一、实验目的熟悉图像的灰度线性变换的原理掌握图像的灰度非线性变换的原理掌握使用Python、OpenCV编程实现图像的灰度线性变换和非线性变换方法。二、实验内容1.图2-1a是一幅数字乳腺图像breast.jpg请使用图像的灰度线性变换获得如b所示的图像。b是a的负片图像即将原图的黑变白白变黑。(a)breast.jpg (b)负片图像图2-1乳腺图像处理import cv2 import numpy as np img1 cv2.imread(r\Experiment2\image\breast.jpg) img2 255-img1 cv2.imshow(org, img1) cv2.imshow(result, img2) cv2. waitKey(0)2.使用伽玛非线性变换对数字乳腺图像breast.jpg进行变换编程实现γ分别取2和0.5时的变换结果显示并比较效果。import cv2 import numpy as np img cv2.imread(r\Experiment2\image\breast.jpg) img1 np.power(img/255, 2) img2 np.power(img/255, 0.5) cv2.imshow(2, img1) cv2.imshow(0.5, img2) cv2. waitKey(0)取2时更好对比度更高看得更清楚3.换成2-2图像airport.jpg,试一下γ分别取345时的变换结果比较哪个增强效果更好。图2-2 airport.jpgimport cv2 import numpy as np img cv2.imread(r\Experiment2\image\airport.jpg) img1 np.power(img/255, 3) img2 np.power(img/255, 4) img3 np.power(img/255, 5) cv2.imshow(3, img1) cv2.imshow(4, img2) cv2.imshow(5, img3) cv2. waitKey(0)取5时更好每个地方都更清晰点4.换成2-3图像spine.jpg,试一下γ分别取0.30.40.6时的变换结果比较哪个增强效果更好。图2-3 spine.jpgimport cv2 import numpy as np img cv2.imread(r\Experiment2\image\spine.jpg) img1 np.power(img/255, 0.3) img2 np.power(img/255, 0.4) img3 np.power(img/255, 0.6) cv2.imshow(0.3, img1) cv2.imshow(0.4, img2) cv2.imshow(0.6, img3) cv2. waitKey(0)取0.6更好前面会有马赛克看不清在0.6时基本看不见也更清晰。