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 marker_segmentation(marker_intensity3D, wall_mask3D, threshold):
"""Return fluorescent marker segmentation."""
marker_intensity3D = marker_intensity3D * wall_mask3D
markers2D = max_intensity_projection(marker_intensity3D)
markers2D = threshold_abs(markers2D, threshold)
markers2D = remove_small_objects(markers2D, min_size=50)
return connected_components(markers2D, background=0)
def test_connected_components_connectivity_option(self):
from jicbioimage.segment import connected_components
from jicbioimage.segment import SegmentedImage
ar = np.array([[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1]], dtype=np.uint8)
segmentation = connected_components(ar, connectivity=1)
self.assertTrue(isinstance(segmentation, SegmentedImage))
self.assertEqual(segmentation.identifiers, set([1, 2, 3, 4]))
segmentation = connected_components(ar, connectivity=2)
self.assertTrue(isinstance(segmentation, SegmentedImage))
self.assertEqual(segmentation.identifiers, set([1, 2]))
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 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_markers(image, wall, threshold):
"""Return segmented markers."""
image = threshold_abs(image, threshold)
image = marker_in_wall(image, wall)
image = remove_small_objects(image, min_size=10)
segmentation = connected_components(image, background=0)
return segmentation
def find_angle(image):
image = equalize_adaptive_clahe(image)
image = threshold_otsu(image)
image = erosion_binary(image, selem=skimage.morphology.disk(3))
image = remove_small_objects(image, min_size=5000)
segmentation = connected_components(image, background=0)
properties = skimage.measure.regionprops(segmentation)
angles = [p["orientation"] for p in properties]
return sum(angles) / len(angles)
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 vertical_cuts(thresholded_image):
"""Return vertical cuts to separate fused seeds."""
n = 50
selem = np.array([0, 1, 0] * n).reshape((n, 3))
cuts = invert_binary(thresholded_image)
cuts = connected_components(cuts, background=0)
cuts = remove_large_regions(cuts, max_size=500).astype(bool)
cuts = dilation_binary(cuts, selem=selem)
cuts = invert_binary(cuts)
return cuts
def segment3D(microscopy_collection, series, threshold):
"""Return segmented plasmodesmata in 3D."""
zstack = microscopy_collection.zstack_array(s=series)
segmentation = np.zeros(zstack.shape, dtype=bool)
ziterator = microscopy_collection.zstack_proxy_iterator(s=series)
for z, proxy_image in enumerate(ziterator):
image = proxy_image.image
image = threshold_abs(image, threshold)
segmentation[:, :, z] = image
return connected_components(segmentation, background=0)
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 segment(image):
cross_section_mask = generate_cross_section_mask(image)
seeds = threshold_adaptive_median(image, 51)
seeds = clip_mask(seeds, cross_section_mask)
seeds = fill_holes(seeds, 10000)
seeds = erode_binary(seeds)
seeds = remove_small_objects(seeds, 10)
seeds = connected_components(seeds, connectivity=1, background=0)
cells = watershed_with_seeds(image, seeds=seeds, mask=cross_section_mask)
return cells
def create_mask(image):
"""Return a mask for the region of interest."""
selem = skimage.morphology.disk(2)
im = equalize_adaptive_clahe(image)
im = threshold_otsu(im)
im = erosion_binary(im, selem)
mask = np.ones(im.shape, dtype=bool)
segmentation = connected_components(im, background=0)
for i in segmentation.identifiers:
region = segmentation.region_by_identifier(i)
if region.area > 5000:
mask[np.where(region.convex_hull)] = False
return Image.from_array(mask)
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 preprocess_and_segment(image):
image = identity(image)
image = threshold_adaptive(image)
image = remove_small_objects(image)
image = invert(image)
image = remove_small_objects(image)
image = invert(image)
segmentation = connected_components(image, background=False)
segmentation = clear_border(segmentation)
segmentation = remove_small_regions(segmentation)
return segmentation
def test_connected_components_acts_like_a_transform(self):
from jicbioimage.segment import connected_components
from jicbioimage.core.image import Image
ar = np.array([[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 2, 2, 2],
[0, 0, 2, 2, 2]], dtype=np.uint8)
im = Image.from_array(ar)
self.assertEqual(len(im.history), 1)
segmentation = connected_components(im)
self.assertEqual(len(segmentation.history), 2)
self.assertEqual(segmentation.history[-1],
"Applied connected_components transform")
def analyse_file_org(fpath, output_directory):
"""Analyse a single file."""
