I want to apply the following example to my data:
https://www.mathworks.com/help/wavelet/ug/digit-classification-with-wavelet-scattering.html
I have more than 4000 images. Images are 224x224x3. In other words 244*244 with 3 channels. After I load images in Matlab I want to apply "Wavelet Image Scattering Feature Extraction". In the beginning, I got the following error:
Error using tall/cellfun (line 21)
Argument 2 to CELLFUN must be one of the following data types: cell.
My codes are:
sf = waveletScattering2('ImageSize',[224 224],'InvarianceScale',112, ...
'NumRotations',[8 8]);
Ttrain = tall(x_train.X);
Ttest = tall(x_test.X);
trainfeatures = cellfun(#(x)helperScatImages(sf,x),Ttrain,'UniformOutput',false);
testfeatures = cellfun(#(x)helperScatImages(sf,x),Ttest,'UniformOutput',false);
As an example Ttrain is in the above code is:
4093x224x224x3 tall single (unevaluated)
How should I change the entire code in https://www.mathworks.com/help/wavelet/ug/digit-classification-with-wavelet-scattering.html to work properly?
Thank you in advance for any help.
Related
I'm attempting to use this tutorial to manipulate and plot ATAC-sequencing data. I have all the libraries listed in that tutorial installed and loaded, except while they use biocLite(BSgenome.Hsapiens.UCSC.hg19) for the human genome, I'm using biocLite(TxDb.Mmusculus.UCSC.mm10.knownGene) for the mouse genome.
Here I have loaded in my BAM file
sorted_AL1.1BAM <-"Sorted_1_S1_L001_R1_001.fastq.gz.subread.bam"
And created an object called TSS, which is transcription start site regions from the mouse genome. I want to ultimately plot the average signal in my read data across mouse transcription start sites.
TSSs <- resize(genes(TxDb.Mmusculus.UCSC.mm10.knownGene), fix = "start", 1)
The problem occurs with the following code:
nucFree <- regionPlot(bamFile = sorted_AL1.1BAM, testRanges = TSSs, style = "point",
format = "bam", paired = TRUE, minFragmentLength = 0, maxFragmentLength = 100,
forceFragment = 50)
The error is as follows:
Reading Bam header information.....Done
Filtering regions which extend outside of genome boundaries.....Done
Filtered 24528 of 24528 regions
Splitting regions by Watson and Crick strand..Error in DataFrame(..., check.names = FALSE) :
different row counts implied by arguments
I assume my BAM file contains empty values that need to be changed to NAs. My issue is that I'm not sure how to visualize and manipulate BAM files in R in order to do this. Any help would be appreciated.
I tried the following:
data.frame(sorted_AL1.1BAM)
sorted_AL1.1BAM[sorted_AL1.1BAM == ''] <- NA
I expected this to resolve the issue of different row counts, but I get the same error message.
I am using IML/SAS in SAS Enterprise Guide for the first time, and want to do the following:
Read some datasets into IML matrices
Average the matrices
Turn the resulting IML matrix back into a SAS data set
My input data sets look something like the following (this is dummy data - the actual sets are larger). The format of the input data sets is also the format I want from the output data sets.
data_set0: d_1 d_2 d_3
1 2 3
4 5 6
7 8 9
I proceed as follows:
proc iml;
/* set the names of the migration matrix columns */
varNames = {"d_1","d_2","d_3"};
/* 1. transform input data set into matrix
USE data_set_0;
READ all var _ALL_ into data_set0_matrix[colname=varNames];
CLOSE data_set_0;
USE data_set_1;
READ all var _ALL_ into data_set1_matrix[colname=varNames];
CLOSE data_set_1;
USE data_set_2;
READ all var _ALL_ into data_set2_matrix[colname=varNames];
CLOSE data_set_2;
USE data_set_3;
READ all var _ALL_ into data_set3_matrix[colname=varNames];
CLOSE data_set_3;
/* 2. find the average matrix */
matrix_sum = (data_set0_matrix + data_set1_matrix +
data_set2_matrix + data_set3_matrix)/4;
/* 3. turn the resulting IML matrix back into a SAS data set */
create output_data from matrix_sum[colname=varNames];
append from matrix_sum;
close output_data;
quit;
I've been trying loads of stuff, but nothing seems to work for me. The error I currently get reads:
ERROR: Matrix matrix_sum has not been set to a value
What am I doing wrong? Thanks up front for the help.
The above code works. In the full version of this code (this is simplified for readability) I had misnamed one of my variables.
I'll leave the question up in case somebody else wants to use SAS / IML to find an average matrix.
I'm running basic edge detection to detect windows region based on this http://www.mathworks.com/videos/edge-detection-with-matlab-119353.html
The edge works successfully :
final_edge = edge(gray_I,'sobel');
BW_out = bwareaopen(imfill(final_edge,'holes'),20);
figure;
imshow(BW_out);
Now when come to these following codes to filter image based on properties, it seems like my MATLAB R2013a can't identify this bwpropfilt method.
% imageRegionAnalyzer(BW);
% Filter image based on image properties
BW_out = bwpropfilt(BW_out,'Area', [400, 467]);
BW_out = bwpropfilt(BW_out,'Solidity',[0.5, 1]);
It says:
Undefined function 'bwpropfilt' for input arguments of type 'char'.
