影像預處理

（1）灰化
（2）二值化

import cv2 as cv
import numpy as np

#全域性閾值
def threshold_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY)  #把輸入影像灰度化
    #直接閾值化是對輸入的單通道矩陣逐畫素進行閾值分割。
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_TRIANGLE)
    print("threshold value %s"%ret)
    cv.namedWindow("binary0", cv.WINDOW_NORMAL)
    cv.imshow("binary0", binary)

#區域性閾值
def local_threshold(image):
    gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY)  #把輸入影像灰度化
    #自適應閾值化能夠根據影像不同區域亮度分佈，改變閾值
    binary =  cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY, 25, 10)
    cv.namedWindow("binary1", cv.WINDOW_NORMAL)
    cv.imshow("binary1", binary)

#使用者自己計算閾值
def custom_threshold(image):
    gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY)  #把輸入影像灰度化
    h, w =gray.shape[:2]
    m = np.reshape(gray, [1,w*h])
    mean = m.sum()/(w*h)
    print("mean:",mean)
    ret, binary =  cv.threshold(gray, mean, 255, cv.THRESH_BINARY)
    cv.namedWindow("binary2", cv.WINDOW_NORMAL)
    cv.imshow("binary2", binary)

src = cv.imread('C:/Users/Administrator/Desktop/test/Crop_test1.jpg')
cv.namedWindow('input_image', cv.WINDOW_NORMAL) #設定為WINDOW_NORMAL可以任意縮放
cv.imshow('input_image', src)
threshold_demo(src)
local_threshold(src)
custom_threshold(src)
cv.waitKey(0)
cv.destroyAllWindows()

（3）按需求濾波