Update app.py

app.py

@@ -14,19 +14,24 @@ depth_model.eval()
 
 def estimate_depth(image):
     """Estimate depth map from image."""
+    image = image.convert("RGB")
     inputs = feature_extractor(images=image, return_tensors="pt")
     with torch.no_grad():
         outputs = depth_model(**inputs)
-        depth = outputs.predicted_depth.squeeze().numpy()
+        depth = outputs.predicted_depth.squeeze().cpu().numpy()
     depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
     return depth.astype(np.uint8)
 
 def warp_design(cloth_img, design_img):
     """Warp the design onto the clothing while preserving folds."""
-    # Convert images to numpy arrays
+    cloth_img = cloth_img.convert("RGB")
+    design_img = design_img.convert("RGB")
     cloth_np = np.array(cloth_img)
     design_np = np.array(design_img)
     
+    # Ensure both images have the same dimensions
+    design_np = cv2.resize(design_np, (cloth_np.shape[1], cloth_np.shape[0]))
+    
     # Estimate depth for fold detection
     depth_map = estimate_depth(cloth_img)
     
@@ -35,15 +40,15 @@ def warp_design(cloth_img, design_img):
     displacement_y = cv2.Sobel(depth_map, cv2.CV_32F, 0, 1, ksize=5)
     
     # Normalize displacement values
-    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)
     
     # Warp design using displacement map
     h, w, _ = cloth_np.shape
     map_x, map_y = np.meshgrid(np.arange(w), np.arange(h))
     map_x = np.float32(map_x + displacement_x)
     map_y = np.float32(map_y + displacement_y)
-    warped_design = cv2.remap(design_np, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
+    warped_design = cv2.remap(design_np, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT)
     
     # Blend images
     blended = cv2.addWeighted(cloth_np, 0.6, warped_design, 0.4, 0)