1.10 openCV-python 图像阈值

图像阈值

简单阈值，自适应阈值，Otsu’s 二值化等

import cv2
import numpy as np
import matplotlib.pyplot as plt

img = cv2.imread('heibai.jpg')

ret, thresh1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img, 127, 255, cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO)
ret, thresh5 = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO_INV)

titles = ['Original Image','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()

image.png

# 自适应阈值
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 中值滤波
img = cv2.imread('lufei.jpg', 0)
img = cv2.medianBlur(img, 3)

ret, th1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
#11 为Block size, 2为C值
th2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)
th3 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 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])
    plt.title(titles[i])
    plt.xlabel([])
    plt.ylabel([])
plt.show()

image.png

# Otsu’s 二值化
import cv2
import numpy as np
import matplotlib.pyplot as plt

img = cv2.imread('yingbi.jpg', 0)

# global thresholding
ret1, th1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)

# Otsu`s thresholding
ret2, th2 = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Otsu`s thresholding after Gaussian filtering
# (5,5)为高斯核的大小， 0 为标准差
blur = cv2.GaussianBlur(img, (5,5), 0)
# 阈值一定要设为0！
ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY+cv2.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()

image.png