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:
Related
I have 2 greyscale images that i am trying to align using scalar scaling 1 , rotation matrix [2,2] and translation vector [2,1]. I can calculate image1's transformed coordinates as
y = s*R*x + t;
Below the resulting images are shown.
The first image is image1 before transformation,
the second image is image1 (red) with attempted interpolation using interp2 shown on top of image2 (green)
The third image is when i manually insert the pixel values from image1 into an empty array (that has the same size as image2) using the transformed coordinates.
From this we can see that the coordinate transformation must have been successful, as the images are aligned although not perfectly (which is to be expected since only 2 coordinates were used in calculating s, R and t) .
How come interp2 is not producing a result more similar to when i manually insert pixel values?
Below the code for doing this is included:
Interpolation code
function [transformed_image] = interpolate_image(im_r,im_t,s,R,t)
[m,n] = size(im_t);
% doesn't help if i use get_grid that the other function is using here
[~, grid_xr, grid_yr] = get_ipgrid(im_r);
[x_t, grid_xt, grid_yt] = get_ipgrid(im_t);
y = s*R*x_t + t;
yx = reshape(y(1,:), m,n);
yy = reshape(y(2,:), m,n);
transformed_image = interp2(grid_xr, grid_yr, im_r, yx, yy, 'nearest');
end
function [x, grid_x, grid_y] = get_ipgrid(image)
[m,n] = size(image);
[grid_x,grid_y] = meshgrid(1:n,1:m);
x = [reshape(grid_x, 1, []); reshape(grid_y, 1, [])]; % X is [2xM*N] coordinate pairs
end
The manual code
function [transformed_image] = transform_image(im_r,im_t,s,R,t)
[m,n] = size(im_t);
[x_t, grid_xt, grid_yt] = get_grid(im_t);
y = s*R*x_t + t;
ymat = reshape(y',m,n,2);
yx = ymat(:,:,1);
yy = ymat(:,:,2);
transformed_image = zeros(m,n);
for i = 1:m
for j = 1:n
% make sure coordinates are inside
if (yx(i,j) < m & yy(i,j) < n & yx(i,j) > 0.5 & yy(i,j) > 0.5)
transformed_image(round(yx(i,j)),round(yy(i,j))) = im_r(i,j);
end
end
end
end
function [x, grid_x, grid_y] = get_grid(image)
[m,n] = size(image);
[grid_y,grid_x] = meshgrid(1:n,1:m);
x = [grid_x(:) grid_y(:)]'; % X is [2xM*N] coordinate pairs
end
Can anyone see what i'm doing wrong with interp2? I feel like i have tried everything
Turns out i got interpolation all wrong.
In my question i calculate the coordinates of im1 in im2.
However the way interpolation works is that i need to calculate the coordinates of im2 in im1 such that i can map the image as shown below.
This means that i also calculated the wrong s,R and t since they were used to transform im1 -> im2, where as i needed im2 -> im1. (this is also called the inverse transform). Below is the manual code, that is basically the same as interp2 with nearest neighbour interpolation
function [transformed_image] = transform_image(im_r,im_t,s,R,t)
[m,n] = size(im_t);
[x_t, grid_xt, grid_yt] = get_grid(im_t);
y = s*R*x_t + t;
ymat = reshape(y',m,n,2);
yx = ymat(:,:,1);
yy = ymat(:,:,2);
transformed_image = zeros(m,n);
for i = 1:m
for j = 1:n
% make sure coordinates are inside
if (yx(i,j) < m & yy(i,j) < n & yx(i,j) > 0.5 & yy(i,j) > 0.5)
transformed_image(i,j) = im_r(round(yx(i,j)),round(yy(i,j)));
end
end
end
end
i want to make a second axis for the same colorbar of an false color image. the second scale should follow this convention : [new values] = Log10([old values]/108000)/-0.4 . i have this code for the first axis:
C = 10
hFig = figure('Name','False Color Luminance Map', 'ToolBar','none','MenuBar','none');
% Create/initialize default colormap of jet.
cmap = parula(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
caxis auto
y_Scl = (1/C);
yticks = get(h,'YTick');
set(h,'YTickLabel',sprintfc('%g', [yticks.*y_Scl]))
in a previous post here i got this lines for an second axis:
BarPos = get(hBar1,'position');
ylabel(hBar1,'label','FontSize',12);
haxes = axes('position',BarPos,'color','none','ytick',0:5:15,'ylim',[0 15],'xtick',[]);
how can i make the second axis use the the yticks of the first axis as an input for the convention?
