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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 have lena image in Matlab. First I need to find the centroid C, and then divide the image into equal number of parts. I can calculate the centroid of the image but from that how can I divide the image into equal number of parts as shown below. Please anyone help me.
Thanks
Using poly2mask to create binary sectors and using the resulting sectors for indexing
Code:
im = imread('peppers.png');
r = 300;
out1 = ones(max(size(im,1),r*2)+2,max(size(im,2),r*2)+2,3).*255;
xoffset = floor((size(out1,2)-size(im,2))/2);
yoffset = floor((size(out1,1)-size(im,1))/2);
out1(yoffset:yoffset+size(im,1)-1,xoffset:xoffset+size(im,2)-1,:) = im(:,:,:);
im = out1;
cy = floor(size(im,1)/2);
cx = floor(size(im,2)/2);
figure;
imshow(uint8(im));
hold on
pos = [cx-r+1 cy-r+1 r*2 r*2];
rectangle('Position',pos,'Curvature',[1 1]);
x1 = [-r, 0, -r*cosd(45), -r*cosd(45); r, 0, r*cosd(45), r*cosd(45)]+cx+1;
y1 = [0, -r, -r*sind(45), r*sind(45); 0, r, r*sind(45), -r*sind(45)]+cy+1;
plot(x1,y1);
hold off
figure;
for i = 0:45:315
t = linspace(-i,-i-45,128);
x = [cx, cx+r*cosd(t), cx];
y = [cy, cy+r*sind(t), cy];
bw = poly2mask( x, y, size(im,1),size(im,2));
bw = repmat(bw,1,1,3);
out = ones(size(im,1),size(im,2),size(im,3)).*155;
out(bw) = im(bw);
subplot(2,4,(i/45)+1); imshow(uint8(out));
end;
Results:
Original Image
Partitions drawn over Original Image
Segments of the image
Update
for getting pixel values of the lines, by using Bresenham function from here
figure;
bw1 = zeros(size(im,1),size(im,2));
outmat = zeros(size(bw1));
[X,Y] = bresenham(cx+1-r,cy+1,cx+1+r,cy+1);
ind = sub2ind(size(outmat), Y, X);
outmat(ind) = 1;
[X,Y] = bresenham(cx+1,cy+1-r,cx+1,cy+1+r);
ind = sub2ind(size(outmat), Y, X);
outmat(ind) = 1;
[X,Y] = bresenham(cx+1-r*cosd(45),cy+1-r*sind(45),cx+1+r*cosd(45),cy+1+r*sind(45));
ind = sub2ind(size(outmat), Y, X);
outmat(ind) = 1;
[X,Y] = bresenham(cx+1-r*cosd(45),cy+1+r*sind(45),cx+1+r*cosd(45),cy+1-r*sind(45));
ind = sub2ind(size(outmat), Y, X);
outmat(ind) = 1;
se = strel('disk',5); %// change the '5' value to affect thickness of the line
outmat = imdilate(outmat,se);
outmat = repmat(boolean(outmat),1,1,3);
outmat1 = zeros(size(outmat));
outmat1(outmat) = im(outmat);
imshow(uint8(outmat1));
Pixel values under each lines
Check the following code. I just did it for a grayscale image. You can now change it to a color image as well. Check and pls confirm this is what you wanted.
clear all;
i = rgb2gray(imread('hestain.png'));
imshow(i);
cr = floor(size(i,1)/2);
cl = floor(size(i,2)/2);
r = min(cr, cl);
a = 90;
r1 = cr;
c1 = size(i,2);
v1=[c1 r1]-[cl cr];
i2 = zeros(size(i,1),size(i,2),ceil(360/a));
for ri = 1:size(i,1)
for ci = 1:size(i,2)
v2=[ci ri]-[cl cr];
a2 = mod(-atan2(v1(1)*v2(2)-v1(2)*v2(1), v1*v2'), 2*pi) * 180/pi;
d2 = pdist([ci ri; cl cr],'euclidean');
if d2<=r
if ceil(a2/a)==0
a2 =1;
end
i2(ri,ci,ceil(a2/a)) = i(ri,ci);
end
end
end
figure;
for i=1:360/a
subplot(2,180/a,i);
imshow(mat2gray(i2(:,:,i)));
end
Sample output:
Code is below. I'm looping through an input image 1 pixel at a time and determining its RGB value. Afterwards i'm trying to find the average RGB value for the image overall. For some reason the averaging portion of my code isnt working though.
im = imread(filename);
[width, height, depth] = size(im);
count = 0;
r=0;
g=0;
b=0;
for x = 1 : width
for y = 1: height
r = r + im(x,y,1);
g = g + im(x,y,2);
b = b + im(x,y,3);
count = count + 1;
end
end
%find averages of each RGB value.
r2 = r/count;
g2 = g/count;
b2 = b/count;
Why not vectorizing and using mean?
mean( reshape( im, [], 3 ), 1 )
The following code would work as well;
pep = imread('peppers.png');
mean(mean(pep))
This will return a 1x1x3 vector which will be the mean values of R, G, and B respectively.
i am able to overlap the binary image with the original RGB image. Through the following code.
inImage = imresize(imread('1.jpg'),0.25);
%imwrite(inImage,'original.jpg');
inImage = skyremoval(inImage);
greyImage = rgb2gray(inImage);
thresh1 = 200;
whiteLayer = greyImage > thresh1;
thresh2 = 125;
lightgreyLayer = greyImage > thresh2 & greyImage <= thresh1;
layer1 = whiteLayer*200;
layer2 = lightgreyLayer*125;
G = layer1 + layer2;
% figure,imshow(G);
se = strel('disk', 15);
Io = imopen(G, se);
figure,imshow(Io);
f = find(Io==0);
mask(:,:,1) = f; % For the red plane
% mask(:,:,2) = f; % For the green plane
% mask(:,:,3) = f; % For the blue plane
inImage(mask)=0;
I = inImage;
figure,imshow(I);
The following are the images.
Here.The first is the binary image derived from the original, second is the original and the third is the result after overlaping both binary and rgb images, by the code given above. As you can see the problem i am facing is that the part except road is cyan all i want is the part which is not road to be black. How can i do that?
Please alter my code if you can help. Thank you.
You don't need the find command, since you can index with a binary image.
Instead of
f = find(Io==0);
mask(:,:,1) = f; % For the red plane
% mask(:,:,2) = f; % For the green plane
% mask(:,:,3) = f; % For the blue plane
inImage(mask)=0;
I = inImage;
figure,imshow(I);
you can write
mask = repmat(Io==0,1,1,3); %# 1 wherever mask is false
I = inImage;
I(mask) = 0;
figure,imshow(I);