Python/Lab 8 at main · AmFruitY/Python · GitHub

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# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""

#%% Lab #8 Mathematical morphology, distance and watershed transforms: Segmentations.

#%% Libraries

import numpy as np

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

from scipy import ndimage
from scipy import signal

from skimage import feature
from skimage import measure
from skimage import morphology
from skimage import segmentation

#%% 8.1 Morphology to the rescue.


im = mpimg.imread('obvvX.jpg')
imL = 0.299*im[:500,500:,0] + 0.587*im[:500,500:,1] + 0.114*im[:500,500:,2]

# Best thresholding technique?

th = 67

imB = np.double(imL<th)
imBl = imL < th

# imM = ndimage.binary_erosion(imB, iterations=1, structure = np.ones((3,3)))
# imM = ndimage.binary_dilation(imM, iterations=1, structure = np.ones((3,3)))
# imM = ndimage.binary_dilation(imM, iterations=1, structure = np.ones((3,3)))
# imM = ndimage.binary_erosion(imM, iterations=1, structure = np.ones((3,3)))

imM = morphology.closing(imB) # Closing first gets rids of the small holes
imM = morphology.opening(imB) # The result in this order is far better than if we do it the other way around


# imM = morphology.remove_small_objects(imBl)
# imM = morphology.remove_small_holes(imM)

# Label
iml = measure.label(imM, connectivity = 2)

# Distance Transform
imDT = ndimage.distance_transform_edt(imB)

# Watershed Transform

# wmax = feature.peak_local_max(imDT, footprint=np.ones((3, 3)), labels=imBl) # Index = False?
# label_max = ndimage.label(wmax)
# imWT = segmentation.watershed(imDT, markers = label_max[0], connectivity = 2, mask = imBl, watershed_line=True) # mask=

segmentation.watershed

plt.figure(1)

plt.subplot(231)
plt.title('OG GL')
plt.imshow(imL, cmap = 'gray')
plt.subplot(232)
plt.title('Binarized')
plt.imshow(imB, cmap = 'gray')
plt.subplot(233)
plt.title('After Morphology')
plt.imshow(imM, cmap = 'gray')
plt.subplot(234)
plt.title('Labelled Image')
plt.imshow(iml, cmap = 'gray')
plt.subplot(235)
plt.title('Distance Transform')
plt.imshow(imDT)
# plt.subplot(236)
# plt.title('Watershed Transform')
# plt.imshow(imWT)

plt.tight_layout()

# im2 = mpimg.imread('800px-Target_cells_and_spherocytes.jpg')
# im2L = 0.299*im2[:,:,0] + 0.587*im2[:,:,1] + 0.114*im2[:,:,2]

# th2 = signal.medfilt(im2L, 5)

# im2B = np.double(im2L<th2)


# plt.figure(2)

# plt.subplot(231)
# plt.imshow(im2L, cmap = 'gray')
# plt.subplot(232)
# plt.imshow(im2B, cmap = 'gray')