Gnuplot is a very powerful library that supports plotting of functions with numerous scientific operations. What my case is I want to read a single channel grayscale image just as we read in matlab or python using imread and store it into a 2D data grid using gnuPlot.
Basically I want to make contours of image gray scale intensities.To do that I am exporting the single channel luminance data of the image as a .dat file using matlab once it is exported I splot it using:
set contour base
splot 'greyScaleImagePixelByPixelData.dat' matrix
This works fine but in case I dont want to use Matlab to export the pixel by pixel data to surface plot the image what is the way around?
The example below has been tested with 8-bit and 16-bit grayscale png images (no alpha channel). If your particular images do not match this, please provide a more complete description of how they are encoded.
You haven't said exactly what you want to do with the pixel data after reading it in, so I show the obvious example of displaying it as an image (e.g. a regular array of pixels). If you want to do further manipulation of the values before plotting, please expand the question to give additional details.
[~/temp] file galaxy-16bitgrayscale.png
galaxy-16bitgrayscale.png: PNG image data, 580 x 363, 16-bit grayscale, non-interlaced
[~/temp] gnuplot
set autoscale noextend
plot 'galaxy-16bitgrayscale.png' binary filetype=png with rgbimage
Note that gnuplot does not have any separate storage mode for grayscale vs. RGB image data. In this case it loads 3 copies of each 16-bit grayscale value into parallel storage as if it were separate R/G/B pixel data.
[2nd edit: show both grayscale image and contour levels]
set autoscale noextend
set view map
set contour surface
set cntrparam levels discrete 100, 200
set cntrparam firstlinetype 1
set key outside title "Contour levels"
splot 'galaxy16bit.png' binary filetype=png with rgbimage notitle, \
'' binary filetype=png with lines nosurface title ""
Related
I am producing a gray level pattern to be loaded on my SLM (Spatial Light Modulator). The pattern is 1920x1080 pixels. I have 255 gray level values. I tried out this code to create a gray level mask . when I open in Matlab I could see it as gray level, but when I write the image as a bmp file and then it becomes a binary file. How can I resolve this. Following is my code.
clear all
close all
mask=zeros(1080,1920);
% imshow(mask,[])
for k=1:500
for i=1:1080
mask(i,k)=randperm(256,1);
end
end
% mask3=Fit_GrayLevel_To_SLM_Vector(mask);
imshow(mask,[])
imwrite(mask,'mymask4.bmp')
imshow does not make the same assumption as imwrite on the input image dynamic. More precisely, from the doc of imwrite:
If A is a grayscale or RGB color image of data type double or single, then imwrite assumes that the dynamic range is [0,1] and automatically scales the data by 255 before writing it to the file as 8-bit values
So store your data either in a uint8 array, or divide the pixel value by 255 before writing the file.
This question already has an answer here:
What does the index refer to when selecting a pixel on an image in Matlab?
(1 answer)
Closed 6 years ago.
I have gray scale image "lena.bmp". I want read this image in matlab using imread() function.
When i use code below to read and show image my image is dark (black).
img = imread('lena.bmp');
imshow(img);
But when i use code below, I have no problem to view.
[img map]= imread('lena.bmp');
imshow(img,map);
It seems that my first code doses not reading image in grayscale mode (like what rgb2gray function generate).
My image is as follows:
What can i do to solve this problem?
Your image is an "indexed" image. That means it contains integer values which act as "labels" more than anything, and each of those labels is mapped to a colour (i.e. an rgb triplet). Your map variable represents that mapping; at row 5 you have the rgb triplet that corresponds to 'label' "5", for instance.
To see what I mean, do unique(img) and you'll see that the values of your img array are in fact quite regular. The command rgbplot can demonstrate the actual colourmap graphically. Run rgbplot(map) on your map variable to see the mapping for each of the red green and blue colours.
Now, save and read the image below on your computer as img2 and compare the array values.
This image was generated by converting from the "indexed" image you linked to, to a "grayscale" one using photoediting software (the GIMP). The difference is that
in a grayscale image, the pixel values represent actual intensities, rather than integer 'labels'. Imread reads grayscale images as uint8 images by default, meaning it assigns intensity values to pixels ranging from 0 (black) to 255 (white). Since these values happen to be integers you could still cheat and treat them as 'labels' and force a colour-mapping on them. But if you assign a 'linear map' (i.e. value 1 = intensity 1, value 2 = intensity 2, etc) then your image will look as you would expect.
You'll see that the values from unique(img2) are quite different. If you imshow(img2) you'll see this displays as you'd expect. If you don't specify a colormap for imshow, it will assume that the map is a linear mapping from the lowest to the highest value in the image array, which explains why your indexed image looked weird, since its values were never supposed to correspond to intensities.
Also try imagesc(img2) which will show this but using the "current" colormap. imagesc causes the colormap to be "scaled", so that the lowest colour goes to the lowest value in the image, and similarly for the highest.
The default colormap is jet so you should see a psychedelic looking image but you should be able to make out lena clearly. If you try colormap gray you should see the gray version again. Also try colormap hot. Now to make sense of the colormaps, try the rgbplot command on them (e.g. rgbplot(gray), rgbplot(hot) etc).
