def to_directory(self, directory):
if not os.path.isdir(directory):
os.mkdir(directory)
xdim, ydim, zdim = self.shape
num_digits = Image3D._num_digits(zdim-1)
for z in range(zdim):
num = str(z).zfill(num_digits)
fname = "z{}.png".format(num)
fpath = os.path.join(directory, fname)
with open(fpath, "wb") as fh:
im = Image.from_array(pretty_color_array(self[:, :, z]))
fh.write(im.png())
def test_pretty_colour_image(self):
from jicbioimage.segment import SegmentedImage
input_array = np.array([[0, 0, 0],
[1, 1, 1],
[2, 2, 2]])
segmented_image = SegmentedImage.from_array(input_array)
pretty_color_image = segmented_image.pretty_color_image
from jicbioimage.core.util.array import pretty_color_array
self.assertTrue(np.array_equal(pretty_color_image,
pretty_color_array(input_array)))
def analyse(microscopy_collection, wall_channel, marker_channel, threshold, max_cell_size):
"""Do the analysis."""
# Prepare the input data for the segmentations.
(wall_intensity2D,
wall_intensity3D,
wall_mask2D,
wall_mask3D) = get_wall_intensity_and_mask_images(microscopy_collection, wall_channel)
(marker_intensity2D,
marker_intensity3D) = get_marker_intensity_images(microscopy_collection, marker_channel)
# Perform the segmentation.
cells = cell_segmentation(wall_intensity2D, wall_mask2D, max_cell_size)
markers = marker_segmentation(marker_intensity3D, wall_mask3D, threshold)
# Get marker in cell wall and project to 2D.
wall_marker = marker_intensity3D * wall_mask3D
wall_marker = max_intensity_projection(wall_marker)
# Get tensors.
tensors = get_tensors(cells, markers)
# Write out tensors to a text file.
fpath = os.path.join(AutoName.directory, "raw_tensors.txt")
with open(fpath, "w") as fh:
tensors.write_raw_tensors(fh)
# Write out intensity images.
fpath = os.path.join(AutoName.directory, "wall_intensity.png")
with open(fpath, "wb") as fh:
fh.write(wall_intensity2D.png())
fpath = os.path.join(AutoName.directory, "marker_intensity.png")
with open(fpath, "wb") as fh:
fh.write(wall_marker.png())
# Shrink the segments to make them clearer.
for i in cells.identifiers:
region = cells.region_by_identifier(i)
mask = region - region.inner.inner
cells[mask] = 0
colorful = pretty_color_array(cells)
pil_im = PIL.Image.fromarray(colorful.view(dtype=np.uint8))
pil_im = make_transparent(pil_im, 60)
fpath = os.path.join(AutoName.directory, "segmentation.png")
pil_im.save(fpath)
def test_pretty_color_array_with_black_background(self):
from jicbioimage.core.util.array import pretty_color_array
input_array = np.array([[0, 0, 0],
[1, 1, 1],
[2, 2, 2]])
c1 = [0, 0, 0]
c2 = [132, 27, 117]
c3 = [20, 134, 44]
expected_output = np.array([[c1, c1, c1],
[c2, c2, c2],
[c3, c3, c3]], dtype=np.uint8)
actual_output = pretty_color_array(input_array)
self.assertTrue(np.array_equal(actual_output, expected_output))
def analyse(microscopy_collection, wall_channel, marker_channel,
threshold, max_cell_size):
"""Do the analysis."""
# Prepare the input data for the segmentations.
cell_wall_stack = microscopy_collection.zstack_array(c=wall_channel)
marker_stack = microscopy_collection.zstack_array(c=marker_channel)
surface = generate_surface_from_stack(cell_wall_stack)
cell_wall_projection = projection_from_stack_and_surface(cell_wall_stack,
surface, 1, 9)
marker_projection = projection_from_stack_and_surface(marker_stack,
surface, 1, 9)
# Perform the segmentation.
cells, wall = segment_cells(cell_wall_projection, max_cell_size)
markers = segment_markers(marker_projection, wall, threshold)
# Get tensors.
tensors = get_tensors(cells, markers)
# Write out tensors to a text file.
fpath = os.path.join(AutoName.directory, "raw_tensors.txt")
with open(fpath, "w") as fh:
tensors.write_raw_tensors(fh)
# Write out intensity images.
fpath = os.path.join(AutoName.directory, "wall_intensity.png")
with open(fpath, "wb") as fh:
fh.write(cell_wall_projection.png())
fpath = os.path.join(AutoName.directory, "marker_intensity.png")
marker_im = marker_in_wall(marker_projection, wall)
with open(fpath, "wb") as fh:
fh.write(marker_im.png())
# Shrink the segments to make them clearer.
for i in cells.identifiers:
region = cells.region_by_identifier(i)
mask = region - region.inner.inner
cells[mask] = 0
colorful = pretty_color_array(cells)
pil_im = PIL.Image.fromarray(colorful.view(dtype=np.uint8))
pil_im = make_transparent(pil_im, 60)
fpath = os.path.join(AutoName.directory, "segmentation.png")
pil_im.save(fpath)
def pretty_color_image(self):
"""Return segmentation as a pretty color image.
:returns: `jicbioimage.core.image.Image`
"""
return Image.from_array(pretty_color_array(self))
