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Add slice attribute for image content-based assertions

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This commit is contained in:
Leonid Kostrykin
2024-09-25 18:26:12 +02:00
parent bd501713c7
commit 6a6a954998
2 changed files with 47 additions and 15 deletions
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lib/galaxy/tool_util/verify/asserts/image.py
+43 -15
View File
@@ -290,6 +290,13 @@ Channel = Annotated[
json_type="typing.Optional[StrictInt]",
),
]
Slice = Annotated[
OptionalXmlInt,
AssertionParameter(
"Restricts the assertion to a specific slice of the image (where ``0`` corresponds to the first image slice).",
json_type="typing.Optional[StrictInt]",
),
]
Frame = Annotated[
OptionalXmlInt,
AssertionParameter(
@@ -398,7 +405,8 @@ def assert_has_image_width(
max: WidthMax = None,
negate: Negate = NEGATE_DEFAULT,
) -> None:
"""Asserts the output is an image and has a specific width (in pixels).
"""
Asserts the output is an image and has a specific width (in pixels).
The width is plus/minus ``delta`` (e.g., ``<has_image_width width="512" delta="2" />``).
Alternatively the range of the expected width can be specified by ``min`` and/or ``max``.
@@ -424,7 +432,8 @@ def assert_has_image_height(
max: HeightMax = None,
negate: Negate = NEGATE_DEFAULT,
) -> None:
"""Asserts the output is an image and has a specific height (in pixels).
"""
Asserts the output is an image and has a specific height (in pixels).
The height is plus/minus ``delta`` (e.g., ``<has_image_height height="512" delta="2" />``).
Alternatively the range of the expected height can be specified by ``min`` and/or ``max``.
@@ -450,7 +459,8 @@ def assert_has_image_channels(
max: ChannelsMax = None,
negate: Negate = NEGATE_DEFAULT,
) -> None:
"""Asserts the output is an image and has a specific number of channels.
"""
Asserts the output is an image and has a specific number of channels.
The number of channels is plus/minus ``delta`` (e.g., ``<has_image_channels channels="3" />``).
@@ -477,7 +487,8 @@ def assert_has_image_depth(
max: DepthMax = None,
negate: Negate = NEGATE_DEFAULT,
) -> None:
"""Asserts the output is an image and has a specific depth (number of slices).
"""
Asserts the output is an image and has a specific depth (number of slices).
The depth is plus/minus ``delta`` (e.g., ``<has_image_depth depth="512" delta="2" />``).
Alternatively the range of the expected depth can be specified by ``min`` and/or ``max``.
@@ -503,7 +514,8 @@ def assert_has_image_frames(
max: FramesMax = None,
negate: Negate = NEGATE_DEFAULT,
) -> None:
"""Asserts the output is an image and has a specific number of frames (number of time steps).
"""
Asserts the output is an image and has a specific number of frames (number of time steps).
The number of frames is plus/minus ``delta`` (e.g., ``<has_image_frames depth="512" delta="2" />``).
Alternatively the range of the expected number of frames can be specified by ``min`` and/or ``max``.
@@ -545,9 +557,12 @@ def _move_char(s: str, pos_src: int, pos_dst: int) -> str:
def _get_image(
output_bytes: bytes,
channel: Optional[Union[int, str]] = None,
slice: Optional[Union[int, str]] = None,
frame: Optional[Union[int, str]] = None,
) -> "numpy.typing.NDArray":
"""Returns the output image with the axes ``TZYXC``, optionally restricted to a specific `channel` and `frame`.
"""
Returns the output image with the axes ``TZYXC``, optionally restricted to a specific `channel`, `slice`,
`frame`, or a combination thereof.
The function tries to read the image using tifffile and Pillow. The image axes are normalized like ``TZYXC``,
treating sample axis ``S`` as an alias for the channel axis ``C``. For images which cannot be read by tifffile,
@@ -640,6 +655,10 @@ def _get_image(
if channel is not None:
im_arr = im_arr[..., [int(channel)]]
# Select the specified slice (if any).
if slice is not None:
im_arr = im_arr[:, [int(slice)], ...]
# Select the specified frame (if any).
if frame is not None:
im_arr = im_arr[[int(frame)], ...]
@@ -651,18 +670,20 @@ def _get_image(
def assert_has_image_mean_intensity(
output_bytes: OutputBytes,
channel: Channel = None,
slice: Slice = None,
frame: Frame = None,
mean_intensity: MeanIntensity = None,
eps: MeanIntensityEps = 0.01,
min: MeanIntensityMin = None,
max: MeanIntensityMax = None,
) -> None:
"""Asserts the output is an image and has a specific mean intensity value.
"""
Asserts the output is an image and has a specific mean intensity value.
The mean intensity value is plus/minus ``eps`` (e.g., ``<has_image_mean_intensity mean_intensity="0.83" />``).
Alternatively the range of the expected mean intensity value can be specified by ``min`` and/or ``max``.
