I am trying to detect two vertical lines shown in the attached images using some image processing methods. The line are in low contrast.
The location is shown in the first image with yellow arrows.
The original image is also attached.
I tried using adaptiveThresholding and normal thresholding using maximum and minimum at local windows. But I can't detect the lines.
Any ideas how to detect the two vertical lines in image processing?
There is some trick when contrast is low in bright pixels. There is thresholding method - otsu thresholding (https://en.wikipedia.org/wiki/Otsu%27s_method), which can be used to detect bright side of histogram. After that, you can normalize that part of histogram to (0,255) and set 0 to darker pixels as in code below:
cv::Mat img = cv::imread("E:\\Workspace\\KS\\excercise\\sjB8q.jpg", 0);
cv::Mat work;
for (int i = 0; i < 4; i++) // number of iterations has to be adjusted
{
cv::threshold(img, work, 30, 255, CV_THRESH_OTSU);
cv::bitwise_and(img, work,img);
cv::normalize(img, img, 0, 255, cv::NORM_MINMAX, -1, work);
}
Then your contrast will be better like in pictures below (for different iterations):
i=2:
i=4:
i =6:
After that preprocessing detecting dark lines should be easier. That answer is just explanation of idea. If you want to know more just ask in comment.
I'm implementing image mosaic in Matlab using SURF.the problem is
outputView = imref2d(size(img1)*2);
Ir = imwarp(img2,tform,'OutputView',outputView);
it produces
i want it something like this
if i change
outputView = imref2d(size(img1)*2);
to
outputView = imref2d(size(img1));
matlab crops the second image so it can fit in first image size after transforming.
Notice that when you warp the image with respect to the target plane, many of the pixels in this new plane are equal to 0. A very rudimentary algorithm is to simply threshold your image so that you find values above 0 then find the largest bounding box that encompasses the non-zero pixels... then crop:
[rows,cols] = find(Ir(:,:,1) > 0);
topLeftRow = min(rows);
topLeftCol = min(cols);
bottomRightRow = max(rows);
bottomRightCol = max(cols);
Ir_crop = Ir(topLeftRow:bottomRightRow, topLeftCol:bottomRightCol, :);
I've been working with the retina image, currently I am submitting to the wavelet, but I have noticed that I have two problems are:
The optical disk which causes me image noise
And the circle delimiting the retina
The original image is the next
My plan is to establish the bottom of the tone of the optical disk in order not to lose any detail of the blood vessels of the retina (I post a code with which I played but still do not understand much as I know the tone of the optical disc and how to set it to the image without altering the blood vessels)
And with respect to the outer circle of the retina, I don´t know that you recommend me (I do not know about masks, I would appreciate if they have to consult my literature can provide)
c = [242 134 72];% Background to change
thresh = 50;
A = imread('E:\Prueba.jpg');
B = zeros(size(A));
Ar = A(:,:,1);
Ag = A(:,:,2);
Ab = A(:,:,3);
Br = B(:,:,1);
Bg = B(:,:,2);
Bb = B(:,:,3);
logmap = (Ar > (c(1) - thresh)).*(Ar < (c(1) + thresh)).*...
(Ag > (c(2) - thresh)).*(Ag < (c(2) + thresh)).*...
(Ab > (c(3) - thresh)).*(Ab < (c(3) + thresh));
Ar(logmap == 1) = Br(logmap == 1);
Ag(logmap == 1) = Bg(logmap == 1);
Ab(logmap == 1) = Bb(logmap == 1);
A = cat(3 ,Ar,Ag,Ab);
imshow(A);
courtesy of the question How can I change the background color of the image?
The image I get is the following
I need a picture like this where the optical disc does not cause me noise when segmenting the blood vessels of the retina.
I want to be uniform background ... and only the veins are perceived
I continued to work and have obtained the following image As you can realize the optical disk removes some parts of the blood vessels (veins) that are above him, so I require eliminating or make uniform the entire bottom of the image.
As Wouter said, you should first correct the inhomogeneity of the image. I would do it in my own way:
First, the parameters you can adjust to optimize the output:
gfilt = 3;
thresh = 0.4;
erode = 3;
brighten = 20;
You will see how they are used in the code.
This is the main step: to apply a Gaussian filter to the image to make it smooth and then subtract the result from the original image. This way you end up with the sharp changes in your data, which happens to be the vessels:
A = imread('Prueba.jpg');
B = imgaussfilt(A, gfilt) - A; % Gaussian filter and subtraction
% figure; imshow(B)
Then I create a binary mask to remove the unwanted area of the image:
% the 'imadjust' makes sure that you get the same result even if you ...
% change the intensity of illumination. "thresh" is the threshold of ...
