Doing this example in Matlab Image Category Classification
I have found an error trying to get the vocabulary of SURF features with this command
bag = bagOfFeatures(trainingSet);
The error is the following
Error using bagOfFeatures/parseInputs (line 1023)
The value of 'imgSets' is invalid. Expected imgSets to be one of these types:
imageSet
Instead its type was matlab.io.datastore.ImageDatastore.
I am using a ImageDatastore input instead of imgSets, but I am following a Mathworks example. Anyone can explain me why is this happening and how can I convert trainingSet into a imgSets type?
You have to convert the ImageDatastore object to an imageSet object. This can simply be done by using the following line instead:
bagOfFeatures(imageSet(trainingSet.Files));
Related
I tried importing a JSONL dataset into Google's Vertex AI and get a weird and seemingly unrelated error:
Error: Could not parse the line, json is invalid or the format does not match the input schema: Cannot find field: classificationAnnotation in message google.cloud.aiplatform.master.schema.ImageBoundingBoxIoFormat. for: gs://[bucketname]/set.jsonl line 10
It happens every 4 lines of code. All of my lines are identical except the image name changes.
Line 10:
{"imageGcsUri":"gs://[mybucket]/path/to/image.png","classificationAnnotation":{"displayName":"MyLabel","annotationResourceLabels":{"aiplatform.googleapis.com/annotation_set_name":"MyLabel"}},"dataItemResourceLabels":{"aiplatform.googleapis.com/ml_use":"training"}}
Why am I getting this error?
From the line you are sharing, it seems like the image you are trying to access doesn't exist in the bucket you are using, so you would need to see if the image is on the same name or format you are calling it.
I am trying to get a set of binary images' eccentricity and solidity values using the regionprops function. I obtain the label matrix using the vision.ConnectedComponentLabeler function.
This is the code I have so far:
files = getFiles('images');
ecc = zeros(length(files)); %eccentricity values
sol = zeros(length(files)); %solidity values
ccl = vision.ConnectedComponentLabeler;
for i=1:length(files)
I = imread(files{i});
[L NUM] = step(ccl, I);
for j=1:NUM
L = changem(L==j, 1, j); %*
end
stats = regionprops(L, 'all');
ecc(i) = stats.Eccentricity;
sol(i) = stats.Solidity;
end
However, when I run this, I get an error says indicating the line marked with *:
Error using ConnectedComponentLabeler/step
Variable-size input signals are not supported when the OutputDataType property is set to 'Automatic'.'
I do not understand what MATLAB is talking about and I do not have any idea about how to get rid of it.
Edit
I have returned back to bwlabel function and have no problems now.
The error is a bit hard to understand, but I can explain what exactly it means. When you use the CVST Connected Components Labeller, it assumes that all of your images that you're going to use with the function are all the same size. That error happens because it looks like the images aren't... hence the notion about "Variable-size input signals".
The "Automatic" property means that the output data type of the images are automatic, meaning that you don't have to worry about whether the data type of the output is uint8, uint16, etc. If you want to remove this error, you need to manually set the output data type of the images produced by this labeller, or the OutputDataType property to be static. Hopefully, the images in the directory you're reading are all the same data type, so override this field to be a data type that this function accepts. The available types are uint8, uint16 and uint32. Therefore, assuming your images were uint8 for example, do this before you run your loop:
ccl = vision.ConnectedComponentLabeler;
ccl.OutputDataType = 'uint8';
Now run your code, and it should work. Bear in mind that the input needs to be logical for this to have any meaningful output.
Minor comment
Why are you using the CVST Connected Component Labeller when the Image Processing Toolbox bwlabel function works exactly the same way? As you are using regionprops, you have access to the Image Processing Toolbox, so this should be available to you. It's much simpler to use and requires no setup: http://www.mathworks.com/help/images/ref/bwlabel.html
I was learning how to do machine learning on mldata.org and I was watching a video on Youtube on how to use the data (https://www.youtube.com/watch?v=zY0UhXPy8fM) (2:50). Using the same data, I tried to follow exactly what he did and create a scatterplot of the dataset. However when he used the scatterplot command, it worked perfectly on his side, but I cannot do it on myside.