So, going back to imshow, imshow basically allows you to display an indexed image, and specify what colormap you want to use to display it. If you don't specify the colormap, it will just use a linear interpolation from the lowest value to the highest as your map. Therefore imshow(img) will show the image pretty much in the same way as imagesc(img) with a gray colormap. And since the values in your first img represent evenly spaced 'labels' rather than actual intensities, you'll get a rubbish picture out.
EDIT: If you want to convert your indexed image to a grayscale image, matlab provides the ind2gray function, e.g.:
[img, map] = imread('lena.bmp');
img_gray = ind2gray(img, map);
This is probably what you need if you mean to process pixel values as intensities.
I have a medical imaging matrix of size [200x200x200].
In order to display it, I am currently using imshow3D function, which is an excellent tool, built by Maysam Shahedi.
This tool displays the 3D image slice by slice, with mouse based slice browsing
In my current project, I generate an RGB image for each z-layer from the original input image. The output is a 3D color image of size [200x200x200x3] (each layer is now represented by 3 channels).
The imshow3D function works great on grayscale images. Is it possible to use it to display RGB images?
I took a look at this nice imshow3D function from Matlab FileExchange, and it is quite straight-forward to change it to allow working with a stack of RGB images.
The magic part of the function is
imshow(Img(:,:,S))
which displays the slice S of the image Img. We can simply change it to show all 3 channels of image S by changing this to Img(:,:,S,:). The result will be of size 200-by-200-by-1-by-3, while MATLAB expects RGB images to be of size 200-by-200-by-3. Simply squeeze this image to get the correct dimension. This results in:
imshow(squeeze(Img(:,:,S,:))
So to show RGB images, do a search-and-replace inside the function imshow3D, to replace all occurrences of Img(:,:,S) with squeeze(Img(:,:,S,:)) and it works!
I am using HDF satellite data to retrieve bands from that I am concluding different vegetation indices. Every band in hdf data is in grey colour format, its a grey colour scale image. After HDF data processed I can convert into colour by using colour map (I am using jet for colourmap). My doubt is how to convert greyscale image into colourmaped while using imwrite. How to use colourmap within imwrite. I have tried many times, but the output is only in full blue colour, this spoil the output image. Please help me to do this.
Why use imwrite? You can use imshow.
Example:
imshow(im)
imshow(im,'Colormap',jet(255))
With reference: http://www.alecjacobson.com/weblog/?p=1655
Try using the ind2rgb function before using imwrite if you want to save to a format like .jpg, but if you are using an indexing image format (e.g. .png) you can just use imwrite directly as shown in the docs:
imwrite(X, map, filename)
where X is your greyscale image, map is your colourmap (i.e. jet) and filename is the is the name of the image you want to save ending in .png
I'm analyzing some sound clips using the spectrogram() function in MATLAB. I would like to save the spectrogram as an image (jpg, png, etc). But regardless of what image format I save the figure in, the resulting image always looks different ("spotty") from what I see in the figure.
Here's an example of the spectrograms: Matlab Figure vs. Saved Image
All I want is to save exactly what I see in the figure as an image. I've already tried saving the figure in all the image formats possible but all of them are producing the same "spotting" effect. I've also tried both manual saving (click on file -> save as) and programmatically using the print() and the saveas() functions. Same result every time.
Any help would be appreciated!
What is the data range of your spectrogram?
One of reasons might be that your spectrogram range is out of the [0,1] region for double images or [0,255] for uint* images (your white spots on saved image are suspiciously close to the local minima on MatLab figure).
Another guess might be that you are using imwrite function, in particular its imwrite(X,map,filename,fmt) syntax. MatLab documentation explains:
imwrite(X,map,filename,fmt) writes the indexed image in X and its associated colormap map to filename in the format specified by fmt. If X is of class uint8 or uint16, imwrite writes the actual values in the array to the file. If X is of class double, imwrite offsets the values in the array before writing, using uint8(X–1). map must be a valid MATLAB colormap. Note that most image file formats do not support colormaps with more than 256 entries.
so the uint8(X–1) might be the source of the white spots.
Though have no idea why they appear after print()'ing.
I found a work-around for this problem by using the pcolor() function, which is essentially a rotated surf() function plotted in a grid format (doc). After tinkering with the spectrogram() function more, I'm convinced that these "spotting" artifacts have nothing to do with the data format, property, or scale. The problem seems to lie in the way MATLAB plots and visualizes 3D plots. I tried plotting with the mesh() function as well and it produced a different kind of "spotting" effect. pcolor() works because it's a 2D visualization of a 3D plot.
This is how spectrogram() plots the image using surf() (adapted from the doc):
[S,T,F,P] = spectrogram(X,256,250,256,2000);
surf(T,F,abs(S),'EdgeColor','none');
axis tight; view(0,90);
... and this is how to use pcolor() to plot a save-friendly image:
[S,T,F,P] = spectrogram(X,256,250,256,2000);
h = pcolor(T,F,abs(S));
set(h,'EdgeColor','none');
The white spots are an OpenGL issue, which is the renderer used in spectrogram()'s internal call to surf().
Since you are interested in plotting a 2D visualization, change the renderer for the current figure to zbuffer:
set(gcf, 'renderer', 'zbuffer');
where gcf means "get current figure". The white spots are now gone.
Note that you can also select the zbuffer renderer when you create the figure, before calling spectrogram():
myNewFig = figure('renderer','zbuffer');