I'm supposed to add another image next to my threshold image with its original color like so: expected image
But I'm unsure how to do it having only achieving the binary image threshold on matlab. How do I show images side by side?
my result
clear all;
close all;
clc;
% read image
palm = imread('palmDown (2).jpg');
%split into RGB
redPalm = palm(:,:,1);
greenPalm = palm(:,:,2);
bluePalm = palm(:,:,3);
redLevel = -0.1;
greenLevel = -0.1;
blueLevel = 0.06;
redThresh = imbinarize(redPalm, redLevel);
greenThresh = imbinarize(greenPalm, greenLevel);
blueThresh = imbinarize(bluePalm, blueLevel);
colorSum = (redThresh&greenThresh&blueThresh);
colorSum2 = imcomplement(colorSum);
thumbFilled = imfill(colorSum2, 'holes');
figure;
imshow(thumbFilled); title('Sum of all');
There are many ways to colorize the thresholded image. One simple way is by multiplication:
palm = im2double(palm); % it’s easier to work with doubles in MATLAB
palm2 = palm * thumbFilled;
imshow([palm, palm2])
The multiplication uses implicit Singleton expansion. If you have an older version of MATLAB it won’t work, you’ll have to use bsxfun instead.
Related
I have written some algorithms for an image but the output has some differences from my ground truth which you can see in image below:
I don't want to make it exactly like the 2nd image but since my images is kinda simple I think there are some filters to at least remove those white curves inside the circles.
Can you suggest me any?
Thanks
You can try using morphological operations like imclose
You need to play with it, to get the desired result.
I used imbinarize to convert from uint8 to black/white.
I = imread('https://i.stack.imgur.com/r8XO7.png'); %Read image directly from URL.
R = I(:,:,1);G = I(:,:,2);B = I(:,:,3);
R = imbinarize(255 - R);G = imbinarize(255 - G);B = imbinarize(255 - B); %Convert to binary (use 255-R to inverse polarity because background is white).
SE = strel('disk', 15);
R = imclose(R, SE); %Close opreation.
G = imclose(G, SE);
B = imclose(B, SE);
J = im2uint8(cat(3, ~R, ~G, ~B)); %Use ~R to invert to original polarity.
figure;imshow(J);
Almost...
[It's weird that image is reversed up/down].
Hello I have a work on this image:
My objective is to count all the sperm in this image .for this I'm thinking to detect just the lines so it make my work easy. because I am beginner, in this step I am completely lost there are any algorithms can help me to detect lines?? ( I have seen that there are hough transformation and scan line algorithm) I don't which algorithm can help me and if there are others
Here's a piece of code that might help you getting started.
By looking at the image it seems very difficult to label sperms by looking at straight lines and hence using Hough transform won't help a lot.
In the example below I focused on filtering the image and counting the number of blobs. The code is commented and should be easy to understand.
img = imread('d9S3Z.png');
figure, imshow(img)
% convert to binary image
[X,map] = rgb2ind(img,0.0);
img = ind2gray(X,map); % Convert indexed to grayscale
level = graythresh(img); % Compute an appropriate threshold
% or use your own, e.g. level = 0.46
img_bw = im2bw(img,level);% Convert grayscale to binary
% create mask to remove edge interference
mask = zeros(size(img_bw));
mask(2:end-2,2:end-2) = 1;
img_bw(mask<1) = 1;
%invert image
img_inv =1-img_bw;
% find blobs
img_blobs = bwmorph(img_inv,'majority',10);
figure, imshow(img_blobs);
% count blobs
CC = bwconncomp(img_blobs);
num_sperm = CC.NumObjects # sperm count
To be exact I need the four end points of the road in the image below.
I used find[x y]. It does not provide satisfying result in real time.
I'm assuming the images are already annotated. In this case we just find the marked points and extract coordinates (if you need to find the red points dynamically through code, this won't work at all)
The first thing you have to do is find a good feature to use for segmentation. See my SO answer here what-should-i-use-hsv-hsb-or-rgb-and-why for code and details. That produces the following image:
we can see that saturation (and a few others) are good candidate colors spaces. So now you must transfer your image to the new color space and do thresholding to find your points.
Points are obtained using matlab's region properties looking specifically for the centroid. At that point you are done.
Here is complete code and results
im = imread('http://i.stack.imgur.com/eajRb.jpg');
HUE = 1;
SATURATION = 2;
BRIGHTNESS = 3;
%see https://stackoverflow.com/questions/30022377/what-should-i-use-hsv-hsb-or-rgb-and-why/30036455#30036455
ViewColoredSpaces(im)
%convert image to hsv
him = rgb2hsv(im);
%threshold, all rows, all columns,
my_threshold = 0.8; %determined empirically
thresh_sat = him(:,:,SATURATION) > my_threshold;
%remove small blobs using a 3 pixel disk
se = strel('disk',3');
cleaned_sat = imopen(thresh_sat, se);% imopen = imdilate(imerode(im,se),se)
%find the centroids of the remaining blobs
s = regionprops(cleaned_sat, 'centroid');
centroids = cat(1, s.Centroid);
%plot the results
figure();
subplot(2,2,1) ;imshow(thresh_sat) ;title('Thresholded saturation channel')
subplot(2,2,2) ;imshow(cleaned_sat);title('After morpphological opening')
subplot(2,2,3:4);imshow(im) ;title('Annotated img')
hold on
for (curr_centroid = 1:1:size(centroids,1))
%prints coordinate
x = round(centroids(curr_centroid,1));
y = round(centroids(curr_centroid,2));
text(x,y,sprintf('[%d,%d]',x,y),'Color','y');
end
%plots centroids
scatter(centroids(:,1),centroids(:,2),[],'y')
hold off
%prints out centroids
centroids
centroids =
7.4593 143.0000
383.0000 87.9911
435.3106 355.9255
494.6491 91.1491
Some sample code would make it much easier to tailor a specific solution to your problem.
