I'd like to use the equivalent of skimage.filters.threshold_multiotsu in OpenCV to detect object in video.
I can get only one threshold in OpenCV, not multiple like in skimage.
OpenCV usually runs faster than skimage for many reasons.
Full code:
#!/usr/bin/env python
__author__ = "Sreenivas Bhattiprolu"
__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"
# https://youtu.be/YdhhiXDQDl4
import matplotlib.pyplot as plt
import numpy as np
from skimage import data, io, img_as_ubyte
from skimage.filters import threshold_multiotsu
# Read an image
image = io.imread("images/BSE.jpg")
# Apply multi-Otsu threshold
thresholds = threshold_multiotsu(image, classes=5)
# Digitize (segment) original image into multiple classes.
#np.digitize assign values 0, 1, 2, 3, ... to pixels in each class.
regions = np.digitize(image, bins=thresholds)
output = img_as_ubyte(regions) #Convert 64 bit integer values to uint8
plt.imsave("images/Otsu_Segmented.jpg", output)
#Let us look at the input image, thresholds on thehistogram and final segmented image
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(10, 3.5))
# Plotting the original image.
ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original')
ax[0].axis('off')
# Plotting the histogram and the two thresholds obtained from
# multi-Otsu.
ax[1].hist(image.ravel(), bins=255)
ax[1].set_title('Histogram')
for thresh in thresholds:
ax[1].axvline(thresh, color='r')
# Plotting the Multi Otsu result.
ax[2].imshow(regions, cmap='Accent')
ax[2].set_title('Multi-Otsu result')
ax[2].axis('off')
plt.subplots_adjust()
plt.show()
The input image:
The output images:
