m2.zip_EMD_fenleishili

clear all; clc; close all; %开始计时 tic %输入需要识别的图像 [filename,pathname,fi]=uigetfile(... {'*.*', 'all image file (*.*)';... '*.bmp','all image file (*.bmp)';... '*.jpg','all image file (*.jpg)'},... '请选择要识别的图片',... 'MultiSelect', 'on'); RGB=imread([pathname filename]); %界面手动选择读入方式 %RGB = imread('D:\Program Files\MATLAB\workspace\timg1.jpg'); %直接路径传入读取方式 figure('name','process'), subplot(2,2,1),imshow(RGB),title('原始RGB'), %convert frame from RGB to YCBCR colorspace（转换到YCBCR空间） YCBCR = rgb2ycbcr(RGB); whos, subplot(2,2,2),imshow(YCBCR),title('YCBCR'), %filter YCBCR image between values and store filtered image to threshold %matrix（用各个通道的阈值对其进行二值化处理） %% 通过数据游标直接选定大致的阈值,后续再手动改进 %Y_MIN = 40; Y_MAX = 160; %Cb_MIN = 80; Cb_MAX = 120; %Cr_MIN = 138; Cr_MAX = 195; Y_MIN = 30; Y_MAX = 215; %到255也可以 Cb_MIN = 65; Cb_MAX = 135; Cr_MIN = 138; Cr_MAX = 195; %已经是最佳,否则会引入背景噪声. threshold1=roicolor(YCBCR(:,:,1),Y_MIN,Y_MAX)&roicolor(YCBCR(:,:,2),Cb_MIN,Cb_MAX)&roicolor(YCBCR(:,:,3),Cr_MIN,Cr_MAX); subplot(2,2,3),imshow(threshold1),title('YCBCR二值化'), %perform morphological operations on thresholded image to eliminate noise %and emphasize the filtered object(s)（进行形态学处理：腐蚀、膨胀、孔洞填充） dilateElement=strel('square',13) ; threshold=imdilate(threshold1, dilateElement); erodeElement = strel('square', 4) ; threshold = imerode(threshold,erodeElement); erodeElement = strel('square', 3) ; threshold = imerode(threshold,erodeElement); erodeElement = strel('square', 2) ; threshold = imerode(threshold,erodeElement); threshold=imfill(threshold,'holes'); subplot(2,2,4),imshow(threshold),title('形态学处理'); % matlab多行注释 %{ erodeElement = strel('square', 2) ; dilateElement=strel('square',4) ; threshold = imerode(threshold,erodeElement); threshold = imerode(threshold,erodeElement); threshold=imdilate(threshold, dilateElement); threshold=imdilate(threshold, dilateElement); threshold=imfill(threshold,'holes'); subplot(2,2,4),imshow(threshold),title('形态学处理'), %} %获取区域的'basic'属性， 'Area', 'Centroid', and 'BoundingBox' figure('name','处理结果'), stats = regionprops(threshold, 'basic'); [C,area_index]=max([stats.Area]); %定位马的区域 face_locate=[stats(area_index).Centroid(1),stats(area_index).Centroid(2)]; imshow(RGB);title('after'),hold on text(face_locate(1),face_locate(2)-40, 'face','color','red'); plot(face_locate(1),face_locate(2), 'b*'); rectangle('Position',[stats(area_index).BoundingBox],'LineWidth',2,'LineStyle','--','EdgeColor','r'), hold off %将五张图片拼接成一张完整的图像 RGB= imresize(RGB ,[1800 2307],'nearest') % 'nearest'最近邻插值（默认）,'bilinear'双线性插值,'bicubic'双三次插值 YCBCR= imresize(YCBCR ,[1800 2307],'nearest') % 'nearest'最近邻插值（默认）,'bilinear'双线性插值,'bicubic'双三次插值 threshold1= imresize(threshold1 ,[1800 2307],'nearest') threshold= imresize(threshold ,[1800 2307],'nearest') figure imshow([RGB,YCBCR]) %面向对象的思想,将对象也当做最特殊的元素尽快尽快撒的科技感ZZ吧 +wq figure imshow([threshold1,threshold]) t=toc