How to read image data sets from a folder at once ?

24 次查看（过去 30 天）

Tousif Ahmed 2017-3-26

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/331975-how-to-read-image-data-sets-from-a-folder-at-once

评论： Tousif Ahmed 2017-3-27

Hello. I have 40 datasets in a folder in C drive. I need to convert those files from RGB to grayscale and should resize it but i am unable to read the file and cant convert all the files from RGB to gray at once and cant resize all the images at once and should save the converted and resized images. Can anyone help me with the coding of that please

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

采纳的回答

Jan 2017-3-26

2
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/331975-how-to-read-image-data-sets-from-a-folder-at-once#answer_260446

编辑：Jan 2017-3-27

在 MATLAB Online 中打开

OutputFolder = 'C:\Temp';  % Set as needed [EDITED]
dinfo = dir('*.jpg');% image extension
for K = 1 : length(dinfo)
  thisimage = dinfo(K).name;
  Img   = imread(thisimage);
  Y     = imshow(Img);
  Gray  = rgb2gray(Img);
  GrayS = imresize(Gray, [112, 92], 'bilinear');
  imwrite(GrayS, fullfile(OutputFolder, thisimage));  % [EDITED]
end

Or any other method for the resizing, see: doc imresize.

[EDITED] See the changed code for writing the changed file. Perhaps you want to modify the file name. Then you can split the file extension and the name by fileparts.

3 个评论
显示 1更早的评论隐藏 1更早的评论

Jan 2017-3-27

See [EDITED]

Tousif Ahmed 2017-3-27

Wow!! awesome Simon thanks a lot,

See if You can help me with this code here, I need to add green rectangular box for the face detection on every person. I need to compare for the accuracy of the face detector like the percentage of the faces which are matching. Here is the code

close all; clear all; clc; %% Simple Face Recognition Example % Copyright 2014-2015 The MathWorks, Inc. %% Load Image Information from ATT Face Database Directory faceDatabase = imageSet('Demo_ds','recursive');

%% Display Montage of First Face figure; montage(faceDatabase(2).ImageLocation); %montage displays multiple images title('Images of Single Face'); %% Display Query Image and Database Side-Side personToQuery = 2; % taken from the data set depending on the numbers for first person image given as 1, for 2nd person image given as 2 and so on galleryImage = read(faceDatabase(personToQuery),1);% in the taken data set of a person out of 10, the number represents that particular image in the respected dataset figure; for i=1:size(faceDatabase,2)% starting from 1 to 40 datasets,here taken 2nd peson dataset imageList(i) = faceDatabase(i).ImageLocation(2); %checking for the images from 1 to 40, for 2nd location end subplot(1,2,1);imshow(galleryImage); subplot(1,2,2);montage(imageList); diff = zeros(1,9);

%% Split Database into Training & Test Sets [training,test] = partition(faceDatabase,[0.8 0.2]);

%% Extract and display Histogram of Oriented Gradient Features for single face person = 5; [hogFeature, visualization]= ... extractHOGFeatures(read(training(person),1)); figure; subplot(2,1,1);imshow(read(training(person),1));title('Input Face'); subplot(2,1,2);plot(visualization);title('HoG Feature');

%% Extract HOG Features for training set trainingFeatures = zeros(size(training,2)*training(1).Count,167796); featureCount = 1; for i=1:size(training,2) for j = 1:training(i).Count trainingFeatures(featureCount,:) = extractHOGFeatures(read(training(i),j)); trainingLabel{featureCount} = training(i).Description; featureCount = featureCount + 1; end personIndex{i} = training(i).Description; end

%% Create 40 class classifier using fitcecoc faceClassifier = fitcecoc(trainingFeatures,trainingLabel);

%% Test Images from Test Set person = 6; queryImage = read(test(person),1); queryFeatures = extractHOGFeatures(queryImage); personLabel = predict(faceClassifier,queryFeatures); % Map back to training set to find identity booleanIndex = strcmp(personLabel, personIndex); integerIndex = find(booleanIndex); subplot(1,2,1);imshow(queryImage);title('Query Face'); subplot(1,2,2);imshow(read(training(integerIndex),1));title('Matched Class');

%% Test First 5 People from Test Set figure; figureNum = 1; for person=1:10 for j = 1:test(person).Count queryImage = read(test(person),j); queryFeatures = extractHOGFeatures(queryImage); personLabel = predict(faceClassifier,queryFeatures); % Map back to training set to find identity booleanIndex = strcmp(personLabel, personIndex); integerIndex = find(booleanIndex); subplot(2,2,figureNum);imshow(imresize(queryImage,3));title('Query Face'); subplot(2,2,figureNum+1);imshow(imresize(read(training(integerIndex),1),3));title('Matched Class'); figureNum = figureNum+2; end figure; figureNum = 1;

end

Link for the data set of the image is here http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html The one which is 4.5 MB file is the data set containing 40 sets Thank You

请先登录，再进行评论。