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:
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 have two polygon images defined by 25 control points. I want to replace one polygon by another one in matlab. Below is an example of TC and BP.
I have added the code. I am not happy with the output texture in the replaced area. Also, I found the that if the polygon shape of the second image is smaller than the first image polygon shape then the output looks very bad.
clc;clear all;close all
im_original = imread('tc.jpg');
im_original=im2double(im_original);
%% ROI (X,Y) coordinates, variable name (pt_original)
load('tc.mat');
im_morphed = imread('bp.jpg');
img_morphed=im2double(im_morphed);
%% ROI (X,Y) coordinates, variable name (pt_morphed)
load('bp.mat');
%% Replace Face
[img_proc,mask] = defineRegion(im_original,pt_original);
img_morphed_proc = histeq_rgb(img_morphed, im_original, mask, mask);
sigma = 5;
se = strel('square',sigma);
mask = imerode(mask,se);
w = fspecial('gaussian',[50 50],sigma);
mask = imfilter(double(mask),w);
img_result = bsxfun(#times,double(img_morphed_proc),double(mask)) + bsxfun(#times,double(im_original),double(1-mask));
imshow(img_result)
function [img_proc,mask] = defineRegion(img, landmark)
sz = size(img);
k =convhull(landmark(:,2),landmark(:,1));
[YY,XX] = meshgrid(1:sz(1),1:sz(2));
in = inpolygon(XX(:),YY(:),landmark(k,1),landmark(k,2));
mask = reshape(in,[sz(2),sz(1)])';
img_proc = bsxfun(#times,im2double(img),double(mask));
end
function img_proc = histeq_rgb(img_src, img_dest, mask_src, mask_dest)
img_proc = img_src;
for i = 1 : 3
tmp_src = img_src(:,:,i);
tmp_src = tmp_src(mask_src);
tmp_dest = img_dest(:,:,i);
tmp_dest = tmp_dest(mask_dest);
t = histeq(tmp_src,imhist(tmp_dest));
tmp_proc = img_proc(:,:,i);
tmp_proc(mask_src) = t;
img_proc(:,:,i) = tmp_proc;
end
end
Output Image
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 using the following code in MATLAB to find the rectangle containing a car's license plate:
clc
clear
close all
%Open Image
I = imread('plate_1.jpg');
figure, imshow(I);
%Gray Image
Ib = rgb2gray(I);
figure,
subplot(1,2,1), imshow(Ib);
%Enhancement
Ih = histeq(Ib);
subplot(1,2,2), imshow(Ih);
figure,
subplot(1,2,1), imhist(Ib);
subplot(1,2,2), imhist(Ih);
%Edge Detection
Ie = edge(Ih, 'sobel');
figure,
subplot(1,2,1), imshow(Ie);
%Dilation
Id = imdilate(Ie, strel('diamond', 1));
subplot(1,2,2), imshow(Id);
%Fill
If = imfill(Id, 'holes');
figure, imshow(If);
%Find Plate
[lab, n] = bwlabel(If);
regions = regionprops(lab, 'All');
regionsCount = size(regions, 1) ;
for i = 1:regionsCount
region = regions(i);
RectangleOfChoice = region.BoundingBox;
PlateExtent = region.Extent;
PlateStartX = fix(RectangleOfChoice(1));
PlateStartY = fix(RectangleOfChoice(2));
PlateWidth = fix(RectangleOfChoice(3));
PlateHeight = fix(RectangleOfChoice(4));
if PlateWidth >= PlateHeight*3 && PlateExtent >= 0.7
im2 = imcrop(I, RectangleOfChoice);
figure, imshow(im2);
end
end
Plates all have white backgrounds. Currently,I use the rectangles' ratio of width to height to select candidate regions for output. This gives the plate rectangle in addition to several other irrelevant ones in the case of a white car. What method can I use to get only one output: the license plate?
Also, I don't find a plate at all when I run the code on a black car. Maybe that's because the car's color is the same as the plate edge.
Are there any alternatives to edge detection to avoid this problem?
Try this !!!
I = imread('http://8pic.ir/images/88146564605446812704.jpg');
im=rgb2gray(I);
sigma=1;
f=zeros(128,128);
f(32:96,32:96)=255;
[g3, t3]=edge(im, 'canny', [0.04 0.10], sigma);
se=strel('rectangle', [1 1]);
BWimage=imerode(g3,se);
gg = imclearborder(BWimage,8);
bw = bwareaopen(gg,200);
gg1 = imclearborder(bw,26);
imshow(gg1);
%Dilation
Id = imdilate(gg1, strel('diamond', 1));
imshow(Id);
%Fill
If = imfill(Id, 'holes');
imshow(If);
%Find Plate
[lab, n] = bwlabel(If);
regions = regionprops(lab, 'All');
regionsCount = size(regions, 1) ;
for i = 1:regionsCount
region = regions(i);
RectangleOfChoice = region.BoundingBox;
PlateExtent = region.Extent;
PlateStartX = fix(RectangleOfChoice(1));
PlateStartY = fix(RectangleOfChoice(2));
PlateWidth = fix(RectangleOfChoice(3));
PlateHeight = fix(RectangleOfChoice(4));
if PlateWidth >= PlateHeight*1 && PlateExtent >= 0.7
im2 = imcrop(I, RectangleOfChoice);
%figure, imshow(I);
figure, imshow(im2);
end
end
I created a 3D stick man walking in matlab and I want to add an image as the floor that he is walking on. I'm not sure how to go about doing this. I looked at an example of someone adding an image as a background. That is kinda what I want but I want it to appear as the floor. I going to give the stick man a trajectory and make him walk across the floor. Can anyone point me in the right direction.
