Update app.py

app.py

@@ -18,7 +18,7 @@ def estimate_depth(image):
     input_tensor = midas_transform(image).unsqueeze(0).to(device)
     with torch.no_grad():
         depth = midas_model(input_tensor).squeeze().cpu().numpy()
-    depth = cv2.resize(depth, (image.width, image.height))
+    depth = cv2.resize(depth, (image.size[0], image.size[1]))
     depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
     return depth.astype(np.uint8)
 
@@ -28,11 +28,13 @@ def apply_tps_warping(design, depth):
     grid_x, grid_y = np.meshgrid(np.arange(w), np.arange(h))
     displacement_x = cv2.Sobel(depth, cv2.CV_32F, 1, 0, ksize=5)
     displacement_y = cv2.Sobel(depth, cv2.CV_32F, 0, 1, ksize=5)
-    displacement_x = cv2.normalize(displacement_x, None, -10, 10, cv2.NORM_MINMAX)
-    displacement_y = cv2.normalize(displacement_y, None, -10, 10, cv2.NORM_MINMAX)
+    displacement_x = cv2.normalize(displacement_x, None, -5, 5, cv2.NORM_MINMAX)
+    displacement_y = cv2.normalize(displacement_y, None, -5, 5, cv2.NORM_MINMAX)
+    
     map_x = np.clip(grid_x + displacement_x, 0, w - 1).astype(np.float32)
     map_y = np.clip(grid_y + displacement_y, 0, h - 1).astype(np.float32)
-    warped_design = cv2.remap(design, map_x, map_y, interpolation=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REFLECT)
+    
+    warped_design = cv2.remap(design, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
     return warped_design
 
 def blend_design(cloth_img, design_img):
@@ -63,9 +65,10 @@ def blend_design(cloth_img, design_img):
     depth_map = estimate_depth(cloth_img)
     warped_design = apply_tps_warping(design_canvas, depth_map)
     
-    # Blend design onto cloth
-    for c in range(3):
-        cloth_np[:, :, c] = (cloth_np[:, :, c] * (1 - alpha_channel) + warped_design[:, :, c] * alpha_channel)
+    # Ensure alpha is applied correctly
+    mask = np.zeros_like(cloth_np, dtype=np.float32)
+    mask[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = np.expand_dims(alpha_channel, axis=-1)
+    cloth_np = (cloth_np * (1 - mask) + warped_design * mask).astype(np.uint8)
     
     return Image.fromarray(cloth_np)