diff --git a/tools/inference.py b/tools/inference.py
index c19531c123..1c42cec3c7 100644
--- a/tools/inference.py
+++ b/tools/inference.py
@@ -21,6 +21,7 @@
 from argparse import ArgumentParser, Namespace
 from importlib import import_module
 from pathlib import Path
+from typing import Optional
 
 import cv2
 import numpy as np
@@ -65,9 +66,11 @@ def add_label(prediction: np.ndarray, scores: float, font: int = cv2.FONT_HERSHE
     return prediction
 
 
-def infer() -> None:
-    """Perform inference on an input image."""
+def stream() -> None:
+    """Stream predictions.
 
+    Show/save the output if path is to an image. If the path is a directory, go over each image in the directory.
+    """
     # Get the command line arguments, and config from the config.yaml file.
     # This config file is also used for training and contains all the relevant
     # information regarding the data, model, train and inference details.
@@ -77,28 +80,51 @@ def infer() -> None:
     # Get the inferencer. We use .ckpt extension for Torch models and (onnx, bin)
     # for the openvino models.
     extension = args.weight_path.suffix
-    inference: Inferencer
+    inferencer: Inferencer
     if extension in (".ckpt"):
         module = import_module("anomalib.deploy.inferencers.torch")
         TorchInferencer = getattr(module, "TorchInferencer")  # pylint: disable=invalid-name
-        inference = TorchInferencer(config=config, model_source=args.weight_path, meta_data_path=args.meta_data)
+        inferencer = TorchInferencer(config=config, model_source=args.weight_path, meta_data_path=args.meta_data)
 
     elif extension in (".onnx", ".bin", ".xml"):
         module = import_module("anomalib.deploy.inferencers.openvino")
         OpenVINOInferencer = getattr(module, "OpenVINOInferencer")  # pylint: disable=invalid-name
-        inference = OpenVINOInferencer(config=config, path=args.weight_path, meta_data_path=args.meta_data)
+        inferencer = OpenVINOInferencer(config=config, path=args.weight_path, meta_data_path=args.meta_data)
 
     else:
         raise ValueError(
             f"Model extension is not supported. Torch Inferencer exptects a .ckpt file,"
             f"OpenVINO Inferencer expects either .onnx, .bin or .xml file. Got {extension}"
         )
+    if args.image_path.is_dir():
+        # Write the output to save_path in the same structure as the input directory.
+        for image in args.image_path.glob("**/*"):
+            if image.is_file() and image.suffix in (".jpg", ".png", ".jpeg"):
+                # Here save_path is assumed to be a directory. Image subdirectories are appended to the save_path.
+                save_path = Path(args.save_path / image.relative_to(args.image_path).parent) if args.save_path else None
+                infer(image, inferencer, save_path)
+    elif args.image_path.suffix in (".jpg", ".png", ".jpeg"):
+        infer(args.image_path, inferencer, args.save_path)
+    else:
+        raise ValueError(
+            f"Image extension is not supported. Supported extensions are .jpg, .png, .jpeg."
+            f" Got {args.image_path.suffix}"
+        )
 
+
+def infer(image_path: Path, inferencer: Inferencer, save_path: Optional[Path] = None) -> None:
+    """Perform inference on a single image.
+
+    Args:
+        image_path (Path): Path to image/directory containing images.
+        inferencer (Inferencer): Inferencer to use.
+        save_path (Path, optional): Path to save the output image. If this is None, the output is visualized.
+    """
     # Perform inference for the given image or image path. if image
     # path is provided, `predict` method will read the image from
     # file for convenience. We set the superimpose flag to True
     # to overlay the predicted anomaly map on top of the input image.
-    output = inference.predict(image=args.image_path, superimpose=True)
+    output = inferencer.predict(image=image_path, superimpose=True)
 
     # Incase both anomaly map and scores are returned add scores to the image.
     if isinstance(output, tuple):
@@ -108,11 +134,17 @@ def infer() -> None:
     # Show or save the output image, depending on what's provided as
     # the command line argument.
     output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
-    if args.save_path is None:
+    if save_path is None:
         cv2.imshow("Anomaly Map", output)
+        cv2.waitKey(0)  # wait for any key press
     else:
-        cv2.imwrite(filename=str(args.save_path), img=output)
+        # Create directory for parents if it doesn't exist.
+        save_path.parent.mkdir(parents=True, exist_ok=True)
+        if save_path.suffix == "":  # This is a directory
+            save_path.mkdir(exist_ok=True)  # Create current directory
+            save_path = save_path / image_path.name
+        cv2.imwrite(filename=str(save_path), img=output)
 
 
 if __name__ == "__main__":
-    infer()
+    stream()