Unable to perform assignment because brace indexing is not supported for variables of this type.

I am doing this code each frame of videos.

Plese let me know how to fix this error.

clear all
close all
%// read the video:
list = dir('*.avi')
% loop through the filenames in the list
for k = 1:length(list)
    reader = VideoReader(list(k).name);
    vid = {};
    while hasFrame(reader)
        vid{end+1} = readFrame(reader);
    end
    for i=1:25
        fIdx(i) = i; %// do it for frame 1 ~ 25
        frameGray{i} = rgb2gray(vid{fIdx(i)});
        
        
        
        data{i} = frameGray{i}';
        data{i} = double(data{i});

        opt.init_num_secants = 180;
        idx1{i} = random('unid',size(data{i}, 2), opt.init_num_secants, 1);
        idx2{i} = random('unid',size(data{i}, 2), opt.init_num_secants, 1);
        secants{i} = data{i}(:, idx1{i})-data{i}(:, idx2{i});
        D1{i} = abs(secants{i});

        numSecants = size(data{i},1);

        options.landmarks = 1:180;
        [geo{i}] = IsomapII(D1{i}, 'k', 3, options); 
        secants{i} = geo{i}/norm(geo{i},2);

    
    
         %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        %%%%%Parameter setting
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        opt.outer_iterations = 1000;
    
        switch 'l-bfgs'
            case 'grad'
            opt.linear_solver = 'grad';
            opt.tau = 1e-1; % gradient step size
            opt.inner_iterations = 10;
            opt.beta1 = 1e-1; opt.beta2 = 1e-1; %penalty parameters
            opt.eta1 = 1; opt.eta2 = 1; %lagrangian update
        case 'cgs'
            opt.linear_solver = 'cgs';
            opt.linear_iterations = 10;
            opt.inner_iterations = 1;
            opt.beta1 = 1; opt.beta2 = 1; %penalty parameters
            opt.eta1 = 1.618; opt.eta2 = 1.618; %lagrangian update
        case 'l-bfgs'
            opt.linear_solver = 'l-bfgs';
            opt.linear_iterations = 3;
            opt.lbfgs_rank = 5;
            opt.inner_iterations = 1;
            opt.beta1 = 1; opt.beta2 = 1; %penalty parameters
            opt.eta1 = 1.618; opt.eta2 = 1.618; %lagrangian update
        end
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    %%End parameters
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    delta = 0.3; %max-margin parameter

    funA = @(z) funA_secants_WY(z, secants{i});
    funAT = @(z) funAT_secants_WY(z, secants{i});

    b = ones(numSecants, 1);

    ticID = tic;
    [P{i}, L{i}, q{i}, Lambda{i}, w{i}] = NuMax(funA, funAT, b, delta, opt);
    toc(ticID);


    [U{i}, S{i}, V{i}] = svd(P{i});
    r{i} = rank(P{i});
    U1{i} = U{i}(:, 1:r{i});
    U1{i} = (U1{i} - min(U1{i}(:)))/(max(U1{i}(:))-min(U1{i}(:)));

    Phi_NuMax = (U{i}(:, 1:r{i})*(S{i}(1:r{i}, 1:r{i}).^(1/2)))';
    data = data{i};
    
    Y = Phi_NuMax * data;

    end
end
    X = cell2mat(Y');

Answer 1

[Walter Roberson]

At the bottom of your loop you have

    data = data{i};

That replaces the cell array data that contains the cell with framegray{1}', with the contents of that entry, so instead of data being a cell array with framegray{1}' inside it, data would become the contents directly, framegray{1}' not inside a cell array. Then in the second iteration of the loop when you go to assign to data{2} then data is not a cell array and you are not permitted to do that.

You should be asking yourself why you are doing the

    data = data{i};

[Kong]

Thank you so much.

I want to multiply the cell, but I don't know how to deal with this problem.

When I delete "data = data{i};", I got error like this.

clear all
close all

clear all
close all
%// read the video:
list = dir('*.avi')
% loop through the filenames in the list
for k = 1:length(list)
    reader = VideoReader(list(k).name);
    vid = {};
    while hasFrame(reader)
        vid{end+1} = readFrame(reader);
    end
    for i=1:25
        fIdx(i) = i; %// do it for frame 1 ~ 60
        frameGray{i} = rgb2gray(vid{fIdx(i)});
        
        
        
        data{i} = frameGray{i}';
        data{i} = double(data{i});

        opt.init_num_secants = 180;
        idx1{i} = random('unid',size(data{i}, 2), opt.init_num_secants, 1);
        idx2{i} = random('unid',size(data{i}, 2), opt.init_num_secants, 1);
        secants{i} = data{i}(:, idx1{i})-data{i}(:, idx2{i});
        D1{i} = abs(secants{i});

        numSecants = size(data{i},1);

        options.landmarks = 1:180;
        [geo{i}] = IsomapII(D1{i}, 'k', 3, options); 
        secants{i} = geo{i}/norm(geo{i},2);

    
    
