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tutorial52_multi_thresholding_and_morph_operators.py
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tutorial52_multi_thresholding_and_morph_operators.py
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#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG
# Image segmentation and morphological operators
from matplotlib import pyplot as plt
import numpy as np
from skimage.filters import threshold_multiotsu
import cv2
img = cv2.imread("images/BSE.tif", 0)
#Denoise for better results
#from skimage.restoration import denoise_tv_chambolle
#denoised_img = denoise_tv_chambolle(img, weight=0.1, eps=0.0002, n_iter_max=200, multichannel=False)
plt.imshow(img, cmap='gray')
plt.hist(img.flat, bins=100, range=(100,255)) #.flat returns the flattened numpy array (1D)
##################MANUAL########################
#Can perform manual segmentation but auto works fine
region1 = (img >= 0) & (img <75)
region2 = (img >= 75) & (img <140)
region3 = (img >= 140) & (img <200)
region4 = (img >= 200) & (img <=255)
all_regions = np.zeros((img.shape[0], img.shape[1], 3)) #Create 3 channel blank image of same size as original
all_regions[region1] = (1,0,0)
all_regions[region2] = (0,1,0)
all_regions[region3] = (0,0,1)
all_regions[region4] = (1,1,0)
plt.imshow(all_regions)
##############################################
####AUTO###########################
# Apply multi-Otsu threshold
thresholds = threshold_multiotsu(img, classes=4)
# Digitize (segment) original image into multiple classes.
#np.digitize assign values 0, 1, 2, 3, ... to pixels in each class.
regions = np.digitize(img, bins=thresholds)
plt.imshow(regions)
segm1 = (regions == 0)
segm2 = (regions == 1)
segm3 = (regions == 2)
segm4 = (regions == 3)
#We can use binary opening and closing operations to clean up.
#Open takes care of isolated pixels within the window
#Closing takes care of isolated holes within the defined window
from scipy import ndimage as nd
segm1_opened = nd.binary_opening(segm1, np.ones((3,3)))
segm1_closed = nd.binary_closing(segm1_opened, np.ones((3,3)))
segm2_opened = nd.binary_opening(segm2, np.ones((3,3)))
segm2_closed = nd.binary_closing(segm2_opened, np.ones((3,3)))
segm3_opened = nd.binary_opening(segm3, np.ones((3,3)))
segm3_closed = nd.binary_closing(segm3_opened, np.ones((3,3)))
segm4_opened = nd.binary_opening(segm4, np.ones((3,3)))
segm4_closed = nd.binary_closing(segm4_opened, np.ones((3,3)))
all_segments_cleaned = np.zeros((img.shape[0], img.shape[1], 3))
all_segments_cleaned[segm1_closed] = (1,0,0)
all_segments_cleaned[segm2_closed] = (0,1,0)
all_segments_cleaned[segm3_closed] = (0,0,1)
all_segments_cleaned[segm4_closed] = (1,1,0)
plt.imshow(all_segments_cleaned) #All the noise should be cleaned now
plt.imsave("images/BSE_segmented.jpg", all_segments_cleaned)