logging.info("Analysing file: {}".format(fpath))
image = Image.from_file(fpath)
image = identity(image)
image = select_red(image)
image = invert(image)
image = threshold_otsu(image)
seeds = remove_small_objects(image, min_size=1000)
seeds = fill_small_holes(seeds, min_size=1000)
seeds = erode_binary(seeds, selem=disk(30))
seeds = connected_components(seeds, background=0)
watershed_with_seeds(-image, seeds=seeds, mask=image)
def segment(image):
"""Return field plots."""
red = red_channel(image)
green = green_channel(image)
image = difference(red, green)
mask = threshold_otsu(image)
mask = remove_small_objects(mask, min_size=1000)
mask = fill_small_holes(mask, min_size=100)
seeds = erode_binary(mask, selem=disk(10))
seeds = remove_small_objects(seeds, min_size=100)
seeds = connected_components(seeds, background=0)
return watershed_with_seeds(-image, seeds=seeds, mask=mask)
def segment(image, seeds=None):
"""Return field plots."""
green = green_channel(image)
image = sklocal(green)
image = skmean(image)
mask = threshold_otsu(image)
mask = remove_small_objects(mask, min_size=1000)
mask = fill_small_holes(mask, min_size=100)
dist = distance_transform(mask)
if seeds is None:
seeds = erode_binary(mask, selem=disk(10))
seeds = remove_small_objects(seeds, min_size=100)
seeds = connected_components(seeds, background=0)
return watershed_with_seeds(image, seeds=seeds, mask=mask)
def segment_cells(wall_projection, surface, mask, **kwargs):
"""Return segmented cells as SegmentedImage."""
seeds = threshold_adaptive_median(
wall_projection,
block_size=kwargs["wall_threshold_adaptive_block_size"])
seeds = remove_small_objects(
seeds, min_size=kwargs["wall_remove_small_objects_in_cell_min_size"])
seeds = invert(seeds)
if "wall_erode_step" in kwargs and kwargs["wall_erode_step"]:
seeds = erode_binary(seeds)
seeds = remove_small_objects(
seeds, min_size=kwargs["wall_remove_small_objects_in_wall_min_size"])
seeds = connected_components(seeds, connectivity=1, background=0)
cells = watershed_with_seeds(-wall_projection, seeds=seeds)
cells = remove_cells_not_in_mask(cells, mask)
return cells
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
def segment(image):
"""Return a segmented image and rotation angle."""
angle = find_angle(image)
image = rotate(image, angle)
mask = create_mask(image)
watershed_mask = equalize_adaptive_clahe(image)
watershed_mask = smooth_gaussian(watershed_mask, sigma=(1, 0))
watershed_mask = threshold_otsu(watershed_mask)
watershed_mask = apply_mask(watershed_mask, mask)
n = 20
selem = np.array([0, 1, 0] * n).reshape((n, 3))
seeds = erosion_binary(watershed_mask, selem=selem)
seeds = apply_mask(seeds, vertical_cuts(watershed_mask))
seeds = remove_small_objects(seeds)
seeds = connected_components(seeds, connectivity=1, background=0)
segmentation = watershed_with_seeds(image, seeds, mask=watershed_mask)
segmentation = remove_cells_touching_border(segmentation, image)
segmentation = remove_cells_touching_border(segmentation, mask)
segmentation = remove_tilted_cells(segmentation)
return segmentation, angle
def find_component_centroids(image):
ccs = connected_components(image, background=0)
return [map(int, r.centroid) for r in yield_regions(ccs)]
def find_seeds(image):
seeds = threshold_abs(image, 200)
seeds = remove_small_objects(seeds, min_size=1000)
seeds = connected_components(seeds, background=0)
return seeds
