Undefined function 'extractLBP' for input arguments of type 'uint8'.

Question

Abdelrahman 2023-8-3

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2004402-undefined-function-extractlbp-for-input-arguments-of-type-uint8

移动：Voss 2023-8-8

I get this errror in my model and want to Extract local patches from the testing images and make predictions using the predict function

Error:

Undefined function 'extractLBP' for input arguments of type 'uint8'.

Error in extractLBPFeatures (line 19)

lbpImg = extractLBP(img, 'NumNeighbors', numNeighbors, 'Radius', radius);

Error in face_recognition_model_PLS (line 14)

trainFeatures{i} = extractLBPFeatures(img, numNeighbors, radius, numBins);

Code:

%Face Recognition using LRR
%% Load a dataset of grayscale face images
Dataset = imageDatastore('ExtendedYaleB', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
%% Split the data into training and testing sets
[trainImgs, testImgs] = splitEachLabel(Dataset, 0.7, 'randomized');
%% Extract local patches from the training images using the extractLBPFeatures function
numNeighbors = 8;
radius = 1;
numBins = numNeighbors*(numNeighbors-1)+3;
trainFeatures = cell(numel(trainImgs.Files),1);
for i = 1:numel(trainImgs.Files)
    img = readimage(trainImgs,i);
    trainFeatures{i} = LBPFeatures(img, numNeighbors, radius, numBins);
end
%% Train the local ridge regression model using the fitrlinear function and local ridge regression
lambda = 1;
span = 0.5;
for i = 1:numel(trainImgs.Files)
    features = trainFeatures{i};
    label = double(trainImgs.Labels(i));
    idx = setdiff(1:numel(trainFeatures),i);
    neighbors = vertcat(trainFeatures{idx});
    neighborLabels = double(trainImgs.Labels(idx));
    mdl = fitrlinear(neighbors,neighborLabels,'Learner','leastsquares','Lambda',lambda);
    yhat = zeros(size(features));
    for j = 1:size(features,1)
        patch = features(j,:);
        pred = predict(mdl,patch);
        dist = pdist2(patch,neighbors);
        w = exp(-dist.^2/(2*span^2));
        yhat(j) = sum(w.*pred)/sum(w);
    end
    trainFeatures{i} = yhat;
end
trainFeatures = cell2mat(trainFeatures);
%% Train the linear regression model on the modified LBP features
mdl = fitrlinear(trainFeatures,double(trainImgs.Labels),'Learner','leastsquares','Lambda',lambda);
%% Save the model to a file
save('face_recognition_model.mat', 'mdl');
%% Extract local patches from the testing images and make predictions using the predict function
testFeatures = cell(numel(testImgs.Files),1);
for i = 1:numel(testImgs.Files)
    img = readimage(testImgs,i);
    testFeatures{i} = extractLBPFeatures(img, numNeighbors, radius, numBins);
end
testFeatures = cell2mat(testFeatures);
predictions = predict(mdl,testFeatures);
%% Evaluate the performance of the model using the confusionmat and classificationReport functions
confMat = confusionmat(testImgs.Labels,predictions);
classificationReport = classificationReport(testImgs.Labels,predictions);
%% Load the saved model from a file
load('face_recognition_model.mat');
%% Use the loaded model for prediction
testImg = imread('test_image.jpg');
testFeatures = extractLBPFeatures(testImg, numNeighbors, radius, numBins);
prediction = predict(mdl,testFeatures);

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Abdelrahman 2023-8-4

You mean that the correct to call extractLBPFeatures first can you replace my code?

to declare

Walter Roberson 2023-8-4

在 MATLAB Online 中打开

testFeatures = extractLBPFeatures(testImg, 'NumNeighbors', numNeighbors, 'Radius', radius);

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Answer 1

recent works 2023-8-4

9
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2004402-undefined-function-extractlbp-for-input-arguments-of-type-uint8#answer_1283342

The error you are encountering (Undefined function 'extractLBP' for input arguments of type 'uint8'.) indicates that the extractLBP function is not recognized. It seems that you might have intended to use extractLBPFeatures function instead of extractLBP.

