I found this code for auto-brightening images to an optimum level.
% AUTOBRIGHTNESS
% -->Automatically adjusts brightness of images to optimum level.
% e.g. autobrightness('Sunset.jpg','Output.jpg')
function autobrightness(input_img,output_img)
my_limit = 0.5;
input_image=imread(input_img);
if size(input_image,3)==3
a=rgb2ntsc(input_image);
else
a=double(input_image)./255;
end
mean_adjustment = my_limit-mean(mean(a(:,:,1)));
a(:,:,1) = a(:,:,1) + mean_adjustment*(1-a(:,:,1));
if size(input_image,3)==3
a=ntsc2rgb(a);
end
imwrite(uint8(a.*255),output_img);
I want to ask, why the value of my_limit is 0.5?
How we determine that value?
Why use the 'ntsc' colorspace instead of another colorspace like hsv, lab or yCbCr?
I want to ask, why the value of my_limit is 0.5? How we determine that
value?
The color space NTSC ranges from 0 to 1 for each of its channel. So essentially 0.5 is the center. This is equivalent of choosing 127 for RGB space
Why use the 'ntsc' colorspace instead of another colorspace like hsv,
lab or yCbCr?
I believe ntsc provides 100% coverage of the color space and so the author of the code choose it. However most modern systems wont display in this color space and hence we use standard RGB for display. I used this website to come to this conclusion NTSC color space
Also, as pointed by Cris in this wikipedia page. NTSC stores Luminance and Chrominance and the author of the code is adjusting the Lumiance(brightness). I am including a modified script I used to come to these conclusions
input_img='lena_std.tif'
output_img='lena2.tif'
my_limit = 0.5;
input_image=imread(input_img);
if size(input_image,3)==3
a=rgb2ntsc(input_image);
k=rgb2ntsc(input_image);
else
a=double(input_image)./255;
end
mean_adjustment = my_limit-mean(mean(a(:,:,1)));
a(:,:,1) = a(:,:,1) + mean_adjustment*(1-a(:,:,1));
if size(input_image,3)==3
a=ntsc2rgb(a);
end
imwrite(uint8(a.*255),output_img);
output=uint8(a.*255);
imwrite(uint8(k.*255),'test.tif');
ntscoutput=uint8(k.*255);
Related
I am trying to use gnuplot 5.0 to plot a 2D array of data with no margins or borders or axes... just a 2D image (.png or .jpg) representing some data. I would like to have each array element to correspond to exactly one pixel in the image with no scaling / interpolation etc and no extra white pixels at the edges.
So far, when I try to set the margins to 0 and even using the pixels flag, I am still left with a row of white pixels on the right and top borders of the image.
How can I get just an image file with pixel-by-pixel representation of a data array and nothing extra?
gnuplot script:
#!/usr/bin/gnuplot --persist
set terminal png size 400, 200
set size ratio -1
set lmargin at screen 0
set rmargin at screen 1
set tmargin at screen 0
set bmargin at screen 1
unset colorbox
unset tics
unset xtics
unset ytics
unset border
unset key
set output "pic.png"
plot "T.dat" binary array=400x200 format="%f" with image pixels notitle
Example data from Fortran 90:
program main
implicit none
integer, parameter :: nx = 400
integer, parameter :: ny = 200
real, dimension (:,:), allocatable :: T
allocate (T(nx,ny))
T(:,:)=0.500
T(2,2)=5.
T(nx-1,ny-1)=5.
T(2,ny-1)=5.
T(nx-1,2)=5.
open(3, file="T.dat", access="stream")
write(3) T(:,:)
close(3)
end program main
Some gnuplot terminals implement "with image" by creating a separate png file containing the image and then linking to it inside the resulting plot. Using that separate png image file directly will avoid any issues of page layout, margins, etc. Here I use the canvas terminal. The plot itself is thrown away; all we keep is the png file created with the desired content.
gnuplot> set term canvas name 'myplot'
Terminal type is now 'canvas'
Options are ' rounded size 600,400 enhanced fsize 10 lw 1 fontscale 1 standalone'
gnuplot> set output '/dev/null'
gnuplot> plot "T.dat" binary array=400x200 format="%f" with image
linking image 1 to external file myplot_image_01.png
gnuplot> quit
$identify myplot_image_01.png
myplot_image_01.png PNG 400x200 400x200+0+0 8-bit sRGB 348B 0.000u 0:00.000
Don't use gnuplot.
