def select_largest_obj(self, img_bin, lab_val=255, fill_holes=False,
smooth_boundary=False, kernel_size=15):
'''Select the largest object from a binary image and optionally
fill holes inside it and smooth its boundary.
Args:
img_bin (2D array): 2D numpy array of binary image.
lab_val ([int]): integer value used for the label of the largest
object. Default is 255.
fill_holes ([boolean]): whether fill the holes inside the largest
object or not. Default is false.
smooth_boundary ([boolean]): whether smooth the boundary of the
largest object using morphological opening or not. Default
is false.
kernel_size ([int]): the size of the kernel used for morphological
operation. Default is 15.
Returns:
a binary image as a mask for the largest object.
'''
n_labels, img_labeled, lab_stats, _ = \
cv2.connectedComponentsWithStats(img_bin, connectivity=8,
ltype=cv2.CV_32S)
largest_obj_lab = np.argmax(lab_stats[1:, 4]) + 1
largest_mask = np.zeros(img_bin.shape, dtype=np.uint8)
largest_mask[img_labeled == largest_obj_lab] = lab_val
# import pdb; pdb.set_trace()
if fill_holes:
bkg_locs = np.where(img_labeled == 0)
bkg_seed = (bkg_locs[0][0], bkg_locs[1][0])
img_floodfill = largest_mask.copy()
h_, w_ = largest_mask.shape
mask_ = np.zeros((h_ + 2, w_ + 2), dtype=np.uint8)
cv2.floodFill(img_floodfill, mask_, seedPoint=bkg_seed,
newVal=lab_val)
holes_mask = cv2.bitwise_not(img_floodfill) # mask of the holes.
largest_mask = largest_mask + holes_mask
if smooth_boundary:
kernel_ = np.ones((kernel_size, kernel_size), dtype=np.uint8)
largest_mask = cv2.morphologyEx(largest_mask, cv2.MORPH_OPEN,
kernel_)
return largest_mask
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