Python灰度变换中灰度切割分析实现

import cv2
import numpy as np
def transform1(x):
    a , b = 150 , 240    #  定义两个阈值，中间部分变换为255
    dst = x.copy()
    dst[(x[:,:] >= a) & (x[:,:] <= b)] = 255        # 中间变换为255
    dst[(x[:,:] <  a) | (x[:,:] >  b)] = 0          # 其余的变换为0
    return dst
def transform2(x):
    a , b = 150 , 240    #  定义两个阈值，中间部分变换为255
    dst = x.copy()
    dst[(x[:,:] >= a) & (x[:,:] <= b)] = 255     # 中间变换255，其余的不变
    return dst
gray = cv2.imread('./img.png',0)
dst1 = transform1(gray)
dst2 = transform2(gray)
cv2.imshow('img',np.hstack((gray,dst1,dst2)))
cv2.waitKey()
cv2.destroyAllWindows()

import cv2
import numpy as np
a = np.arange(0,256).reshape(1,-1).astype(np.uint8) # 0-255
img = cv2.resize(a,(800,100),interpolation=cv2.INTER_AREA)    #  创建渐变图像
ret1,img1 = cv2.threshold(img,200,255,cv2.THRESH_BINARY) # 二值化阈值处理
ret2,img2 = cv2.threshold(img,200,255,cv2.THRESH_BINARY_INV) # 反二值化阈值处理
ret3,img3 = cv2.threshold(img,200,255,cv2.THRESH_TOZERO) # 低于阈值零处理
ret4,img4 = cv2.threshold(img,200,255,cv2.THRESH_TOZERO_INV) # 超出阈值零处理
ret5,img5 = cv2.threshold(img,200,255,cv2.THRESH_TRUNC) # 截断阈值处理
cv2.imshow('img',np.vstack((img,img1,img2,img3,img4,img5)))
cv2.waitKey()
cv2.destroyAllWindows()