I have an Image, converted into binary, i got all the black pixel's coordinates.
The 'matrix' contains the x and y coordinates arranged by columns.
Now i Need to make a Simulation, to see if my Programme works.
I have to generate an Matrix Image with my results.
im=imread('square.jpg');
imshow(im); c=im2bw(im); figure; imshow(c);
dim = size(c) % size of the image
x = [];
y = [];
xdif = [];
newx = [];
matrix = [];
for i = 1:dim(1)
for j = 1:dim(2)
if c(i,j)==0;
x = [x i];
y = [y j];
end
end
end
% show black pixel's coordinates
p = [x;y];
%number of pixels
nr = length(x)
dimp = size(p);
xval = p(1,:);
yval = p(2,:);
j=1;
i=1;
for z = 1:dimp(2)-1
xdif = xval(z+1)-xval(z);
ff=find(xdif > 0);
if ff == 1
i = 1;
else
i=i+1;
end
newx(i,j)= xval(z);
newy(i,j)= yval(z);
if ff == 1
j= j+1;
end
end
xsize = size(newx);
ysize = size(newy);
matrix_size = xsize(2)+ysize(2)
xinc = 1;
yinc = 1;
x=1;
for ct = 1:1:matrix_size/2
x;
matrix(:,x) = newx(:,xinc);
matrix(:,x+1) = newy(:,yinc);
matrix;
xinc = xinc+1;
yinc = yinc+1;
x=x+3;
end
matrix
this is my Programme, now i need to make a simulation, by generating an image with my coordinates.
how can i do that?
thank's
Related
I have the code that generate a plot which has points plotted both inside and outside the contour line. I want to eliminate the points outside the outermost contour line. I'm using gaussian copula function.
plot(givenData(:,1),givenData(:,2),'b.','MarkerSize',3);
givenData is a 5000x2 matrix and I want only those values that lie inside the outer contour line to be plotted.
plot i'm getting
plot i want to generate
i want to eliminate the points lying outside the red contour which are shown as geen dots.
function [xgrid,ygrid,Z] = biVariateContourPlotsGMMCopula(givenData,gmmObject,~,numMeshPoints,x_dim,y_dim)
d = 2;
if nargin < 5
x_dim = 1;
y_dim = 2;
end
if x_dim == y_dim
hist(givenData(:,x_dim),10);
return;
end
numMeshPoints = min(numMeshPoints,256);
givenData = givenData(:,[x_dim y_dim]);
alpha = gmmObject.alpha;
mu = gmmObject.mu(:,[x_dim y_dim]);
sigma = gmmObject.sigma([x_dim y_dim],[x_dim y_dim],:) + 0.005*repmat(eye(d),[1 1 numel(alpha)]);
gmmObject = gmdistribution(mu,sigma,alpha);
bin_num = 256;
for j = 1:2
l_limit = min(gmmObject.mu(:,j))-3*(max(gmmObject.Sigma(j,j,:))^0.5);
u_limit = max(gmmObject.mu(:,j))+3*(max(gmmObject.Sigma(j,j,:))^0.5);
xmesh_inverse_space{j} = (l_limit:(u_limit-l_limit)/(bin_num-1):u_limit);
end
[~,pdensity{i},xmesh{i}]=kde(currentVar,numMeshPoints);
pdensity{i}(pdensity{i}<0) = 0;
cdensity{i} = cumsum(pdensity{i});
cdensity{i} = (cdensity{i}-min(cdensity{i}))/(max(cdensity{i})-min(cdensity{i})); % scaling the cdensity value to be between [0 1]
end
[xgrid,ygrid] = meshgrid(xmesh{1}(2:end-1),xmesh{2}(2:end-1));
for k = 1:d
marginalLogLikelihood_grid{k} = log(pdensity{k}(2:end-1)+eps);
marginalCDFValues_grid{k} = cdensity{k}(2:end-1);
end
[marg1,marg2] = meshgrid(marginalLogLikelihood_grid{1},marginalLogLikelihood_grid{2});
[xg,yg] = meshgrid(marginalCDFValues_grid{1},marginalCDFValues_grid{2});
inputMatrix = [reshape(xg,numel(xg),1) reshape(yg,numel(yg),1)];
copulaLogLikelihoodVals = gmmCopulaPDF(inputMatrix,gmmObject,xmesh_inverse_space);
Z = reshape(copulaLogLikelihoodVals,size(marg1,1),size(marg1,2));
Z = Z+marg1+marg2;
Z = exp(Z);
plot(givenData(:,1),givenData(:,2),'b.','MarkerSize',3);hold
contour(xgrid,ygrid,Z,40,'EdgeColor',[1 0 0]);
axis tight;
I am working on a project and my aim is to color and 20 randomly generated lines of all fixed length, then count all lines crossing y=0 and color them green else color them blue.
