I'm saving my images with
for 1:30
file = 'stuff.tif' % 2269 x 1452 image
RGBImage = imread(file);
filename = sprintf('example.tif');
saveas(gcf,filename)
end
However when doing so Matlab down-sizes my images and saves the image as a smaller, "zoomed out" version of itself, which cripples its quality.
Input, 'stuff.tif' when zoomed in
Output, 'example.tif' when zoomed in
How can I save my images automatically in a for loop while retaining their original size and quality?
I highly recommend using Oliver Woodford's function export_fig from the File Exchange. That should solve your problem. (Now Yair Altman takes care of it).
Related
Apologies if this is a dupe, I've been searching for over an hour but the search terms are all really broad and I just keep getting the same results. Also I'm fairly new to matlab so apologies for any misunderstandings.
Anywho, I have a matlab program which needs to frequently save an image generated from a matrix, but I just can't figure out how to do that without displaying it first. Basically I'm caught in between two functions, image and imwrite, both only do half of what I want.
image is able to take my matrix and create the desired output, but it just displays it to a figure window
imwrite is able to save an image to a file without displaying it, but the image is completely wrong and I can't find any parameters that would fix it.
Other questions I've seen deal with using imread and managing figures and stuff, but I'm just doing (for example)
matrix = rand(20);
colormap(winter);
image(matrix, 'CDataMapping', 'scaled');
or
matrix = rand(20);
imwrite(matrix, winter(256), 'filename.png');
Is there some way to call the image function such that it doesn't display a figure window and then gets saved to a file? Something analogous to calling imshow and then savefig in matplotlib.
Just do this:
matrix = rand(20);
f = figure('visible', 'off');
colormap(winter);
image(matrix, 'CDataMapping', 'scaled');
print(f, '-dpng', 'filename.png');
I wrote matlab code for face detection.In my code it is detecting face for first 100 frames and it crop faces from each frame and saves it in database folder.Problems iam facing
1.Detecting frame by frame is very slow.Is there any idea to run faster since i have to work on 4000 frames.
2.In my database folder it has to show 1 to 100 face images but it is not showing 11th and 12th face images directly it showing 13th face image after 10th image.23rd face image is blurr.Likewise so many images are missing and some are blurr.Last image number it is showing as 216.But total 106 face images are there in database folder.In that 12 images are blurr.Remaining are correct images.
clc;
clear all;
obj=vision.VideoFileReader('basu.avi');
for k=0:99
videoFrame = step(obj);
%using viola-jones algorithm
FaceDetect = vision.CascadeObjectDetector;
%FaceDetect
BB = step(FaceDetect,videoFrame);
%BB
figure(2),imshow(videoFrame);
for i = 1:size(BB,1)
rectangle('Position',BB(i,:),'LineWidth',3,'LineStyle','-','EdgeColor','r');
end
%crop faces and convert it to gray
for i = 1:size(BB,1)
J= imcrop(videoFrame,BB(i,:));
I=rgb2gray(imresize(J,[292,376]));
%save cropped faces in database folder
filename = ['G:\matlab_installed\bin\database\' num2str(i+k*(size(BB,1))) '.jpg'];
imwrite(I,filename);
end
end
There are a few of things you can try:
Definitely move FaceDetect = vision.CascadeObjectDetector; outside of the loop. You only need to create the face detector object once. Re-creating it for every frame is definitely your performance bottleneck.
vision.VideoFileReader returns a frame of class 'single' by default. If you change the output data type to 'uint8', that should speed up the face detector. Use obj=vision.VideoFileReader('basu.avi', 'VideoOutputDataType', 'uint8');
vision.VideoFileReader can also do the conversion to grayscale for you. Use obj=vision.VideoFileReader('basu.avi', 'VideoOutputDataType', 'uint8', 'ImageColorSpace', 'Intensity'); This may be faster than calling rgb2gray.
Try limiting the size of the faces being detected using 'MinSize' and 'MaxSize' options of vision.CascadeObjectDetector and/or try downsampling the frame before detecting faces.
I'm having an issue with attempting to save some plots with transparent ellipsoids on them if I attempt to save them with .ps/.eps extensions.
Here's the plot saved as a .png:
If I choose to save it as a .ps/.eps here is what it looks like:
How I got around this, was to use ImageMagick to convert the original png to a ps. The only problem is that the image in png format is about 90k, and it becomes just under 4M after conversion. This is not good since I have a lot of these images, and it will take too much time to compile my latex document. Does anyone have a solution to this?
The problem is that eps does not support transparencies natively.
There are few options:
rasterize the image and embed in a eps file (like #Molly suggests) or exporting to pdf and converting with some external tool (like gs) (which usually relies as well on rasterization)
'mimic' transparency, giving a colour that looks like the transparent one on a given background.
I discussed this for sure once on the matplotlib mailing list, and I got the suggestion to rasterize, which is not feasible as you get either pixellized or huge figures. And they don't scale very nicely when put into, e.g., a publication.
