圖像閾值處理是實現圖像分割的一種方法，常用的閾值分割方法有簡單閾值，自適應閾值，Otsu's二值化等。
cv2.threshold()可以用來進行圖像閾值處理，cv2.threshold(src, thresh, maxval, type) 第一個參數是原圖像，第二個參數是對像素值進行分類的閾值，第三個參數是當像素值高於(小於)閾值時，應該被賦予的新的像素值，第四個參數是閾值方法。函數有兩個返回值，一個為retVal, 一個閾值化處理之后的圖像。
常用的閾值方法有：
全局閾值：
自適應閾值
當同一幅圖像不同部分具有不同的亮度時，全局閾值並不適用，此時我們會采用自適應閾值，自適應閾值會在圖像上每一個小區域計算與其對應的閾值。可以使用cv2.adaptiveThreshold()要傳入三個參數：閾值計算方法，鄰域大小，常數C, 閾值等於平均值或者加權平均值減去這個常數。
import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('/home/vincent/Pictures/work/uy_gnix_iac.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)
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in range(6):
plt.subplot(2,3, i+1)
plt.imshow(images[i])
plt.suptitle('fixed threshold')
img = cv2.imread('/home/vincent/Pictures/work/barbara.png', 0)
print(img.shape)
thresh6 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)
thresh7 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
ret, thresh8 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
images2 = [img, thresh6, thresh7, thresh8]
plt.figure(2)
for i in range(4):
plt.subplot(1,4,i+1)
plt.imshow(images2[i],'gray')
plt.suptitle('adaptive threshold')
plt.show()
