Draw line to connect centroids - matlab

I have an image
.
After I process to find centroid, it has four centroids.
My goal is I want to connect them using line and measure the angle between this area. To be clear about the centroid and my goal, you can open .
Here it is my code to achieve the centroid
I = imread('22c.jpg');
Ibw = im2bw(I);
Ibw = imfill(Ibw,'holes');
Ilabel = bwlabel(Ibw);
stat = regionprops(Ilabel,'centroid');
imshow(I); hold on;
for x = 1: numel(stat)
plot(stat(x).Centroid(1),stat(x).Centroid(2),'ro');
end
The problem is I am still confused to do the next (to connect each centroids and measure the angle). I need your help, thanks

Here is a file exchange link to bresenham.m
Changed your code to get all the 4 centroids
%// read your input image
im = imread('http://i.stack.imgur.com/xeqe8.jpg');
BW = im>220;
CC = bwconncomp(BW);
stat = regionprops(CC,'Centroid');
figure; imshow(BW); hold on
for x = 1: numel(stat)
plot(stat(x).Centroid(1),stat(x).Centroid(2),'ro');
end
Here is the output:
Further implementation:
%// putting all the Centroid coordinates into corresponding x,y variable
x = [stat(1).Centroid(1),stat(2).Centroid(1),stat(3).Centroid(1),stat(4).Centroid(1)];
y = [stat(1).Centroid(2),stat(2).Centroid(2),stat(3).Centroid(2),stat(4).Centroid(2)];
%// obtain row and col dim
[r,c] = size(BW);
%// get all x,y values connecting the centroid points
[xAll{1},yAll{1}] = bresenham(x(1),y(1),x(4),y(4));
[xAll{2},yAll{2}] = bresenham(x(2),y(2),x(3),y(3));
[xAll{3},yAll{3}] = bresenham(x(3),y(3),x(4),y(4));
%// change row and col subs to linear index
for ii = 1:3
idx{ii} = sub2ind(size(BW),yAll{ii},xAll{ii});
end
%// change grayscale image to 3D (as you want red line)
out = repmat(im,[1,1,3]);
%// obtaining corresponding index of all 3 slices
for ii = 1:3
idxall{ii} = bsxfun(#plus, idx{ii},[0:2].*(r*c));
end
%// keep only the index of 1st slice to 255 and changing rest to 0 to obtain a red line.
%// Similar process for blue line except keep the index in the 3rd slice to 255
out(cat(1,idxall{:})) = 0;
out(idx{1}) = 255;
out(idx{2}) = 255;
out(idx{3}+2*(r*c)) = 255;
%// see what you have obtained
figure; imshow(out);hold on
for x = 1: numel(stat)
plot(stat(x).Centroid(1),stat(x).Centroid(2),'bo');
end
Result:
Note: The line may look dotted due to the picture's large size, but its continuous
Last figure zoomed to see continuous line:
Going further:
You may have to take the advice of #Spektre to find the angle of inclination using atan2. Also refer his answer for more explanation.

Related

Matlab image translation matrix

I'm very new to Matlab. I'm learning some image manipulation basics, and I'm a bit confused on how to write a translation without using imtranslate.
this is my code but it just displays a black background. Thank you.
img = imread('name2.png');
figure(1);
% pixel matrix
[orig_x, orig_y,z] = size(img);
final_x = 600;
final_y = 600;
% define the final array with calculated dimensions and fill the array with zeros ie.,black
final_img = uint8(zeros([final_x final_y 3 ]));
for i = 1 : size(final_img, 1)
for j = 1 : size(final_img, 2)
new_x = img(i) + 5;
new_y = img(j) + 5;
% fprintf('X: %f\n',new_x); % prints 255
final_img(i) = new_x;
final_img(j) = new_y;
end
end
imshow(final_img);
This is one solution for 'translation only' transformation.
I = imread('Lenna.png');
shiftX = 5; % shift columns
shiftY = 5; % shift rows
% Assigning empty matrix for result, expected to be shiftX-1 larger in rows and shiftY-1 larger in columns
nI = uint8( zeros(size(I,1)+shiftY-1, size(I,2)+shiftX-1, size(I,3));
% Translate
nI(shiftY:end, shiftX:end, :) = I;
imshow(nI)
Now the image will start from (x,y) = (5,5) instead of (1,1). Also note that in matlab image coordinate system, x and y axis start from upper left corner (documentation).
final_img:
You've defined "final_img" with new x and new y but you haven't replaced the zeros in the red/green/blue values. It's all black because of your initialisation filling the final_img with all zeros.
Maybe try this instead of what you've written:
%{
[X,map] = imread('name2.png');
figure(1);
% X should be 600 by 600
%Translate X however you wish, e.g.:
X = X +5;
%Verify that the colormap, map, is not empty, and convert
%the data in X to RGB and store as your final_img.
if ~isempty(map)
final_img = ind2rgb(X,map);
end
%}
I am also not sure if you want to be indexing img with just a single i without the the other dimensions like you have:
new_x = img(i) + 5;
?
For the problems in your specific code, I wrote in the comments some of them.
A short way to achieve image translation is by 2D convolution with a filter of zeros and just one 1, that will preserve the values of the image, but relocate them according to the size of the filter and the position of the 1 in it.
That seems you want to move the image but preserve the size of the total image, if I get it right. So:
r=3; c=5; % number of rows and columns to move
filt=zeros(r*2+1, c*2+1); filt(end)=1; % the filetr
img2=conv2(img,filt,'same'); % the translated image
Just for the example, lets translate "cameraman" with 20 rows and columns:
img=imread('cameraman.tif');
imshow(img)
r=20; c=20;
filt=zeros(r*2+1, c*2+1); filt(end)=1;
img2=conv2(img,filt,'same');
figure; imshow(img2,[])