Instead, write a script that reads your data and converts it into one of the Portable Anymap formats. Here's an example in Python:
#!/usr/bin/env python3
import math
import struct
width = 400
height = 200
levels = 255
raw_datum_fmt = '=d' # native, binary double-precision float
raw_datum_size = struct.calcsize(raw_datum_fmt)
with open('T.dat', 'rb') as f:
print("P2")
print("{} {}".format(width, height))
print("{}".format(levels))
raw_data = f.read(width * height * raw_datum_size)
for y in range(height):
for x in range(width):
raw_datum, = struct.unpack_from(raw_datum_fmt, raw_data, (y * width + x) * raw_datum_size)
datum = math.floor(raw_datum * levels) # assume a number in the range [0, 1]
print("{:>3} ".format(datum), end='')
print()
If you can modify the program which generates the data file, you can even skip the above step and instead generate the data directly in a PNM format.
Either way, you can then use ImageMagick to convert the image to a format of your choice:
./convert.py | convert - pic.png
This should be an easy task, however, apparently it's not.
The following might be a (cumbersome) solution because all other attempts failed. My suspicion is that some graphics library has an issue which you probably cannot solve as a gnuplot user.
You mentioned that ASCII matrix data is also ok. The "trick" here is to plot data with lines where the data is "interrupted" by empty lines, basically drawing single points. Check this in case you need to get your datafile 1:1 into a datablock.
However, if it is not already strange enough, it seems to work for png and gif terminal but not for pngcairo or wxt.
I guess the workaround is probably slow and inefficient but at least it creates the desired output. I'm not sure if there is a limit on size. Tested with 100x100 pixels with Win7, gnuplot 5.2.6. Comments and improvements are welcome.
Code:
### pixel image from matrix data without strange white border
reset session
SizeX = 100
SizeY = 100
set terminal png size SizeX,SizeY
set output "tbPixelImage.png"
# generate some random matrix data
set print $Data2
do for [y=1:SizeY] {
Line = ''
do for [x=1:SizeX] {
Line = Line.sprintf(" %9d",int(rand(0)*0x01000000)) # random color
}
print Line
}
set print
# print $Data2
# convert matrix data into x y z data with empty lines inbetween
set print $Data3
do for [y=1:SizeY] {
do for [x=1:SizeX] {
print sprintf("%g %g %s", x, y, word($Data2[y],x))
print ""
}
}
set print
# print $Data3
set margins 0,0,0,0
unset colorbox
unset border
unset key
unset tics
set xrange[1:SizeX]
set yrange[1:SizeY]
plot $Data3 u 1:2:3 w l lw 1 lc rgb var notitle
set output
### end of code
Result: (100x100 pixels)
(enlarged with black background):
Image with 400x200 pixels (takes about 22 sec on my 8 year old laptop).
What I ended up actually using to get what I needed even though the question / bounty asks for a gnuplot solution:
matplotlib has a function matplotlib.pyplot.imsave which does what I was looking for... i.e. plotting 'just data pixels' and no extras like borders, margins, axes, etc. Originally I only knew about matplotlib.pyplot.imshow and had to pull a lot of tricks to eliminate all the extras from the image file and prevent any interpolation/smoothing etc (and therefore turned to gnuplot at a certain point). With imsave it's fairly easy, so I'm back to using matplotlib for an easy yet still flexible (in terms of colormap, scaling, etc) solution for 'pixel exact' plots. Here's an example:
#!/usr/bin/env python3
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
nx = 400
ny = 200
data = np.fromfile('T.dat', dtype=np.float32, count=nx*ny)
data = data.reshape((nx,ny), order='F')
matplotlib.image.imsave('T.png', np.transpose(data), origin='lower', format='png')
OK, here is another possible solution (I separated it from my first cumbersome approach). It creates the plot immediately, less than a second. No renaming necessary or creation of a useless file.
I guess key is to use term png and ps 0.1.
I don't have a proof but I think ps 1 would be ca. 6 pixels large and would create some overlap and/or white pixels at the corner. Again, for whatever reason it seems to work with term png but not with term pngcairo.
What I tested (Win7, gnuplot 5.2.6) is a binary file having the pattern 00 00 FF repeated all over (I can't display null bytes here). Since gnuplot apparently reads 4 bytes per array item (format="%d"), this leads to an alternating RGB pattern if I am plotting with lc rgb var.