In your code, you've defined LBPFeatures instead of extractLBPFeatures, so you should use LBPFeatures instead of extractLBP in the loop where you extract local features from training images. Modify the corresponding part of your code as follows:

% ... for i = 1:numel(trainImgs.Files)

img = readimage(trainImgs,i);

trainFeatures{i} = LBPFeatures(img, numNeighbors, radius, numBins);

end % ...

Make sure that you have properly implemented the LBPFeatures function or it is a valid function accessible in your MATLAB environment. If the function is not implemented yet, you need to define the LBPFeatures function with the desired functionality, or you can use the appropriate function for extracting LBP features that suits your application.

Additionally, double-check any other custom functions you are using and verify that they are defined and accessible in your code.

After making these changes, the error should be resolved, and your face recognition model should work as expected

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Abdelrahman 2023-8-4

编辑：Abdelrahman 2023-8-4

在 MATLAB Online 中打开

Hello Dear,

You mean that?

%Face Recognition using LRR
%% Load a dataset of grayscale face images
Dataset = imageDatastore('ExtendedYaleB', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
%% Split the data into training and testing sets
[trainImgs, testImgs] = splitEachLabel(Dataset, 0.7, 'randomized');
%% Extract local patches from the training images using the extractLBPFeatures function
numNeighbors = 8;
radius = 1;
numBins = numNeighbors*(numNeighbors-1)+3;
trainFeatures = cell(numel(trainImgs.Files),1);
for i = 1:numel(trainImgs.Files)
    img = readimage(trainImgs,i);
    trainFeatures{i} = LBPFeatures(img, numNeighbors, radius, numBins);
end
%% Train the local ridge regression model using the fitrlinear function and local ridge regression
lambda = 1;
span = 0.5;
for i = 1:numel(trainImgs.Files)
    features = trainFeatures{i};
    label = double(trainImgs.Labels(i));
    idx = setdiff(1:numel(trainFeatures),i);
    neighbors = vertcat(trainFeatures{idx});
    neighborLabels = double(trainImgs.Labels(idx));
    mdl = fitrlinear(neighbors,neighborLabels,'Learner','leastsquares','Lambda',lambda);
    yhat = zeros(size(features));
    for j = 1:size(features,1)
        patch = features(j,:);
        pred = predict(mdl,patch);
        dist = pdist2(patch,neighbors);
        w = exp(-dist.^2/(2*span^2));
        yhat(j) = sum(w.*pred)/sum(w);
    end
    trainFeatures{i} = yhat;
end
trainFeatures = cell2mat(trainFeatures);
%% Train the linear regression model on the modified LBP features
mdl = fitrlinear(trainFeatures,double(trainImgs.Labels),'Learner','leastsquares','Lambda',lambda);
%% Save the model to a file
save('face_recognition_model.mat', 'mdl');
%% Extract local patches from the testing images and make predictions using the predict function
testFeatures = cell(numel(testImgs.Files),1);
for i = 1:numel(testImgs.Files)
    img = readimage(testImgs,i);
    testFeatures{i} = LBPFeatures(img, numNeighbors, radius, numBins);
end
testFeatures = cell2mat(testFeatures);
predictions = predict(mdl,testFeatures);
%% Evaluate the performance of the model using the confusionmat and classificationReport functions
confMat = confusionmat(testImgs.Labels,predictions);
classificationReport = classificationReport(testImgs.Labels,predictions);
%% Load the saved model from a file
load('face_recognition_model.mat');
%% Use the loaded model for prediction
testImg = imread('test_image.jpg');
testFeatures = extractLBPFeatures(testImg, numNeighbors, radius, numBins);
prediction = predict(mdl,testFeatures);

recent works 2023-8-6

在 MATLAB Online 中打开

right.

Assumed that the Labels in testImgs are numeric, but they are likely text or categorical labels. Since fitrlinear returns a RegressionLinear object, its predict function will return numeric predictions, which might lead to issues when comparing them with text labels.

