多模块内容网页布局设计_商城app源码_seo优化关键词_关键一招

一、cv2.threshold()

res, dst = cv2.threshold(src=*,thresh=*,maxval=*,type=*) 

 res：函数返回的阈值。

dst：阈值处理后的函数。

src：要处理的图像。

thresh：阈值。

maxval：设定像素最大值。

type：阈值函数处理方法，常见方法如下表所示：

方法	值	解释
THRESH_BINARY	0	大于阈值取最大值，其他值取0
THRESH_BINARY_INV	1	大于阈值取0，其他为最大值
THRESH_TRUNC	2	大于阈值取阈值，其他值不变
THRESH_TOZERO	3	大于阈值不变，其他值取0
THRESH_TOZERO_INV	4	大于阈值取0，其他值不变
THRESH_OTSU	8	使用算法自动计算阈值
THRESH_TRIANGLE	16	使用三角形算法自动计算阈值

1.1 自定义阈值 

import cv2
import numpy as npcar = cv2.imread('car.png',cv2.IMREAD_GRAYSCALE )
car = car[::2,::2]res1, dst1 = cv2.threshold(src=car,thresh=100,maxval=255,type=cv2.THRESH_BINARY)
res2, dst2 = cv2.threshold(src=car,thresh=100,maxval=255,type=cv2.THRESH_BINARY_INV)
res3, dst3 = cv2.threshold(src=car,thresh=100,maxval=255,type=cv2.THRESH_TRUNC)
res4, dst4 = cv2.threshold(src=car,thresh=100,maxval=255,type=cv2.THRESH_TOZERO)
res5, dst5 = cv2.threshold(src=car,thresh=100,maxval=255,type=cv2.THRESH_TOZERO_INV)cv2.imshow('car',car)
cv2.imshow('THRESH_BINARY',dst1)
cv2.imshow('THRESH_BINARY_INV',dst2)
cv2.imshow('THRESH_TRUNC',dst3)
cv2.imshow('THRESH_TOZERO',dst4)
cv2.imshow('THRESH_TOZERO_INV',dst5)cv2.waitKey(0)
cv2.destroyAllWindows()

 1.2  自动阈值

import cv2car = cv2.imread('car.png',cv2.IMREAD_GRAYSCALE )
car = car[::2,::2]res1, dst1 = cv2.threshold(src=car,thresh=100,maxval=255,type=cv2.THRESH_BINARY)
res2, dst2 = cv2.threshold(src=car,thresh=100,maxval=255,type=(cv2.THRESH_BINARY+cv2.THRESH_OTSU))
res3, dst3 = cv2.threshold(src=car,thresh=100,maxval=255,type=(cv2.THRESH_BINARY+cv2.THRESH_TRIANGLE))cv2.imshow('car',car)
cv2.imshow('THRESH_BINARY',dst1)
cv2.imshow('THRESH_OTSU',dst2)
cv2.imshow('THRESH_TRIANGLE',dst3)print("THRESH_OTSU方法的计算阈值：",res2)
print("THRESH_TRIANGLE方法的计算阈值：",res3)
cv2.waitKey(0)
cv2.destroyAllWindows()

 从结果可以看出THRESH_OTSU方法的计算阈值比较可靠，能够很好的显示物体的轮廓。

 二、cv2.adaptiveThreshold()

res, dst = cv2.adaptiveThreshold(src=*,maxval=*,adaptiveMethod=*,thresholdType=*,blockSize=,C=*)

res：函数返回的阈值。

maxval：设定像素最大值。

adaptiveMethod：

算术平均法	cv2.ADAPTIVE_THRESH_MEAN_C、	将周围区域的平均值当作阈值，再减去参数C
高斯加权和法	cv2.ADAPTIVE_THRESH_GAUSSIAN_C	利用高斯函数方法对中心点和周围区域进行加权计算当作阈值，在减去参数C

thresholdType：阈值处理方法。必须为：THRESH_BINARY 或 THRESH_BINARY_INV

blockSize：阈值计算核，3*3或5*5或7*7等。

C：常数。

import cv2car = cv2.imread('car.png',cv2.IMREAD_GRAYSCALE )
car = car[::2,::2]res1, dst1 = cv2.threshold(src=car,thresh=100,maxval=255,type=cv2.THRESH_BINARY)
dst2 = cv2.adaptiveThreshold(src=car,maxValue=255,adaptiveMethod=cv2.ADAPTIVE_THRESH_MEAN_C,thresholdType=cv2.THRESH_BINARY,blockSize=7,C=0)
dst3 = cv2.adaptiveThreshold(src=car,maxValue=255,adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,thresholdType=cv2.THRESH_BINARY,blockSize=7,C=0)cv2.imshow('car',car)
cv2.imshow('THRESH_BINARY',dst1)
cv2.imshow('ADAPTIVE_THRESH_MEAN_C',dst2)
cv2.imshow('ADAPTIVE_THRESH_GAUSSIAN_C',dst3)cv2.waitKey(0)
cv2.destroyAllWindows()

可以调整 blockSize=？进行调试，blockSize越小，绘制的二值图像轮廓越细节。