In the same way (hopefully) we can figure out how to read format="%f" and use it together with a color palette. I guess that's what you are looking for, right?
Further test results, comments, improvements and explanations are welcome.
Code:
### pixel image from matrix data without strange white border
reset session
SizeX = 400
SizeY = 200
set terminal png size SizeX,SizeY
set output "tbPixelImage.png"
set margins 0,0,0,0
unset colorbox
unset border
unset key
unset tics
set xrange[0:SizeX-1]
set yrange[0:SizeY-1]
plot "tbBinary.dat" binary array=(SizeX,SizeY) format="%d" w p pt 5 ps 0.1 lc rgb var
### end of code
Result:
Here is the code that i used to display the DICOM image, When i give the full display range it shows burred image like left, and when i increase the lower display range the image looks more clear.
[img, map] = dicomread('D:\Work 2017\Mercy\trial\trial4\Export0000\MG0001.dcm');
info = dicominfo('D:\Work 2017\Mercy\trial\trial4\Export0000\MG0001.dcm' );
mini = min(img(:));
maxi = max(img(:));
figure,
subplot(131), imshow(img, [mini maxi]); title('Full display range')
subplot(132), imshow(img, [maxi*0.7 maxi]); title('70% and above display range')
subplot(133), imshow(img, [maxi*0.8 maxi]); title('80% and above display range')
I want to always see an image something similar to right side image without giving the display range that I used in the above code
Typically a DICOM will have WindowCenter and WindowWidth tags that specify the recommended window/level settings. You can convert these to color limits in the following way
% Get the DICOM header which contains the WindowCenter and WindowWidth tags
dcm = dicominfo(filename);
% Compute the lower and upper ranges for display
lims = [dcm.WindowCenter - (dcm.WindowWidth / 2), ...
dcm.WindowCenter + (dcm.WindowWidth / 2)];
% Load in the actual image data
img = dicomread(dcm);
% Display with the limits computed above
imshow(img, lims);
Or more briefly
lims = dcm.WindowCenter + [-0.5 0.5] * dcm.WindowWidth;
If those values aren't acceptable, then it's likely best to provide a user-adjustable window/level (such as the contrast tool in imtool) as there is unlikely any way to reliably get "acceptable" contrast since it is subjective.
I want to read in the right ascension (in hour angles), declination (in degrees) and size (in arcmin) of a catalogue of galaxies and draw all of them in a large image of specified pixel size.
I tried converting the ra, dec and size into pixels to create a Bounds object for each galaxy, but get an error that "BoundsI must be initialized with integer values." I understand that pixels have to be integers...
But is there a way to center the large image at a specified ra and dec, then input the ra and dec of each galaxy as parameters to draw it in?
Thank you in advance!
GalSim uses the CelestialCoord class to handle coordinates in the sky and any of a number of WCS classes to handle the conversion from pixels to celestial coordinates.
The two demos in the tutorial series that use a CelestialWCS (the base class for WCS classes that use celestial coordinates for their world coordinate system) are demo11 and demo13. So you might want to take a look at them. However, neither one does something very close to what you're doing.
So here's a script that more or less does what you described.
import galsim
import numpy
# Make some random input data so we can run this.
# You would use values from your input catalog.
ngal = 20
numpy.random.seed(123)
ra = 15 + 0.02*numpy.random.random( (ngal) ) # hours
dec = -34 + 0.3*numpy.random.random( (ngal) ) # degrees
size = 0.1 * numpy.random.random( (ngal) ) # arcmin
e1 = 0.5 * numpy.random.random( (ngal) ) - 0.25
e2 = 0.5 * numpy.random.random( (ngal) ) - 0.25
# arcsec is usually the more natural units for sizes, so let's
# convert to that here to make things simpler later.
# There are options throughout GalSim to do things in different
# units, such as arcmin, but arcsec is the default, so it will
# be simpler if we don't have to worry about that.
size *= 60 # size now in arcsec
# Some plausible location at which to center the image.
# Note that we are now attaching the right units to these
# so GalSim knows what angle they correspond to.
cen_ra = numpy.mean(ra) * galsim.hours
cen_dec = numpy.mean(dec) * galsim.degrees
# GalSim uses CelestialCoord to handle celestial coordinates.
# It knows how to do all the correct spherical geometry calculations.
cen_coord = galsim.CelestialCoord(cen_ra, cen_dec)
print 'cen_coord = ',cen_coord.ra.hms(), cen_coord.dec.dms()
