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
Related
I am currently trying to make some TensorFlow Inference (C backend) using Boost::GIL (challenging). I need a few thinks, I have been able to load my png image (rgb8_image_t)
and did a conversion to rgb32_f_image_t.
I still need 3 thinks, the raw pointer of the data, memory allocated, and dimensions.
for the memory allocated unfortunately the function total_allocated_size_in_bytes() is private, so I did this:
boost::gil::view(dest).size() * boost::gil::view(dest).num_channels() * sizeof(value_type);
Which is valid, if I do not have any extra padding for alignment story. But does it exist any nice alternative?
For the dimension, I should match with numpy (from PILLOW), I hope both libraries are using the same memory layout pattern. From my understanding, by default, datas are interleaved and contiguous so, it should be good.
Last the raw pointer _memory, it is a private data member of the Image class with no dedicated function. boost::gil::view(dest).row_begin(0) returns a iterator on the first pixel but I not sure how I could get the pointer of the data _memory. Any suggestions ?
Thank you very much,
++t
ps: TensorFlow proposes a C++ backend, however, it is not installed from any package managers, and manipulate Bazel is beyond my strength.
GIL documentation pretty accurately documents the various memory layouts.
The point of the library, though, is to abstract away the memory layouts. If you require some representation (planar/interleaved, packed or unpacked) you are doing things "the hard way" for the library interface.
So, I think you can read and convert in one go, e.g. for a jpeg:
gil::rgb32f_image_t img;
gil::image_read_settings<gil::jpeg_tag> settings;
read_and_convert_image("input.jpg", img, settings);
Now getting the raw data is possible:
auto* raw_data = gil::interleaved_view_get_raw_data(view(img));
It happens to be the case that the preferred implementation storage is interleaved, which is likely what you're expecting. If your particular image storage is planar, the call will not compile (and you'd probably want planar_view_get_raw_data(vw, plane_index) instead).
Note that you'll have to reinterpret_cast to float [const]* if you need that, because there is not public interface to get a reference to the scoped_channel_value<>::value_, but the BaseChannelValue type is indeed float and you can assert that the wrapper doesn't add additional weight:
static_assert(sizeof(float) == sizeof(raw_data[0]));
Alternative Approach:
Conversely, you can setup your own raw pixel buffer, mount a mutable view into it and use that to read/convert your initial load into:
// get dimension
gil::image_read_settings<gil::jpeg_tag> settings;
auto info = gil::read_image_info("input.jpg", settings).get_info();
// setup raw pixel buffer & view
using pixel = gil::rgb32f_pixel_t;
auto data = std::make_unique<pixel[]>(info._width * info._height);
auto vw = gil::interleaved_view(info._width, info._height, data.get(),
info._width * sizeof(pixel));
// load into buffer
read_and_convert_view("input.jpg", vw, settings);
I've actually checked that it works correctly by writing out the resulting view:
//// just for test - doesn't work for 32f, so choose another pixel format
//gil::write_view("output.png", vw, gil::png_tag());
The Format module
The Format module is used to model and combine pretty printers with a syntactic extension that allows typed formats and it helps a lot when you are writing something like a code generator or a data structure printer.
The problem
However, there is a limit of 78 columns that is initialized on the margin of the formatter and will pull to the left anything that takes more than this limit.
I'm printing a lighter version of a Yojson.Basic.json program using the Format module, but when the input is too large, the output is collapsed, and that is not really "prettily".
Preview
Here is how it is is formatted when it is short:
Here is how it is formatted when the indentation becomes too large
I've been trying to exceed and configure this limit to 120 columns, but didn't have any success.
What have I tried?
Using Format.pp_set_margin ppf 120 to reconfigure
Using Format.pp_set_max_indent to a larger value
But they doesn't seem to have any effect and there is no documentation easily available about this limit. I've discovered it only by reading the source code.
What am I doing?
let string_of_cst program =
let ppf = Format.str_formatter in
(* I've enabled colors. *)
Format.pp_set_tags ppf colors;
Format.pp_set_formatter_tag_functions ppf with_colors;
(* [print_json] is my printer. *)
print_json ppf program;
(* Get string out of printer. *)
Format.flush_str_formatter ()
How can I configure a larger limit?
The issue is that the values for margin and max_indent are implicitly constrained to the cone 1 < max_indent < margin and the function set_max_indent silently fails and does nothing if this constraint is not respected.
To avoid this issue, in OCaml ≥4.08, it would be possible to use the new set_geometry function that requires to set both value simultaneously and fails with an exception if the required max_indent is greater than the margin.
Otherwise, you should always set both values at the same time, and always in the order
margin first, and max_indent second. If you don't know which value to chose for max_indent, margin - 10 is generally an alright choice.
In an old version of my code, I used to do a hardcopy() with a given resolution, ie:
frame = hardcopy(figHandle, ['-d' renderer], ['-r' num2str(round(pixelsperinch))]);
For reference, hardcopy saves a figure window to file.
Then I would typically perform:
ZZ = rgb2gray(frame) < 255/2;
se = strel('disk',diskSize);
ZZ2 = imdilate(ZZ,se); %perform dilation.
Surface = bwarea(ZZ2); %get estimated surface (in pixels)
This worked until I switched to Matlab 2017, in which the hardcopy() function is deprecated and we are left with the print() function instead.
I am unable to extract the data from figure handler at a specific resolution using print. I've tried many things, including:
frame = print(figHandle, '-opengl', strcat('-r',num2str(round(pixelsperinch))));
But it doesn't work. How can I overcome this?
EDIT
I don't want to 'save' nor create a figure file, my aim is to extract the data from the figure in order to mesure a surface after a dilation process. I just want to keep this information and since 'im processing a LOT of different trajectories (total is approx. 1e7 trajectories), i don't want to save each file to disk (this is costly, time execution speaking). I'm running this code on a remote server (without a graphic card).
The issue I'm struggling with is: "One or more output arguments not assigned during call to "varargout"."
getframe() does not allow for setting a specific resolution (it uses current resolution instead as far as I know)
EDIT2
Ok, figured out how to do, you need to pass the '-RGBImage' argument like this:
frame = print(figHandle, ['-' renderer], ['-r' num2str(round(pixelsperinch))], '-RGBImage');
it also accept custom resolution and renderer as specified in the documentation.
I think you must specify formattype too (-dtiff in my case). I've tried this in Matlab 2016b with no problem:
print(figHandle,'-dtiff', '-opengl', '-r600', 'nameofmyfig');
EDIT:
If you need the CData just find the handle of the corresponding axes and get its CData
f = findobj('Tag','mytag')
Then depending on your matlab version use:
mycdata = get(f,'CData');
or directly
mycdta = f.CData;
EDIT 2:
You can set the tag of your image programatically and then do what I said previously:
a = imshow('peppers.png');
set(a,'Tag','mytag');
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.