数字图像处理：一些基本灰度变换

包括图像反转、对数变换、幂律（伽马）变换、分段线性变换函数，测试图选的不咋地

import cv2
import numpy as np

#灰度图反转
def grayReversal(gray):
    gray_reversal = 255 - gray #灰度图反转
    return gray_reversal

#彩色图像反转
def imgReversal(img):
    img_reversal = np.zeros(img.shape, np.uint8)#初始模板
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            b, g, r = img[i, j] #注意是bgr，不是rgb
            img_reversal[i, j] = 255 - b, 255 - g, 255 - r
    return img_reversal

#对数变换
def logTrans(gray, c):
    gray_log = np.uint8((c * np.log(1.0 + gray)))
    return gray_log

#幂律（伽马）变换
def powerTrans(gray, c, y):
    gray_power = np.uint8(c * (gray ** y))
    return gray_power

path = "_kdy.jpg"
img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #转换为灰度图
gray_reversal = grayReversal(gray)
img_reversal = imgReversal(img)
gray_log = logTrans(gray, c=30)
gray_power = powerTrans(gray, c=30, y =0.35)

cv2.imshow("img", img)
cv2.imshow("gray", gray)
cv2.imshow("gray_reversal", gray_reversal)
cv2.imshow("img_reversal", img_reversal)
cv2.imshow("gray_log", gray_log)
cv2.imshow("gray_power", gray_power)

cv2.waitKey(0)

（图分别为原图、灰度图、灰度反转图、对数变换图、幂律（伽马）变换图）

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分段线性变换函数

就是用拉格朗日插值法实现分段线性变换函数，然后，注意数据类型的转换就完事了，可用来对比度拉伸和灰度级分层

import cv2
import numpy as np

#拉格朗日插值法,对比度拉伸
def plf(x, X, Y):
    x = np.float32(x)#得把数据类型给转换下
    y = 0
    for i in range(len(X)):
        t = 1
        for j in range(len(Y)):
            if j != i:
                t = t * ((x - X[j]) / (X[i] - X[j]))
        y = np.uint8(y + t * Y[i])#再把数据类型换下
    return y

path = "_plf.jpg"
img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #转换为灰度图

#参数设置
X = [0, 60, 190, 255]
Y = [0, 30, 230, 255]

gray_plf = plf(gray, X, Y)

cv2.imshow("gray", gray)
cv2.imshow("gray_plf", gray_plf)
cv2.waitKey(0)

左图是灰度图，右图是对比图拉伸图