One solution to this general problem is using impoint.
Something like
h = figure();
ax = gca;
% ... drawing your image
points = {};
points = [points; impoint(ax,initialX,initialY)];
% ... generate more points
indx = 1 % or whatever point you care about
[currentX,currentY] = getPosition(points{indx});
should do the trick.
Edit: First argument of impoint is an axis object, not a figure object.
I need to find similar corners in image (for example: 4 corners of a rectangle, same corners, just different orientation?).
I have this code:
% read the image into MATLAB and convert it to grayscale
I = imread('s2.jpg');
Igray = rgb2gray(I);
figure, imshow(I);
% We can see that the image is noisy. We will clean it up with a few
% morphological operations
Ibw = im2bw(Igray,graythresh(Igray));
se = strel('line',3,90);
cleanI = imdilate(~Ibw,se);
figure, imshow(cleanI);
% Perform a Hough Transform on the image
% The Hough Transform identifies lines in an image
[H,theta,rho] = hough(cleanI);
peaks = houghpeaks(H,10);
lines = houghlines(Ibw,theta,rho,peaks);
figure, imshow(cleanI)
% Highlight (by changing color) the lines found by MATLAB
hold on
After running this code I convert my starting image into a binary image with:
binary = im2bw(I);
after this I get a product from those 2 binary images and I think I get corners..
product = binary .* cleanI;
now I imfuse this picture with grayscale starting picture and get this:
I dont know what to do to get only those 4 corners!
Ok second try. Below a code that does not finally do the job but it might help.
Edge identifies the contours and with regionprops you get the characteristica of each identified element. As soon as you know what characteristics your desired object has you can filter it and plot it. I went through the Areas in shapedata.Area and the 6th largest was the one you were searching for. If you combine Area with some of the other charateristica you might get the one you want. As I said not ideal and final but perhaps a start ...
clear all
close all
source = imread('Mobile Phone.jpg');
im = rgb2gray(source);
bw = edge(im ,'canny',[],sqrt(2));
shapedata=regionprops (bwlabel(bw,8),'all');
%index = find([shapedata.Area]== max([shapedata.Area]));
index = 213;
data = shapedata(index).PixelList;
figure
imshow(im)
hold on
plot(data(:,1),data(:,2),'ro');
after I applied watershed segmentation, I want to extract remained leaf from image,and only I want to get without background like image-2. Please can you help me. Thanks a lot. I attach below also my code.
I'm new at stackoverflow, therefore I'm not allowed to post images.I asked the same qustion in mathworks, you can check the images from there if you will.
Thanks a lot in advance.
http://www.mathworks.com/matlabcentral/answers/237106-extracting-leaf-from-background
image-1: after watershed segmentation(colored version):
image-2:image to be ;
my code:
% I -- intensity image
% Gmag -- gradient mag.
se = strel('disk', 30);
Ie = imerode(I, se);
Iobr = imreconstruct(Ie, I);
figure
imshow(Iobr), title('Opening-by-reconstruction (Iobr)')
Iobrd = imdilate(Iobr, se);
Iobrcbr = imreconstruct(imcomplement(Iobrd), imcomplement(Iobr));
Iobrcbr = imcomplement(Iobrcbr);
figure
imshow(Iobrcbr), title('Opening-closing by reconstruction (Iobrcbr)')
fgm = imregionalmax(Iobrcbr);
figure
imshow(fgm), title('Regional maxima of opening-closing by reconstruction (fgm)')
% modify area
I2 = I;
I2(fgm) = 255;
figure
imshow(I2), title('Regional maxima superimposed on original image (I2)')
se2 = strel(ones(10,10));
fgm2 = imclose(fgm, se2);
fgm3 = imerode(fgm2, se2);
fgm4 = bwareaopen(fgm3, 100);
I3 = I;
I3(fgm4) = 255;
figure
imshow(I3)
title('Modified regional maxima superimposed on original image (fgm4)')
% background markers
bw = im2bw(Iobrcbr, graythresh(Iobrcbr));
figure
imshow(bw), title('Thresholded opening-closing by reconstruction (bw)')
D = bwdist(bw);
DL = watershed(D);
bgm = DL == 0;
figure
imshow(bgm), title('Watershed ridge lines (bgm)')
gradmag2 = imimposemin(Gmag, bgm | fgm4);
L = watershed(gradmag2);
I4 = I;
I4(imdilate(L == 0, ones(3, 3)) | bgm | fgm4) = 255;
figure
imshow(I4)
title('Markers and object boundaries superimposed on original image (I4)')
Lrgb = label2rgb(L, 'jet', 'w', 'shuffle');
figure
imshow(Lrgb)
title('Colored watershed label matrix (Lrgb)')
figure
imshow(I)
hold on
himage = imshow(Lrgb);
himage.AlphaData = 0.3;
title('Lrgb superimposed transparently on original image')
props = regionprops(L);
[~,ind] = max([props.Area]);
imshow(L == ind);
It's not possible to extract the leaf according to the segmented image, because the yellow component cuts the leaf in different parts.
Moreover, according to the source code, I understand that you use a basic watershed that produces an over segmentation. Use a watershed constrained, also known as watershed with markers.
Find a way to share the original image and the image after processing.
I confirm that you certainly have an issue with the watershed you use. I ran your code on my own library, and I used: one inner marker (biggest component, consequently the leaf on the corner is discarded), one outer (dilation of the inner), the gradient image.
And here is my result: www.thibault.biz/StackOverflow/ResultLeaf.png. So only one component, because I use only one inner marker. It's not perfect, but already closer and easier to post-process.