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 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 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 test_watershed_with_seeds_mask_option(self):
from jicbioimage.segment import watershed_with_seeds
ar = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 9, 0, 0],
[9, 9, 9, 9, 9, 9],
[0, 0, 0, 9, 0, 0],
[0, 0, 0, 9, 0, 0]], dtype=np.uint8)
sd = np.array([[1, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 4]], dtype=np.uint8)
ma = np.array([[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0]], dtype=bool)
segmentation = watershed_with_seeds(ar, seeds=sd, mask=ma)
self.assertEqual(segmentation.identifiers, set([1, 2, 3]))
mask_size = len(segmentation[np.where(segmentation == 0)])
self.assertEqual(mask_size, 6)
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 segment(zstack):
image = max_intensity_projection(zstack)
seeds = generate_seeds(image)
mask = generate_mask(image)
segmentation = watershed_with_seeds(image, seeds=seeds, mask=mask)
annotate_segmentation(image, segmentation)
return segmentation
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 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 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 analyse_file(fpath, output_directory, test_data_only=False):
"""Analyse a single file."""
logging.info("Analysing file: {}".format(fpath))
AutoName.directory = output_directory
image = Image.from_file(fpath)
negative = get_negative_single_channel(image)
seeds = find_seeds(negative)
mask = find_mask(negative)
eaten_leaf_segmentation = watershed_with_seeds(negative,
seeds=seeds,
mask=mask)
whole_leaf_segmentation = post_process_segmentation(
eaten_leaf_segmentation.copy())
ann = annotate(image, whole_leaf_segmentation, eaten_leaf_segmentation)
ann_fpath = os.path.join(output_directory, "annotated.png")
with open(ann_fpath, "wb") as fh:
fh.write(ann.png())
def test_watershed_with_seeds(self):
from jicbioimage.segment import watershed_with_seeds
from jicbioimage.segment import SegmentedImage
ar = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 9, 0, 0],
[9, 9, 9, 9, 9, 9],
[0, 0, 0, 9, 0, 0],
[0, 0, 0, 9, 0, 0]], dtype=np.uint8)
sd = np.array([[1, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 4]], dtype=np.uint8)
segmentation = watershed_with_seeds(ar, seeds=sd)
self.assertTrue(isinstance(segmentation, SegmentedImage))
self.assertEqual(segmentation.identifiers, set([1, 2, 3, 4]))
def test_watershed_with_seeds_acts_like_a_transform(self):
from jicbioimage.segment import watershed_with_seeds
from jicbioimage.core.image import Image
ar = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 9, 0, 0],
[9, 9, 9, 9, 9, 9],
[0, 0, 0, 9, 0, 0],
[0, 0, 0, 9, 0, 0]], dtype=np.uint8)
im = Image.from_array(ar)
self.assertEqual(len(im.history), 1)
sd = np.array([[1, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 4]], dtype=np.uint8)
segmentation = watershed_with_seeds(im, seeds=sd)
self.assertEqual(len(segmentation.history), 2)
self.assertEqual(segmentation.history[-1],
"Applied watershed_with_seeds transform")
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
