diff --git a/zoedepth/models/depth_model.py b/zoedepth/models/depth_model.py
index fc421c108..479288ab6 100644
--- a/zoedepth/models/depth_model.py
+++ b/zoedepth/models/depth_model.py
@@ -149,4 +149,37 @@ def infer_pil(self, pil_img, pad_input: bool=True, with_flip_aug: bool=True, out
             return out_tensor.squeeze().cpu()
         else:
             raise ValueError(f"output_type {output_type} not supported. Supported values are 'numpy', 'pil' and 'tensor'")
-    
\ No newline at end of file
+
+    @torch.no_grad()
+    def infer_cv(self, cv_img, pad_input: bool=True, with_flip_aug: bool=True, output_type: str="numpy", **kwargs) -> Union[np.ndarray, torch.Tensor]:
+        """
+        Inference interface for the model for a NumPy image loaded via OpenCV (BGR format)
+        Args:
+            cv_img (np.ndarray): input image in BGR format as loaded by OpenCV
+            pad_input (bool, optional): whether to use padding augmentation. Defaults to True.
+            with_flip_aug (bool, optional): whether to use horizontal flip augmentation. Defaults to True.
+            output_type (str, optional): output type. Supported values are 'numpy' and 'tensor'. Defaults to "numpy".
+        """
+        # Convert BGR to RGB
+        cv_img_rgb = cv_img[:, :, ::-1]
+
+        # Ensure the image is in float format (required for correct scaling)
+        if cv_img_rgb.dtype != np.float32:
+            cv_img_rgb = cv_img_rgb.astype(np.float32)
+            cv_img_rgb /= 255.0
+
+        # Convert the image to a PyTorch tensor, add a batch dimension, and transfer to device
+        x = torch.from_numpy(cv_img_rgb).permute(2, 0, 1).unsqueeze(0).to(self.device)
+
+        # Perform inference
+        out_tensor = self.infer(
+            x, pad_input=pad_input, with_flip_aug=with_flip_aug, **kwargs
+        )
+
+        # Convert the output tensor to the requested output type
+        if output_type == "numpy":
+            return out_tensor.squeeze().cpu().numpy()
+        elif output_type == "tensor":
+            return out_tensor.squeeze().cpu()
+        else:
+            raise ValueError(f"output_type {output_type} not supported. Supported values are 'numpy' and 'tensor'")