how to extract graph in a hand written word image

I have taken one hand written word image as input for my program, I want to resize that image into 100x100 after resizing am skeletonised that image and i divide that image into equal segments of 10x10 in that segment i want to find the center of mass for foreground pixels in each segments. in my program i am finding the center of mass for foreground pixel. in my algorithm which am trying to implement they told that use that center of mass as a node add it into resulting graph and add the Add edges based on edge extraction like Minimal Spanning
Tree (MST).my question is i know the center of mass of foreground pixels im my code values of x and y coordinates stored in CXX and CYY matrix using plot(CXX,CYY,'g*'); am plotting on image. but i do not know how to use that center of mass as a node add it into resulting graph and how to add edges using MST.
this is my output image:
I want to use that plotted points as a node to resulting graph, after adding that points as a node i want add the edges based on Minimum Spanning Tree. please help me to how can i add those plotted point as nodes to resulting graph and how can i add edges to those nodes using minimum spanning tree.
clc;
clear all;
close all;
X=imread('math1.jpg');
imfinfo('math1.jpg')
figure,imshow(X)
b = imresize(X,[100,100]);
si = size(b,1);
sj = size(b,2);
%figure;imshow(b);
% Binarization
th = graythresh(b);
I = im2bw(b,th);
% w = 5;
% h = 5;
% c=si/w;
% r=sj/h;
kl=bwmorph(~I,'thin',inf);
%figure,imshow(kl)
R(:,:)=kl(:,:);
%grid size
t1=5;
D=100;
I=1;
U1=t1;
J=1;
U2=t1;
E=1;
t2=D/t1;
%Z=1;
for i=1:t2
for j=1:t2
B(I:U1,J:U2)=R(I:U1,J:U2);
[x,y]=find(B==1);
CX=mean(x);
CY=mean(y);
CXXX(E)=CX;
CYYY(E)=CY;
CXX(i,j)=CX;
CYY(i,j)=CY;
T(I:U1,J:U2)=B(I:U1,J:U2);
J=J+t1;
U2=U2+t1;
E=E+1;
clear B x y
end
I=I+t1;
U1=U1+t1;
J=1;
U2=t1;
end
%plot and grid
figure,imshow(R)
hold on
M = size(R,1);
N = size(R,2);
a=t1;
b=t1;
for k = 1:a:M
x = [1 N];
y = [k k];
plot(x,y,'Color','white');
set(findobj('Tag','MyGrid'),'Visible','on')
end
for k = 1:b:N
x = [k k];
y = [1 M];
plot(x,y,'Color','white');
set(findobj('Tag','MyGrid'),'Visible','on')
end
plot(CYY,CXX,'c*')
this is my input image please try to run this code.
my input image
input image