I have come up with the code below but it doesn't work well in the if statement.
Can someone please have at look? Thank you if you can help!
Question:
How do I correct the if statement to display all the lines and count those lines crossing y = 0?
clear
clc
L = 1.5;
a = -5;
b = 5;
GLines = 0:5:5;
m = 0;
for i = 1:20
X1 = rand(1,i)*(b-a)+a;
Y1 = rand(1,i)*(b-a)+a;
Angle = rand(1,i)*360;
X2 = L*cosd(Angle) + X1;
Y2 = L*sind(Angle) + X2;
if X1(i) < L/2* sind(Angle)
m = m + 1;
plot([X1(i); X2(i)],[Y1(i); Y2(i)], '-g');
else
plot([X1(i); X2(i)],[Y1(i); Y2(i)], '-b');
end
for j = 1:length(GLines)
axis square
ylim([-5 5]);
xlim([-5 5]);
y = yline(GLines(j));
end
end
disp(m)
If a line crosses zero, the sign of Y1 and Y2 will be opposite, so you can do the following:
clear; clc
L = 1.5;
a = -5;
b = 5;
GLines = 0:5:5;
m = 0;
figure;
hold all;
for i = 1:20
X1 = rand*(b-a)+a;
Y1 = rand*(b-a)+a;
Angle = rand*360;
X2 = L*cosd(Angle) + X1;
Y2 = L*sind(Angle) + Y1;
if Y1*Y2 < 0 % if line crosses zero
m = m + 1;
c = 'g'; % color = green
else
c = 'b'; % color = blue
end
plot([X1; X2],[Y1; Y2],'color',c);
end
axis equal
disp(m)
which gives the follwing plot
and correctly outputs m = 2.
I am trying to reconstruct an image using the projections from the Neutron image scanner. I am using the following code. I am not able to obtain a meaningful reconstructed image.
Can anybody advise me on where I am going wrong.
much appreciated,
Vani
filename = strcat(' Z:\NIST_Data\2016\SEPT\example reconstructed\carboxylic\carboxylic reconstructed part 3\Coral\',srcFiles(i).name);
I=imread(filename);
P = im2double(I);
if i == 1
array3d = P;
else
array3d = cat(3, array3d, P);
end
end
num = size(array3d,3);
for p = 1:num
PR = double(squeeze(array3d(p,:,:)));
[L,C]=size(PR);
w = [-pi : (2*pi)/L : pi-(2*pi)/L];
Filt = abs(sin(w));
Filt = Filt(1:463);
for i = 1:C,
IMG = fft(PR(:,i));
end
FiltIMG = IMG*Filt; %FiltIMG = filter (b, a, IMG);
% Remove any remaining imaginary parts
FIL = real(FiltIMG);
% filter the projections
%filtPR = projfilter(PR);
%filtPR = filterplus(PR);
filtPR = FIL;
THETA=0:180;
% figure out how big our picture is going to be.
n = size(filtPR,1);
sideSize = n;
% convert THETA to radians
th = (pi/180)*THETA;
% set up the image
m = length(THETA);
BPI = zeros(sideSize,sideSize);
% find the middle index of the projections
midindex = (n+1)/2;
% set up x and y matrices
x = 1:sideSize;
y = 1:sideSize;
[X,Y] = meshgrid(x,y);
xpr = X - (sideSize+1)/2;
ypr = Y - (sideSize+1)/2;
% loop over each projection
%figure
%colormap(jet)
%M = moviein(m);
for i = 1:m
tic
disp(['On angle ', num2str(THETA(i))]);
% figure out which projections to add to which spots
filtIndex = round(midindex + xpr*sin(th(i)) - ypr*cos(th(i)));
% if we are "in bounds" then add the point
BPIa = zeros(sideSize,sideSize);
spota = find((filtIndex > 0) & (filtIndex <= n));
newfiltIndex = filtIndex(spota);
BPIa(spota) = filtPR(newfiltIndex(:),i);
%keyboard
BPI = BPI + BPIa;
toc
%imagesc(BPI)
%M(:,i) = getframe;
%figure(2)
%plot(filtPR(:,i));
%keyboard
end
BPI = BPI./m;
h=figure
imagesc(BPI)
saveas(h,sprintf('filtsli-FIG%d.tif',p));end
I have to draw a hipsometric map on a 3D plot. I have two vectors 1x401 (named xLabels and yLabels) which are the geo coordinates, and401x401(namedA`) matrix with the altitude data. To plot the data I use:
surf(xLabels, yLabels,A,'EdgeColor','None','Marker','.');
which leads to something like that:
But i would like to have something like that:
On the second image, only the surface is plotted, while my image looks like pillars.
