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I have an image and I would like to blur it in one specific direction and distance using Matlab.
I found out there is a filter called fspecial('motion',len,theta).
Here there is an example:
I = imread('cameraman.tif');
imshow(I);
H = fspecial('motion',20,45);
MotionBlur = imfilter(I,H,'replicate');
imshow(MotionBlur);
However the blurred picture is blurred in 2 directions! In this case 225 and 45 degrees.
What should it do in order to blur it just in a specific direction (e.g. 45) and not both?
I think you want what's called a "comet" kernel. I'm not sure what kernel is used for the "motion" blur, but I'd guess that it's symmetrical based on the image you provided.
Here is some code to play with that applies the comet kernel in one direction. You'll have to change things around if you want an arbitrary angle. You can see from the output that it's smearing in one direction, since there is a black band on only one side (due to the lack of pixels there).
L = 5; % kernel width
sigma=0.2; % kernel smoothness
I = imread('cameraman.tif');
x = -L:1.0:L;
[X,Y] = meshgrid(x,x);
H1 = exp((-sigma.*X.^2)+(-sigma.*Y.^2));
kernel = H1/sum((H1(:)));
Hflag = double((X>0));
comet_kernel = Hflag.*H1;
comet_kernel=comet_kernel/sum(comet_kernel(:));
smearedImage = conv2(double(I),comet_kernel,'same');
imshow(smearedImage,[]);
Updated code: This will apply an arbitrary rotation to the comet kernel. Note also the difference between sigma in the previous example and sx and sy here, which control the length and width parameters of the kernel, as suggested by Andras in the comments.
L = 5; % kernel width
sx=3;
sy=10;
theta=0;
I = imread('cameraman.tif');
x = -L:1.0:L;
[X,Y] = meshgrid(x,x);
rX = X.*cos(theta)-Y.*sin(theta);
rY = X.*sin(theta)+Y.*cos(theta);
H1 = exp(-((rX./sx).^2)-((rY./sy).^2));
Hflag = double((0.*rX+rY)>0);
H1 = H1.*Hflag;
comet_kernel = H1/sum((H1(:)))
smearedImage = conv2(double(I),comet_kernel,'same');
imshow(smearedImage,[]);
Based on Anger Density's answer I wrote this code that solves my problem completely:
L = 10; % kernel width
sx=0.1;
sy=100;
THETA = ([0,45,90,135,180,225,270,320,360])*pi/180;
for i=1:length(THETA)
theta=(THETA(i)+pi)*-1;
I = imread('cameraman.tif');
x = -L:1.0:L;
[X,Y] = meshgrid(x,x);
rX = X.*cos(theta)-Y.*sin(theta);
rY = X.*sin(theta)+Y.*cos(theta);
H1 = exp(-((rX./sx).^2)-((rY./sy).^2));
Hflag = double((0.*rX+rY)>0);
H1 = H1.*Hflag;
comet_kernel = H1/sum((H1(:)));
smearedImage = conv2(double(I),comet_kernel,'same');
% Fix edges
smearedImage(:,[1:L, end-L:end]) = I(:,[1:L, end-L:end]); % Left/Right edge
smearedImage([1:L, end-L:end], :) = I([1:L, end-L:end], :); % Top/bottom edge
% Keep only inner blur
smearedImage(L:end-L,L:end-L) = min(smearedImage(L:end-L,L:end-L),double(I(L:end-L,L:end-L)));
figure
imshow(smearedImage,[]);
title(num2str(THETA(i)*180/pi))
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0 1 1]);
end
How can I scale the colorbar axis of a false color image?
I read this post,and copied the code but it seems not to work correctly:
MATLAB Colorbar - Same colors, scaled values
Please see the two images below. In the first (without the scaling) the coloraxis goes
[1 2 3 4 5 6]*10^4
In the second image, it goes
[0.005 0.01 0.015 0.02 0.025]
The correct scaling (with C = 100000) would be
[0.1 0.2 0.3 0.4 0.5 0.6]
Without scaling
Wrong scaling
I want that the coloraxis is scaled by 1/C and I can freely choose C, so that when the pixel value = 10^4 and C=10^6 the scale should show 10^-2.
The reason why I multiply my image first by C is to get more decimals places, because all values below 1 will be displayed as zero without the C scaling.