MATLAB's treeplot: align by node height from top

Here's what I get by using the treeplot function on MATLAB (this is the example image):
Here's what I'd like to get:
As you can see, I'd like to have the position of each node according to its distance from the root. Is that possible?
I was also looking for a "root-aligned" treeplot in Matlab and found no solution. Here is what I came up with, in case someone is still in need of it (I'm using the same example as in the Matlab documentation):
nodes = [0 1 2 2 4 4 4 1 8 8 10 10];
At first we need to get the x and y coordinates of every node in the original tree plot and find all leaves in it:
[x,y] = treelayout(nodes);
leaves = find( y == min(y) );
Next, we reconstruct every chain in the tree plot and store it in a matrix (by doing so, we can later change the y position of the nodes):
num_layers = 1/min(y)-1;
chains = zeros(num_layers, length(leaves));
for l=1:length(leaves)
index = leaves(l);
chain = [];
chain(1) = index;
parent_index = nodes(index);
j = 2;
while (parent_index ~= 0)
chain(j) = parent_index;
parent_index = nodes(parent_index);
j = j+1;
end
chains(:,l) = padarray(flip(chain), [0, num_layers-length(chain)], 'post');
end
Now we compute the new y-coordinates determined by the row index in the matrix and dependent on the number of layers in the tree:
y_new = zeros(size(y));
for i=1:length(nodes)
[r,c] = find(chains==i, 1);
y_new(i) = max(y) - (r-1)*1/(num_layers+1);
end
We can now plot the re-positioned nodes and add the connecting lines:
plot(x, y_new, 'o');
hold on
for c=1:size(chains, 2)
line_x = x(chains(chains(:,c)>0, c));
line_y = y_new(chains(chains(:,c)>0, c));
line(line_x, line_y);
end
If you like, you can also add the node labels to the plot:
for t=1:length(nodes)
text(x(t)+0.025, y_new(t), num2str(t));
end
xlim([0 1]);
ylim([0 1]);
The resulting figure looks as follows:

How can i render lineseries/contour/etc objects to array of pixel data?

I have an array of pixel data frames for use with VideoWriter. I want to overlay lineseries/contour objects into each frame. I don't want to make the movie by iteratively drawing each frame to a figure and capturing it with getframe, because that gives poor resolution and is slow. I tried using getframe on a plot of just the contour, but that returns images scaled to the wrong size with weird margins, especially when using 'axis equal,' which I need.
Updated to accommodate feedback from OP
Getting the contour data as pixel data is not trivial (if possible at all) since using getframe doesn't yield predictable results
What we can do is to extract the contour data and then overlay it on the pixel data frames, forcing them to be to the same scale and then do a getframe on the resultant merged image. This will at least ensure that they two data sets area aligned.
The following code shows the principle though you'd need to modify it for your own needs:
%% Generate some random contours to use
x = linspace(-2*pi,2*pi);
y = linspace(0,4*pi);
[X,Y] = meshgrid(x,y);
Z = sin(X)+cos(Y);
[~,h] = contour(X,Y,Z);
This yields the following contours
Now we get the handles of the children of this image. These will all be 'patch' type objects
patches = get(h,'Children');
Also get the axis limits for the contours
lims = axis;
Next, create a new figure and render the pixel frame data into it
In this example I'm just loading an image but you get the idea.
%% Render frame data
figure
i = imread( some_image_file_png );
This image is actually 194 x 259 x 3. I can display it and rescale the X and Y axes using
%% Set image axes
image(flipdim(i,1),'XData',[lims(1) lims(2)],'YData',[lims(4) lims(3)]);
Note the use of flipdim() to vertically flip the image since the image Y-axis runs in the opposite sense to the contour Y axis. This gives me:
Now I can plot the contours (patches) form the contour plot over the top of the image in the same coordinate space
%% Plot patches
for p =1:length(patches)
xd = get( patches(p), 'XData' );
yd = get( patches(p), 'YData' );
% This causes all contours to be rendered in white. You may
% want to play with this a little
cd = zeros(size(xd));
patch( xd, yd, cd, 'EdgeColor', 'w');
end
This yields
You can now use getframe to extract the frame. If it's important to have coloured contours, you will need to extract colour data from the original contour map and use it to apply an appropriate colouring in the overlaid image.
As a short cut, it's also possible to compile all patch data into a single MxN matrix and render with a single call to patch but I wrote it this way to demonstrate the process.
Well, here's a Bresenham-esque solution based on the ContourMatrix. Not ideal cuz doesn't handle line width, antialiasing, or any more than a single color. But it's pretty efficient (not quite Bham efficient).
function renderContour
clc
close all
x = randn(100,70);
[c,h] = contour(x,[0 0],'LineColor','r');
axis equal
if ~isnumeric(h.LineColor)
error('not handled')
end
cs = nan(size(c,2),4);
k = 0;
ci = 1;
for i = 1:size(c,2)
if k <= 0
k = c(2,i);
else
if k > 1
cs(ci,:) = reshape(c(:,i+[0 1]),[1 4]);
ci = ci + 1;
end
k = k - 1;
end
end
pix = renderLines(cs(1:ci-1,:),[1 1;fliplr(size(x))],10,h.LineColor);
figure
image(pix)
axis equal
end
function out = renderLines(cs,rect,res,color)
% cs = [x1(:) y1(:) x2(:) y2(:)]
% rect = [x(1) y(1);x(2) y(2)]
% doesnt handle line width, antialiasing, etc
% could do those with imdilate, imfilter, etc.
test = false;
if test
if false
cs = [0 0 5 5; 0 5 2.5 2.5];
rect = [0 0; 10 10];
else
cs = 100 * randn(1000,4);
rect = 200 * randn(2);
end
res = 10;
color = [1 .5 0];
end
out = nan(abs(res * round(diff(fliplr(rect)))));
cs = cs - repmat(min(rect),[size(cs,1) 2]);
d = [cs(:,1) - cs(:,3) cs(:,2) - cs(:,4)];
lens = sqrt(sum(d.^2,2));
for i = 1:size(cs,1)
n = ceil(sqrt(2) * res * lens(i));
if false % equivalent but probably less efficient
pts = linspace(0,1,n);
pts = round(res * (repmat(cs(i,1:2),[length(pts) 1]) - pts' * d(i,:)));
else
pts = round(res * [linspace(cs(i,1),cs(i,3),n);linspace(cs(i,2),cs(i,4),n)]');
end
pts = pts(all(pts > 0 & pts <= repmat(fliplr(size(out)),[size(pts,1) 1]),2),:);
out(sub2ind(size(out),pts(:,2),pts(:,1))) = 1;
end
out = repmat(flipud(out),[1 1 3]) .* repmat(permute(color,[3 1 2]),size(out));
if test
image(out)
axis equal
end
end

