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how to overcome the following error? errors are described in the comment

2 visualizaciones (últimos 30 días)
clear all; close all; clc; %deviceInfo = gpuDevice;
rootFolder = fullfile('E:\education\Matlab\bin\Endoscopy\', '101_ObjectCategories'); categories = {'airplanes', 'ferry', 'laptop'};
imds = imageDatastore(fullfile(rootFolder, categories), 'LabelSource', 'foldernames');
tbl = countEachLabel(imds) minSetCount = min(tbl{:,2}); % determine the smallest amount of images in a category
% Use splitEachLabel method to trim the set. imds = splitEachLabel(imds, minSetCount, 'randomize');
% Notice that each set now has exactly the same number of images. countEachLabel(imds)
% Find the first instance of an image for each category airplanes = find(imds.Labels == 'airplanes', 1); ferry = find(imds.Labels == 'ferry', 1); laptop = find(imds.Labels == 'laptop', 1); figure subplot(1,3,1); imshow(readimage(imds,airplanes)) subplot(1,3,2); imshow(readimage(imds,ferry)) subplot(1,3,3); imshow(readimage(imds,laptop))
% Load pre-trained AlexNet net = alexnet() net.Layers
net.Layers(1)
net.Layers(end) % Number of class names for ImageNet classification task numel(net.Layers(end).ClassNames)
imds.ReadFcn = @(filename)readAndPreprocessImage(filename); %% function Iout = readAndPreprocessImage(filename) I = imread(filename); % Some images may be grayscale. Replicate the image 3 times to % create an RGB image. if ismatrix(I) I = cat(3,I,I,I); end % Resize the image as required for the CNN. Iout = imresize(I, [227 227]); % Note that the aspect ratio is not preserved. In Caltech 101, the % object of interest is centered in the image and occupies a % majority of the image scene. Therefore, preserving the aspect % ratio is not critical. However, for other data sets, it may prove % beneficial to preserve the aspect ratio of the original image % when resizing. end %% [trainingSet, testSet] = splitEachLabel(imds, 0.3, 'randomize'); %%
% Get the network weights for the second convolutional layer w1 = net.Layers(2).Weights;
% Scale and resize the weights for visualization w1 = mat2gray(w1); w1 = imresize(w1,5);
% Display a montage of network weights. There are 96 individual sets of % weights in the first layer. figure montage(w1) title('First convolutional layer weights') %% featureLayer = 'fc7'; %% trainingFeatures = activations(net, trainingSet, featureLayer, ... 'MiniBatchSize', 32, 'OutputAs', 'columns','ExecutionEnvironment','cpu'); %% trainingLabels = trainingSet.Labels;
% Train multiclass SVM classifier using a fast linear solver, and set % 'ObservationsIn' to 'columns' to match the arrangement used for training % features. classifier = fitcecoc(trainingFeatures, trainingLabels, ... 'Learners', 'Linear', 'Coding', 'onevsall', 'ObservationsIn', 'columns');
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Aman Swaraj
Aman Swaraj el 10 de Feb. de 2020
can anyone answer his question though? I'm having similar error:
Error using matlab.io.datastore.ImageDatastore/readimage (line 36)
Error using ReadFcn @(filename)readAndPreprocessImage(filename) function handle for file
E:\IndianCulturalEventRecognition\1\1.jpg.
Undefined function 'readAndPreprocessImage' for input arguments of type 'char'.
Error in CNN_main>@(filename)readAndPreprocessImage(filename)

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