Using scikit library I was analysing the defects' area and mean diameter. Here is the code and the respective segmented regions.
import cv2
import numpy as np
from matplotlib import pyplot as plt
from skimage import measure, io, img_as_ubyte
from skimage.color import label2rgb, rgb2gray
img = cv2.imread("F:\py_image_pro\pore.jpg", 0)
scale = 0.086 #1 pixel in microns
from skimage.filters import threshold_otsu
threshold = threshold_otsu(img)
thresholded_img = img < threshold
#plt.imshow(thresholded_img, cmap='gray')
#plt.show()
from skimage.segmentation import clear_border
edge_touching_removed = clear_border(thresholded_img)
label_image = measure.label(edge_touching_removed, connectivity=img.ndim)
#plt.imshow(label_image)
#plt.show()
image_label_overlay = label2rgb(label_image, image=img)
plt.imshow(image_label_overlay)
plt.show()
props = measure.regionprops_table(label_image, img, properties=['label', 'area', 'equivalent_diameter', 'mean_intensity', 'solidity'])
import pandas as pd
df = pd.DataFrame(props)
df = df[df['area'] > 20]
df['area_in_microns'] = df['area'] * (scale**2)
df['equivalent_diameter_microns'] = df['equivalent_diameter'] * (scale)
print(df.head())Used regionprops to measure the segmented regions. Segmented imageI would like to know if there is any way to display the labels in the output image so that segmented labels' corresponding measurements can be known?
1 Answer
Are you asking whether you can display a colormapped version of the measurements on top of the image? If so, the answer is yes! You can use skimage.util.map_array for this.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from skimage import ( color, data, filters, measure, morphology, segmentation, util )
# grab the image
coins = data.coins()
# segment the image; from:
#
edges = filters.farid(coins)
markers = np.zeros_like(coins)
markers[coins < 30] = 1
markers[coins > 150] = 2
watershed = segmentation.watershed(edges, markers)
segmented_raw = measure.label(watershed == 2)
# remove tiny background objects due to noise
segmented = morphology.remove_small_objects(segmented_raw, 64)
# measure regionprops
table = pd.DataFrame(measure.regionprops_table( segmented, coins, properties=('label', 'area') ))
# map the labels to measured properties
colored_by_area = util.map_array( segmented, np.asarray(table['label']), np.asarray(table['area']).astype(float), )
# set 0 to nan, so it appears as transparent in pyplot.imshow
colored_by_area[colored_by_area==0] = np.nan
# display the results
fig, axes = plt.subplots(1, 2, sharex=True, sharey=True)
colored_by_label = color.label2rgb(segmented, image=coins, bg_label=0)
axes[0].imshow(colored_by_label)
axes[0].set_axis_off()
axes[0].set_title('segmentation')
axes[1].imshow(coins, cmap='gray')
axim = axes[1].imshow(colored_by_area, cmap='viridis')
axes[1].set_axis_off()
axes[1].set_title('segment area')
plt.colorbar(axim, ax=axes[1], fraction=0.05, label='area (px)')
plt.show()
