr.Radio(["Red", "Blue", "Green", "Yellow", "Purple", "Orange", "Cyan", "Magenta", "White", "Black"], label="Choose New Dress Color")
     ],
     outputs=gr.Image(type="pil", label="Color Changed Dress"),
     title="Dress Color Changer",