I tried even make my vectors to 401x401 using meshgrid but it did not have any effect.
Do you have any idea what I should change?
#EDIT
I checked for X and Y data. I quess is too small interval (0.0083), but when i try plot good second of upper plots with same interval it draws correctly.
#EDIT2:
sizeX = 4800;
sizeY = 6000;
pixdegree = 0.0083; % 1 pixel is 0.0083 degree on map
intSize = 2;
lon = 37 + (35/60);
lat = 55+ (45/60);
fDEM = 'E020N90';
fHDR = 'E020N90.HDR';
[startXY, endXY] = calcFirstPixel(lon, lat); %calc borders for my area
f = fopen('E020N90.DEM');
offset = (startXY(1,2)*sizeX*intSize)+(startXY(1,1)*intSize);
fseek(f, offset,0); %seek from curr file pos
x = 0;
A = [];
BB = [];
jump = (intSize*sizeX)-(401*2);
while x<401
row = fread(f, 802);
fseek(f, jump, 0); %jump 2 next row
A = [A row];
x = x+1;
end
fclose(f);
A = A';
A = A(:,2:2:802);
m1 = min(A(:)); %wartość minimalna dla naszej podziałki
m2 = max(A(:)); %wartość maksymalna dla naszej podziałki
step = m2/8; % będzie 8 kolorów
highScale = m1:step:m2-step; %wartości graniczne dla każdego z nich
%handles.axes1 = A;
colormap(hObject, jet(8));
startXtick = 20 + pixdegree*startXY(1,1);
endXtick = 20 + pixdegree*endXY(1,1);
startYtick = 90 - pixdegree*endXY(1,2);
endYtick = 90 - pixdegree*startXY(1,2);
[XX,YY] = ndgrid(startXtick:pixdegree:endXtick,startYtick:pixdegree:endYtick);
xLabels = startXtick:pixdegree:endXtick;
yLabels = startYtick:pixdegree:endYtick;
surf(xLabels, yLabels,A,'EdgeColor','None','Marker','.');
set(gca,'YDir','normal');
grid on;
view([45 45])
And .DEM files
function [startXY, endXY] = calcFirstPixel(lon,lat)
global fHDR;
format = '%s %s';
f = fopen(fHDR);
cont = textscan(f, format);
LonStart = str2double(cont{1,2}{11,1});
LatStart = str2double(cont{1,2}{12,1});
diffPerPix = str2double(cont{1,2}{13,1});
fclose(f);
x = LonStart;
countX = 0
y = LatStart;
countY= 0;
while x<lon
x=x+diffPerPix
countX = countX +1;
end
while y>lat
y=y-diffPerPix
countY = countY+1;
end
startXY= [countX-200 countY-200];
endXY = [countX+200 countY+200];
end
I have to create an algorithm with Matlab that, with a image of a hand, can know the form of the hand by the number of raised fingers and the presence or absence of the thumb. So far, the algorithm is almost complete but I don't know what more I can do that could find the peaks that represents the fingers. We tried a lot of things but nothing works. The idea is to find when there is a sudden increasement but as the pixels are never completely aligned, nothing that we tried worked. Someone has any idea? Here is the code so far.
The image that he is reading is this one:
To know if the finger is relevant or not, we already have an idea that might work... but we need to find the fingers first.
