def find_grains(input_file, output_dir=None):
"""Return tuple of segmentaitons (grains, difficult_regions)."""
name = fpath2name(input_file)
name = "grains-" + name + ".png"
if output_dir:
name = os.path.join(output_dir, name)
image = Image.from_file(input_file)
intensity = mean_intensity_projection(image)
# Median filter seems more robust than Otsu.
# image = threshold_otsu(intensity)
image = threshold_median(intensity, scale=0.8)
image = invert(image)
image = erode_binary(image, selem=disk(2))
image = dilate_binary(image, selem=disk(2))
image = remove_small_objects(image, min_size=200)
image = fill_holes(image, min_size=50)
dist = distance(image)
seeds = local_maxima(dist)
seeds = dilate_binary(seeds) # Merge spurious double peaks.
seeds = connected_components(seeds, background=0)
segmentation = watershed_with_seeds(dist, seeds=seeds, mask=image)
# Remove spurious blobs.
segmentation = remove_large_segments(segmentation, max_size=3000)
segmentation = remove_small_segments(segmentation, min_size=100)
return segmentation
def find_grains(input_file, output_dir=None):
"""Return tuple of segmentaitons (grains, difficult_regions)."""
name = fpath2name(input_file)
name = "grains-" + name + ".png"
if output_dir:
name = os.path.join(output_dir, name)
image = Image.from_file(input_file)
intensity = mean_intensity_projection(image)
image = remove_scalebar(intensity, np.mean(intensity))
image = threshold_abs(image, 75)
image = invert(image)
image = fill_holes(image, min_size=500)
image = erode_binary(image, selem=disk(4))
image = remove_small_objects(image, min_size=500)
image = dilate_binary(image, selem=disk(4))
dist = distance(image)
seeds = local_maxima(dist)
seeds = dilate_binary(seeds) # Merge spurious double peaks.
seeds = connected_components(seeds, background=0)
segmentation = watershed_with_seeds(dist, seeds=seeds, mask=image)
# Need a copy to avoid over-writing original.
initial_segmentation = np.copy(segmentation)
# Remove spurious blobs.
segmentation = remove_large_segments(segmentation, max_size=3000)
segmentation = remove_small_segments(segmentation, min_size=500)
props = skimage.measure.regionprops(segmentation)
segmentation = remove_non_round(segmentation, props, 0.6)
difficult = initial_segmentation - segmentation
return segmentation, difficult
def segment(line_image, dilation):
lines = convert_to_signal(line_image)
lines = skeletonize(lines)
lines = remove_small_objects(lines, min_size=10, connectivity=2)
lines = dilate_binary(lines, selem=np.ones((1, dilation)))
segmentation = connected_components(lines, background=0)
return segmentation
def test_dilate_binary(self):
from jicbioimage.transform import dilate_binary
from jicbioimage.core.image import Image
array = np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
dtype=np.bool)
expected = np.array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 1, 1, 0],
[0, 0, 1, 0, 0], [0, 0, 0, 0, 0]],
dtype=np.bool)
dilated = dilate_binary(array)
self.assertTrue(np.array_equal(expected, dilated))
self.assertTrue(isinstance(dilated, Image))
# The dilate_binary function only makes sense on dtype bool.
with self.assertRaises(TypeError):
dilate_binary(array.astype(np.uint8))
def analyse_image(image):
image = normalise(image) * 255
canvas = AnnotatedImage.from_grayscale(image)
image = smooth_gaussian(image.astype(float), 5)
image = threshold_abs(image, 30)
image = erode_binary(image)
image = remove_small_objects(image, 5)
salem = skimage.morphology.disk(2)
image = dilate_binary(image, salem)
segmentation = connected_components(image, background=0)
for i in segmentation.identifiers:
color = pretty_color_from_identifier(i)
region = segmentation.region_by_identifier(i)
convex_hull = region.convex_hull
outline = convex_hull.inner.border.dilate()
canvas.mask_region(outline, color=color)
return canvas
def cell_segmentation(wall_intensity2D, wall_mask2D, max_cell_size):
"""Return image segmented into cells."""
seeds = dilate_binary(wall_mask2D)
seeds = invert(seeds)
seeds = remove_small_objects(seeds, min_size=10)
seeds = connected_components(seeds, background=0)
segmentation = watershed_with_seeds(-wall_intensity2D, seeds=seeds)
segmentation = remove_large_segments(segmentation, max_cell_size)
return segmentation
def generate_cross_section_mask(image):
image = find_edges_sobel(image)
image = threshold_otsu(image)
image = dilate_binary(image, selem=skimage.morphology.disk(5))
image = remove_small_objects(image, 5000)
image = fill_holes(image, 50000)
image = erode_binary(image, selem=skimage.morphology.disk(10))
image = remove_small_objects(image, 50000)
return image
def generate_mask(image):
mask = threshold_abs(image, 6500)
mask = remove_small_objects(mask, 50)
mask = fill_holes(mask, 5)
selem = disk(3)
mask = erode_binary(mask, selem)
mask = dilate_binary(mask, selem)
return mask
def generate_seeds(image):
seeds = white_tophat(image, 10)
seeds = threshold_abs(seeds, 1500)
seeds = remove_small_objects(seeds, 50)
selem = disk(5)
seeds = dilate_binary(seeds, selem)
return connected_components(seeds, background=0)
def test_dilate_binary_with_selem(self):
from jicbioimage.transform import dilate_binary
selem = np.ones((3, 3))
array = np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
dtype=np.bool)
expected = np.array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0],
[0, 1, 1, 1, 0], [0, 0, 0, 0, 0]],
dtype=np.bool)
dilated = dilate_binary(array, selem=selem)
self.assertTrue(np.array_equal(expected, dilated))
def segment_cells(image, max_cell_size):
"""Return segmented cells."""
image = identity(image)
wall = threshold_adaptive_median(image, block_size=101)
seeds = remove_small_objects(wall, min_size=100)
seeds = dilate_binary(seeds)
seeds = invert(seeds)
seeds = remove_small_objects(seeds, min_size=5)
seeds = connected_components(seeds, background=0)
segmentation = watershed_with_seeds(-image, seeds=seeds)
segmentation = remove_large_segments(segmentation, max_cell_size)
return segmentation, wall
def find_tubes(input_file, output_dir=None):
"""Return pollen tube segmentation."""
name = fpath2name(input_file)
name = "tubes-" + name + ".png"
if output_dir:
name = os.path.join(output_dir, name)
image = Image.from_file(input_file)
intensity = mean_intensity_projection(image)
image = find_edges_sobel(intensity)
image = remove_scalebar(image, 0)
image = threshold_otsu(image)
image = dilate_binary(image, selem=disk(3))
image = erode_binary(image, selem=disk(3))
image = remove_small_objects(image, min_size=500)
image = fill_holes(image, min_size=500)
image = erode_binary(image, selem=disk(3))
image = remove_small_objects(image, min_size=200)
segmentation = connected_components(image, background=0)
return segmentation