"""
im_arr = _get_image(output_bytes, channel, frame)
im_arr = _get_image(output_bytes, channel, slice, frame)
_assert_float(
actual=im_arr.mean(),
label="mean intensity",
@@ -677,16 +698,18 @@ def assert_has_image_center_of_mass(
output_bytes: OutputBytes,
center_of_mass: CenterOfMass,
channel: Channel = None,
slice: Slice = None,
frame: Frame = None,
eps: CenterOfMassEps = 0.01,
) -> None:
"""Asserts the specified output is an image and has the specified center of mass.
"""
Asserts the specified output is an image and has the specified center of mass.
Asserts the output is an image and has a specific center of mass,
or has an Euclidean distance of ``eps`` or less to that point (e.g.,
``<has_image_center_of_mass center_of_mass="511.07, 223.34" />``).
"""
im_arr = _get_image(output_bytes, channel, frame)
im_arr = _get_image(output_bytes, channel, slice, frame)
center_of_mass_parts = [c.strip() for c in center_of_mass.split(",")]
assert len(center_of_mass_parts) == 2
center_of_mass_tuple = (float(center_of_mass_parts[0]), float(center_of_mass_parts[1]))
@@ -701,6 +724,7 @@ def assert_has_image_center_of_mass(
def _get_image_labels(
output_bytes: bytes,
channel: Optional[Union[int, str]] = None,
slice: Optional[Union[int, str]] = None,
frame: Optional[Union[int, str]] = None,
labels: Optional[Union[str, List[int]]] = None,
exclude_labels: Optional[Union[str, List[int]]] = None,
@@ -709,7 +733,7 @@ def _get_image_labels(
Determines the unique labels in the output image or a specific channel.
"""
assert labels is None or exclude_labels is None
im_arr = _get_image(output_bytes, channel, frame)
im_arr = _get_image(output_bytes, channel, slice, frame)
def cast_label(label):
label = label.strip()
@@ -744,6 +768,7 @@ def _get_image_labels(
def assert_has_image_n_labels(
output_bytes: OutputBytes,
channel: Channel = None,
slice: Slice = None,
frame: Frame = None,
labels: Labels = None,
exclude_labels: ExcludeLabels = None,
@@ -753,14 +778,15 @@ def assert_has_image_n_labels(
max: NumLabelsMax = None,
negate: Negate = NEGATE_DEFAULT,
) -> None:
"""Asserts the output is an image and has the specified labels.
"""
Asserts the output is an image and has the specified labels.
Labels can be a number of labels or unique values (e.g.,
``<has_image_n_labels n="187" exclude_labels="0" />``).
The primary usage of this assertion is to verify the number of objects in images with uniquely labeled objects.
"""
present_labels = _get_image_labels(output_bytes, channel, frame, labels, exclude_labels)[1]
present_labels = _get_image_labels(output_bytes, channel, slice, frame, labels, exclude_labels)[1]
_assert_number(
len(present_labels),
n,
@@ -776,6 +802,7 @@ def assert_has_image_n_labels(
def assert_has_image_mean_object_size(
output_bytes: OutputBytes,
channel: Channel = None,
slice: Slice = None,
frame: Frame = None,
labels: Labels = None,
exclude_labels: ExcludeLabels = None,
@@ -784,13 +811,14 @@ def assert_has_image_mean_object_size(
min: MeanObjectSizeMin = None,
max: MeanObjectSizeMax = None,
) -> None:
"""Asserts the output is an image with labeled objects which have the specified mean size (number of pixels),
"""
Asserts the output is an image with labeled objects which have the specified mean size (number of pixels),
The mean size is plus/minus ``eps`` (e.g., ``<has_image_mean_object_size mean_object_size="111.87" exclude_labels="0" />``).
The labels must be unique.
"""
im_arr, present_labels = _get_image_labels(output_bytes, channel, frame, labels, exclude_labels)
im_arr, present_labels = _get_image_labels(output_bytes, channel, slice, frame, labels, exclude_labels)
assert im_arr.shape[-1] == 1, f"has_image_mean_object_size is undefined for multi-channel images (channels: {im_arr.shape[-1]})"
object_sizes = sum(
[
test/functional/tools/validation_image.xml
+4
View File
@@ -118,6 +118,8 @@
<has_image_depth depth="25" />
<has_image_frames frames="1" />
<has_image_mean_intensity mean_intensity="26.56" />
<has_image_mean_intensity mean_intensity="12.68" slice="0" />
<has_image_mean_intensity mean_intensity="9.05" slice="24" />
</assert_contents>
</output>
</test>
@@ -133,6 +135,8 @@
<has_image_mean_intensity mean_intensity="14.18" />
<has_image_mean_intensity mean_intensity="12.61" channel="0" />
<has_image_mean_intensity mean_intensity="15.75" channel="1" />
<has_image_mean_intensity mean_intensity="4.03" slice="0" />
<has_image_mean_intensity mean_intensity="3.44" slice="0" channel="0" />
</assert_contents>
</output>
</test>