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Otsu's method

In computer vision and image processing, Otsu's method, named after Nobuyuki Otsu (大津展之, Ōtsu Nobuyuki), is used to perform automatic image thresholding.

About

이미지의 히스토그램이 두 개의 피크로 구성된 Bimodal image 에 적용 가능하다.

Otsu_method_-_Bimodal_image_histogram.png

Example 

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

img = cv.imread('noisy2.png',0)

# global thresholding
ret1,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)

# Otsu's thresholding
ret2,th2 = cv.threshold(img,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)

# Otsu's thresholding after Gaussian filtering
blur = cv.GaussianBlur(img,(5,5),0)
ret3,th3 = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)

# plot all the images and their histograms
images = [img, 0, th1, img, 0, th2, blur, 0, th3]

titles = [
    'Original Noisy Image',
    'Histogram',
    'Global Thresholding (v=127)',
    'Original Noisy Image',
    'Histogram',
    "Otsu's Thresholding",
    'Gaussian filtered Image',
    'Histogram',
    "Otsu's Thresholding"
]

for i in range(3):
    plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
    plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])
    plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
    plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([])
    plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray')
    plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([])

plt.show()

Otsu_method_-_result.png

가우시안 필터링을 거친 마지막 이미지가 보다 잘 처리된다.

See also

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