Structured Edge Detection Toolbox

Hi there !

first thanks pdollar for sharing your work, the results are astonishing !

however i'm new to matlab and I got some problems trying to make it work :

I did everything carefully in the installation method but then when I try to run edgesDemo.m I got this error :

Error using imPadMex
Requested 137438953888x1726576853504x7598263500303858035 (17179869184.0GB) array exceeds 
maximum array size preference. Creation of arrays greater than 
this limit may take a long time and cause MATLAB to become unresponsive.
See array size limit or preference panel for more information.

and several others but they come from the first one I think

Error in imPad (line 39)
J = imPadMex( I, pad, type );

Error in edgesDetect (line 52)
  I = imPad(I,p,'symmetric');

Error in edgesDemo (line 25)
tic, E=edgesDetect(I,model); toc

As I said i'm quite new to matlab and didn't find yet where this imPadMex error does come from, any help would be greatly appreciated.

I've seen in README there are additional models online, but I can't find them on the Internet. Would you give a specific link where I can download them? Many thanks!

I've installed the toolbox and trying to compile the mex file. But I keep getting this error with or without [OMPPARAMS]. I'm using Linux and 6.3 version of gcc.

private/edgesDetectMex.cpp:103:71: error: cannot convert ‘const int*’ to ‘const mwSize* {aka const long unsigned int*}’ for argument ‘2’ to ‘mxArray* mxCreateNumericArray_730(mwSize, const mwSize*, mxClassID, mxComplexity)’
   if(nl>2) pl[2] = mxCreateNumericArray(5,segDims,mxUINT8_CLASS,mxREAL);

Any idea what the issue is?

Hi,

I have a problem in understanding the Alpha parameter from the edgeBoxesMex.c file. In which function the Alpha parameter is used? I want to see how the step size alpha is related to the IoU

Hi! Thanks for your work! Is it possible to get xml of your model for this example I am trying to convert this file to xml.

Also I didn't look yet deep inside implementation. But maybe there is some other implementation (I suppose it's your code)? It looks heavy and slowly. Thank you!

Hello,

I want to use the edge box on my dataset and compare its performance with Selective search. In the edgeBoxesDemo.m, I have run the code with split='val' which i used the training set of Voc2007. The 2 graphs with the default setting like

model=load('models/forest/modelBsds'); model=model.model;
model.opts.multiscale=0; model.opts.sharpen=2; model.opts.nThreads=4;

%% set up opts for edgeBoxes (see edgeBoxes.m)
opts = edgeBoxes;
opts.alpha = .65;     % step size of sliding window search
opts.beta  = .75;     % nms threshold for object proposals
opts.minScore = .01;  % min score of boxes to detect
opts.maxBoxes = 1e4;  % max number of boxes to detect

it shows me an output: EdgeBoxes70 T=0.70 A=0.46 M= 804 R=0.87 Which is similar to Figure 5 (I guess). But it is mentioned in the paper that for the comparison of Edge Box with other methods you used test set.

So for the test set I have changed the training set with testing set in the \boxes folder and changed this part in the code split='test'; data=boxesData('split',split);

and I am getting this output EdgeBoxes70 T=0.70 A=0.47 M= 656 R=0.88

Therefore, I have couple of questions:

1. Why the test result is different from the paper. like According to table 2, the value of should be 800 not 656 ? Or i did something wrong

2. How can I plot the Figure 5 graph for the selective search method using VOC 2007 dataset?

3. Also, if i used the Edge box for different dataset do I need to train new model? Please help me to solve this

??? Error: File: edgesTrain.m Line: 157 Column: 18 The variable eBins in a parfor cannot be classified. See Parallel for Loops in MATLAB, "Overview".

I am getting this error while trying to run edgesDemo. It looks like the error stems from using two different indices within the loop. The variables need sliced to operate correctly.

