diff --git a/python/pyproject.toml b/python/pyproject.toml
index ee11852610..25d48c7077 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -116,7 +116,8 @@ mlflow = [
   "mlflow-skinny"
 ]
 image = [
-  "Pillow"
+  "Pillow",
+  "numpy"
 ]
 fugue = [
   "fugue",
diff --git a/python/tests/extras/test_image_metric.py b/python/tests/extras/test_image_metric.py
index ecf5e2fe5b..1d1db36b9f 100644
--- a/python/tests/extras/test_image_metric.py
+++ b/python/tests/extras/test_image_metric.py
@@ -10,11 +10,17 @@
 from whylogs.core.preprocessing import ListView, PreprocessedColumn
 from whylogs.core.resolvers import Resolver
 from whylogs.core.schema import ColumnSchema, DatasetSchema
-from whylogs.extras.image_metric import ImageMetric, ImageMetricConfig, log_image
+from whylogs.extras.image_metric import (
+    ImageMetric,
+    ImageMetricConfig,
+    init_image_schema,
+    log_image,
+)
 
 logger = logging.getLogger(__name__)
 
 try:
+    import numpy as np
     from PIL.Image import Image as ImageType
 except ImportError as e:
     ImageType = None
@@ -80,6 +86,20 @@ def test_image_metric() -> None:
     assert "ints" not in metric.submetrics["Software"]
 
 
+def test_log_np_image() -> None:
+    image_path = os.path.join(TEST_DATA_PATH, "images", "flower2.jpg")
+    img = np.array(image_loader(image_path))
+
+    schema = init_image_schema()
+    profile = why.log({"image": img}, schema=schema)
+    df = profile.profile().view().to_pandas()
+
+    # Ensure a few columns are in the data frame from the image metric
+    assert "image/Brightness.mean:cardinality/est" in df.columns
+    assert "image/Brightness.mean:cardinality/lower_1" in df.columns
+    assert "image/entropy:types/tensor" in df.columns
+
+
 def test_allowed_exif_tags() -> None:
     image_path = os.path.join(TEST_DATA_PATH, "images", "flower2.jpg")
     img = image_loader(image_path)
diff --git a/python/whylogs/extras/image_metric.py b/python/whylogs/extras/image_metric.py
index 8dfceed174..58e918750d 100644
--- a/python/whylogs/extras/image_metric.py
+++ b/python/whylogs/extras/image_metric.py
@@ -29,6 +29,8 @@
 logger = logging.getLogger(__name__)
 
 try:
+    import numpy as np  # type: ignore
+    from PIL import Image
     from PIL.Image import Image as ImageType  # type: ignore
     from PIL.ImageStat import Stat  # type: ignore
     from PIL.TiffImagePlugin import IFDRational  # type: ignore
@@ -211,6 +213,9 @@ def _update_relevant_submetrics(self, name: str, data: PreprocessedColumn) -> No
     def columnar_update(self, view: PreprocessedColumn) -> OperationResult:
         count = 0
         for image in list(chain.from_iterable(view.raw_iterator())):
+            if isinstance(image, np.ndarray):
+                image = Image.fromarray(image.astype(np.uint8))
+
             if isinstance(image, ImageType):
                 metadata = get_pil_exif_metadata(image)
                 for name, value in metadata.items():
@@ -244,6 +249,24 @@ def zero(cls, config: Optional[MetricConfig] = None) -> "ImageMetric":
         )
 
 
+def init_image_schema(column_prefix: str = "image") -> DatasetSchema:
+    """
+    Initialize a DatasetSchema for logging images. This can be passed into a logger or why.log.
+
+    Args:
+        column_prefix (str): The prefix that appears in the dataset profiles along with all of the
+        image features. If the prefix is "image", then you'll log image with why.log({image: image_data}).
+    """
+
+    class ImageResolver(Resolver):
+        def resolve(self, name: str, why_type: DataType, column_schema: ColumnSchema) -> Dict[str, Metric]:
+            return {ImageMetric.get_namespace(): ImageMetric.zero(column_schema.cfg)}
+
+    return DatasetSchema(
+        types={column_prefix: Image.Image}, default_configs=ImageMetricConfig(), resolvers=ImageResolver()
+    )
+
+
 def log_image(
     images: Union[ImageType, List[ImageType], Dict[str, ImageType]],
     default_column_prefix: str = "image",