图像处理

import cv2 as cv
import numpy as np

# 全局阈值
def threshold_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY)  # 把输入图像灰度化
    # 直接阈值化是对输入的单通道矩阵逐像素进行阈值分割。
    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)

# 局部阈值
def local_threshold(image):
    gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY)  # 把输入图像灰度化
    # 自适应阈值化能够根据图像不同区域亮度分布，改变阈值
    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)

# 用户自己计算阈值
def custom_threshold(image):
    gray = cv.cvtColor(image, cv.COLOR_RGB2GRAY)  # 把输入图像灰度化
    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(r'C:\Users\Milky\Desktop\1.jpg')
cv.namedWindow('input_image', cv.WINDOW_NORMAL)
# 设置为WINDOW_NORMAL可以任意缩放
cv.imshow('input_image', src)
threshold_demo(src)
local_threshold(src)
custom_threshold(src)
cv.waitKey(0)
cv.destroyAllWindows()

　　

    dst = cv.GaussianBlur(image, (3, 3), 0) #高斯模糊去噪
    gray = cv.cvtColor(dst, cv.COLOR_RGB2GRAY)
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU) #用大律法、全局自适应阈值方法进行图像二值化
    cv.imshow("binary image", binary)

　　

来源：https://www.cnblogs.com/Twinko/p/11945081.html