Merge branch 'master' into rj/storage-info

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ### Features
 - Added `get_workspace_status()` method to management API ([#1662](https://github.com/neptune-ai/neptune-client/pull/1662))
+- Added auto-scaling pixel values for image logging ([#1664](https://github.com/neptune-ai/neptune-client/pull/1664))
 
 ### Fixes
 - Restored support for SSL verification exception ([#1661](https://github.com/neptune-ai/neptune-client/pull/1661))

src/neptune/internal/utils/images.py

@@ -13,6 +13,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+from __future__ import annotations
+
 __all__ = [
     "get_image_content",
     "get_html_content",
@@ -37,6 +39,7 @@
 )
 from typing import Optional
 
+import numpy as np
 from packaging import version
 from pandas import DataFrame
 
@@ -45,6 +48,7 @@
 
 logger = get_logger()
 SEABORN_GRID_CLASSES = {"FacetGrid", "PairGrid", "JointGrid"}
+ALLOWED_IMG_PIXEL_RANGES = ("[0, 255]", "[0.0, 1.0]")
 
 try:
     from numpy import array as numpy_array
@@ -65,8 +69,8 @@ def pilimage_fromarray():
         pass
 
 
-def get_image_content(image) -> Optional[bytes]:
-    content = _image_to_bytes(image)
+def get_image_content(image, autoscale=True) -> Optional[bytes]:
+    content = _image_to_bytes(image, autoscale)
 
     return content
 
@@ -83,12 +87,12 @@ def get_pickle_content(obj) -> Optional[bytes]:
     return content
 
 
-def _image_to_bytes(image) -> bytes:
+def _image_to_bytes(image, autoscale) -> bytes:
     if image is None:
         raise ValueError("image is None")
 
     elif is_numpy_array(image):
-        return _get_numpy_as_image(image)
+        return _get_numpy_as_image(image, autoscale)
 
     elif is_pil_image(image):
         return _get_pil_image_data(image)
@@ -97,10 +101,10 @@ def _image_to_bytes(image) -> bytes:
         return _get_figure_image_data(image)
 
     elif _is_torch_tensor(image):
-        return _get_numpy_as_image(image.detach().numpy())
+        return _get_numpy_as_image(image.detach().numpy(), autoscale)
 
     elif _is_tensorflow_tensor(image):
-        return _get_numpy_as_image(image.numpy())
+        return _get_numpy_as_image(image.numpy(), autoscale)
 
     elif is_seaborn_figure(image):
         return _get_figure_image_data(image.figure)
@@ -196,38 +200,48 @@ def _image_content_to_html(content: bytes) -> str:
     return "<img src='data:image/png;base64," + str_equivalent_image + "'/>"
 
 
-def _get_numpy_as_image(array):
+def _get_numpy_as_image(array: np.ndarray, autoscale: bool) -> bytes:
     array = array.copy()  # prevent original array from modifying
+    if autoscale:
+        array = _scale_array(array)
 
-    data_range_warnings = []
-    array_min = array.min()
-    array_max = array.max()
-    if array_min < 0:
-        data_range_warnings.append(f"the smallest value in the array is {array_min}")
-    if array_max > 1:
-        data_range_warnings.append(f"the largest value in the array is {array_max}")
-    if data_range_warnings:
-        data_range_warning_message = (" and ".join(data_range_warnings) + ".").capitalize()
-        logger.warning(
-            "%s To be interpreted as colors correctly values in the array need to be in the [0, 1] range.",
-            data_range_warning_message,
-        )
-    array *= 255
-    shape = array.shape
-    if len(shape) == 2:
+    if len(array.shape) == 2:
         return _get_pil_image_data(pilimage_fromarray(array.astype(numpy_uint8)))
-    if len(shape) == 3:
-        if shape[2] == 1:
+    if len(array.shape) == 3:
+        if array.shape[2] == 1:
             array2d = numpy_array([[col[0] for col in row] for row in array])
             return _get_pil_image_data(pilimage_fromarray(array2d.astype(numpy_uint8)))
-        if shape[2] in (3, 4):
+        if array.shape[2] in (3, 4):
             return _get_pil_image_data(pilimage_fromarray(array.astype(numpy_uint8)))
     raise ValueError(
         "Incorrect size of numpy.ndarray. Should be 2-dimensional or"
         "3-dimensional with 3rd dimension of size 1, 3 or 4."
     )
 
