def get_mask_factories(self):
if self.dataset.shape.sig.dims != 2:
raise ValueError("can only handle 2D signals currently")
(detector_y, detector_x) = self.dataset.shape.sig
cx = self.parameters['cx']
cy = self.parameters['cy']
r = self.parameters['r']
dtype = self.dtype
def disk_mask():
return masks.circular(
centerX=cx, centerY=cy,
imageSizeX=detector_x,
imageSizeY=detector_y,
radius=r,
)
return [
disk_mask,
lambda: masks.gradient_x(
imageSizeX=detector_x,
imageSizeY=detector_y,
dtype=dtype,
) * disk_mask(),
lambda: masks.gradient_y(
imageSizeX=detector_x,
imageSizeY=detector_y,
dtype=dtype,
) * disk_mask(),
]
def com_masks_generic(detector_y, detector_x, base_mask_factory):
"""
Create a CoM mask stack with a generic selection mask factory
Parameters
----------
detector_y : int
The detector height
detector_x : int
The detector width
base_mask_factory : () -> np.array
A factory function for creating the selection mask
Returns
-------
List[Function]
The mask stack as a list of factory functions
"""
return [
base_mask_factory,
lambda: masks.gradient_y(
imageSizeX=detector_x,
imageSizeY=detector_y,
) * base_mask_factory(),
lambda: masks.gradient_x(
imageSizeX=detector_x,
imageSizeY=detector_y,
) * base_mask_factory(),
]
def get_mask_factories(self):
cx = self.parameters['cx']
cy = self.parameters['cy']
r = self.parameters['r']
frame_size = self.dataset.shape[2:]
def disk_mask():
return masks.circular(
centerX=cx,
centerY=cy,
imageSizeX=frame_size[1],
imageSizeY=frame_size[0],
radius=r,
)
return [
disk_mask,
lambda: masks.gradient_x(
imageSizeX=frame_size[1],
imageSizeY=frame_size[0],
dtype=self.dtype,
) * (np.ones(frame_size) * disk_mask()),
lambda: masks.gradient_y(
imageSizeX=frame_size[1],
imageSizeY=frame_size[0],
dtype=self.dtype,
) * (np.ones(frame_size) * disk_mask()),
]
def do_com(fn, tileshape):
ds = H5DataSet(
path=fn,
ds_path="data",
tileshape=tileshape,
target_size=512*1024*1024,
)
masks = [
# summation of all pixels:
lambda: np.ones(shape=ds.shape[2:]),
# gradient from left to right
lambda: gradient_x(*ds.shape[2:]),
# gradient from top to bottom
lambda: gradient_y(*ds.shape[2:]),
]
job = ApplyMasksJob(dataset=ds, mask_factories=masks)
print(job.masks.computed_masks)
print("\n\n")
executor = InlineJobExecutor()
full_result = np.zeros(shape=(3,) + ds.shape[:2])
color = np.zeros(shape=(3,) + ds.shape[:2])
for result in executor.run_job(job):
for tile in result:
print(tile)
print(tile.data[0])
color[tile.tile_slice.get()[:2]] += 1
tile.copy_to_result(full_result)
x_centers = np.divide(full_result[1], full_result[0])
y_centers = np.divide(full_result[2], full_result[0])
print(color)
return full_result, x_centers, y_centers
def com_masks_factory(detector_y, detector_x, cy, cx, r):
def disk_mask():
return masks.circular(
centerX=cx, centerY=cy,
imageSizeX=detector_x,
imageSizeY=detector_y,
radius=r,
)
return [
disk_mask,
lambda: masks.gradient_y(
imageSizeX=detector_x,
imageSizeY=detector_y,
) * disk_mask(),
lambda: masks.gradient_x(
imageSizeX=detector_x,
imageSizeY=detector_y,
) * disk_mask(),
]
def _make_com(cx, cy, r, shape):
disk_mask = masks.circular(
centerX=cx,
centerY=cy,
imageSizeX=frame_size[1],
imageSizeY=frame_size[0],
radius=r,
)
return [
lambda: disk_mask,
lambda: masks.gradient_x(
imageSizeX=frame_size[1],
imageSizeY=frame_size[0],
dtype=dtype,
) * (np.ones(frame_size) * disk_mask),
lambda: masks.gradient_y(
imageSizeX=frame_size[1],
imageSizeY=frame_size[0],
dtype=dtype,
) * (np.ones(frame_size) * disk_mask),
]