% conversion to black and white:
circ = im2bw(imadjust(A(:,:,1)), thresh);
% here I am shrinking the "circ" for "erode" pixels:
circ = imerode(circ, strel('disk', erode));
circ3 = repmat(circ, 1, 1, 3); % and here I extended it to 3D.
% figure; imshow(circ)
And finally, I remove everything on the surrounding dark area and show the result:
B(~circ3) = 0; % ignore the surrounding area
figure; imshow(B * brighten) % brighten and show the output
Notes:
I do not see the last image as a final result, but probably you could apply some thresholds to it and separate the vessels from the rest.
The quality of the image you provided is quite low. I expect good results with a better data.
Although the intensity of blue channel is less than the rest, the vessels are expressed there better than the other channels, because blood is red!
If you are acquiring this data or you have access to the person, I suggest you to use blue light for illumination, since it provides you with higher contrast of the vessels.
Morphological operations are good for working with sphagetti images.
Original image:
Convert to grayscale:
original = rgb2gray(gavrF);
Estimate the background via morphological closing:
se = strel('disk', 3);
background = imclose(original, se);
Estimate of the background:
You could then for example subtract this background from the original grayscale image. You can do this straight by doing a bottom hat transform on the grayscale image:
flatImage = imbothat(original, strel('disk', 4));
With a output:
Noisy, but now you got access to global thresholding methods. Remember to change the datatypes to double if you wish to do some subtraction or division manually.
I know this thread about converting black color to white and white to black simultaneously.
I would like to convert only black to white.
I know this thread about doing this what I am asking but I do not understand what goes wrong.
Picture
Code
rgbImage = imread('ecg.png');
grayImage = rgb2gray(rgbImage); % for non-indexed images
level = graythresh(grayImage); % threshold for converting image to binary,
binaryImage = im2bw(grayImage, level);
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
% Make the black parts pure red.
redChannel(~binaryImage) = 255;
greenChannel(~binaryImage) = 0;
blueChannel(~binaryImage) = 0;
% Now recombine to form the output image.
rgbImageOut = cat(3, redChannel, greenChannel, blueChannel);
imshow(rgbImageOut);
Which gives
Where seems to be something wrong in red color channel.
The Black color is just (0,0,0) in RGB so its removal should mean to turn every (0,0,0) pixel to white (255,255,255).
Doing this idea with
redChannel(~binaryImage) = 255;
greenChannel(~binaryImage) = 255;
blueChannel(~binaryImage) = 255;
Gives
So I must have misunderstood something in Matlab. The blue color should not have any black. So this last image is strange.
How can you turn only black color to white?
I want to keep the blue color of the ECG.
If I understand you properly, you want to extract out the blue ECG plot while removing the text and axes. The best way to do that would be to examine the HSV colour space of the image. The HSV colour space is great for discerning colours just like the way humans do. We can clearly see that there are two distinct colours in the image.
We can convert the image to HSV using rgb2hsv and we can examine the components separately. The hue component represents the dominant colour of the pixel, the saturation denotes the purity or how much white light there is in the pixel and the value represents the intensity or strength of the pixel.
Try visualizing each channel doing:
im = imread('http://i.stack.imgur.com/cFOSp.png'); %// Read in your image
hsv = rgb2hsv(im);
figure;
subplot(1,3,1); imshow(hsv(:,:,1)); title('Hue');
subplot(1,3,2); imshow(hsv(:,:,2)); title('Saturation');
subplot(1,3,3); imshow(hsv(:,:,3)); title('Value');
Hmm... well the hue and saturation don't help us at all. It's telling us the dominant colour and saturation are the same... but what sets them apart is the value. If you take a look at the image on the right, we can tell them apart by the strength of the colour itself. So what it's telling us is that the "black" pixels are actually blue but with almost no strength associated to it.
We can actually use this to our advantage. Any pixels whose values are above a certain value are the values we want to keep.
Try setting a threshold... something like 0.75. MATLAB's dynamic range of the HSV values are from [0-1], so:
mask = hsv(:,:,3) > 0.75;
When we threshold the value component, this is what we get:
There's obviously a bit of quantization noise... especially around the axes and font. What I'm going to do next is perform a morphological erosion so that I can eliminate the quantization noise that's around each of the numbers and the axes. I'm going to make it the mask a bit large to ensure that I remove this noise. Using the image processing toolbox:
se = strel('square', 5);
mask_erode = imerode(mask, se);
We get this:
Great, so what I'm going to do now is make a copy of your original image, then set any pixel that is black from the mask I derived (above) to white in the final image. All of the other pixels should remain intact. This way, we can remove any text and the axes seen in your image:
im_final = im;
mask_final = repmat(mask_erode, [1 1 3]);
im_final(~mask_final) = 255;
I need to replicate the mask in the third dimension because this is a colour image and I need to set each channel to 255 simultaneously in the same spatial locations.