Then what should be my alternative to change this bwpropfilt?
bwpropfilt simply takes a look at the corresponding attribute that is output from regionprops and gives you objects that conform to that certain range and also filtering out those that are outside of the range. You can rewrite the algorithm by explicitly calling regionprops, creating a logical array to index into the structure to retain only the values within the right range (seen in the third input of bwpropfilt) corresponding to the property you want to examine (seen in the second input of bwpropfilt). If you want to finally reconstruct the image after filtering, you'll need to use the column major linear indices found in the PixelIdxList attribute, stack them all into a single vector and write to a new output image by setting all of these values to true.
Specifically, you can use the following code to reproduce the last two lines of code you have shown:
% Run regionprops and get all properties
s = regionprops(BW_out, 'all');
%%% For the first line of code
values = [s.Area];
s = s(values > 400 & values < 467);
%%% For the second line of code
values = [s.Solidity];
s = s(values > 0.5 & values < 1);
% Stack column major indices
ind = vertcat(s.PixelIdxList);
% Create output image
final_out = false(size(BW_out));
final_out(ind) = true;
final_out contains the filtered image only retaining the values within the range specified by the desired property.
Caution
The above logic only works for attributes returned from regionprops that contain only a single scalar value per unique region. If you examine the supported properties found in bwpropfilt, you will see that this list is a subset of the full list found in regionprops. This makes sense as certain regionprops properties return a vector or a matrix depending on what you choose so using a range to filter out properties becomes ambiguous if you have multiple values that characterize a particular unique region returned by regionprops.
Minor Note
Being curious, I opened up bwpropfilt to see how it is implemented as I currently have MATLAB R2016a. The above logic, with the exception of some exception handling, is essentially how bwpropfilt has been implemented so the code that I wrote is in line with the logic of the function.
I have an image:
I want to divide this image into 3 equal parts and calculate the SIFT for each part individually and then concatenate the results.
I found out that Matlab's blockproc does just that, but I do not know how to get it to work with my function. Here is what I have:
[r c] = size(image);
c_new = floor(c/3); %round it
B = blockproc(image, [r c_new], #block_fun)
So according to Matlabs documentation the function, block_fun will be applied to the original image in blocks of size r and c_new.
this is what I wrote as block_fun
function feats = block_fun(img)
[keypoints, descriptors] = vl_sift(single(img));
feats = descriptors;
end
So, my matrix B should be a concatenation of the SIFT descriptors of all three parts of the same image? right?
But the error that I get when I run the command:
B = blockproc(image, [r c_new], #block_fun)
Function BLOCKPROC encountered an error while evaluating the user
supplied function handle, FUN.
The cause of the error was:
Error using single Conversion to single from struct is not possible.
For your custom function, blockproc sends in a structure where the image data is stored in a field called data. As such, you simply need to change your function so that it accesses the data field in the input. Like so:
function feats = block_fun(block_struct) %// Change
[keypoints, descriptors] = vl_sift(single(block_struct.data)); %// Change
feats = descriptors;
end
This error is caused by the fact that the function that is called via its handle by blockproc expects a block struct.
The real problem is that blockproc will attempt to concatenate all results and you will have a different set of 128xN feature vectors for each block, which blockproc doesn't allow.
I think that using im2col and reshape would be much more simple.
I am quite new to image processing and would like to produce an array that stores 10 images. After which I would like to run a for loop through some code that identifies some properties of the images, specifically the surface area of a biological specimen, which then spits out an array containing 10 areas.
Below is what I have managed to scrap up so far, and this is the ensuing error message:
??? Index exceeds matrix dimensions.
Error in ==> Testing1 at 14
nova(i).img = imread([myDir B(i).name]);
Below is the code I've been working on so far:
my_Dir = 'AC04/';
ext_img='*.jpg';
B = dir([my_Dir ext_img]);
nfile = max(size(B));
nova = zeros(1,nfile);
for i = 1:nfile
nova(i).img = imread([myDir B(i).name]);
end
areaarray = zeros(1,nfile);
for k = 1:nfile
[nova(k), threshold] = edge(nova(k), 'sobel');
.
.
.
.%code in this area is irrelevant to the problem I think%
.
.
.
areaarray(k) = bwarea(BWfinal);
end
areaarray
There are few ways you could store an image in a kind of an array structure in Matlab. You could use array of structs. In that case you could do as you did:
nova(i).img = imread([myDir B(i).name]);
You access first image with nova(1).img, second one with nova(2).img etc.
Other way to do it is to use cell array (similar to arrays but are more flexible in the sense that members could be of the different type):
nova{i} = imread([myDir B(i).name]);
You access first image with nova{1}, second one with nova{2} etc.
[ IMPORTANT ] In both cases you should remove this line from code:
nova = zeros(1,nfile);
I suppose you've tried to pre-allocate memory for images, and since you're beginner I advise you not to be concerned with it. It is an optimization concern to be addressed if you come across some performance issues - and if you don't come across them, take advantage of Matlab's automatic memory (re)allocation.