EDIT: here is what i came up with. the thing is the values are wrong :/
fname='IMG_0041'; % select target image
C = 1000; % Constant to adjust image
K = 480; % Cameraconstant
RGB = imread([fname, '.tif']);% Read Image as tif
info = imfinfo([fname,'.CR2']); % get Metadata from CR2
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'); % save DateTimeOriginal
I = rgb2gray(RGB); % convert Image to greyscale
% N_s = K*(t*S)/power(f,2))*L
L = power(f,2)/(K*t*S)*C; % calculate L/N_s
J = immultiply(I,L);
hFig = figure('Name','False Color Luminance Map', 'ToolBar','none', 'MenuBar','none');
% Create/initialize default colormap of jet.
cmap = parula(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
caxis auto
y_Scl = (1/C);
yticks = get(h,'YTick');
set(h,'YTickLabel',sprintfc('%g', [yticks.*y_Scl]))
BarPos = get(h,'position');
haxes = axes('position',BarPos,'color','none','ylim',[0 150]);
set(haxes,'YTickLabel', sprintfc('%g', log10(yticks.*y_Scl/108000)/-0.4));
https://www.sendspace.com/file/39wwm9 -> files for testing the code
I'm trying to write a MATLAB script that does the following:
Given: pixel coordinates(x,y) for a .jpg image
Goal: Check, within a 5 pixel radius of given coordinates, if there is a pixel of a certain value.
For example, let's say I'm given the coordinates (100,100), then I want to check the neighborhood of (100,100) within my image for any pixels that are black (0,0,0). So perhaps, pixel (103, 100) and (104,100) might have the value (0,0,0).
Current code:
x_coord = uint32(coord(:,1));
y_coord = uint32(coord(:,2));
count = 0;
for i = 1:length(x_coord)
%(img(x,y) returns pixel value at that (x,y)
%Note 0 = black. Indicating that, at that position, the image is just
% black
if img(x_coord(i),y_coord(i)) == 0
count = count + 1;
end
end
It currently only checks at an exact location. Not in a local neighborhood. How to could I extend this?
EDIT: Also note, as long as there as at least one pixel in the neighborhood with the value, I increment count. I'm not trying to enumerate how many pixels in the neighborhood have that value, just trying to find evidence of at least one pixel that has that value.
EDIT:
Even though I am unable to identify an error with the code, I am not able to get the exact results I want. Here is the code I am using.
val = 0; %pixel value to check
N = 50; % neighbourhood radius
%2D grid of coordinates surrounding center coordinate
[R, C] = ndgrid(1 : size(img, 1), 1 : size(img, 2));
for kk = 1 : size(coord, 1)
r = coord(kk, 1); c = coord(kk, 2); % Get pixel locations
% mask of valid locations within the neighbourhood (avoid boundary problems)
mask = (R - r).^2 + (C - c).^2 <= N*N;
pix = img(mask); % Get the valid pixels
valid = any(pix(:) ~= val);
% Add either 0 or 1 depending if we have found any matching pixels
if(valid == 1)
img = insertMarker(img, [r c], 'x', 'color', 'red', 'size', 10);
imwrite(img, images(i).name,'tiff');
end
count = count + valid;
end
An easier way to do this would be to use indexing to grab a neighbourhood, then to check to see if any of the pixels in the neighbourhood have the value that you're looking for, use any on a flattened version of this neighbourhood. The trick with grabbing the right neighbourhood is to first generate a 2D grid of coordinates that span the entire dimensions of your image, then simply use the equation of a circle with the centre of it being each coordinate you are looking at and determine those locations that satisfy the following equation:
(x - a)^2 + (y - b)^2 <= N^2
N is the radius of the observation window, (a, b) is a coordinate of interest while (x, y) is a coordinate in the image. Use meshgrid to generate the coordinates.
You would use the above equation to create a logical mask, index into your image to pull the locations that are valid within the mask and check how many pixels match the one you want. Another added benefit with the above approach is that you are not subject to any out of bounds errors. Because you are pre-generating the list of all valid coordinates in your image, generating the mask will confine you within the boundaries of the image so you never have to check for out of boundaries conditions.... even when you specify coordinates to search that are out of bounds.