clear all
close all
image=imread('mao2.jpg');
YCBCR = rgb2ycbcr(image);
image=YCBCR;
cb = image(:,:,2);
cr = image(:,:,3);
imagek(:,1) = cb(:);
imagek(:,2) = cr(:);
imagek = double(imagek);
[IDX, C] = kmeans(imagek, 2, 'EmptyAction', 'singleton');
s=size(image);
IDX= uint8(IDX);
C2=round(C);
imageNew = zeros(s(1),s(2));
temp = reshape(IDX, [s(1) s(2)]);
for i = 1 : 1 : s(1)
for j = 1 : 1 : s(2)
imageNew(i,j,:) = C2(temp(i,j));
end
end
imageNew=uint8(imageNew);
[m,n]=size(imageNew);
for i=1:1:m
for j = 1:1:n
if(imageNew(i,j)>=127)
pretobranco(i,j)=0;
else
pretobranco(i,j)=1;
end
end
end
I2=imfill(pretobranco);
imshow(I2);
imwrite(I2, 'mao1trab.jpg');
[m,n]=size(I2);
B=edge(I2);
figure
imshow(B);
hold on;
stats=regionprops(I2,'BoundingBox');
rect=rectangle('position', [stats(1).BoundingBox(1), stats(1).BoundingBox(2), stats(1).BoundingBox(3), stats(1).BoundingBox(4)], 'EdgeColor', 'r');
stats(1).BoundingBox(1)
stats(1).BoundingBox(2)
stats(1).BoundingBox(3)
stats(1).BoundingBox(4)
figure
Bound = B( stats(1).BoundingBox(2): stats(1).BoundingBox(2)+stats(1).BoundingBox(4)-1, stats(1).BoundingBox(1):stats(1).BoundingBox(1)+stats(1).BoundingBox(3)-1);
imshow(Bound)
y1 = round(stats(1).BoundingBox(2))
y2 = round(stats(1).BoundingBox(2)+stats(1).BoundingBox(4)-1)
x1 = round(stats(1).BoundingBox(1))
x2 = round(stats(1).BoundingBox(1)+stats(1).BoundingBox(3)-1)
% Bounding box contida em imagem[M, N].
[M,N] = size(Bound)
vertical=0;
horizontal=0;
if M > N
vertical = 1 %imagem vertical
else
horizontal = 1 %imagem horizontal
end
%Find thumb
MaoLeft = 0;
MaoRight = 0;
nPixelsBrancos = 0;
if vertical==1
for i = x1:1:x2
for j= y1:1:y2
if I2(j,i) == 1
nPixelsBrancos = nPixelsBrancos + 1; %Numero de pixels da mão
end
end
end
for i=x1:1:x1+30
for j=y1:1:y2
if I2(j,i) == 1
MaoLeft = MaoLeft + 1; %Number of pixels of the hand between the 30 first colums
end
end
end
for i=x2-30:1:x2
for j=y1:1:y2
if I2(j,1) == 1
MaoRight = MaoRight + 1; %Number of pixels of the hand between the 30 last colums
end
end
end
TaxaBrancoLeft = MaoLeft/nPixelsBrancos
TaxaBrancoRight = MaoRight/nPixelsBrancos
if TaxaBrancoLeft <= (7/100)
if TaxaBrancoRight <= (7/100)
Thumb = 0 %Thumb in both borders is defined as no Thumb.
else
ThumbEsquerdo = 1 %Thumb on left
end
end
if TaxaBrancoRight <= (7/100) && TaxaBrancoLeft >= (7/100)
ThumbDireito = 1 %Thumb on right
end
end
if horizontal==1
for i = x1:1:x2
for j= y1:y2
if I2(i,j) == 1
nPixelsBrancos = nPixelsBrancos + 1; %Numero de pixels da mão
end
end
end
for i=x1:1:x2
for j=y1:1:y1+30
if I2(i,j) == 1
MaoLeft = MaoLeft + 1; %Numero de pixels da mão entre as 30 primeiras colunas
end
end
end
for i=x1:1:x2
for j=y2-30:1:y2
if I2(j,1) == 1
MaoRight = MaoRight + 1; %Numero de pixels da mão entre as 30 ultimas colunas
end
end
end
TaxaBrancoLeft = MaoLeft/nPixelsBrancos
TaxaBrancoRight = MaoRight/nPixelsBrancos
if TaxaBrancoLeft <= (7/100)
if TaxaBrancoRight <= (7/100)
Thumb = 0 %Polegar nas duas bordas. Definimos como sem polegar.
else
ThumbEsquerdo = 1 %Polegar na borda esquerda
end
end
if TaxaBrancoRight <= (7/100) && TaxaBrancoLeft >= (7/100)
ThumbDireito = 1 %Polegar na borda direita
end
end
figure
imshow(I2);
%detecção da centroid
Ibw = im2bw(I2);
Ilabel = bwlabel(Ibw);
stat = regionprops(Ilabel,'centroid');
figure
imshow(I2); hold on;
for x = 1: numel(stat)
plot(stat(x).Centroid(1),stat(x).Centroid(2),'ro');
end
centroid = [stat(x).Centroid(1) stat(x).Centroid(2)] %coordenadas x e y da centroid
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Seemed like an interesting problem, so I gave it a shot. Basically you start with a Sobel filter to find the edges in your image (after slight denoising). Then clean up the resulting lines, use them to separate regions within your binary mask of the hand, use a watershed transform to find the wrist, some distance transforms to find other landmarks, then remove the palm. What you're left with is separate regions for each finger and thumb. You can count those regions easily enough or find which way they are pointing, or whatever you'd like.