When I run the code I get yticks as a workspace variable with the following values:
[500 1000 1500 2000 2500]
My code:
RGB = imread('IMG_0043.tif');% Read Image
info = imfinfo('IMG_0043.CR2'); % get Metadata
C = 1000000; % Constant to adjust image
x = info.DigitalCamera; % get EXIF
t = getfield(x, 'ExposureTime');% save ExposureTime
f = getfield(x, 'FNumber'); % save FNumber
S = getfield(x, 'ISOSpeedRatings');% save ISOSpeedRatings
date = getfield(x,'DateTimeOriginal');
I = rgb2gray(RGB); % convert Image to greyscale
K = 480; % Kamerakonstante(muss experimentel eavaluiert werden)
% N_s = K*(t*S)/power(f,2))*L
L = power(f,2)/(K*t*S)*C; %
J = immultiply(I,L); % multiply each value with constant , so the Image is Calibrated to cd/m^2
hFig = figure('Name','False Color Luminance Map', 'ToolBar','none','MenuBar','none');
% Create/initialize default colormap of jet.
cmap = jet(16); % or 256, 64, 32 or whatever.
% Now make lowest values show up as black.
cmap(1,:) = 0;
% Now make highest values show up as white.
cmap(end,:) = 1;
imshow(J,'Colormap',cmap) % show Image in false color
colorbar % add colorbar
h = colorbar; % define colorbar as variable
y_Scl = (1/C);
yticks = get(gca,'YTick');
set(h,'YTickLabel',sprintfc('%g', [yticks.*y_Scl]))
ylabel(h, 'cd/m^2')% add unit label
title(date); % Show date in image
caxis auto % set axis to auto
datacursormode on % enable datacursor
img = getframe(gcf);
nowstr = datestr(now, 'yyyy-mm-dd_HH_MM_SS');
folder = 'C:\Users\Taiko\Desktop\FalseColor\';
ImageFiles = dir( fullfile(folder, '*.jpg') );
if isempty(ImageFiles)
next_idx = 1;
else
lastfile = ImageFiles(end).name;
[~, basename, ~] = fileparts(lastfile);
file_number_str = regexp('(?<=.*_)\d+$', basename, 'match' );
last_idx = str2double(file_number_str);
next_idx = last_idx + 1;
end
newfilename = fullfile( folder, sprintf('%s_%04d.jpg', nowstr, next_idx) );
imwrite(img.cdata, newfilename);
Problems:
1) You are getting YTick of the figure (gca) but not the color bar. That would give you the "pixel" coordinates of the graph, instead of the actual values. Use yticks = get(h,'YTick');.
2) caxis auto Should come before overwriting YTicks (and after enabling the color bar); otherwise the scale and ticks will mismatch.
3) Do you mean C = 100000?
Result:
I'm trying to plot small images on a larger plot... Actually its isomap algorithm, I got many points, now each point correspond to some image, I know which image is it... The porblem is how to load that image and plot on the graph?
One more thing I have to plot both image and the points, so, basically the images will overlap the points.
Certainly, the type of image given here
Something like this should get you started. You can use the low-level version of the image function to draw onto a set of axes.
% Define some random data
N = 5;
x = rand(N, 1);
y = rand(N, 1);
% Load an image
rgb = imread('ngc6543a.jpg');
% Draw a scatter plot
scatter(x, y);
axis([0 1 0 1]);
% Offsets of image from associated point
dx = 0.02;
dy = 0.02;
width = 0.1;
height = size(rgb, 1) / size(rgb, 2) * width;
for i = 1:N
image('CData', rgb,...
'XData', [x(i)-dx x(i)-(dx+width)],...
'YData', [y(i)-dy y(i)-(dy+height)]);
end
I have a set of Dicom images on matlab and i would like to add a midline going through all the images
I am outputting the images via imshow3d function
thanks
Edit: here's what i have, the random points are not in the middle they just run through the image
>> clc;
>>clear;
>>%imports dicom images
>>run DicomImport.m;
>>%random points for shortest distance test
>>a = [1 10 200];
>>b = [500 512 300];
>>ab = b - a;
>>n = max(abs(ab)) + 1;
>>s = repmat(linspace(0, 1, n)', 1, 3);
>>for d = 1:3
>> s(:, d) = s(:, d) * ab(d) + a(d);
>>end
>>s = round(s);
>>Z = 593;
>>N = 512;
>>X = zeros(N, N, Z);
>>X(sub2ind(size(X), s(:, 1), s(:, 2), s(:, 3))) = 1;
>>C = find(X);
>>ans.Img(C) = 5000;
>> %shows image
>>imshow3D(ans.Img);
So it looks like ans.Img contains the 3D matrix consisting of your image stack. It looks like you've got something going, but allow me to do this a bit differently. Basically, you need to generate a set of coordinates where we can access the image stack and draw a vertical line in the middle of the each image in the image stack. Do something like this. First get the dimensions of the stack, then determine the halfway point for the columns. Next, generate a set of coordinates that will draw a line down the middle for one image. After you do this, repeat this for the rest of the slices and get the column major indices for these:
%// Get dimensions
[rows,cols,slices] = size(ans.Img);
%// Get halfway point for columns
col_half = floor(cols/2);
%// Generate coordinates for vertical line for one slice
coords_middle_row = (1:rows).';
coords_middle_col = repmat(col_half, rows, 1);
%// Generate column major indices for the rest of the slices:
ind = sub2ind(size(ans.Img), repmat(coords_middle_row, slices, 1), ...
