computer vision systems toolbox question

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Hae-Jong 2015-3-30

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/195289-computer-vision-systems-toolbox-question

评论： Hae-Jong 2015-3-30

When I generated an executable file using mcc -m BowClassifierTest_executable, it succeeded, but when I run it in command window, it complains about one error as below. The source code is the all the way bottom.

C:\FP\DB2_orig>BoWClassifierTest_executable.exe "0.5"

Creating Bag-Of-Features from 10 image sets. -------------------------------------------- * Image set 1: 1. * Image set 2: 10. * Image set 3: 2. * Image set 4: 3. * Image set 5: 4. * Image set 6: 5. * Image set 7: 6. * Image set 8: 7. * Image set 9: 8. * Image set 10: 9.

Extracting SURF features using the Grid selection method. The GridStep is [8 8] and the BlockWidth is [32 64 96 128].
Extracting features from 6 images in image set 1...done. Extracted 84000 features.
Extracting features from 6 images in image set 2...done. Extracted 84000 features.
Extracting features from 6 images in image set 3...done. Extracted 84000 features.
Extracting features from 6 images in image set 4...done. Extracted 84000 features.
Extracting features from 6 images in image set 5...done. Extracted 84000 features.
Extracting features from 6 images in image set 6...done. Extracted 84000 features.
Extracting features from 6 images in image set 7...done. Extracted 84000 features.
Extracting features from 6 images in image set 8...done. Extracted 84000 features.
Extracting features from 6 images in image set 9...done. Extracted 84000 features.
Extracting features from 6 images in image set 10...done. Extracted 84000 features.
Keeping 80 percent of the strongest features from each image set.
Using K-Means clustering to create a 500 word visual vocabulary.
Number of features : 672000
Number of clusters (K) : 500
Clustering...done.
Finished creating Bag-Of-Features

Training an image category classifier for 10 categories. -------------------------------------------------------- * Category 1: 1 * Category 2: 10 * Category 3: 2 * Category 4: 3 * Category 5: 4 * Category 6: 5 * Category 7: 6 * Category 8: 7 * Category 9: 8 * Category 10: 9

Encoding features for category 1...done.
Encoding features for category 2...done.
Encoding features for category 3...done.
Encoding features for category 4...done.
Encoding features for category 5...done.
Encoding features for category 6...done.
Encoding features for category 7...done.
Encoding features for category 8...done.
Encoding features for category 9...done.
Encoding features for category 10...done.

Undefined function 'fitcecoc' for input arguments of type 'classreg.learning.Fit Template'.

Error in imageCategoryClassifier/trainEcocClassifier (line 476)

Error in imageCategoryClassifier (line 436)

Error in imageCategoryClassifier.create (line 320)

Error in trainImageCategoryClassifier (line 82)

Error in BoWClassifierTest_executable (line 23)

MATLAB:UndefinedFunction

C:\FP\DB2_orig>

//////////////////////////////////////////////////////////////////////////

function BoWClassifierTest_executable(ratio)

if(isdeployed) ratio = str2num(ratio); end imgSets = imageSet(pwd,'recursive'); %% [trainingSets,testSets] = partition(imgSets,ratio,'randomize'); %% bag = bagOfFeatures(trainingSets,'Verbose',true); %% img = read(imgSets(1), 1); featureVector = encode(bag, img);

% Plot the histogram of visual word occurrences figure bar(featureVector) title('Visual word occurrences') xlabel('Visual word index') ylabel('Frequency of occurrence') %% opts = templateSVM('BoxConstraint', 1.1, 'KernelFunction', 'gaussian'); categoryClassifier = trainImageCategoryClassifier(trainingSets, bag, 'LearnerOptions', opts, 'Verbose',true );

%% confMatrix_train = evaluate(categoryClassifier, trainingSets);

%% confMatrix_test = evaluate(categoryClassifier, testSets);

save([ 'result_' num2str(ratio) '.mat']);