I personally use the second approach, and although not ideal, I found it good enough. I wrote a small python script that implements the algorithm from this SO post to obtain a solid RGB representation of a colour with a give transparency
EDIT
In the specific case of your plot try to use the zorder keyword to order the parts plotted. Try to use zorder=10 for the blue ellipse, zorder=11 for the green and zorder=12 for the hexbins.
This way the blue should be below everything, then the green ellipse and finally the hexbins. And the plot should be readable also with solid colors. And if you like the shades of blue and green that you have in png, you can try to play with mimic_alpha.py.
EDIT 2
If you are 100% sure that you have to use eps, there are a couple of workarounds that come to my mind (and that are definitely uglier than your plot):
Just draw the ellipse borders on top of the hexbins.
Get centre and amplitude of each hexagon, (possibly discard all zero bins) and make a scatter plot using the same colour map as in hexbin and adjusting the marker size and shape as you like. You might want to redraw the ellipses borders on top of that
Another alternative would be to save them to pdf
savefig('myfigure.pdf')
That works with pdflatex, if that was the reason why you needed to use eps and not svg.
You can rasterize the figure before saving it to preserve transparency in the eps file:
ax.set_rasterized(True)
plt.savefig('rasterized_fig.eps')
I had the same problem. To avoid rasterizing, you can save the image as a pdf and then run (on unixish systems at least) in a terminal:
pdftops -eps my.pdf my.eps
Which gives a .eps file as output.
I solved this by:
1) adding a set_rasterization_zorder(1) when defining the figure area:
fxsize=16
fysize=8
f = figure(num=None, figsize=(fxsize, fysize), dpi=180, facecolor='w',
edgecolor='k')
plt.subplots_adjust(
left = (18/25.4)/fxsize,
bottom = (13/25.4)/fysize,
right = 1 - (8/25.4)/fxsize,
top = 1 - (8/25.4)/fysize)
subplots_adjust(hspace=0,wspace=0.1)
#f.suptitle('An overall title', size=20)
gs0 = gridspec.GridSpec(1, 2)
gs11 = gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs0[0])
ax110 = plt.Subplot(f, gs11[0,0])
f.add_subplot(ax110)
ax110.set_rasterization_zorder(1)
2) a zorder=0 in each alpha=anynumber in the plot:
ax110.scatter(xs1,ys1 , marker='o', color='gray' , s=1.5,zorder=0,alpha=0.3)#, label=label_bg)
and
3) finally a rasterized=True when saving:
P.savefig(str(PLOTFILENAME)+'.eps', rasterized=True)
Note that this may not work as expected with the transparent keyword to savefig because an RGBA colour with alpha<1 on transparent background will be rendered the same as the RGB colour with alpha=1.
As mentioned above, the best and easiest choice (if you do not want to loose resolution) is to rasterized the figure
f = plt.figure()
f.set_rasterized(True)
ax = f.add_subplot(111)
ax.set_rasterized(True)
f.savefig('figure_name.eps',rasterized=True,dpi=300)
This way, you can manage the size by dpi option as well. In fact, you can also play with the zorder below you want to apply the rasterization:
ax.set_rasterization_zorder(0)
Note: It is important to keep f.set_rasterized(True) when you use plt.subplot and plt.subplot2grid functions. Otherwise, label and tick area will not appear in the .eps file
My solution is to export the plot as .eps, load it up to Inkscape for example, then Ungroup the plot, select the object that I want to set the transparency and just edit the Opacity of the Fill in the "Fill and Stroke" tab.
You can save the file as .svg if you want to tweak it later, or export the image for a publication.
If you are writing the academic paper in latex, I would recommend you export the .pdf file rather than .eps. The .pdf format supports transparency perfectly and has good compression efficiency, and most importantly, can be easily edited in Adobe Illustrator.
If you wanna further edit the graph (NOT EDITING DATA! I MEAN, FOR GOOD-LOOKING), you could open the exported graph, in Adobe Acrobat - Edit - Copy elements into Adobe Illustrator. The two software can handle everything perfectly.
I work happily with this method. Everything clear, editable and small-size. Hope can help.
I am trying to modify the default I-beam cursor image. I'm using [[[NSCursor IBeamCursor] image] representations], passing each one through a CIFilter and adding it to a new image. However, the resulting cursor looks as though it is rendering the low-resolution images.
The High Resolution Guidelines say:
For custom cursors, you can pass a multirepresentation TIFF to the NSCursor class method initWithImage:hotSpot:.
So I would expect this to work. Additionally, if I get the -TIFFRepresentation of the original image and my modified image, and write them to disk, they both look like multi-page TIFF files with the same size images. What could I be doing wrong?
I have a somewhat-temporary solution: manually call -setSize: on each image representation, dividing the pixel height and width by the screen's scale factor. However, this technique doesn't seem like it will work ideally with multiple screens.
You're right on. I've been debugging this all day and I'm pretty sure I've got it nailed. I'm not doing exactly the same thing you are (my images are loaded from a file) but the end result is exactly the same.