When I do that, this is what I get:
Now you'll notice that there are gaps in the graph.... which is to be expected due to quantization noise. We can do something further by converting this image to grayscale and thresholding the image, then filling joining the edges together by a morphological dilation. This is safe because we have already eliminated the axies and text. We can then use this as a mask to index into the original image to obtain our final graph.
Something like this:
im2 = rgb2gray(im_final);
thresh = im2 < 200;
se = strel('line', 10, 90);
im_dilate = imdilate(thresh, se);
mask2 = repmat(im_dilate, [1 1 3]);
im_final_final = 255*ones(size(im), class(im));
im_final_final(mask2) = im(mask2);
I threshold the previous image that we got without the text and axes after I convert it to grayscale, and then I perform dilation with a line structuring element that is 90 degrees in order to connect those lines that were originally disconnected. This thresholded image will contain the pixels that we ultimately need to sample from the original image so that we can get the graph data we need.
I then take this mask, replicate it, make a completely white image and then sample from the original image and place the locations we want from the original image in the white image.
This is our final image:
Very nice! I had to do all of that image processing because your image basically has quantization noise to begin with, so it's going to be a bit harder to get the graph entirely. Ander Biguri in his answer explained in more detail about colour quantization noise so certainly check out his post for more details.
However, as a qualitative measure, we can subtract this image from the original image and see what is remaining:
imshow(rgb2gray(abs(double(im) - double(im_final_final))));
We get:
So it looks like the axes and text are removed fine, but there are some traces in the graph that we didn't capture from the original image and that makes sense. It all has to do with the proper thresholds you want to select in order to get the graph data. There are some trouble spots near the beginning of the graph, and that's probably due to the morphological processing that I did. This image you provided is quite tricky with the quantization noise, so it's going to be very difficult to get a perfect result. Also, these thresholds unfortunately are all heuristic, so play around with the thresholds until you get something that agrees with you.
Good luck!
What's the problem?
You want to detect all black parts of the image, but they are not really black
Example:
Your idea (or your code):
You first binarize the image, selecting the pixels that ARE something against the pixels that are not. In short, you do: if pixel>level; pixel is something
Therefore there is a small misconception you have here! when you write
% Make the black parts pure red.
it should read
% Make every pixel that is something (not background) pure red.
Therefore, when you do
redChannel(~binaryImage) = 255;
greenChannel(~binaryImage) = 255;
blueChannel(~binaryImage) = 255;
You are doing
% Make every pixel that is something (not background) white
% (or what it is the same in this case, delete them).
Therefore what you should get is a completely white image. The image is not completely white because there has been some pixels that were labelled as "not something, part of the background" by the value of level, in case of your image around 0.6.
A solution that one could think of is manually setting the level to 0.05 or similar, so only black pixels will be selected in the gray to binary threholding. But this will not work 100%, as you can see, the numbers have some very "no-black" values.
How would I try to solve the problem:
I would try to find the colour you want, extract just that colour from the image, and then delete outliers.
Extract blue using HSV (I believe I answered you somewhere else how to use HSV).
rgbImage = imread('ecg.png');
hsvImage=rgb2hsv(rgbImage);
I=rgbImage;
R=I(:,:,1);
G=I(:,:,2);
B=I(:,:,3);
th=0.1;
R((hsvImage(:,:,1)>(280/360))|(hsvImage(:,:,1)<(200/360)))=255;
G((hsvImage(:,:,1)>(280/360))|(hsvImage(:,:,1)<(200/360)))=255;
B((hsvImage(:,:,1)>(280/360))|(hsvImage(:,:,1)<(200/360)))=255;
I2= cat(3, R, G, B);
imshow(I2)
Once here we would like to get the biggest blue part, and that would be our signal. Therefore the best approach seems to first binarize the image taking all blue pixels
% Binarize image, getting all the pixels that are "blue"
bw=im2bw(rgb2gray(I2),0.9999);
And then using bwlabel, label all the independent pixel "islands".
% Label each "blob"
lbl=bwlabel(~bw);
The label most repeated will be the signal. So we find it and separate the background from the signal using that label.
% Find the blob with the highes amount of data. That will be your signal.
r=histc(lbl(:),1:max(lbl(:)));
[~,idxmax]=max(r);
% Profit!