# Define some reasonable pixel size.
pixel_scale = 0.4 # arcsec / pixel
# Make the full image of some size.
# Powers of two are typical, but not required.
image_size = 2048
image = galsim.Image(image_size, image_size)
# Define the WCS we'll use to connect pixels to celestial coords.
# For real data, this would usually be read from the FITS header.
# Here, we'll need to make our own. The simplest one that properly
# handles celestial coordinates is TanWCS. It first goes from
# pixels to a local tangent plane using a linear affine transformation.
# Then it projects that tangent plane into the spherical sky coordinates.
# In our case, we can just let the affine transformation be a uniform
# square pixel grid with its origin at the center of the image.
affine_wcs = galsim.PixelScale(pixel_scale).affine().withOrigin(image.center())
wcs = galsim.TanWCS(affine_wcs, world_origin=cen_coord)
image.wcs = wcs # Tell the image to use this WCS
for i in range(ngal):
# Get the celestial coord of the galaxy
coord = galsim.CelestialCoord(ra[i]*galsim.hours, dec[i]*galsim.degrees)
print 'gal coord = ',coord.ra.hms(), coord.dec.dms()
# Where is it in the image?
image_pos = wcs.toImage(coord)
print 'position in image = ',image_pos
# Make some model of the galaxy.
flux = size[i]**2 * 1000 # Make bigger things brighter...
gal = galsim.Exponential(half_light_radius=size[i], flux=flux)
gal = gal.shear(e1=e1[i],e2=e2[i])
# Pull out a cutout around where we want the galaxy to be.
# The bounds needs to be in integers.
# The fractional part of the position will go into offset when we draw.
ix = int(image_pos.x)
iy = int(image_pos.y)
bounds = galsim.BoundsI(ix-64, ix+64, iy-64, iy+64)
# This might be (partially) off the full image, so get the overlap region.
bounds = bounds & image.bounds
if not bounds.isDefined():
print ' This galaxy is completely off the image.'
continue
# This is the portion of the full image where we will draw. If you try to
# draw onto the full image, it will use a lot of memory, but if you go too
# small, you might see artifacts at the edges. You might need to
# experiment a bit with what is a good size cutout.
sub_image = image[bounds]
# Draw the galaxy.
# GalSim by default will center the object at the "true center" of the
# image. We actually want it centered at image_pos, so provide the
# difference as the offset parameter.
# Also, the default is to overwrite the image. But we want to add to
# the existing image in case galaxies overlap. Hence add_to_image=True
gal.drawImage(image=sub_image, offset=image_pos - sub_image.trueCenter(),
add_to_image=True)
# Probably want to add a little noise...
image.addNoise(galsim.GaussianNoise(sigma=0.5))
# Write to a file.
image.write('output.fits')
GalSim deals with image bounds and locations using image coordinates. The way to connect true positions on the sky (RA, dec) into image coordinates is using the World Coordinate System (WCS) functionality in GalSim. I gather from your description that there is a simple mapping from RA/dec into pixel coordinates (i.e., there are no distortions).
So basically, you would set up a simple WCS defining the (RA, dec) center of the big image and its pixel scale. Then for a given galaxy (RA, dec), you can use the "toImage" method of the WCS to figure out where on the big image the galaxy should live. Any subimage bounds can be constructed using that information.
For a simple example with a trivial world coordinate system, you can check out demo10 in the GalSim repository.
I'm trying to combine the two images based on the information from the mask. I'm using the color information from the background image if the mask is 0 and color information from foreground image if the mask is 1. Because the mask and both
Images are of the same size, I would like to use logical indexing of matrices to achieve this.
My attempt:
mask = imread('mask.png');
foreground = imread('fg.jpg');
background = imread('bg.jpg');
[r,c,~]=size(mask);
A = zeros(size(mask));
for i=1:r
for j=1:c
if mask(i,j) == 0
A(i,j,:) = background(i,j,:);
end
if mask(i,j) > 0
A(i,j,:) = foreground(i,j,:);
end
end
end
imshow(A);
The result looks like a flickering blue image, but I don't want that. Please help.
You can do this a bit more concisely:
f = double(foreground).*double(mask);
b = double(background).*double(~mask);
blend = f+b;
imshow(blend, []);
Using logical indexing you could also do
foreground(logical(mask)) = 0;
background(logical(~mask)) = 0;
blend = foreground+background;
The ISNOT operator '~' inverts your matrix in the second line, so you cut out the area you would like for background.
