Python图像处理之二值化处理

import cv2
import matplotlib.pyplot as plt
img = cv2.imread('dog.jpeg', 0)  # 直接读为灰度图像
ret, thresh1 = cv2.threshold(img, 127, 255, 0)
ret, thresh2 = cv2.threshold(img, 127, 255, 1)
ret, thresh3 = cv2.threshold(img, 127, 255, 2)
ret, thresh4 = cv2.threshold(img, 127, 255, 3)
ret, thresh5 = cv2.threshold(img, 127, 255, 4)
titles = ['img', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in range(6):
    plt.subplot(2, 3, i + 1), plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])
plt.show()

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
img = cv.imread('dog.jpeg',0)
img = cv.medianBlur(img,5)
ret,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
th2 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C,0,11,2)
th3 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,0,11,2)
titles = ['Original Image', 'Global Thresholding (v = 127)',
            'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
images = [img, th1, th2, th3]
for i in range(4):
    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()

import cv2
import matplotlib.pyplot as plt
img = cv2.imread('cat.jpg', 0)  # 直接读为灰度图像
# 简单滤波
ret1, th1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
print(ret1)
# Otsu 滤波
ret2, th2 = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
print(ret2)
plt.figure()
plt.subplot(221), plt.imshow(img,'gray')
plt.subplot(222), plt.hist(img.ravel(), 256)  # .ravel方法将矩阵转化为一维
plt.subplot(223), plt.imshow(th1,'gray')
plt.subplot(224), plt.imshow(th2,'gray')
plt.show()