Ok now I got it thanks to Andrey.
clear all
cyl = UnitCylinder(2);
sph = UnitSphere(2);
% Head
L1 = 2;
Head = translate(scale(sph,L1/2, L1/2, L1/2),0,0,L1+4.5);
Head.facecolor = 'yellow';
%Shoulder
r2 = 0.3;
L2 = 3;
Shoulder = translate(rotateX(scale(cyl,r2/2,r2/2,L2/2),90),0,0,5);
Shoulder.facecolor = 'red';
%Left Upper Arm
w1_s = [-20:4:20 20:-4:-20];
r3 = 0.3;
L3 = 2;
Upper_Arm_left = translate(scale(cyl,r3/2,r3/2,L3/2),0,0,-L3/2);
Upper_Arm_left.facecolor = 'red';
%Right Upper Arm
Upper_Arm_right = translate(scale(cyl,r3/2,r3/2,L3/2),0,0,-L3/2);
Upper_Arm_right.facecolor = 'red';
%Left Forearm
w2_s = [-5:1:5 5:-1:-5];
L3_f = 2.5;
Fore_Arm_left = translate(scale(cyl,r3/2,r3/2,L3_f/2),0,0,-L3_f/2);
Fore_Arm_left.facecolor = 'red';
%Right Forearm
Fore_Arm_right = translate(scale(cyl,r3/2,r3/2,L3_f/2),0,0,-L3_f/2);
Fore_Arm_right.facecolor = 'red';
%Chest
r4 = 2;
L4 = 2;
Chest = translate(scale(cyl,r4/2,r4/2,L4/2),0, 0, 5-L4/2);
Chest.facecolor = 'yellow';
%Weist
r5 = 1;
L5 = 2;
Weist = translate(scale(cyl,r5/2,r5/2,L5/2),0, 0, 5-L4-L5/2);
Weist.facecolor = 'yellow';
%Hip
L6 = 1.5;
Hip = translate(rotateX(scale(cyl,r2/2,r2/2,L6/2),90),0,0,5-L4-L5-r2/2);
Hip.facecolor = 'green';
%Left Upper Leg
r7 = 0.4;
L7 = 2.5;
L71 = (L6/2+r7/2);
L72 = 5-L7/2-L4-L5;
Upper_Leg_left = translate(scale(cyl,r7/2,r7/2,L7/2),0,0,-L7/2);
Upper_Leg_left.facecolor = 'green';
%Right Upper Leg
Upper_Leg_right = translate(scale(cyl,r7/2,r7/2,L7/2),0,0,-L7/2);
Upper_Leg_right.facecolor = 'green';
%Left Lower Leg
L7_f = 3;
Lower_Leg_left = translate(scale(cyl,r7/2,r7/2,L7_f/2),0,0,-L7_f/2);
Lower_Leg_left.facecolor = 'green';
%Right Lower Leg
Lower_Leg_right = translate(scale(cyl,r7/2,r7/2,L7_f/2),0,0,-L7_f/2);
Lower_Leg_right.facecolor = 'green';
angle1 = 0;
angle2 = 0;
for i = 1:120
angle1 = w1_s(rem(i,length(w1_s))+1);
angle2 = w2_s(rem(i,length(w1_s))+1);
Arm_left = combine(translate(rotateY(Fore_Arm_left,angle2),0,0,-L3), Upper_Arm_left);
Arm_right = combine(translate(rotateY(Fore_Arm_right,-angle2),0,0,-L3), Upper_Arm_right);
Arm_left = translate(rotateY(Arm_left,angle1),0,-L2/2,(5-L3/2)+L3/2);
Arm_right = translate(rotateY(Arm_right,-angle1),0,L2/2,(5-L3/2)+L3/2);
Leg_left = combine(translate(rotateY(Lower_Leg_left,-angle2),0,0,-L7), Upper_Leg_left);
Leg_right = combine(translate(rotateY(Lower_Leg_right,angle2),0,0,-L7), Upper_Leg_right);
Leg_left = translate(rotateY(Leg_left,-angle1),0,-L71,L72+L7/2);
Leg_right = translate(rotateY(Leg_right,angle1),0,L71,L72+L7/2);
Upper_Body = combine(Head, Shoulder, Arm_left, Arm_right, Chest, Weist);
Lower_Body = combine(Hip, Leg_left, Leg_right);
walker = combine(Upper_Body, Lower_Body);
cla
img = imread('peppers.png');
[X,Y] = ndgrid([-10 10],[-10 10]);
zImage = [-5 -5; -5 -5];
surf(X,Y,zImage,'CData',img,'FaceColor','texturemap');
% view([1 1 1]);
hold on
view(3)
set(gca,'xlim',[-10 10],'ylim',[-10 10],'zlim',[-6 6]);
renderpatch(walker);
camlight
box on
drawnow
pause(0.04)
end
I want to get something like this floor but I don't know how to do it. This person used a .fig in their code.This is a piece of what they used.
F1 = open('background1.fig');
background_gca = gca;
F2 = figure(2);
.
.
.
clf(F2)
copyobj(background_gca,F2);
view([-40,25])
set(gca,'xlim',[-5 5],'ylim',[-5 5],'zlim',[0 10]);
renderpatch(walker);
camlight
box on
% axis off
drawnow;
You had a small syntax error:
First, you reversed z and y :
surf(xImage,zImage,yImage,...)
Also, you should think about surf as a surface. Thus, all z should be 0
img = imread('peppers.png');
[X,Y] = ndgrid([-5 5],[-5 5]);
zImage = [0 0; 0 0];
surf(X,Y,zImage,'CData',img,'FaceColor','texturemap');
view([1 1 1]);
make that background image as GIF image, it feels like man walking on floor
otherwise visit here, useful video:http://www.youtube.com/watch?v=Ztb6_kIkXb8