 
+def _scale_array(array: np.ndarray) -> np.ndarray:
+    array_min = array.min()
+    array_max = array.max()
+
+    if array_min >= 0 and 1 < array_max <= 255:
+        return array
+
+    if array_min >= 0 and array_max <= 1:
+        return array * 255
+
+    _warn_about_incorrect_image_data_range(array_min, array_max)
+    return array
+
+
+def _warn_about_incorrect_image_data_range(array_min: int | float, array_max: int | float) -> None:
+    msg = f"Image data is in range [{array_min}, {array_max}]."
+    logger.warning(
+        "%s To be interpreted as colors correctly values in the array need to be in the %s or %s range.",
+        msg,
+        *ALLOWED_IMG_PIXEL_RANGES,
+    )
+
+
 def _get_pil_image_data(image: PILImage) -> bytes:
     with io.BytesIO() as image_buffer:
         image.save(image_buffer, format="PNG")

src/neptune/types/atoms/file.py

@@ -171,7 +171,7 @@ def from_stream(stream: IOBase, *, seek: Optional[int] = 0, extension: Optional[
         return File(file_composite=file_composite)
 
     @staticmethod
-    def as_image(image) -> "File":
+    def as_image(image, autoscale: bool = True) -> "File":
         """Static method for converting image objects or image-like objects to an image File value object.
 
         This way you can upload `Matplotlib` figures, `Seaborn` figures, `PIL` images, `NumPy` arrays, as static images.
@@ -207,7 +207,7 @@ def as_image(image) -> "File":
         .. _as_image docs page:
            https://docs.neptune.ai/api/field_types#as_image
         """
-        content_bytes = get_image_content(image)
+        content_bytes = get_image_content(image, autoscale=autoscale)
         return File.from_content(content_bytes if content_bytes is not None else b"", extension="png")
 
     @staticmethod

tests/unit/neptune/new/internal/utils/test_images.py

@@ -39,6 +39,7 @@
     IS_WINDOWS,
 )
 from neptune.internal.utils.images import (
+    _scale_array,
     get_html_content,
     get_image_content,
 )
@@ -75,23 +76,13 @@ def test_get_image_content_from_2d_grayscale_array(self):
     def test_get_image_content_from_3d_grayscale_array(self):
         # given
         image_array = numpy.array([[[1], [0]], [[-3], [4]], [[5], [6]]])
-        expected_array = numpy.array([[1, 0], [-3, 4], [5, 6]]) * 255
+        expected_array = numpy.array([[1, 0], [-3, 4], [5, 6]])
         expected_image = Image.fromarray(expected_array.astype(numpy.uint8))
 
         # when
-        _log = partial(format_log, "WARNING")
 
         # expect
-        stdout = io.StringIO()
-        with contextlib.redirect_stdout(stdout):
-            self.assertEqual(get_image_content(image_array), self._encode_pil_image(expected_image))
-        self.assertEqual(
-            stdout.getvalue(),
-            _log(
-                "The smallest value in the array is -3 and the largest value in the array is 6."
-                " To be interpreted as colors correctly values in the array need to be in the [0, 1] range.\n",
-            ),
-        )
+        self.assertEqual(get_image_content(image_array), self._encode_pil_image(expected_image))
 
     def test_get_image_content_from_rgb_array(self):
         # given
@@ -292,3 +283,46 @@ def _random_image_array(w=20, h=30, d: Optional[int] = 3):
             return numpy.random.rand(w, h, d)
         else:
             return numpy.random.rand(w, h)
+
+
+def test_scale_array_when_array_already_scaled():
+    # given
+    arr = numpy.array([[123, 32], [255, 0]])
+
+    # when
+    result = _scale_array(arr)
+
+    # then
+    assert numpy.all(arr == result)
+
+
+def test_scale_array_when_array_not_scaled():
+    # given
+    arr = numpy.array([[0.3, 0], [0.5, 1]])
+
+    # when
+    result = _scale_array(arr)
+    expected = numpy.array([[76.5, 0.0], [127.5, 255.0]])
+
+    # then
+    assert numpy.all(expected == result)
+
+
+def test_scale_array_incorrect_range():
+    # given
+    arr = numpy.array([[-12, 7], [300, 0]])
+
+    # when
+    _log = partial(format_log, "WARNING")
+
+    stdout = io.StringIO()
+    with contextlib.redirect_stdout(stdout):
+        result = _scale_array(arr)
+
+    # then
+    assert numpy.all(arr == result)  # returned original array
+
+    assert stdout.getvalue() == _log(
+        "Image data is in range [-12, 300]. To be interpreted as colors "
+        "correctly values in the array need to be in the [0, 255] or [0.0, 1.0] range.\n",
+    )