signal=rgbImage;
signal(repmat((lbl~=idxmax),[1 1 3]))=255;
background=rgbImage;
background(repmat((lbl==idxmax),[1 1 3]))=255;
Here there is a plot with the signal, background and difference (using the same equation as #rayryang used)
Here is a variation on #rayryeng's solution to extract the blue signal:
%// retrieve picture
imgRGB = imread('http://i.stack.imgur.com/cFOSp.png');
%// detect axis lines and labels
imgHSV = rgb2hsv(imgRGB);
BW = (imgHSV(:,:,3) < 1);
BW = imclose(imclose(BW, strel('line',40,0)), strel('line',10,90));
%// clear those masked pixels by setting them to background white color
imgRGB2 = imgRGB;
imgRGB2(repmat(BW,[1 1 3])) = 255;
%// show extracted signal
imshow(imgRGB2)
To get a better view, here is the detected mask overlayed on top of the original image (I'm using imoverlay function from the File Exchange):
figure
imshow(imoverlay(imgRGB, BW, uint8([255,0,0])))
Here is a code for this:
rgbImage = imread('ecg.png');
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
black = ~redChannel&~greenChannel&~blueChannel;
redChannel(black) = 255;
greenChannel(black) = 255;
blueChannel(black) = 255;
rgbImageOut = cat(3, redChannel, greenChannel, blueChannel);
imshow(rgbImageOut);
black is the area containing the black pixels. These pixels are set to white in each color channel.
In your code you use a threshold and a grayscale image so of course you have much bigger area of pixels that is set to white resp. red color. In this code only pixel that contain absolutly no red, green and blue are set to white.
The following code does the same with a threshold for each color channel:
rgbImage = imread('ecg.png');
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
black = (redChannel<150)&(greenChannel<150)&(blueChannel<150);
redChannel(black) = 255;
greenChannel(black) = 255;
blueChannel(black) = 255;
rgbImageOut = cat(3, redChannel, greenChannel, blueChannel);
imshow(rgbImageOut);
After I did a 'imclearborder', there are still a bit of unwanted object around the barcode. How can I remove those objects to isolate the barcode? I have pasted my code for your reference.
rgb = imread('barcode2.jpg');
% Resize Image
rgb = imresize(rgb,0.33);
figure(),imshow(rgb);
% Convert from RGB to Gray
Igray = double(rgb2gray(rgb));
% Calculate the Gradients
[dIx, dIy] = gradient(Igray);
B = abs(dIx) - abs(dIy);
% Low-Pass Filtering
H = fspecial('gaussian', 20, 10);
C = imfilter(B, H);
C = imclearborder(C);
figure(),imagesc(C);colorbar;
Well, i have already explained it in your previous question How to find the location of red region in an image using MATLAB? , but with a opencv code and output images.
Instead of asking for code, try to implement it yourself.
Below is what to do next.
1) convert image 'C' in your code to binary.
2) Apply some erosion to remove small noises.( this time, barcode region also shrinks)
3) Apply dilation to compensate previous erosion.(most of noise will have removed in previous erosion. So they won't come back)
4) Find contours in the image.
5) Find their area. Most probably, contour which has maximum area will be the barcode, because other things like letters, words etc will be small ( you can understand it in the grayscale image you have provided)
6) Select contour with max. area. Draw a bounding rectangle for it.
Its result is already provided in your previous question. It works very nice. Try to implement it yourself with help of MATLAB documentation. Come here only when you get an error which you don't understand.
%%hi, i am ading my code to yours at the end of your code%%%%
clear all;
rgb = imread('barcode.jpeg');
% Resize Image
rgb = imresize(rgb,0.33);
figure(),imshow(rgb);
% Convert from RGB to Gray
Igray = double(rgb2gray(rgb));
Igrayc = Igray;
% Calculate the Gradients
[dIx, dIy] = gradient(Igray);
B = abs(dIx) - abs(dIy);
% Low-Pass Filtering
H = fspecial('gaussian', 10, 5);
C = imfilter(B, H);
C = imclearborder(C);
imshow(Igray,[]);
figure(),imagesc(C);colorbar;
%%%%%%%%%%%%%%%%%%%%%%%%from here my code starts%%%%%%%%%%%%%%%%
bw = im2bw(C);%%%binarising the image
% imshow(bw);
%%%%if there are letters or any other noise is present around the barcode
%%Note: the size of the noise and letters should be smaller than the
%%barcode size
labelImage = bwlabel(bw,8);
len=0;labe=0;
for i=1:max(max(labelImage))
a = find(labelImage==i);
if(len<length(a))
len=length(a);
labe=i;
end
end
imag = zeros(size(l));
imag(find(labelImage==labe))=255;
% imtool(imag);
%%%if Necessary do errossion
% se2 = strel('line',10,0);
% imag= imerode(imag,se2);
% imag= imerode(imag,se2);
[r c]= find(imag==255);
minr = min(r);
maxc = max(c);
minc = min(c);
maxr = max(r);
imag1 = zeros(size(l));
for i=minr:maxr
for j=minc:maxc
imag1(i,j)=255;
end
end
% figure,imtool(imag1);
varit = find(imag1==0);
Igrayc(varit)=0;
%%%%%result image having only barcode
imshow(Igrayc,[]);
%%%%%original image
figure(),imshow(Igray,[]);
Hope it is useful