Specifically, assuming your image is stored in img, you would do:
count = 0; % Remembers total count of pixels matching a value
val = 0; % Value to match
N = 50; % Radius of neighbourhood
% Generate 2D grid of coordinates
[x, y] = meshgrid(1 : size(img, 2), 1 : size(img, 1));
% For each coordinate to check...
for kk = 1 : size(coord, 1)
a = coord(kk, 1); b = coord(kk, 2); % Get the pixel locations
mask = (x - a).^2 + (y - b).^2 <= N*N; % Get a mask of valid locations
% within the neighbourhood
pix = img(mask); % Get the valid pixels
count = count + any(pix(:) == val); % Add either 0 or 1 depending if
% we have found any matching pixels
end
The proposed solution:
fc = repmat(-5:5,11,1);
I = (fc.^2+fc'.^2)<=25;
fc_x = fc(I);
fc_y = fc'; fc_y = fc_y(I);
for i = 1:length(x_coord)
x_toCheck = fc_x + x_coord(i);
y_toCheck = fc_y + y_coord(i);
I = x_toCheck>0 & x_toCheck<=yourImageWidth;
I = I.*(y_toCheck>0 & y_toCheck<=yourImageHeight);
x_toCheck = x_toCheck(logical(I));
y_toCheck = y_toCheck(logical(I));
count = sum(img(x_toCheck(:),y_toCheck(:)) == 0);
end
If your img function can only check one pixel at a time, just add a for loop:
for i = 1:length(x_coord)
x_toCheck = fc_x + x_coord(i);
y_toCheck = fc_y + y_coord(i);
I = x_toCheck>0 & x_toCheck<=yourImageWidth;
I = I.*(y_toCheck>0 & y_toCheck<=yourImageHeight);
x_toCheck = x_toCheck(logical(I));
y_toCheck = y_toCheck(logical(I));
for j = 1:length(x_toCheck)
count = count + (img(x_toCheck(j),y_toCheck(j)) == 0);
end
end
Step-by-step:
You first need to get all the coordinates within 5 pixels range of the given coordinate.
We start by building a square of 11 pixels in length/width.
fc = repmat(-5:5,11,1);
fc_x = fc;
fc_y = fc';
plot(fc_x,fc_y,'.');
We now need to build a filter to get rid of those points outside the 5-pixel radius.
I = (fc.^2+fc'.^2)<=25;
Apply the filter, so we can get a circle of 5-pixel radius.
fc_x = fc_x(I);
fc_y = fc_y(I);
Next translate the centre of the circle to the given coordinate:
x_toCheck = fc_x + x_coord(i);
y_toCheck = fc_y + y_coord(i);
You need to check whether part of the circle is outside the range of your image:
I = x_toCheck>0 & x_toCheck<=yourImageWidth;
I = I.*(y_toCheck>0 & y_toCheck<=yourImageHeight);
x_toCheck = x_toCheck(logical(I));
y_toCheck = y_toCheck(logical(I));
Finally count the pixels:
count = sum(img(x_toCheck,y_toCheck) == 0);
The matlab code below splits up an image into a number of smaller images. It then counts the number of black pixels in the image and displays it as a percentage of the total number of pixels in the picture. example of image
My question is - instead of counting the black pixels and displaying the percentage, how can I count the white pixels? (essentially the opposite!)
Thanks
% Divide an image up into blocks (non-overlapping tiles).
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
workspace; % Make sure the workspace panel is showing.
fontSize = 20;
% Read the image from disk.
rgbImage = imread('edge-diff.jpg');
% Display image full screen.
imshow(rgbImage);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
drawnow;
% Get the dimensions of the image. numberOfColorBands should be = 3.
[rows columns numberOfColorBands] = size(rgbImage)
%==========================================================================
% The first way to divide an image up into blocks is by using mat2cell().
blockSizeR = 400; % Rows in block.
blockSizeC = 400; % Columns in block.
% Figure out the size of each block in rows.
% Most will be blockSizeR but there may be a remainder amount of less than that.
wholeBlockRows = floor(rows / blockSizeR);
blockVectorR = [blockSizeR * ones(1, wholeBlockRows), rem(rows, blockSizeR)];
% Figure out the size of each block in columns.
wholeBlockCols = floor(columns / blockSizeC);
blockVectorC = [blockSizeC * ones(1, wholeBlockCols), rem(columns, blockSizeC)];
% Create the cell array, ca.
% Each cell (except for the remainder cells at the end of the image)
% in the array contains a blockSizeR by blockSizeC by 3 color array.
