We need to detect the ground using simple filling starting from the bottom of the image shown below. Any suggestions?
This codes what I have done so far,
rgb=imread('obstacle_scene_1.jpg');
figure, imshow(rgb);
rgbImage = imread('obstacle_scene_1.jpg');
hsvInt = rgb2hsv(rgbImage);
hsvDouble = rgb2hsv(double(rgbImage));
figure, imshow(hsvInt);
figure, imshow(hsvDouble);
level = graythresh(hsvInt);
bw = im2bw(hsvInt,level);
bw = bwareaopen(bw, 50);
figure, imshow(bw)
what I want is
Using GraphCut, constraining the top and the obstacles to "background" and the bottom to "foreground":
img = imread('http://i.stack.imgur.com/xJQBP.jpg');
bw = img(4:243,6:325,1) > 128 ;
sz = size(bw);
Create pixel-wise tendency not to belong to background, stronger tendency at the bottom
bgp = linspace(1,0,sz(1))'*ones(1,sz(2));
Constrain the last row not to belong to background
bgp(end,:) = 1000.*(1-bw(end,:));
Constraint top row and obstacles not to belong to "foreground":
fgp = 1000.*bw;
fgp(1,:) = 1000;
Create the graph (using sparse_adj_matrix):
[ii jj] = sparse_adj_matrix(sz, 1, 1);
sel = ii<jj;
ii=ii(sel);
jj=jj(sel);
W = sparse(ii, jj, double(bw(ii)==bw(jj)), numel(bw), numel(bw));
Using GraphCut to split the image:
gch = GraphCut('open',[bgp(:) fgp(:)]', 500*[0 1; 1 0], W+W' );
[gch L] = GraphCut('expand', gch);
gch = GraphCut('close', gch);
Resulting with:
L = reshape(L, sz);
Related
I = imread('Sub1.png');
figure, imshow(I);
I = imcomplement(I);
I = double(I)/255;
I = adapthisteq(I,'clipLimit',0.0003,'Distribution','exponential');
k = 12;
beta = 2;
maxIter = 100;
for i=1:length(beta)
[seg,prob,mu,sigma,it(i)] = ICM(I, k, beta(i), maxIter,5);
pr(i) = prob(end);
hold on;
end
figure, imshow(seg,[]);
and ICM function is defined as
function [segmented_image,prob,mu,sigma,iter] = ICM(image, k, beta, max_iterations, neigh)
[width, height, bands] = size(image);
image = imstack2vectors(image);
segmented_image = init(image,k,1);
clear c;
iter = 0;
seg_old = segmented_image;
while(iter < max_iterations)
[mu, sigma] = stats(image, segmented_image, k);
E1 = energy1(image,mu,sigma,k);
E2 = energy2(segmented_image, beta, width, height, k);
E = E1 + E2;
[p2,~] = min(E2,[],2);
[p1,~] = min(E1,[],2);
[p,segmented_image] = min(E,[],2);
prob(iter+1) = sum(p);
%find mismatch with previous step
[c,~] = find(seg_old~=segmented_image);
mismatch = (numel(c)/numel(segmented_image))*100;
if mismatch<0.1
iter
break;
end
iter = iter + 1;
seg_old = segmented_image;
end
segmented_image = reshape(segmented_image,[width height]);
end
Output of my algorithm is a logical matrix (seg) of size 305-by-305. When I use
imshow(seg,[]);
I am able to display the image. It shows different component with varying gray value. But bwlabel returns 1. I want to display the connected components. I think bwlabel thresholds the image to 1. unique(seg) returns values 1 to 10 since number of classes used in k-means is 10. I used
[label n] = bwlabel(seg);
RGB = label2rgb(label);
figure, imshow(RGB);
I need all the ellipse-like structures which are in between the two squares close to the middle of the image. I don't know the number of classes present in it.
Input image:
Ground truth:
My output:
If you want to explode the label image to different connected components you need to use a loop to extract labels for each class and sum label images to get the out label image.
u = unique(seg(:));
out = zeros(size(seg));
num_objs = 0;
for k = 1: numel(u)
mask = seg==u(k);
[L,N] = bwlabel(mask);
L(mask) = L(mask) + num_objs;
out = out + L;
num_objs = num_objs + N ;
end
mp = jet(num_objs);
figure,imshow(out,mp)
Something like this is produced:
I have tried to do everything out of scratch. I wish it is of some help.