repmat(coords_middle_col, slices, 1), ...
reshape(kron(1:slices, ones(rows, 1)), [], 1));
%// Set the pixels accordingly
ans.Img(ind) = 5000;
This code is quite similar to the answer I provided to one of your earlier question; i.e. I don't use imshow3D but the framework is similar and simpler to modify in order to suit your need. In this case, upon pressing a pushbutton a line appears at the middle of the stack and you can scroll through it with the slider. I hope this can be of help.
function LineDicom(~)
clc
clear
close all
%// Load demo data
S = load('mri');
%// Get dimensions and number of slices.
ImageHeight = S.siz(1); %// Not used here
ImageWidth = S.siz(2); %// Not used here
NumSlices = S.siz(3);
S.D = squeeze(S.D);
%// Create GUI
hFig = figure('Position',[100 100 400 400],'Units','normalized');
%// create axes with handle
handles.axes1 = axes('Position', [0.2 0.2 0.6 0.6]);
%// create y slider with handle
handles.y_slider = uicontrol('style', 'Slider', 'Min', 1, 'Max', NumSlices, 'Value',1, 'Units','normalized','position', [0.08 0.2 0.08 0.6], 'callback', #(s,e) UpdateY);
handles.SlideryListener = addlistener(handles.y_slider,'Value','PostSet',#(s,e) YListenerCallBack);
%// Create pusbutton to draw line
handles.DrawLineButton= uicontrol('style', 'push','position', [40 40 100 30],'String','Draw line', 'callback', {#DrawLine,handles});
%// Flag to know whether pushbutton has been pushed
handles.LineDrawn = false;
%// Show 1st slice
imshow(S.D(:,:,1))
guidata(hFig,handles);
%// Listeners callbacks followed by sliders callbacks. Used to display each
%// slice smoothly.
function YListenerCallBack
handles = guidata(hFig);
%// Get current slice
CurrentSlice = round(get(handles.y_slider,'value'));
hold on
imshow(S.D(:,:,CurrentSlice));
%// If button was button, draw line
if handles.LineDrawn
line([round(ImageWidth/2) round(ImageWidth/2)],[1 ImageHeight],'Color','r','LineWidth',2);
end
drawnow
guidata(hFig,handles);
end
function UpdateY(~)
handles = guidata(hFig); %// Get handles.
CurrentSlice = round(get(handles.y_slider,'value'));
hold on
imshow(S.D(:,:,CurrentSlice));
if handles.LineDrawn
line([round(ImageWidth/2) round(ImageWidth/2)],[1 ImageHeight],'Color','r','LineWidth',2);
end
drawnow
guidata(hFig,handles);
end
%// Pushbutton callback to draw line.
function DrawLine(~,~,handles)
line([round(ImageWidth/2) round(ImageWidth/2)],[1 ImageHeight],'Color','r','LineWidth',2);
handles.LineDrawn = true;
guidata(hFig,handles);
end
end
Sample output:
and after moving the slider up:
Is this what you meant? If not I'll remove that answer haha and sorry.
I've an image over which I would like to compute a local histogram within a circular neighborhood. The size of the neighborhood is given by a radius. Although the code below does the job, it's computationally expensive. I run the profiler and the way I'm accessing to the pixels within the circular neighborhoods is already expensive.
Is there any sort of improvement/optimization based maybe on vectorization? Or for instance, storing the neighborhoods as columns?
I found a similar question in this post and the proposed solution is quite in the spirit of the code below, however the solution is still not appropriate to my case. Any ideas are really welcomed :-) Imagine for the moment, the image is binary, but the method should also ideally work with gray-level images :-)
[rows,cols] = size(img);
hist_img = zeros(rows, cols, 2);
[XX, YY] = meshgrid(1:cols, 1:rows);
for rr=1:rows
for cc=1:cols
distance = sqrt( (YY-rr).^2 + (XX-cc).^2 );
mask_radii = (distance <= radius);
bwresponses = img(mask_radii);
[nelems, ~] = histc(double(bwresponses),0:255);
% do some processing over the histogram
...
