def run(
self, stack: ImageStack,
) -> Tuple[IntensityTable, ConnectedComponentDecodingResult]:
"""decode pixels and combine them into spots using connected component labeling
Parameters
----------
stack : ImageStack
ImageStack containing spots
Returns
-------
IntensityTable :
IntensityTable containing decoded spots
ConnectedComponentDecodingResult :
Results of connected component labeling
"""
pixel_intensities = IntensityTable.from_image_stack(
stack, crop_x=self.crop_x, crop_y=self.crop_y, crop_z=self.crop_z)
decoded_intensities = self.codebook.metric_decode(
pixel_intensities,
max_distance=self.distance_threshold,
min_intensity=self.magnitude_threshold,
norm_order=self.norm_order,
metric=self.metric
)
caf = CombineAdjacentFeatures(
min_area=self.min_area,
max_area=self.max_area,
mask_filtered_features=True
)
decoded_spots, image_decoding_results = caf.run(intensities=decoded_intensities)
return decoded_spots, image_decoding_results
def test_imagestack_to_intensity_table_no_noise(
synthetic_spot_pass_through_stack):
codebook, intensity_table, image = synthetic_spot_pass_through_stack
pixel_intensities = IntensityTable.from_image_stack(image)
pixel_intensities = codebook.metric_decode(pixel_intensities,
max_distance=0,
min_intensity=1000,
norm_order=2)
assert isinstance(pixel_intensities, IntensityTable)
def test_imagestack_to_intensity_table():
codebook, intensity_table, image = codebook_intensities_image_for_single_synthetic_spot(
)
pixel_intensities = IntensityTable.from_image_stack(image)
pixel_intensities = codebook.metric_decode(pixel_intensities,
max_distance=0,
min_intensity=1000,
norm_order=2)
assert isinstance(pixel_intensities, IntensityTable)
def test_reshaping_between_stack_and_intensities():
"""
transform an pixels of an ImageStack into an IntensityTable and back again, then verify that
the created Imagestack is the same as the original
"""
np.random.seed(777)
image = ImageStack.from_numpy_array(
np.random.rand(1, 2, 3, 4, 5).astype(np.float32))
pixel_intensities = IntensityTable.from_image_stack(image, 0, 0, 0)
image_shape = (image.shape['z'], image.shape['y'], image.shape['x'])
image_from_pixels = pixel_intensities_to_imagestack(
pixel_intensities, image_shape)
assert np.array_equal(image.xarray, image_from_pixels.xarray)
def run(
self,
primary_image: ImageStack,
n_processes: Optional[int] = None,
*args,
) -> Tuple[IntensityTable, ConnectedComponentDecodingResult]:
"""decode pixels and combine them into spots using connected component labeling
Parameters
----------
primary_image : ImageStack
ImageStack containing spots
n_processes : Optional[int]
The number of processes to use for CombineAdjacentFeatures.
If None, uses the output of os.cpu_count() (default = None).
Returns
-------
IntensityTable :
IntensityTable containing decoded spots
ConnectedComponentDecodingResult :
Results of connected component labeling
"""
pixel_intensities = IntensityTable.from_image_stack(
primary_image, crop_x=self.crop_x, crop_y=self.crop_y, crop_z=self.crop_z)
decoded_intensities = self.codebook.metric_decode(
pixel_intensities,
max_distance=self.distance_threshold,
min_intensity=self.magnitude_threshold,
norm_order=self.norm_order,
metric=self.metric
)
caf = CombineAdjacentFeatures(
min_area=self.min_area,
max_area=self.max_area,
mask_filtered_features=True
)
decoded_spots, image_decoding_results = caf.run(intensities=decoded_intensities,
n_processes=n_processes)
transfer_physical_coords_from_imagestack_to_intensity_table(image_stack=primary_image,
intensity_table=decoded_spots)
return decoded_spots, image_decoding_results