Sorting two column vectors into 3D matrix based on position

Using the imfindcircles function in MATLAB to track circles in two images. I start with approximately a grid of circles which deforms. I am trying to sort the two column vector from imfindcircles into matrices so that neighbouring circles are neighbouring elements in the matrices. The first image the circles conform to a grid and the following code works:
[centXsort,IX] = sortrows(centres1,1); %sort by x
centYsort =zeros(289,2); %preallocate
for i = 1:17:289
[sortedY,IY] = sortrows(centXsort(i:i+16,:),2); %sort by y within individual column
centYsort(i:i+16,:) = sortedY;
end
cent1mat = reshape(centYsort,17,17,2); %reshape into centre matrices
This doesn't work for the second image as some of the circles overlap in the x or y direction, but neighbouring circles never overlap. This means that in the second set of matrices the neighbouring circles aren't neighbouring elements after sorting.
Is there a way to approximate a scatter of points into a matrix?
This answer doesn't work in every single case, but it seems good enough for situations where the points don't vary too wildly.
My idea is to start at the grid corners and work our way along the outside diagonals of the matrix, trying to "grab" the nearest points that seem like they fit into the grid-points based any surrounding points we've already captured.
You will need to provide:
The number of rows (rows) and columns (cols) in the grid.
Your data points P arranged in a N x 2 array, rescaled to the unit square on [0,1] x [0,1]. (I assume the you can do this through visual inspection of the corner points of your original data.)
A weight parameter edge_weight to tell the algorithm how much the border points should be attracted to the grid border. Some tests show that 3-5 or so are good values.
The code, with a test case included:
%// input parameters
rows = 11;
cols = 11;
edge_weight = 4;
%// function for getting squared errors between the points list P and a specific point pt
getErr =#(P,pt) sqrt( sum( bsxfun(#minus,P,pt(:)').^2, 2 ) ); %'
output_grid = zeros(rows,cols,2); %// output grid matrix
check_grid = zeros(rows,cols); %// matrix flagging the gridpoints we have covered
[ROW,COL] = meshgrid(... %// coordinate points of an "ideal grid"
linspace(0,1,rows),...
linspace(0,1,cols));
%// create a test case
G = [ROW(:),COL(:)]; %// the actual grid-points
noise_factor = 0.35; %// noise radius allowed
rn = noise_factor/rows;
cn = noise_factor/cols;
row_noise = -rn + 2*rn*rand(numel(ROW),1);
col_noise = -cn + 2*cn*rand(numel(ROW),1);
P = G + [row_noise,col_noise]; %// add noise to get points
%// MAIN LOOP
d = 0;
while ~isempty(P) %// while points remain...
d = d+1; %// increase diagonal depth (d=1 are the outer corners)
for ii = max(d-rows+1,1):min(d,rows)%// for every row number i...
i = ii;
j = d-i+1; %// on the dth diagonal, we have d=i+j-1
for c = 1:4 %// repeat for all 4 corners
if i<rows & j<cols & ~check_grid(i,j) %// check for out-of-bounds/repetitions
check_grid(i,j) = true; %// flag gridpoint