end
end
EDIT 1 Given the received feedback, I tried to update the solution. However, it's not yet correct
radius = sqrt(2.0);
disk = diskfilter(radius);
fun = #(x) histc( x(disk>0), min(x(:)):max(x(:)) );
output = im2col(im, size(disk), fun);
function disk = diskfilter(radius)
height = 2*ceil(radius)+1;
width = 2*ceil(radius)+1;
[XX,YY] = meshgrid(1:width,1:height);
dist = sqrt((XX-ceil(width/2)).^2+(YY-ceil(height/2)).^2);
circfilter = (dist <= radius);
end
Following on the technique I described in my answer to a similar question you could try to do the following:
compute the index offsets from a particular voxel that get you to all the neighbors within a radius
Determine which voxels have all neighbors at least radius away from the edge
Compute the neighbors for all these voxels
Generate your histograms for each neighborhood
It is not hard to vectorize this, but note that
It will be slow when the neighborhood is large
It involves generating an intermediate matrix that is NxM (N = voxels in image, M = voxels in neighborhood) which could get very large
Here is the code:
% generate histograms for neighborhood within radius r
A = rand(200,200,200);
radius = 2.5;
tic
sz=size(A);
[xx yy zz] = meshgrid(1:sz(2), 1:sz(1), 1:sz(3));
center = round(sz/2);
centerPoints = find((xx - center(1)).^2 + (yy - center(2)).^2 + (zz - center(3)).^2 < radius.^2);
centerIndex = sub2ind(sz, center(1), center(2), center(3));
% limit to just the points that are "far enough on the inside":
inside = find(xx > radius+1 & xx < sz(2) - radius & ...
yy > radius + 1 & yy < sz(1) - radius & ...
zz > radius + 1 & zz < sz(3) - radius);
offsets = centerPoints - centerIndex;
allPoints = 1:prod(sz);
insidePoints = allPoints(inside);
indices = bsxfun(#plus, offsets, insidePoints);
hh = histc(A(indices), 0:0.1:1); % <<<< modify to give you the histogram you want
toc
A 2D version of the same code (which might be all you need, and is considerably faster):
% generate histograms for neighborhood within radius r
A = rand(200,200);
radius = 2.5;
tic
sz=size(A);
[xx yy] = meshgrid(1:sz(2), 1:sz(1));
center = round(sz/2);
centerPoints = find((xx - center(1)).^2 + (yy - center(2)).^2 < radius.^2);
centerIndex = sub2ind(sz, center(1), center(2));
% limit to just the points that are "far enough on the inside":
inside = find(xx > radius+1 & xx < sz(2) - radius & ...
yy > radius + 1 & yy < sz(1) - radius);
offsets = centerPoints - centerIndex;
allPoints = 1:prod(sz);
insidePoints = allPoints(inside);
indices = bsxfun(#plus, offsets, insidePoints);
hh = histc(A(indices), 0:0.1:1); % <<<< modify to give you the histogram you want
toc
You're right, I don't think that colfilt can be used as you're not applying a filter. You'll have to check the correctness, but here's my attempt using im2col and your diskfilter function (I did remove the conversion to double so it now output logicals):
function circhist
% Example data
im = randi(256,20)-1;
% Ranges - I do this globally for the whole image rather than for each neighborhood
mini = min(im(:));
maxi = max(im(:));
edges = linspace(mini,maxi,20);
% Disk filter
radius = sqrt(2.0);
disk = diskfilter(radius); % Returns logical matrix
% Pad array with -1
im_pad = padarray(im, (size(disk)-1)/2, -1);
% Convert sliding neighborhoods to columns
B = im2col(im_pad, size(disk), 'sliding');
% Get elements from each column that correspond to disk (logical indexing)
C = B(disk(:), :);
% Apply histogram across columns to count number of elements
out = histc(C, edges)
% Display output
figure
imagesc(out)
h = colorbar;
ylabel(h,'Counts');
xlabel('Neighborhood #')
ylabel('Bins')
axis xy
function disk = diskfilter(radius)
height = 2*ceil(radius)+1;
width = 2*ceil(radius)+1;
[XX,YY] = meshgrid(1:width,1:height);
dist = sqrt((XX-ceil(width/2)).^2+(YY-ceil(height/2)).^2);
disk = (dist <= radius);
If you want to set your ranges (edges) based on each neighborhood then you'll need to make sure that the vector is always the same length if you want to build a big matrix (and then the rows of that matrix won't correspond to each other).
You should note that the shape of the disk returned by fspecial is not as circular as what you were using. It's meant to be used a smoothing/averaging filter so the edges are fuzzy (anti-aliased). Thus when you use ~=0 it will grab more pixels. It'd stick with your own function, which is faster anyways.
You could try processing with an opposite logic (as briefly explained in the comment)
hist = zeros(W+2*R, H+2*R, Q);
for i = 1:R+1;
for j = 1:R+1;
if ((i-R-1)^2+(j-R-1)^2 < R*R)
for q = 0:1:Q-1;
hist(i:i+W-1,j:j+H-1,q+1) += (image == q);
end
end
end
end