How can i separate the image as a three region(dark, mid, bright)?any matlab commands are available for that?
The image processing toolbox has some options for segmenting images based on color. More information here. If you only need to select pixels based on intensity you can use Boolean operators. Here's an example of that approach:
% create an example image with three regions of intensity
im = ones(100,100);
im(1:25, 1:30) = 256;
im(75:end, 7:end) = 128;
% add some random noise
im = im + 10*rand(size(im));
% display image
figure
subplot(2,2,1)
image(im)
colormap gray
% segment image based on intensity
bottomThird = 256/3;
topThird = 2*256/3;
index1 = find(im < bottomThird);
index2 = find(and((im > bottomThird),(im <topThird)));
index3 = find(im > topThird);
%create images for each segmented region
im1 = ones(size(im));
im2 = ones(size(im));
im3 = ones(size(im));
im1(index1) = 0;
im2(index2) = 0;
im3(index3) = 0;
%display sub-regions
subplot(2,2,2)
imagesc(im1)
subplot(2,2,3)
imagesc(im2)
subplot(2,2,4)
imagesc(im3)
Related
I want to pad an object based on edge pixel to replace the zeros.
I am not sure if padarray is applicable to this, I am showing a sample code below to replicate my need. I am able to do it but I think this is not an efficient way as I am scanning each row at a time to find and pad the zeros.
%% Example code to recreate my need
image = imread('moon.tif');
[~, ncols] = size(image);
image(image <50) = 0;
image = fliplr(image(:,1:round(ncols/2)));
% figure, imshow(image,[])
BW = bwareafilt(logical(image),1);
% create bounding box
boxProps=regionprops(BW,'BoundingBox');
cords_BoundingBox = boxProps(1).BoundingBox;
% Extract sub_Image
sub_Image = imcrop(image, cords_BoundingBox);
% figure, imshow(sub_Image,[])
%% This is the part I want to use better or existing function for padding
duplicate_sub_Image = sub_Image;
[nrows, ~] = size(duplicate_sub_Image);
for nrow = 1:nrows
% current_row_inverted = fliplr(sub_Image(nrow,:));
[~,col,pad_value] = find(duplicate_sub_Image(nrow,:),1,'last');
duplicate_sub_Image(nrow,col+1:end) = pad_value;
end
figure,
subplot(131),imshow(image,[]), title('original image');
subplot(132),imshow(sub_Image,[]), title('bounding box image');
subplot(133),imshow(duplicate_sub_Image,[]), title('row padded image');
Any suggestions to improve this code or use of existing functions to address this issue?
Thanks
Here is a way without using loops:
[~,imin] = min(sub_Image, [], 2);
col = max(1, imin-1);
ind = sub2ind(size(sub_Image), (1:numel(col)).', col);
duplicate_sub_Image = sub_Image(ind) .* ~sub_Image + sub_Image;
Currently, I try to draw a rectangular box around a white object with a label indicating its size.
I want create 4 categories of sizes:
Don't have
Small
Medium
Big
I don't have clue how to create this.
use insertObjectAnnotation:
% generate image
bw = zeros(1000);
[xg,yg] = meshgrid(1:1000);
rads = 10:20:100;
for ii = 1:length(rads)
r = rads(ii);
c = randi([1+r,1000-r],[1,2]);
bw = bw | ( ((xg - c(1)).^2 + (yg - c(2)).^2) < r^2);
end
% extract region properties
props = regionprops(bw,'Area','BoundingBox');
% add labels
sizes = [0,2000,6000,10000];
labels = {'tiny','small','medium','large'};
colors = autumn(numel(labels));
bw = double(bw);
for ii = 1:numel(props)
labelidx = find(props(ii).Area > sizes,1,'last');
bw = insertObjectAnnotation(bw,'rectangle',...
props(ii).BoundingBox,labels{labelidx},...
'Color',colors(labelidx,:),'FontSize',32);
end
imshow(bw);
EDIT: applying this to the OP's image:
I'd like to plot a graph over an image. I followed this tutorial to Plot over an image background in MATLAB and it works fine:
% replace with an image of your choice
img = imread('myimage.png');
% set the range of the axes
% The image will be stretched to this.
min_x = 0;
max_x = 8;
min_y = 0;
max_y = 6;
% make data to plot - just a line.
x = min_x:max_x;
y = (6/8)*x;
imagesc([min_x max_x], [min_y max_y], img);
hold on;
plot(x,y,'b-*','linewidth',1.5);
But when I apply the procedure to my study case, it doesn't work. I'd like to do something like:
I = imread('img_png.png'); % here I load the image
DEM = GRIDobj('srtm_bigtujunga30m_utm11.tif');
FD = FLOWobj(DEM,'preprocess','c');
S = STREAMobj(FD,flowacc(FD)>1000);
% with the last 3 lines I calculated the stream network on a geographic area using the TopoToolBox
imagesc(I);
hold on
plot(S)
The aim is to plot the stream network over the satellite image of the same area.
