def threshold(img, thresh=None, size=9):
"""
Segment image using a thresholding algorithm
"""
if thresh is None:
thresh = Stats.mode(img)
thresh += np.std(img)
# Take the binary image version "splitted" on the 'thresh' value
img_bin = (img > thresh)
# Clean fluctuations
img_bin = Clean.spurious(img_bin)
# Clean small objects
img_bin = Clean.small(img_bin,size=9)
return img_bin
def regionGrow(img, x, y, thresh=None):
"""
Segment using a Region Growing technique
"""
flag = 1
x_o = x
y_o = y
if thresh is None:
thresh = Stats.mode(img)
thresh += np.std(img)
# Initialize region with the seed point
region = np.zeros(img.shape,dtype=np.bool)
reg_old = (region==flag)
if img[y_o,x_o] < thresh:
return region
region[y_o,x_o] = flag
reg_cur = (region==flag)
# For future morphological operations (MO)
strct_elem = ndi.generate_binary_structure(2,2)
# While region stills changes (grow), do...
while not np.all(region == reg_old):
reg_old = region.copy()
reg_mean = np.mean(img[region==flag])
#reg_area = np.where(region==flag)[0].size
# Define pixel neighbours using MO dilation
tmp = ndi.binary_dilation(region,strct_elem, 1)
neigbors = tmp - region
inds = np.where(neigbors)
# Check for the new neighbors; do they fullfil requirements
# to become members of the growing region?
for y_i,x_i in zip(*inds):
if (img[y_i,x_i] >= thresh):# and (img[y_i,x_i] <= reg_mean):
region[y_i,x_i] = flag
return region