Switching to something like this:

parfor i=1:nTrees*nNodes if(model.child(i) || model.nSegs(i)==1), continue;end %#ok<PFBNS> E=gradientMag(single(model.segs(:,:,i)))>.01; E0=0; sliceBins = eBins{i}; sliceBnds = eBnds(i); for j=1:nBnds, %eBins{i,j}=uint16(find(E & ~E0)'-1); E0=E; %eBnds(i,j)=length(eBins{i,j}); E=convTri(single(E),1)>.01; end sliceBins{j} = unit16(find(E&~E0)'-1); E0=E; sliceBnds(j)=length(sliceBins{j}); E=convTri(single(E),1)>0.1; end

Solves the issue.

EDIT: Solved the issue that results from the solution of this issue. I needed to add the toolbox path back in because it got removed.

Hi Piotr,

Have you thought of extending the future version to classifying edges in 3D? I have been using now your code on a slice-by-slice basis for our 2-PM microscope stacks trained with our own ground truths, and the results are quite good :) I would assume that the results would be even better if full 3D context would be taken into account?

Petteri

Thanks for your continuous work on edge detection and kind sharing.

Excuse me. I came up against a problem when testing my model on NYU depth dataset.

I could not find the NYUD edge ground truth file after training on its training sets, could you please give me any advice?

Thanks in advance.

I'm running this in R2014a.

After running edgesDemo, I get this error message:

Error using imPadMex Out of memory. Type HELP MEMORY for your options.

Error in imPad (line 39) J = imPadMex( I, pad, type );

Error in edgesDetect (line 52) I = imPad(I,p,'symmetric');

Error in edgesDemo (line 26) tic, E=edgesDetect(I,model); toc

I don't think it's a memory problem, however. I believe this because if I go into the edgesDemo.m code and adapt the 'peppers.png' image, called on line 24, to make it a 25:25:3 image (by adding a line at line 25, "I = I(1:25, 1:25, :);" it still outputs "Out of memory". Any ideas would be appreciated.

Hi,

The link to Piotr's Matlab Toolbox (http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html) is not accessible. Is there any other web page where I can download the toolbox?

Thank you!

Dear @pdollar or anyone :)

First of all @pdollar thanks for your contribution. Secondly, in more detailed info, I would like to know-how is the AP computed?

Cheers

Xavier

Hi! Thanks pdollar for this excellent work. I'm trying to run edgesDemo.m, before that, I install it as Installation in readme.txt. But when the program running to the 25 line in edgesDemo.m, it call the edgesDetect.m, and then edgesDetect call the edgesChns.m, in 58 lines in edgesChns.m, it call gradientMag.cpp, and Matlab was down. I have tried several times and it is same. Help!

The environment I use: Mac OS Mojave 10.14 5 Matlab 2017 Xcode Clang++ to configure mex

I am confused about the function of correspondPixels().And there is a compiled lib so I can't see code. Can you give some explanation about it? Thank you

Hi,pdollar. I try train tree myself,but it alaways have the error: Unable to read file 'BSR/BSDS500/data//groundTruth/train/Thumb.mat'. No such file or directory. PS:when I reduce the number of the patch,it run ok.I guess if it is related to the number of data? can you help me?thanks a lot

i'm new to matlab and I got some problems trying to make it work :

I did everything carefully in the installation method a but then when I try to run edgesDemo.m I got this error :

Error using imPadMex
Requested 137438953888x1726576853504x7598263500303858035 (17179869184.0GB) array exceeds 
maximum array size preference. Creation of arrays greater than 
this limit may take a long time and cause MATLAB to become unresponsive.
See array size limit or preference panel for more information.

and several others but they come from the first one I think

Error in imPad (line 39)
J = imPadMex( I, pad, type );

Error in edgesDetect (line 52)
  I = imPad(I,p,'symmetric');

Error in edgesDemo (line 25)
tic, E=edgesDetect(I,model); toc

As I said i'm quite new to matlab and didn't find yet where this imPadMex error does come from, any help would be greatly appreciated.