The trick is to set the first representation of the multi-representation image to the non-retina size. If you are loading your cursors from an image file, you must take this extra step to adjust the size of the representations to match. It doesn't work 'out-of-the-box' as you would expect.
I've tested this on a machine with two monitors and dragging the window from the retina display to the non-retina display acts as it should, displaying the high/low resolution images for the cursor.
I had a similar problem a while ago: I had my cursor in a PDF, and it always drew as if it was a pixel image at 1:1 size, blown up. There's a solution to that in NSCursor: Using high-resolution cursors with cursor zoom (or retina).
Maybe someone can use that technique to solve this problem? My guess is creating an image with the same size but a different CTM marks it as the same size but Retina. What #jtbrandes is doing probably marks it as a different size and non-Retina. So you're effectively losing the scale factor information. If you create an image with a CTM in the hints, maybe you can draw the filtered images into it and it'll be detected right.
I'm currently creating my figures in matlab to embed themvia latex into a pdf for later printing. I save the figures and save them via the script export_fig! Now I wonder which is the best way to go:
Which size of the matlab figure window to chose
Which -m option to take for the script? It will change the resolution and the size of the image...
I'm wondering about those points in regards to the following two points:
When chosing the figure-size bigger, there are more tickmarks shown and the single point markers are better visible
When using a small figure and using a big -m option, I still have only some tickmarks
When I generate a image which is quite huge (e.g. resolution 300 and still 2000*2000px) and than embed it into the document: Does this than look ugly? Will this be embedded in a nice scaling mode or is it the same ugliness as if you upload a 1000*1000px image onto a homepage and embed it via the widht and height tags in html -> the browser displays it quite ugly because the browser doesn't do a real resize. So it looks unsharp and ugly.
Thanks in advance!
The MATLAB plots are internally described as vector graphics, and PDF files are also described using vector graphics. Rendering the plot to a raster format is a bad idea, because you end up having to choose resolution and end up with bigger files.
Just save the plot to EPS format, which can be directly embedded into a PDF file using latex. I usually save my MATLAB plots for publication using:
saveas(gcf, 'plot.eps', 'epsc');
and embed them directly into my latex file using:
\includegraphics[width=0.7\linewidth]{plot.eps}
Then, you only need to choose the proportion of the line the image is to take (in this case, 70%).
Edit: IrfanView and others (XnView) don't display EPS very well. You can open them in Adobe Illustrator to get a better preview of what it looks like. I always insert my plots this way and they always look exactly the same in the PDF as in MATLAB.
One bonus you also get with EPS is that you can actually specify a font size so that the text is readable even when you resize the image in the document.
As for the number of ticks, you can look at the axes properties in the MATLAB documentation. In particular, the XTick and YTick properties are very useful manually controlling how many ticks appear no matter what the window resolution is.
Edit (again): If you render the image to a raster format (such as PNG), it is preferable to choose the exact same resolution as the one used in the document. Rendering a large image (by using a big window size) and making it small in the PDF will yield bad results mainly because the size of the text will scale directly with the size of the image. Rendering a small image will obviously make for a very bad effect because of stretching.
That is why you should use a vector image format. However, the default MATLAB settings for figures produce some of the same problems as raster images: text size is not specified as a font size and the number of ticks varies with the window size.
To produce optimal plots in the final render, follow the given steps:
Set the figure's font size to a decent setting (e.g. 11pt)
Render the plot
Decide on number of ticks to get a good effect and set the ticks manually
Render the image to color EPS
In MATLAB code, this should look somewhat like the following:
function [] = nice_figure ( render )
%
% invisible figure, good for batch renders.
f = figure('Visible', 'Off');
% make plots look nice in output PDF.
set(f, ...
'DefaultAxesFontSize', 11, ...
'DefaultAxesLineWidth', 0.7, ...
'DefaultLineLineWidth', 0.8, ...
'DefaultPatchLineWidth', 0.7);
% actual plot to render.
a = axes('Parent', f);
% show whatever it is we need to show.
render(a);
% save file.
saveas(f, 'plot.eps', 'epsc');
% collect garbarge.
close(f);
end
Then, you can draw some fancy plot using:
function [] = some_line_plot ( a )
%
% render data.
x = -3 : 0.001 : +3;
y = expm1(x) - x - x.^2;
plot(a, x, y, 'g:');
title('f(x)=e^x-1-x-x^2');
xlabel('x');
ylabel('f(x)');
% force use of 'n' ticks.
n = 5;
xlimit = get(a, 'XLim');
ylimit = get(a, 'YLim');
xticks = linspace(xlimit(1), xlimit(2), n);
yticks = linspace(ylimit(1), ylimit(2), n);
set(a, 'XTick', xticks);
set(a, 'YTick', yticks);
end
And render the final output using:
nice_figure(#some_line_plot);
With such code, you don't need to worry about the window size at all. Notice that I haven't even showed the window for you to play with its size. Using this code, I always get beautiful output and small EPS and PDF file sizes (much smaller than when using PNG).
The only thing this solution does not address is adding more ticks when the plot is made larger in the latex code, but that can't be done anyways.