= [] for i in range(256): lut.append(np.int32(i*weight)) return np.array(lut) def rgb2gray...== "__main__": src = cv.imread("D:/images/flower.png") cv.imshow("input", src) gray1 = rgb2gray...(src, 1, lut(0.299), lut(0.587), lut(0.114)) gray2 = rgb2gray(src, 1, lut(0.21), lut(0.72), lut(0.07...)) gray3 = rgb2gray(src, 2) gray4 = rgb2gray(src, 3) gray5 = rgb2gray(src, 4) gray6 =...rgb2gray(src, 5) cv.imshow("gray1", gray1) cv.imshow("gray2", gray2) cv.imshow("gray3",
matlab图像处理基础实例 ·边缘检测(edge)边缘检测时先要把其他格式图像转化为灰度图像>> f=imread( lbxx.bmp );>> a=rgb2gray(f);>> [g,t]=edge...>> subplot(121);imshow(a);>> subplot(122);imshow(b);·roicolor按灰度值选择的区域>> a=imread( onion.png );>> i=rgb2gray...imshow(b);>> holdCurrent plot held>> plot(x,y, b , LineWidth ,2)·roifilt2区域滤波a=imread( onion.png );i=rgb2gray...256,256);F2=fftshift(F);subplot(122);imshow(log(abs(F2))) ·离散余弦变换(dct)>> a=imread( onion.png );>> i=rgb2gray...马赫带效应(同等差色带条)·减采样>> a=imread( football.jpg );>> b=rgb2gray(a);>> [wid,hei]=size(b);>> quarting=zeros(
一书代码公布的说明 http://blog.csdn.net/baimafujinji/article/details/40987807 P44 i = imread(‘theatre.jpg’); i = rgb2gray...out2), title(‘image, p = 0.035’); subplot(224), imhist(out2), title(‘histgram, p = 0.035’); P47 i = rgb2gray...2.5’); subplot(223), imshow(y2), title(‘p =0.4’); subplot(224), imhist(y2), title(‘p =0.4’); P48 i = rgb2gray...), title(‘c=0.050’); subplot(224), imhist(y2), title(‘c=0.050’); P52 i = imread(‘theatre.jpg’); i = rgb2gray...(imread(‘theatre.jpg’)); img_ref = rgb2gray(imread(‘rpic.jpg’)); [hgram, x] = imhist(img_ref); J = histeq
.^(2)); Week_04_Lec_03_Code.m RGB = imread('saturn.png'); I = rgb2gray(RGB); I2G = imnoise(I,'gaussian...figure, imshow(I2); figure, imshow(I20); Week_04_Lec_08_Code.m RGB = imread('saturn.png'); I = rgb2gray...; figure, imshow(blurred); figure, imshow(K); Week_04_Lec_08_Code.m RGB = imread('saturn.png'); I = rgb2gray...; figure, imshow(blurred); figure, imshow(K); Week_04_Lec_08_Code.m RGB = imread('saturn.png'); I = rgb2gray
import pyplot as pltfrom skimage.color import rgb2grayimport numpy as np image = data.coffee()#grayimg = rgb2gray...k]=int(image[i,j,k]/ratio1)*ratio1 img3[i, j, k] = int(image[i, j, k] / ratio2) * ratio2img1 = rgb2gray...(img1)img2 = rgb2gray(img2)img3 = rgb2gray(img3)plt.subplot(221),plt.imshow(image)plt.title('Input Image
% 读入图像 imshow(RGB), % 显示原始图像 GRAY = rgb2gray...subplot(2,2,1),imshow(I); title('原始图像'); axis([50,250,50,200]); axis on; %显示坐标系 I1=rgb2gray...5.线性平滑滤波器 用MATLAB实现领域平均法抑制噪声程序： I=imread('xian.bmp'); subplot(231) imshow(I) title('原始图像') I=rgb2gray...9.LOG算子检测边缘 用MATLAB程序实现如下： I=imread('xian.bmp'); subplot(2,2,1); imshow(I); title('原始图像'); I1=rgb2gray....Canny算子检测边缘 用MATLAB程序实现如下： I=imread('xian.bmp'); subplot(2,2,1); imshow(I); title('原始图像') I1=rgb2gray
第七章：图像类型转换 7.1 RGB转换为灰度 语法规则 A=rgb2gray(B) A:原图像，B:结果图 将真彩色图像B转换为灰度图像A。...rgb2gray函数通过消除色调和饱和度信息，同时保留亮度，来将B图像转换为灰度图。...clc;clear; s=what; p=s.path; I=imread([p,'\图像素材\','lenaRGB.bmp']); gray=rgb2gray(I);%灰度化 bin=imbinarize
''' ''' 上文的png图片是单通道图片（灰度），如果test.png是rgb通道的图片，可以rgb2gray进行转化，代码如下： def rgb2gray(rgb): return np.dot...(rgb[...,:3], [0.299, 0.587, 0.114]) img = rgb2gray(img) ''' 关于图片的通道，我们可以在photoshop里直观的查看： 先查看下读取的图片数组维度