         %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        %%%%%Parameter setting
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        opt.outer_iterations = 1000;
    
        switch 'l-bfgs'
            case 'grad'
            opt.linear_solver = 'grad';
            opt.tau = 1e-1; % gradient step size
            opt.inner_iterations = 10;
            opt.beta1 = 1e-1; opt.beta2 = 1e-1; %penalty parameters
            opt.eta1 = 1; opt.eta2 = 1; %lagrangian update
        case 'cgs'
            opt.linear_solver = 'cgs';
            opt.linear_iterations = 10;
            opt.inner_iterations = 1;
            opt.beta1 = 1; opt.beta2 = 1; %penalty parameters
            opt.eta1 = 1.618; opt.eta2 = 1.618; %lagrangian update
        case 'l-bfgs'
            opt.linear_solver = 'l-bfgs';
            opt.linear_iterations = 3;
            opt.lbfgs_rank = 5;
            opt.inner_iterations = 1;
            opt.beta1 = 1; opt.beta2 = 1; %penalty parameters
            opt.eta1 = 1.618; opt.eta2 = 1.618; %lagrangian update
        end
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    %%End parameters
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    delta = 0.3; %max-margin parameter

    funA = @(z) funA_secants_WY(z, secants{i});
    funAT = @(z) funAT_secants_WY(z, secants{i});

    b = ones(numSecants, 1);

    ticID = tic;
    [P{i}, L{i}, q{i}, Lambda{i}, w{i}] = NuMax(funA, funAT, b, delta, opt);
    toc(ticID);


    [U{i}, S{i}, V{i}] = svd(P{i});
    r{i} = rank(P{i});
    U1{i} = U{i}(:, 1:r{i});
    U1{i} = (U1{i} - min(U1{i}(:)))/(max(U1{i}(:))-min(U1{i}(:)));

    Phi_NuMax{i} = (U{i}(:, 1:r{i})*(S{i}(1:r{i}, 1:r{i}).^(1/2)))';
    
    
    Y = Phi_NuMax{i} * data{i}';

    end
end
    X = cell2mat(Y');

[Walter Roberson]

Your original code had

    data = data{i};
    Y = Phi_NuMax * data;

which for the calculation of Y was equivalent to

    Y = phi_NuMax * data{i};

That code worked for the first iteration.

Your revised code has

   Y = Phi_NuMax{i} * data{i}';

with data{i} being transposed relative to what worked for one iteration before.

[Kong]

Thank you so much!

I used this code.

Y = Phi_NuMax{i} * data{i};

[Kong]

I have same problem another example.

There are lots of videos in folder.

Could you check this error too?

clear all
close all
%// read the video:
list = dir('*.avi')
% loop through the filenames in the list
for k = 1:length(list)
    reader = VideoReader(list(k).name);
    vid = {};
    while hasFrame(reader)
        vid{end+1} = readFrame(reader);
    end
    for i=1:25
        fIdx(i) = i; %// do it for frame 1 ~ 60
        frameGray{i} = rgb2gray(vid{fIdx(i)});
        
        
        
        data{i} = frameGray{i};
        data{i} = double(data{i});
        n = size(frameGray{i}, 1);

        opt.init_num_secants = n;
        idx1{i} = random('unid',size(data{i}, 2), opt.init_num_secants, 1);
        idx2{i} = random('unid',size(data{i}, 2), opt.init_num_secants, 1);
        secants{i} = data{i}(:, idx1{i})-data{i}(:, idx2{i});
        D1{i} = abs(secants{i});

        numSecants = size(data{i},1);

        options.landmarks = 1:n;
        [geo{i}] = IsomapII(D1{i}, 'k', 3, options); 
        secants{i} = geo{i}/norm(geo{i},2);

    
    
         %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        %%%%%Parameter setting
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        opt.outer_iterations = 1000;
    
        switch 'l-bfgs'
            case 'grad'
            opt.linear_solver = 'grad';
            opt.tau = 1e-1; % gradient step size
            opt.inner_iterations = 10;
            opt.beta1 = 1e-1; opt.beta2 = 1e-1; %penalty parameters
            opt.eta1 = 1; opt.eta2 = 1; %lagrangian update
        case 'cgs'
            opt.linear_solver = 'cgs';
            opt.linear_iterations = 10;
            opt.inner_iterations = 1;
            opt.beta1 = 1; opt.beta2 = 1; %penalty parameters
            opt.eta1 = 1.618; opt.eta2 = 1.618; %lagrangian update
        case 'l-bfgs'
            opt.linear_solver = 'l-bfgs';
            opt.linear_iterations = 3;
            opt.lbfgs_rank = 5;
            opt.inner_iterations = 1;
            opt.beta1 = 1; opt.beta2 = 1; %penalty parameters
            opt.eta1 = 1.618; opt.eta2 = 1.618; %lagrangian update
        end
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
        %%End parameters
        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

        delta = 0.3; %max-margin parameter

        funA = @(z) funA_secants_WY(z, secants{i});
        funAT = @(z) funAT_secants_WY(z, secants{i});

        b = ones(numSecants, 1);

        ticID = tic;
        [P{i}, L{i}, q{i}, Lambda{i}, w{i}] = NuMax(funA, funAT, b, delta, opt);
        toc(ticID);


        [U{i}, S{i}, V{i}] = svd(P{i});
        r{i} = rank(P{i});
        U1{i} = U{i}(:, 1:r{i});
        U1{i} = (U1{i} - min(U1{i}(:)))/(max(U1{i}(:))-min(U1{i}(:)));

        Phi_NuMax{i} = (U{i}(:, 1:r{i})*(S{i}(1:r{i}, 1:r{i}).^(1/2)))';
    
    
        Y{i} = Phi_NuMax{i} * data{i};
        Y{i} = reshape(Y{i},[],1);
    end
    Y = cell2mat(Y);
    % make csv file whose name matches the avi file
    [~,name] = fileparts(list(k).name); % get the file name without the extension
    outfile = strcat(name,'.csv')
    csvwrite(outfile,Y); % assumes you have already assigned the array X earlier

end

[Walter Roberson]

    Y = cell2mat(Y);

is within your for k loop. You build up cell Y the first time, doing all for i iterations, and then you replace Y with a matrix instead of a cell, and then you go for the second iteration of k, but Y is still the matrix, not a cell array.