Update app.py

app.py

@@ -27,7 +27,7 @@ def estimate_depth(image):
 def blend_design(cloth_img, design_img):
     """Blend design onto clothing naturally."""
     cloth_img = cloth_img.convert("RGB")
-    design_img = design_img.convert("RGB")
+    design_img = design_img.convert("RGBA")
     cloth_np = np.array(cloth_img)
     design_np = np.array(design_img)
     
@@ -36,18 +36,21 @@ def blend_design(cloth_img, design_img):
     dh, dw, _ = design_np.shape
     scale_factor = min(w / dw, h / dh) * 0.4  # Scale to 40% of clothing area
     new_w, new_h = int(dw * scale_factor), int(dh * scale_factor)
-    design_np = cv2.resize(design_np, (new_w, new_h))
+    design_np = cv2.resize(design_np, (new_w, new_h), interpolation=cv2.INTER_AREA)
     
-    # Convert design to grayscale and darken for print effect
-    design_gray = cv2.cvtColor(design_np, cv2.COLOR_RGB2GRAY)
-    design_np = cv2.cvtColor(design_gray, cv2.COLOR_GRAY2RGB)
-    design_np = cv2.convertScaleAbs(design_np, alpha=1.5, beta=-40)  # Increase contrast
+    # Convert design to include transparency effect
+    alpha_channel = design_np[:, :, 3] / 255.0  # Extract alpha channel
+    design_np = design_np[:, :, :3]  # Remove alpha channel for processing
     
     # Create a blank canvas and paste the resized design at the center
-    design_canvas = np.zeros_like(cloth_np)
     x_offset = (w - new_w) // 2
     y_offset = int(h * 0.35)  # Move slightly upward for a natural position
-    design_canvas[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = design_np
+    
+    for c in range(3):
+        cloth_np[y_offset:y_offset+new_h, x_offset:x_offset+new_w, c] = (
+            cloth_np[y_offset:y_offset+new_h, x_offset:x_offset+new_w, c] * (1 - alpha_channel) + 
+            design_np[:, :, c] * alpha_channel
+        )
     
     # Estimate depth for fold detection
     depth_map = estimate_depth(cloth_img)
@@ -63,13 +66,9 @@ def blend_design(cloth_img, design_img):
     map_x, map_y = np.meshgrid(np.arange(w), np.arange(h))
     map_x = np.clip(np.float32(map_x + displacement_x), 0, w - 1)
     map_y = np.clip(np.float32(map_y + displacement_y), 0, h - 1)
-    warped_design = cv2.remap(design_canvas, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
-    
-    # Use Poisson blending for seamless integration
-    mask = (warped_design > 0).astype(np.uint8) * 255
-    blended = cv2.seamlessClone(warped_design, cloth_np, mask, (w//2, int(h * 0.35 + new_h//2)), cv2.NORMAL_CLONE)
+    warped_cloth = cv2.remap(cloth_np, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
     
-    return Image.fromarray(blended)
+    return Image.fromarray(warped_cloth)
 
 def main(cloth, design):
     return blend_design(cloth, design)