代码示例： X = imread('rabbit.jpg'); subplot(221);X = rgb2gray(X);imshow(X); subplot(222);Y = wcodemat(X,150...代码示例： X = imread('girl.jpg'); subplot(221);X = rgb2gray(X);imshow(X); subplot(222);Y = wcodemat(X,150...[CA，CH，CV，CD] = dwt2(…,‘mode’,MODE) 代码示例： X = imread('rabbit.jpg'); X = rgb2gray(X); [LoD,HiD] = wfilters...‘wname’ 为使用的小波基函数； 代码示例： [X,map] = imread('girl.jpg'); X = rgb2gray(X); [c,s]=wavedec2(X,2,'haar');%进行...代码示例： [X,map] = imread('girl.jpg'); X = rgb2gray(X); [Lo_D,Hi_D]=wfilters('db2','d'); [c,s]=wavedec2
YCBCR = rgb2ycbcr(img); ii=imresize(YCBCR, [128 128]); s = strel('ball',2,2); f = imclose(ii,s); i1=rgb2gray...YCBCR = rgb2ycbcr(img); ii=imresize(YCBCR, [128 128]); s = strel('ball',2,2); f = imclose(ii,s); i1=rgb2gray...YCBCR = rgb2ycbcr(img); ii=imresize(YCBCR, [128 128]); s = strel('ball',2,2); f = imclose(ii,s); i1=rgb2gray
import cv2 from numpy import sqrt from skimage.color import rgb2gray from skimage.feature import blob_dog..., blob_log, blob_doh im=cv2.imread('C:/Users/xpp/Desktop/Lena.png')#原始图像 im_gray=rgb2gray(im)#将彩色图片转换为灰度图片
读取图像并转化为灰度图像 image = imread('C:\Users\Administrator\Desktop\图像降噪\src\original image\100.png'); image = rgb2gray...(image); image = imread('C:\Users\Administrator\Desktop\图像降噪\src\original image\1.bmp'); image = rgb2gray
import cv2 from numpy import sqrt from skimage.color import rgb2gray from matplotlib import pylab as...skimage.feature import blob_dog, blob_log, blob_doh im=cv2.imread('C:/Users/xpp/Desktop/Lena.png')#原始图像 im_gray=rgb2gray
2 matlab 实现 clc clear all img=imread('lena.jpg'); figure,imshow(img); title('lena'); AW=edge(rgb2gray...sobel');%将真彩图像转为灰度图，再用edge函数调用sobel prewitt,laplacian算子 figure,imshow(AW); title('lena sobel'); BW=edge(rgb2gray...(img),'prewitt'); figure,imshow(BW); title('lena prewitt'); CW=edge(rgb2gray(img),'log'); figure,imshow...(CW); title('lena laplacian'); DW=edge(rgb2gray(img),'canny'); figure,imshow(DW); title('lena canny')
import cv2 import numpy as np from skimage.io import imread from skimage.color import rgb2gray from...skimage.exposure import rescale_intensity img=cv2.imread('C:/Users/xpp/Desktop/Lena.png')#原始图像 image_gray=rgb2gray
imread('C:\Users\xpp\Desktop\FCIS-Image-Processing-Course-s-Tasks--master\Morphology\test.png'); i = rgb2gray...imread('C:\Users\xpp\Desktop\FCIS-Image-Processing-Course-s-Tasks--master\Morphology\test.png'); i = rgb2gray
语法： I = rgb2gray(RGB) newmap = rgb2gray(map) 相关命令： ind2gray, ntsc2rgb, rgb2ind, rgb2ntsc 92.rgb2hsv...flowers.tif'); [X,map] = rgb2ind(RGB,128); imshow(X,map) 相关命令： cmunique, dither, imapprox, ind2rgb, rgb2gray
[注:我对源码略有修改] 1.1 全局阈值分割程序 original_image=imread('test1.png'); gray_image=rgb2gray(original_image); gray_image...result=uint8(result); figure imshow(result); 1.2 局部阈值分割程序 original_image=imread('test1.png'); gray_image=rgb2gray...function [ result ] = partialostu( image,part,isrgb ) %PARTIALOSTU partial image ostu if isrgb image=rgb2gray
import transform as transform from skimage.feature import (match_descriptors, ORB, plot_matches) img1=rgb2gray...rotation=0.5,translation=(0,-200))#图像仿射 img3=transform.warp(img1,affine_trans) img4=transform.resize(rgb2gray
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