% This line is where the image is actually divided up into blocks.
if numberOfColorBands > 1
% It's a color image.
ca = mat2cell(rgbImage, blockVectorR, blockVectorC, numberOfColorBands);
else
ca = mat2cell(rgbImage, blockVectorR, blockVectorC);
end
percentBlack = cellfun(#(x)sum(sum(all(x == 0, 3))) / (numel(x) / size(x,3)), ca);
% Now display all the blocks.
plotIndex = 1;
numPlotsR = size(ca, 1);
numPlotsC = size(ca, 2);
for r = 1 : numPlotsR
for c = 1 : numPlotsC
fprintf('plotindex = %d, c=%d, r=%d\n', plotIndex, c, r);
% Specify the location for display of the image.
subplot(numPlotsR, numPlotsC, plotIndex);
ax2 = subplot(numPlotsR, numPlotsC, plotIndex);
% Extract the numerical array out of the cell
% just for tutorial purposes.
rgbBlock = ca{r,c};
imshow(rgbBlock); % Could call imshow(ca{r,c}) if you wanted to.
[rowsB columnsB numberOfColorBandsB] = size(rgbBlock);
set(ax2, 'box', 'on', 'Visible', 'on', 'xtick', [], 'ytick', []);
% Make the caption the block number.
averageBlack = percentBlack(r,c);
disp(numPlotsR);
disp(averageBlack);
caption = sprintf('Frame #%d of %d\n Percentage information content %0.2f', ...
plotIndex, numPlotsR*numPlotsC, averageBlack*100);
title(caption);
drawnow;
% Increment the subplot to the next location.
plotIndex = plotIndex + 1;
end
end
This line:
percentBlack = cellfun(#(x)sum(sum(all(x == 0, 3))) / (numel(x) / size(x,3)), ca);
specifically the part that says all(x == 0, 3) means "all color channels have value 0". You want to change it to "all color channels have value 1 (or 255 depends on your image)"
So basically, change that 0 to 1 or 255, deependinf if your image is unit8 or double
I have the following function that is successful in creating a grey circular mask over the image input, such that the new image is a grey border around a circular image. Example: Grey circular mask.
All I want to do is make the mask a very specific green, but I haven't been successful.
Here is the code:
function [newIm] = myCircularMask(im)
%Setting variables
rad = size(im,1)/2.1; %Radius of the circle window
im = double(im);
[rows, cols, planes]= size(im);
newIm = zeros(rows, cols, planes);
%Generating hard-edged circular mask with 1 inside and 0 outside
M = rows;
[X,Y] = meshgrid(-M/2:1:(M-1)/2, -M/2:1:(M-1)/2);
mask = double(zeros(M,M));
mask(X.^2 + Y.^2 < rad^2) = 1;
% Soften edge of mask
gauss = fspecial('gaussian',[12 12],0.1);
mask = conv2(mask,gauss,'same');
% Multiply image by mask, i.e. x1 inside x0 outside
for k=1:planes
newIm(:,:,k) = im(:,:,k).*mask;
end
% Make mask either 0 inside or -127 outside
mask = (abs(mask-1)*127);
% now add mask to image
for k=1:planes
newIm(:,:,k) = newIm(:,:,k)+mask;
end
newIm = floor(newIm)/255;
The type of green I would like to use is of RGB values [59 178 74].
I'm a beginner with MATLAB, so any help would be greatly appreciated.
Cheers!
Steve
After masking your image, create a color version of your mask:
% test with simple mask
mask = ones(10,10);
mask(5:7,5:7)=0;
% invert mask, multiply with rgb-values, make rgb-matrix:
r_green=59/255; g_green=178/255; b_green=74/255;
invmask=(1-mask); % use mask with ones/zeroes
rgbmask=cat(3,invmask*r_green,invmask*g_green,invmask*b_green);
Add this to your masked image.
Edit:
function [newIm] = myCircularMask(im)
%Setting variables
rad = size(im,1)/2.1; %Radius of the circle window
im = double(im);
[rows, cols, planes]= size(im);
newIm = zeros(rows, cols, planes);
%Generating hard-edged circular mask with 1 inside and 0 outside
M = rows;
[X,Y] = meshgrid(-M/2:1:(M-1)/2, -M/2:1:(M-1)/2);
mask = double(zeros(M,M));
mask(X.^2 + Y.^2 < rad^2) = 1;
% Soften edge of mask
gauss = fspecial('gaussian',[12 12],0.1);
mask = conv2(mask,gauss,'same');
% Multiply image by mask, i.e. x1 inside x0 outside
for k=1:planes
newIm(:,:,k) = im(:,:,k).*mask;
end
% Here follows the new code:
% invert mask, multiply with rgb-values, make rgb-matrix:
r_green=59/255; g_green=178/255; b_green=74/255;
invmask=(1-mask); % use mask with ones/zeroes
rgbmask=cat(3,invmask*r_green,invmask*g_green,invmask*b_green);
newIm=newIm+rgbmask;
Note that I haven't been able to test my suggestion, so there might be errors.