current_gridpoint = [ROW(i,j),COL(i,j)];
%// get error between all remaining points and the next gridpoint's neighbours
if i>1
errI = getErr(P,output_grid(i-1,j,:));
else
errI = edge_weight*getErr(P,current_gridpoint);
end
if check_grid(i+1,j)
errI = errI + edge_weight*getErr(P,current_gridpoint);
end
if j>1
errJ = getErr(P,output_grid(i,j-1,:));
else
errJ = edge_weight*getErr(P,current_gridpoint);
end
if check_grid(i,j+1)
errJ = errJ + edge_weight*getErr(P,current_gridpoint);
end
err = errI.^2 + errJ.^2;
%// find the point with minimal error, add it to the grid,
%// and delete it from the points list
[~,idx] = min(err);
output_grid(i,j,:) = permute( P(idx,:), [1 3 2] );
P(idx,:) = [];
end
%// rotate the grid 90 degrees and repeat for next corner
output_grid = cat(3, rot90(output_grid(:,:,1)), rot90(output_grid(:,:,2)));
check_grid = rot90(check_grid);
ROW = rot90(ROW);
COL = rot90(COL);
end
end
end
Code for plotting the resulting points with edges:
%// plotting code
figure(1); clf; hold on;
axis([-0.1 1.1 -0.1 1.1])
for i = 1:size(output_grid,1)
for j = 1:size(output_grid,2)
scatter(output_grid(i,j,1),output_grid(i,j,2),'b')
if i < size(output_grid,1)
plot( [output_grid(i,j,1),output_grid(i+1,j,1)],...
[output_grid(i,j,2),output_grid(i+1,j,2)],...
'r');
end
if j < size(output_grid,2)
plot( [output_grid(i,j,1),output_grid(i,j+1,1)],...
[output_grid(i,j,2),output_grid(i,j+1,2)],...
'r');
end
end
end
I've developed a solution, which works for my case but might not be as robust as required for some. It requires a known number of dots in a 'square' grid and a rough idea of the spacing between the dots. I find the 'AlphaShape' of the dots and all the points that lie along the edge. The edge vector is shifted to start at the minimum and then wrapped around a matrix with the corresponding points are discarded from the list of vertices. Probably not the best idea for large point clouds but good enough for me.
R = 50; % search radius
xy = centres2;
x = centres2(:,1);
y = centres2(:,2);
for i = 1:8
T = delaunay(xy); % delaunay
[~,r] = circumcenter(triangulation(T,x,y)); % circumcenters
T = T(r < R,:); % points within radius
B = freeBoundary(triangulation(T,x,y)); % find edge vertices
A = B(:,1);
EdgeList = [x(A) y(A) x(A)+y(A)]; % find point closest to origin and rotate vector
[~,I] = min(EdgeList);
EdgeList = circshift(EdgeList,-I(3)+1);
n = sqrt(length(xy)); % define zeros matrix
matX = zeros(n); % wrap x vector around zeros matrix
matX(1,1:n) = EdgeList(1:n,1);
matX(2:n-1,n) = EdgeList(n+1:(2*n)-2,1);
matX(n,n:-1:1) = EdgeList((2*n)-1:(3*n)-2,1);
matX(n-1:-1:2,1) = EdgeList((3*n)-1:(4*n)-4,1);
matY = zeros(n); % wrap y vector around zeros matrix
matY(1,1:n) = EdgeList(1:n,2);
matY(2:n-1,n) = EdgeList(n+1:(2*n)-2,2);
matY(n,n:-1:1) = EdgeList((2*n)-1:(3*n)-2,2);
matY(n-1:-1:2,1) = EdgeList((3*n)-1:(4*n)-4,2);
centreMatX(i:n+i-1,i:n+i-1) = matX; % paste into main matrix
centreMatY(i:n+i-1,i:n+i-1) = matY;
xy(B(:,1),:) = 0; % discard values
xy = xy(all(xy,2),:);
x = xy(:,1);
y = xy(:,2);
end
centreMatX(centreMatX == 0) = x;
centreMatY(centreMatY == 0) = y;