NOTE: This works for black and white (one channel). For coloured images see rayryeng's solution.
There are two problems with your code. The first problem is that you are trying to assign colour pixels to the output image A, yet this image is only two-dimensional. You want an image with three channels, not two. In addition, the output image type you are specifying is wrong. By default, the output image A is of type double, yet you are copying values into it that aren't double... most likely unsigned 8-bit integer.
As such, cast the image to the same type as the input images. Assuming both input images are the same type, initialize your A so that:
A = zeros(size(foreground), class(foreground));
This correctly makes a colour image with the same type as any of the inputs, assuming that they're both the same type.
Now, your for loop is fine, but it's better if you do this in one shot with logical indexing. If you want to use logical indexing, create a new image that's initially blank like what you've done, but then make sure your mask has three channels to match the number of channels the other images have. After, you simply need to index into each image and set the right locations accordingly:
mask = imread('mask.png');
foreground = imread('fg.jpg');
background = imread('bg.jpg');
[r,c,d]=size(mask); %// Change
%// If your mask isn't three channels, make it so
%// Change
if d ~= 3
mask = cat(3, mask, mask, mask);
end
A = zeros(size(foreground), class(foreground)); %// Change
A(mask) = foreground(mask); %// Assign pixels to foreground
A(~mask) = background(~mask); %// Assign pixels to background
imshow(A);
I'm trying to use the answers I found in these questions:
How to save a plot into a PDF file without a large margin around
Get rid of the white space around matlab figure's pdf output
External source
to print a matlab plot to pdf without having the white margins included.
However using this code:
function saveTightFigure( h, outfilename, orientation )
% SAVETIGHTFIGURE(H,OUTFILENAME) Saves figure H in file OUTFILENAME without
% the white space around it.
%
% by ``a grad student"
% http://tipstrickshowtos.blogspot.com/2010/08/how-to-get-rid-of-white-margin-in.html
% get the current axes
ax = get(h, 'CurrentAxes');
% make it tight
ti = get(ax,'TightInset');
set(ax,'Position',[ti(1) ti(2) 1-ti(3)-ti(1) 1-ti(4)-ti(2)]);
% adjust the papersize
set(ax,'units','centimeters');
pos = get(ax,'Position');
ti = get(ax,'TightInset');
set(h, 'PaperUnits','centimeters');
set(h, 'PaperSize', [pos(3)+ti(1)+ti(3) pos(4)+ti(2)+ti(4)]);
set(h, 'PaperPositionMode', 'manual');
set(h, 'PaperPosition',[0 0 pos(3)+ti(1)+ti(3) pos(4)+ti(2)+ti(4)]);
% save it
%saveas(h,outfilename);
if( orientation == 1)
orient portrait
else
orient landscape
end
print( '-dpdf', outfilename );
end
Results in this output:
As you can see the 'PaperSize' seems to be set not properly. Any idea of possible fixes?
NOTE
If I change the orientation between landscape and portrait the result is the same, simply the image is chopped in a different way.
However if I save the image with the saveas(h,outfilename); instruction the correct output is produced.
Why is this? And what is the difference between the two saving instructions?
Alltogether the answers you mentioned offer a lot of approaches, but most of them didn't worked for me neither. Most of them screw up your papersize when you want to get the tight inset, the only which worked for me was:
set(axes_handle,'LooseInset',get(axes_handle,'TightInset'));
I finally wrote a function, where I specify the exact height and width of the output figure on paper, and the margin I want (or just set it to zero). Be aware that you also need to pass the axis handle. Maybe this functions works for you also.
function saveFigure( fig_handle, axes_handle, name , height , width , margin)
set(axes_handle,'LooseInset',get(axes_handle,'TightInset'));
set(fig_handle, 'Units','centimeters','PaperUnits','centimeters')
% the last two parameters of 'Position' define the figure size
set(fig_handle,'Position',[-margin -margin width height],...
'PaperPosition',[0 0 width+margin height+margin],...
'PaperSize',[width+margin height+margin],...
'PaperPositionMode','auto',...
'InvertHardcopy', 'on',...
'Renderer','painters'... %recommended if there are no alphamaps
);
saveas(fig_handle,name,'pdf')
end
Edit: if you use painters as renderer saveas and print should produce similar results. For jpegs print is preferable as you can specify the resolution.