I have a treatment chain that get at first contours with parameters tuned on a trial-and-error basis, I confess. The last "image" is given at the bottom ; with it, you can easily select the connected components and do for example a reconstruction by markers using "imreconstruct" operator.
clear all;close all;
I = imread('C:\Users\jean-marie.becker\Desktop\imagesJPG10\spinalchord.jpg');
figure,imshow(I);
J = I(:,:,1);% select the blue channel because jpg image
J=double(J<50);% I haven't inverted the image
figure, imshow(J);
se = strel('disk',5);
J=J-imopen(J,se);
figure, imshow(J);
J=imopen(J,ones(1,15));% privilegizes long horizontal strokes
figure, imshow(J);
K=imdilate(J,ones(20,1),'same');
% connects verticaly not-to-far horizontal "segments"
figure, imshow(K);
I want to find the corners of objects.
I tried the following code:
Vstats = regionprops(BW2,'Centroid','MajorAxisLength','MinorAxisLength',...
'Orientation');
u = [Vstats.Centroid];
VcX = u(1:2:end);
VcY = u(2:2:end);
[VcY id] = sort(VcY); % sorting regions by vertical position
VcX = VcX(id);
Vstats = Vstats(id); % permute according sort
Bv = Bv(id);
Vori = [Vstats.Orientation];
VRmaj = [Vstats.MajorAxisLength]/2;
VRmin = [Vstats.MinorAxisLength]/2;
% find corners of vertebrae
figure,imshow(BW2)
hold on
% C = corner(VER);
% plot(C(:,1), C(:,2), 'or');
C = cell(size(Bv));
Anterior = zeros(2*length(C),2);
Posterior = zeros(2*length(C),2);
for i = 1:length(C) % for each region
cx = VcX(i); % centroid coordinates
cy = VcY(i);
bx = Bv{i}(:,2); % edge points coordinates
by = Bv{i}(:,1);
ux = bx-cx; % move to the origin
uy = by-cy;
[t, r] = cart2pol(ux,uy); % translate in polar coodinates
t = t - deg2rad(Vori(i)); % unrotate
for k = 1:4 % find corners (look each quadrant)
fi = t( (t>=(k-3)*pi/2) & (t<=(k-2)*pi/2) );
ri = r( (t>=(k-3)*pi/2) & (t<=(k-2)*pi/2) );
[rp, ip] = max(ri); % find farthest point
tc(k) = fi(ip); % save coordinates
rc(k) = rp;
end
[xc,yc] = pol2cart(tc+1*deg2rad(Vori(i)) ,rc); % de-rotate, translate in cartesian
C{i}(:,1) = xc + cx; % return to previous place
C{i}(:,2) = yc + cy;
plot(C{i}([1,4],1),C{i}([1,4],2),'or',C{i}([2,3],1),C{i}([2,3],2),'og')
% save coordinates :
Anterior([2*i-1,2*i],:) = [C{i}([1,4],1), C{i}([1,4],2)];
Posterior([2*i-1,2*i],:) = [C{i}([2,3],1), C{i}([2,3],2)];
end
My input image is :
I got the following output image
The bottommost object in the image is not detected properly. How can I correct the code? It fails to work for a rotated image.
You can get all the points from the image, and use kmeans clustering and partition the points into 8 groups. Once partition is done, you have the points in and and you can pick what ever the points you want.
rgbImage = imread('your image') ;
%% crop out the unwanted white background from the image
grayImage = min(rgbImage, [], 3);
binaryImage = grayImage < 200;
binaryImage = bwareafilt(binaryImage, 1);
[rows, columns] = find(binaryImage);
row1 = min(rows);
row2 = max(rows);
col1 = min(columns);
col2 = max(columns);
% Crop
croppedImage = rgbImage(row1:row2, col1:col2, :);
I = rgb2gray(croppedImage) ;
%% Get the white regions
[y,x,val] = find(I) ;
%5 use kmeans clustering
[idx,C] = kmeans([x,y],8) ;
%%
figure
imshow(I) ;
hold on
for i = 1:8
xi = x(idx==i) ; yi = y(idx==i) ;
id1=convhull(xi,yi) ;
coor = [xi(id1) yi(id1)] ;
[id,c] = kmeans(coor,4) ;
plot(coor(:,1),coor(:,2),'r','linewidth',3) ;
plot(c(:,1),c(:,2),'*b')
end
Now we are able to capture the regions..the boundary/convex hull points are in hand. You can do what ever math you want with the points.
