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 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(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
