四、图像增强系列------线性增强

线性增强基本算法

python实现线性增强基本算法

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
#### 线性增强基本算法

# 绘制直方图函数
def grayHist(img):
    h, w = img.shape[:2]
    pixelSequence = img.reshape([h * w, ])
    numberBins = 256
    histogram, bins, patch = plt.hist(pixelSequence, numberBins,
                                      facecolor='black', histtype='bar')
    plt.xlabel("gray label")
    plt.ylabel("number of pixels")
    plt.axis([0, 255, 0, np.max(histogram)])
    plt.show()


img = cv.imread("peng.png", 0)
## b可以省略
b = 0
out = -0.5 * img + b
# 进行数据截断，大于255的值截断为255
out[out > 255] = 255
# 数据类型转换
out = np.around(out)
out = out.astype(np.uint8)
# 分别绘制处理前后的直方图
grayHist(img)
grayHist(out)
cv.imshow("img", img)
cv.imshow("out", out)
cv.waitKey()

效果图如下

变换前：

变换后：

分段线性变换

python实现分段线性变换

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt


# 绘制直方图函数
def grayHist(img):
    h, w = img.shape[:2]
    pixelSequence = img.reshape([h * w, ])
    numberBins = 256
    histogram, bins, patch = plt.hist(pixelSequence, numberBins,
                                      facecolor='black', histtype='bar')
    plt.xlabel("gray label")
    plt.ylabel("number of pixels")
    plt.axis([0, 255, 0, np.max(histogram)])
    plt.show()

img = cv.imread("peng.png", 0)
img = cv.resize(img, None, fx=0.3, fy=0.3)
h, w = img.shape[:2]
out = np.zeros(img.shape, np.uint8)
for i in range(h):
    for j in range(w):
        pix = img[i][j]
        if pix < 50:
            out[i][j] = 0.5 * pix
        elif pix < 150:
            out[i][j] = 3.6 * pix - 310
        else:
            out[i][j] = 0.238 * pix + 194
        # 数据类型转换
out = np.around(out)
out = out.astype(np.uint8)
grayHist(img)
grayHist(out)
cv.imshow("img", img)
cv.imshow("out", out)
cv.waitKey()

分段线性变换效果图

变换前：

变换后：

线性增强统计量算法

待更新。。。