Did you solve the problem? I Looked into it and it seems that the rotation given by 'regionprops' seems to be off. To fix that I've prepared a quick solution: I've dilated the image to close the gaps, found 4 most distant peaks of each spine, and then validated if a peak is on the left, or on the right of the centerline (that I have obtained by extrapolating form sorted centroids). This method seems to work for this particular problem.
BW2 = rgb2gray(Image);
BW2 = imbinarize(BW2);
%dilate and erode will help to remove extra features of the vertebra
se = strel('disk',4,4);
BW2_dilate = imdilate(BW2,se);
BW2_erode = imerode(BW2_dilate,se);
sb = bwboundaries(BW2_erode);
figure
imshow(BW2)
hold on
centerLine = [];
corners = [];
for bone = 1:length(sb)
x0 = sb{bone}(:,2) - mean(sb{bone}(:,2));
y0 = sb{bone}(:,1) - mean(sb{bone}(:,1));
%save the position of the centroid
centerLine = [centerLine; [mean(sb{bone}(:,1)) mean(sb{bone}(:,2))]];
[th0,rho0] = cart2pol(x0,y0);
%make sure that the indexing starts at the dip, not at the corner
lowest_val = find(rho0==min(rho0));
rho1 = [rho0(lowest_val:end); rho0(1:lowest_val-1)];
th00 = [th0(lowest_val:end); th0(1:lowest_val-1)];
y1 = [y0(lowest_val:end); y0(1:lowest_val-1)];
x1 = [x0(lowest_val:end); x0(1:lowest_val-1)];
%detect corners, using smooth data to remove noise
[pks,locs] = findpeaks(smooth(rho1));
[pksS,idS] = sort(pks,'descend');
%4 most pronounced peaks are where the corners are
edgesFndCx = x1(locs(idS(1:4)));
edgesFndCy = y1(locs(idS(1:4)));
edgesFndCx = edgesFndCx + mean(sb{bone}(:,2));
edgesFndCy = edgesFndCy + mean(sb{bone}(:,1));
corners{bone} = [edgesFndCy edgesFndCx];
end
[~,idCL] = sort(centerLine(:,1),'descend');
centerLine = centerLine(idCL,:);
%extrapolate the spine centerline
yDatExt= 1:size(BW2_erode,1);
extrpLine = interp1(centerLine(:,1),centerLine(:,2),yDatExt,'spline','extrap');
plot(centerLine(:,2),centerLine(:,1),'r')
plot(extrpLine,yDatExt,'r')
%find edges to the left, and to the right of the centerline
for bone = 1:length(corners)
x0 = corners{bone}(:,2);
y0 = corners{bone}(:,1);
for crn = 1:4
xCompare = extrpLine(y0(crn));
if x0(crn) < xCompare
plot(x0(crn),y0(crn),'go','LineWidth',2)
else
plot(x0(crn),y0(crn),'ro','LineWidth',2)
end
end
end
Solution
I just wanted to recognize the smaller squares group of squares, I needed some tips for that effect.thaks
I = imread('q1.jpg');
Ibw = ~im2bw(I,graythresh(I));
Ifill = imfill(Ibw,'holes');
Iarea = bwareaopen(Ifill,100100);
stat = regionprops(Iarea,'boundingbox');
imshow(I); hold on;
for cnt = 1 : numel(stat)
bb = stat(cnt).BoundingBox;
rectangle('position',bb,'edgecolor','r','linewidth',2);
end
I have a set of images of some shapes on black backgrounds. I want to overlay these shapes on another image. This is a sample:
m = 200; n = m*3/2; p = m / 2;
background = im2double(rgb2gray(imresize(imread('pears.png'), [m, n])));
[x, y] = meshgrid(linspace(-1, 1, 64));
shape1 = imadjust(im2double(imresize(imread('moon.tif'), [m, m])), [.1 .9], [0, 1]);
shape2 = imadjust(im2double(rgb2gray(imresize(imread('saturn.png'), [m, m]))), [.1 .9], [0, 1]);
mask1 = double(shape1>0);
mask2 = double(shape2>0);
I = background;
I(:, (1:m)+0) = (1-mask1).*I(:, (1:m)+0) + mask1.*shape1;
I(:, (1:m)+p) = (1-mask2).*I(:, (1:m)+p) + mask2.*shape2;
And the result:
How can I remove those sharp black edges?