imgURL = 'https://encrypted-tbn2.gstatic.com/imgs?q=tbn:ANd9GcRQsqJtlrOnSbJNTnj35Z0uG9BXsecX2AXn1vV0YDKodq-zSuqnnQ';
imgIn=imread(imgURL);
gaussfilt = fspecial('gaussian', 3, .5); % Blur starting image
blurImg = imfilter(double(img(:,:,1)), gaussfilt);
edgeImg = edge(blurImg, 'sobel'); % Use Sobel edge filter to pick out contours of hand + fingers
% Clean up contours
edgeImg = bwmorph(edgeImg, 'close', 1);
edgeImg = bwmorph(edgeImg, 'thin', Inf);
% Clean up rogue spots in corners
edgeImg([2 end-1], 2) = 0;
edgeImg([2 end-1], end-1) = 0;
% Extend lines to edge of image (correct for 'close' operation above
edgeImg([1 end],:) = edgeImg([2 end-1],:);
edgeImg(:, [1 end]) = edgeImg(:, [2 end-1]);
% Remove all but the longest line
regs = regionprops(edgeImg, 'Area', 'PixelIdxList');
regs(vertcat(regs.Area) ~= max(vertcat(regs.Area))) = [];
lineImg = false(size(edgeImg, 1), size(edgeImg, 2));
lineImg(regs.PixelIdxList) = 1;
fillImg = edgeImg;
% Close in wrist
if any(fillImg(1,:))
fillImg(1,:) = 1;
end
if any(fillImg(end,:))
fillImg(end,:) = 1;
end
if any(fillImg(:,1))
fillImg(:,1) = 1;
end
if any(fillImg(:,end))
fillImg(:,end) = 1;
end
fillImg = imfill(fillImg, 'holes');
fillImg([1 end], :) = 0;
fillImg(:, [1 end]) = 0;
fillImg([1 end],:) = fillImg([2 end-1],:);
fillImg(:, [1 end]) = fillImg(:, [2 end-1]);
% Start segmenting out hand + fingers
handBin = fillImg;
% Set lines in above image to 0 to separate closely-spaced fingers
handBin(lineImg) = 0;
% Erode these lines to make fingers a bit more separate
handBin = bwmorph(handBin, 'erode', 1);
% Segment out just hand (remove wrist)
distImg = bwdist(~handBin);
[cDx, cDy] = find(distImg == max(distImg(:)));
midWrist = distImg;
midWrist = max(midWrist(:)) - midWrist;
midWrist(distImg == 0) = Inf;
wristWatershed = watershed(imerode(midWrist, strel('disk', 10)));
whichRegion = wristWatershed(cDx, cDy);
handBin(wristWatershed ~= whichRegion) = 0;
regs = regionprops(handBin, 'Area', 'PixelIdxList');
regs(vertcat(regs.Area) ~= max(vertcat(regs.Area))) = [];
handOnly = zeros(size(handBin, 1), size(handBin, 2));
handOnly(regs.PixelIdxList) = 1;
% Find radius of circle around palm centroid that excludes wrist and splits
% fingers into separate regions.
% This is estimated as D = 1/3 * [(Centroid->Fingertip) + 2*(Centroid->Wrist)]
% Find Centroid-> Wrist distance
dist2w = wristWatershed ~= whichRegion;
dist2w = bwdist(dist2w);
distToWrist = dist2w(cDx, cDy);
% Find Centroid-> Fingertip distance
dist2FE = zeros(size(handOnly, 1), size(handOnly, 2));
dist2FE(cDx, cDy) = 1;
dist2FE = bwdist(dist2FE).*handOnly;
distToFingerEnd = max(dist2FE(:));
circRad = mean([distToFingerEnd, distToWrist, distToWrist]); % Estimage circle diameter
% Draw circle
X = bsxfun(#plus,(1:size(handOnly, 1))',zeros(1,size(handOnly, 2)));
Y = bsxfun(#plus,(1:size(handOnly, 2)),zeros(size(handOnly, 1),1));
B = sqrt(sum(bsxfun(#minus,cat(3,X,Y),reshape([cDx, cDy],1,1,[])).^2,3))<=circRad;
% Cut out binary mask within circle
handOnly(B) = 0;
% Label separate regions, where each now corresponds to a separate digit
fingerCount = bwlabel(handOnly);
% Display overlay image
figure()
imshow(imgIn)
hold on
overlayImg = imshow(label2rgb(fingerCount, 'jet', 'k'));
set(overlayImg, 'AlphaData', 0.5);
hold off
Results:
http://imgur.com/ySn1fPy