Error using vision.internal.Kdtree/index Invalid input class.

Question

Pradip Gupta 2017-3-16

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/330118-error-using-vision-internal-kdtree-index-invalid-input-class

评论： Raju Pal 2019-11-23

Error using bagOfFeatures. It was earlier working fine but suddenly it stopped. I am using custom extractor function.

extractorFcn= @calcFeatures;
bag = bagOfFeatures(trainingSet,'CustomExtractor',extractorFcn,'VocabularySize',500);
% function [T, TMetric] = calcFeatures(pic) Custom Extractor looks like this

The Error I get is:

Clustering...completed 0/100 iterations Error using vision.internal.Kdtree/index
Invalid input class.
Error in vision.internal.approximateKMeans>assignDataToClustersSerial (line 172)
    searcher.index(centers);
Error in vision.internal.approximateKMeans>assignDataToClusters (line 153)
    [assignments, dists, varargout{1:nargout-2}] = assignDataToClustersSerial(features, centers, randState);
Error in vision.internal.approximateKMeans (line 73)
    [assignments, dists, isValid] = assignDataToClusters(features, centers, params);
Error in bagOfFeatures/createVocabulary (line 640)
            clusterCenters = vision.internal.approximateKMeans(descriptors, K, ...
Error in bagOfFeatures (line 197)
                this.Vocabulary = this.createVocabulary(descriptorSet, ...

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Suvidha Tripathi 2018-10-18

在 MATLAB Online 中打开

I am using CNN for custom feature extraction and using BoF for feature selection.

imageDir=fullfile(oldfolder,dataset,newfold,strip(list2(subfold,:)));
imds=imageDatastore(imageDir);         
imds=imageDatastore(imageDir,'Labels',labelIds);
imds.ReadFcn= @(loc)imresize3(imread(loc),[227 227 3]);
extractorFcn = @exampleBagofFeaturesExtractor;
FeatureBag=bagOfFeatures(imds,'CustomExtractor',extractorFcn,'vocabularySize',100);
function [features,featureMetric] = exampleBagofFeaturesExtractor(patchimds)
            %patchimds.ReadFcn = @(loc)imresize3(imread(loc),[227 227 3]);
            net = alexnet;
            layer = 'pool5';
            trainFeatures = activations(net,patchimds,layer,'OutputAs','rows','ExecutionEnvironment','gpu');
            features=reshape(trainFeatures,[size(trainFeatures,1)*size(trainFeatures,2)*size(trainFeatures,3),size(trainFeatures,4)]);
%             features=features';
            featureMetric=var(features,[],2);
end
    Creating Bag-Of-Features.
    -------------------------
    * Image category 1: 1
    * Image category 2: 2
    * Image category 3: 3
    * Image category 4: 4
    * Extracting features using a custom feature extraction function: exampleBagofFeaturesExtractor.
    * Extracting features from 43735 images...done. Extracted 403061760 features.
    * Keeping 80 percent of the strongest features from each category.
    * Balancing the number of features across all image categories to improve clustering.
    ** Image category 3 has the least number of strongest features: 35278848.
    ** Using the strongest 35278848 features from each of the other image categories.
    * Using K-Means clustering to create a 100 word visual vocabulary.
    Starting parallel pool (parpool) using the 'local' profile ...
    IdleTimeout has been reached.
    Parallel pool using the 'local' profile is shutting down.
    connected to 8 workers.
    * Number of features          : 141115392
    * Number of clusters (K)      : 100
    * Initializing cluster centers...100.00%.
    * Clustering...completed 0/100 iterations Error using vision.internal.approximateKMeans>assignDataToClustersSerial (line
    172)
    Invalid input class.
    Error in vision.internal.approximateKMeans>assignDataToClustersParallel (line
    214)
        parfor n = 1:size(featuresCube,3)
    Error in vision.internal.approximateKMeans>assignDataToClusters (line 151)
        [assignments, dists, varargout{1:nargout-2}] =
        assignDataToClustersParallel(features, centers, randState);
    Error in vision.internal.approximateKMeans (line 73)
        [assignments, dists, isValid] = assignDataToClusters(features, centers,
        params);
    Error in bagOfFeatures/createVocabulary (line 639)
                clusterCenters = vision.internal.approximateKMeans(descriptors, K,
                ...
    Error in bagOfFeatures (line 197)
                    this.Vocabulary = this.createVocabulary(descriptorSet, ...

Walter Roberson 2018-10-22

I chopped out a lot of images and was able to trigger the input class problem you mention. Unfortunately it also triggered a MATLAB kernel crash, so I am trying again.

Raju Pal 2019-11-23

在 MATLAB Online 中打开

I have faced the problem in knn search when using alexnet features

* Clustering...completed 0/100 iterations Error using vision.internal.Kdtree/index
Invalid input class.
Error in vision.internal.approximateKMeans>assignDataToClustersSerial (line 172)
    searcher.index(centers);
Error in vision.internal.approximateKMeans>assignDataToClusters (line 153)
    [assignments, dists, varargout{1:nargout-2}] = assignDataToClustersSerial(features, centers, randState);
Error in vision.internal.approximateKMeans (line 73)
    [assignments, dists, isValid] = assignDataToClusters(features, centers, params);
 

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Answer 1

Walter Roberson 2018-10-23

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/330118-error-using-vision-internal-kdtree-index-invalid-input-class#answer_342834

Digging into this: that error is being triggered by the fact that the features array that vision.internal.approximateKMeans is being asked to work on is a single column; it is expecting a minimum of two columns.

I am still tracing through to figure out what the one column is at that point, and why it is not multiple columns.

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Walter Roberson 2018-10-23

在 MATLAB Online 中打开

Your exampleBagofFeaturesExtractor uses activations('OutputAs','rows'), which is going to return either a vector or a 2D array. But the next line does

              features=reshape(trainFeatures,[size(trainFeatures,1)*size(trainFeatures,2)*size(trainFeatures,3),size(trainFeatures,4)]);

which will reduce anything of less than 4 dimensions into a column vector.

There is a transpose in the next line, but it is commented out, so the output is a single column.

If the output were a row instead of a column, then that would have succeeded in the approximateKMeans -- just watch out for which dimension you are taking the feature metric relative to.

Suvidha Tripathi 2018-10-23

在 MATLAB Online 中打开

Yes, You are right. I ran the example code by MATLAB, and figured out the error yesterday. I corrected it by reshaping my array like this

features=reshape(trainFeatures,[size(trainFeatures,1)*size(trainFeatures,2) ,size(trainFeatures,3),size(trainFeatures,4)]);

This worked for me. Thank you for your time.

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Answer 2

Dr. Murtaza Ali Khan 2019-1-23

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/330118-error-using-vision-internal-kdtree-index-invalid-input-class#answer_357738

I got the same error when my features matrix was of size M-by-1 in the custom feature extractor function. I changed it to size 1-by-M then the error did not appear.

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Error using vision.internal.Kdtree/index Invalid input class.

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