You can erode your masks slightly with imerode to remove the black edges, then filter them with imfilter to blend the images together smoothly. Here's a simple example with disk filters of radius 2:
erode1 = imerode(mask1, strel('disk', 2));
erode2 = imerode(mask2, strel('disk', 2));
blend1 = imfilter(erode1, fspecial('disk', 2));
blend2 = imfilter(erode2, fspecial('disk', 2));
I = background;
I(:, (1:m)+0) = (1-blend1).*I(:, (1:m)+0) + blend1.*shape1;
I(:, (1:m)+p) = (1-blend2).*I(:, (1:m)+p) + blend2.*shape2;
To refine the result you can experiment with the types and sizes of the structure elements and filters created by strel and fspecial, respectively.
I am trying to extract plankton from a scanned image.
I segmented the plankton using the technique I found here, http://www.mathworks.com/help/images/examples/detecting-a-cell-using-image-segmentation.html
The outline is not bad, however, now I am not sure how to extract the images so each individual plankton can be saved individually. I tried to use labels but there is a lot of noise and it labels every single spec. I am wondering if there is a better way to do this.
Here is my code:
I = imread('plankton_2.jpg');
figure, imshow(I), title('original image');
[~, threshold] = edge(I, 'sobel');
fudgeFactor = .5;
BWs = edge(I,'sobel', threshold * fudgeFactor);
figure, imshow(BWs), title('binary gradient mask');
se90 = strel('line', 3, 90);
se0 = strel('line', 3, 0);
BWsdil = imdilate(BWs, [se90 se0]);
figure, imshow(BWsdil), title('dilated gradient mask');
BWdfill = imfill(BWsdil, 'holes');
figure, imshow(BWdfill);
title('binary image with filled holes');
BWnobord = imclearborder(BWdfill,1);
figure, imshow(BWnobord), title('cleared border image');
seD = strel('diamond',1);
BWfinal = imerode(BWnobord,seD);
BWfinal = imerode(BWfinal,seD);
figure, imshow(BWfinal), title('segmented image');
BWoutline = bwperim(BWfinal);
Segout = I;
Segout(BWoutline) = 0;
figure, imshow(Segout), title('outlined original image');
label = bwlabel(BWfinal);
max(max(label))
for j = 1:max(max(label))
[row, col] = find(label == j);
len = max(row) - min(row)+2;
breadth = max(col)-min(col) +2;
target = uint8(zeros([len breadth]));
sy = min(col)-1;
sx = min(row)-1;
for i = 1:size(row,1)
x = row(i,1)-sx;
y = col(i,1) - sy;
target(x,y)=I(row(i,1),col(i,1));
end
mytitle =strcat('Object Number:',num2str(j));
figure, imshow(target);mytitle;
end
for j = 1:max(max(label))
[row, col] = find(label == j);
len = max(row) - min(row)+2;
breadth = max(col)-min(col) +2;
target = uint8(zeros([len breadth]));
sy = min(col)-1;
sx = min(row)-1;
for i = 1:size(row,1)
x = row(i,1)-sx;
y = col(i,1) - sy;
target(x,y)=I(row(i,1),col(i,1));
end
mytitle =strcat('Object Number:',num2str(j));
figure, imshow(target);mytitle;
end
You should use the regionprops function to filter the detected objects by size and/or shape characteristics.