To handle the situation where the labels are text or categorical, you can follow these steps:

Convert text labels to numeric values: Before training the model, convert the text or categorical labels in trainImgs.Labels to numeric values. You can use the grp2idx function to achieve this:

[trainImgs.Labels, labelIdx] = grp2idx(trainImgs.Labels);

Train the model with numeric labels: Now, use fitrlinear with the numeric labels:

mdl = fitrlinear(neighbors, labelIdx, 'Learner', 'leastsquares', 'Lambda', lambda);

Convert test labels to numeric for prediction: Similar to the training data, convert the test labels to numeric using grp2idx:

[testImgs.Labels, testLabelIdx] = grp2idx(testImgs.Labels);

Perform prediction and convert numeric predictions back to text labels: After obtaining numeric predictions, convert them back to their original text labels using the grp2idx reverse function, idx2grp:

predictionsIdx = predict(mdl, testFeatures);
predictions = idx2grp(predictionsIdx, labelIdx);

By converting the labels to numeric and then back to text, you ensure that the comparison between predictions and test labels is done correctly.

Remember that the success of this approach depends on the uniqueness and consistency of the labels. If there are any label mismatches or inconsistencies, the conversion might not be accurate. Therefore, ensure that your labels are properly formatted and unique.

Abdelrahman 2023-8-7

移动：Voss 2023-8-8

在 MATLAB Online 中打开

Hello sir you mean Like that or there are any issue:

%Face Recognition using LRR
%% Load a dataset of grayscale face images
Dataset = imageDatastore('ExtendedYaleB', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
%% Split the data into training and testing sets
[trainImgs, testImgs] = splitEachLabel(Dataset, 0.8, 'randomized');
%% Extract local patches from the training images using the extractLBPFeatures function
numNeighbors = 8;
radius = 1;
numBins = numNeighbors*(numNeighbors-1)+3;
trainFeatures = cell(numel(trainImgs.Files),1);
for i = 1:numel(trainImgs.Files)
    img = readimage(trainImgs,i);
    trainFeatures{i} = LBPFeatures(img, numNeighbors, radius, numBins);
end
%% Train the local ridge regression model using the fitrlinear function and local ridge regression
lambda = 1;
span = 0.5;
for i = 1:numel(trainImgs.Files)
    features = trainFeatures{i};
    label = double(trainImgs.Labels(i));
    idx = setdiff(1:numel(trainFeatures),i);
    neighbors = vertcat(trainFeatures{idx});
    neighborLabels = double(trainImgs.Labels(idx));
    mdl = fitrlinear(neighbors,neighborLabels,'Learner','leastsquares','Lambda',lambda);
    yhat = zeros(size(features));
    for j = 1:size(features,1)
        patch = features(j,:);
        pred = predict(mdl,patch);
        dist = pdist2(patch,neighbors);
        w = exp(-dist.^2/(2*span^2));
        yhat(j) = sum(w.*pred)/sum(w);
    end
    trainFeatures{i} = yhat;
end
trainFeatures = cell2mat(trainFeatures);
%% Convert text labels to numeric values
[trainImgs.Labels, labelIdx] = grp2idx(trainImgs.Labels);
%% Train the linear regression model on the modified LBP features
mdl = fitrlinear(neighbors, labelIdx, 'Learner', 'leastsquares', 'Lambda', lambda);
%% Save the model to a file
save('face_recognition_model.mat', 'mdl');
%% Extract local patches from the testing images and make predictions using the predict function
testFeatures = cell(numel(testImgs.Files),1);
for i = 1:numel(testImgs.Files)
    img = readimage(testImgs,i);
    testFeatures{i} = LBPFeatures(img, numNeighbors, radius, numBins);
end
testFeatures = cell2mat(testFeatures);
%% Convert test labels to numeric for prediction
[testImgs.Labels, testLabelIdx] = grp2idx(testImgs.Labels);
%% Perform prediction and convert numeric predictions back to text labels
predictionsIdx = predict(mdl, testFeatures);
predictions = idx2grp(predictionsIdx, labelIdx);
%% Evaluate the performance of the model using the confusionmat and classificationReport functions
confMat = confusionmat(testImgs.Labels, predictions);
disp(confMat);
classification_report = classificationReport(testImgs.Labels,predictions);
disp(classification_report)
%% Load the saved model from a file
load('face_recognition_model.mat');
%% Use the loaded model for prediction
testImg = imread('ExtendedYaleB\yaleB11\yaleB11_P00A+000E+00_result.jpg');
testFeatures = LBPFeatures(testImg, numNeighbors, radius, numBins);
prediction = predict(mdl,testFeatures);