Can anyone explain what's wrong and what I should do?
octave:2> load banana_data.octave
octave:3> pkg load communications
octave:4> whos
Variables in the current scope:
Attr Name Size Bytes Class
==== ==== ==== ===== =====
data 2x5300 84800 double
label 1x5300 42400 double
Total is 15900 elements using 127200 bytes
octave:5> scatterplot(data, label)
error: scatterplot: real X must be a vector or a 2-column matrix
error: called from:
error: /home/anthony/octave/communications-1.2.0/scatterplot.m at line 69, column 7
The error message says it all. Your data is a 2-row matrix, and not a 2-column matrix as it should be. Just transpose it with .'.
scatterplot(data.')
I dropped the label argument since it is not compatible with the communications toolbox, either in matlab or in octave.
Update:
According to news('communications'),
The plotting functions eyediagram' andscatterplot' have improved Matlab compatibility
This may be why the behaviour is different. Be ready to find other glitches, as the octave 3.2.4 used in this course is about 5 years old.
In order to use the label, you should rather use the standard octave scatter function.
Colors could be changed by choosing another colormap.
colormap(cool(256))
scatter(data(1,:), data(2,:), 6, label, "filled")
I am trying to save an image using opencv cvSaveImage function. The problem is that I am performing a DCT on the image and then changing the coefficients that are obtained after performing the DCT, after that I am performing an inverse DCT to get back the pixel values. But this time I get the pixel values in Decimals(e.g. 254.34576). So when I save this using cvSaveImage function it discards all the values after decimals(e.g. saving 254.34576 as 254) and saves the image. Due to this my result gets affected. Please Help
"The function cvSaveImage saves the image to the specified file. The image format is chosen depending on the filename extension, see cvLoadImage. Only 8-bit single-channel or 3-channel (with 'BGR' channel order) images can be saved using this function. If the format, depth or channel order is different, use cvCvtScale and cvCvtColor to convert it before saving, or use universal cvSave to save the image to XML or YAML format."
I'd suggest investigating the cvSave function.
HOWEVER, a much easier way is to just write your own save/load functions, this would be very easy:
f = fopen("image.dat","wb");
fprintf(f,"%d%d",width,height);
for (y=0 to height)
for (x=0 to width)
fprintf(f,"%f",pixelAt(x,y));
And a corresponding mirror function for reading.
P.S. Early morning and I can't remember for the life of me if fprintf works with binary files. But you get the idea. You could use fwrite() instead.
I have a homework in which i have to convert some images to grayscale and compress them using huffman encoding. I converted them to grayscale and then i tried to compress them but i get an error. I used the code i found here.
Here is the code i'm using:
A=imread('Gray\36.png');
[symbols,p]=hist(A,unique(A))
p=p/sum(p)
[dict,avglen]=huffmandict(symbols,p)
comp=huffmanenco(A,dict)
This is the error i get. It occurs at the second line.
Error using eps
Class must be 'single' or 'double'.
Error in hist (line 90)
bins = xx + eps(xx);
What am i doing wrong?
Thanks.
P.S. how can i find the compression ratio for each image?
The problem is that when you specify the bin locations (the second input argument of 'hist'), they need to be single or double. The vector A itself does not, though. That's nice because sometimes you don't want to convert your whole dataset from an integer type to floating precision. This will fix your code:
[symbols,p]=hist(A,double(unique(A)))
Click here to see this issue is discussed more in detail.
first, try :
whos A
Seems like its type must be single or double. If not, just do A = double(A) after the imread line. Should work that way, however I'm surprised hist is not doing the conversion...
[EDIT] I have just tested it, and I am right, hist won't work in uint8, but it's okay as soon as I convert my image to double.