The only difference between the two examples that doesn't let the code working is in the plot line, in the first case "plot(x,y)" works, in the other one "plot(S)" doesn't.
Thanks guys.
This is the satellite image, imagesc(I)
It is possible that the plot method of the STREAMobj performs it's own custom plotting including creating new figures, axes, toggling hold states, etc. Because you can't easily control what their plot routine does, it's likely easier to flip the order of your plotting so that you plot your stuff after the toolbox plots the STREAMobj. This way you have completely control over how your image is added.
% Plot the STREAMobj
hlines = plot(S);
% Make sure we plot on the same axes
hax = ancestor(hlines, 'axes');
% Make sure that we can add more plot objects
hold(hax, 'on')
% Plot your image data on the same axes
imagesc(I, 'Parent', hax)
Maybe I am preaching to the choir or overlooking something here but the example you used actually mapped the image to the data range of the plot, hence the lines:
% set the range of the axes
% The image will be stretched to this.
min_x = 0;
max_x = 8;
min_y = 0;
max_y = 6;
imagesc([min_x max_x], [min_y max_y], img);
where you directly plot your image
imagesc(I);
If now your data coordinates and your image coordinates are vastly different you either see one or the other.
Thanks guys, I solved in this way:
I = imread('orto.png'); % satellite image loading
DEM = GRIDobj('demF1.tif');
FD = FLOWobj(DEM,'preprocess','c');
S = STREAMobj(FD,flowacc(FD)>1000); % Stream network extraction
x = S.x; % [node attribute] x-coordinate vector
y = S.y; % [node attribute] y-coordinate vector
min_x = min(x);
max_x = max(x);
min_y = min(y);
max_y = max(y);
imagesc([min_x max_x], [min_y max_y], I);
hold on
plot(S);
Here's the resulting image: stream network over the satellite image
Actually the stream network doesn't match the satellite image just because I'm temporarily using different images and DEM.
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.
I'm using normxcorr2 to find the area that exactly match with my pattern and i also want to find the other area(in the red rectangle) that is look like the pattern. I think it will be works if i can find the next maximum and so on and that value must not in the first maximum area or the first one that it has been detected but i can't do it. Or if you have any idea that using normxcorr2 to find the others area please advise me, I don't have any idea at all.
Here's my code. I modified from this one http://www.mathworks.com/products/demos/image/cross_correlation/imreg.html
onion = imread('pattern103.jpg'); %pattern image
peppers = imread('rsz_1jib-159.jpg'); %Original image
onion = rgb2gray(onion);
peppers = rgb2gray(peppers);
%imshow(onion)
%figure, imshow(peppers)
c = normxcorr2(onion,peppers);
figure, surf(c), shading flat
% offset found by correlation
[max_c, imax] = max(abs(c(:)));
[ypeak, xpeak] = ind2sub(size(c),imax(1));
corr_offset = [(xpeak-size(onion,2))
(size(onion,1)-ypeak)]; %size of window show of max value
offset = corr_offset;
xoffset = offset(1);
yoffset = offset(2);
xbegin = round(xoffset+1); fprintf(['xbegin = ',num2str(xbegin)]);fprintf('\n');
xend = round(xoffset+ size(onion,2));fprintf(['xend = ',num2str(xbegin)]);fprintf('\n');
ybegin = round(yoffset+1);fprintf(['ybegin = ',num2str(ybegin)]);fprintf('\n');
yend = round(yoffset+size(onion,1));fprintf(['yend = ',num2str(yend)]);fprintf('\n');
% extract region from peppers and compare to onion
extracted_onion = peppers(ybegin:yend,xbegin:xend,:);
if isequal(onion,extracted_onion)
disp('pattern103.jpg was extracted from rsz_org103.jpg')
end
recovered_onion = uint8(zeros(size(peppers)));
recovered_onion(ybegin:yend,xbegin:xend,:) = onion;
figure, imshow(recovered_onion)
[m,n,p] = size(peppers);
mask = ones(m,n);
i = find(recovered_onion(:,:,1)==0);
mask(i) = .2; % try experimenting with different levels of
% transparency
% overlay images with transparency
figure, imshow(peppers(:,:,1)) % show only red plane of peppers
hold on
h = imshow(recovered_onion); % overlay recovered_onion
set(h,'AlphaData',mask)