I am writing a matlab code that takes in a photo and detects the circular object. After using some filters, I got below image.
To detect circular object(it is not a perfect circle), I tried to apply Hough Transform passing different values of radius and threshold, but it couldn't detect properly. Why this happens? Is it about shape of object or background of image?
Also is it possible to detect same object at the following image using Hough Transform?
Edge of circular object seems by human eye, but I am not sure that background can be eliminated from image completely via Hough Transform.
You can use imfindcircles in the Image Processing Toolbox. Using morphology to fill in the circle and cranking up sensitivity may help:
im = imread('pattern.jpg');
im2 = rgb2gray(im(100:end-100, 100:end-100, :));
im3 = im2bw(im2, 0.1);
im4 = imclose(im3, strel('disk', 4, 4));
im5 = imfill(im4, 'holes');
imshow(im5);
[centers, radii] = imfindcircles(im5, [180, 200], 'Sensitivity', .99);
viscircles(centers, radii);
Related
I need to transform my tilted image in a way I can find coins on an A4 paper. So far, I have been getting four coordinates of edges of my paper by manually selecting them with ginput.
targetImageData = imread('coin1.jpg');
imshow(targetImageData);
fprintf('Corner selection must be clockwise or anti-clockwise.\n');
[X,Y] = ginput(4);
Is there a way to automate this process, say, apply some edge detector and then find coordinates of each vertex and then pass them as the coordinates needed for transformation?
Manual selection:
Result:
You can try using detectHarrisFeatures on the S color channel of HSV color space:
I was looking for a color space that gets maximum contrast of the paper.
It looks like the saturation color channel of HSV makes a good contrast between the paper and the background.
Image is resized the image by a factor of 0.25, for removing noise.
detectHarrisFeatures finds the 4 corners of the paper, but it might not be robust enough.
You may need to find more features, and find the 4 correct features, using some logic.
Here is a code sample:
%Read input image
I = imread('im.png');
%Remove the margins, and replace them using padding (just because the image is a MATLAB figure)
I = padarray(I(11:end-10, 18:end-17, :), [10, 17], 'both', 'replicate');
HSV = rgb2hsv(I);
%H = HSV(:, :, 1);%figure;imshow(H);title('H');
S = HSV(:, :, 2);%figure;imshow(S);title('S');
%V = HSV(:, :, 3);%figure;imshow(V);title('V');
%Reduce image size by a factor of 0.25 in each axis
S = imresize(S, 0.25);
%S = imclose(S, ones(3)); %May be requiered
%Detect corners
corners = detectHarrisFeatures(S);
imshow(S); hold on;
plot(corners.selectStrongest(4));
Result:
Different approach you may try:
Take a photo without the coins.
Mark the corners manually, and extract features of the 4 corners.
Use image matching techniques to match the image with the coins with the image without the coins (mach basted on the 4 corners).
I am calling some images inside a for loop and then doing some processing on those images. After that, I am using the step function to display those frames and their masks inside a video player. How can I add a boundary to an object inside the mask image? Also, how can I make the boundary thicker and plot the centroids of each blob in the mask in the mask image? Below is the rough sketch of the code.
videoPlayer = vision.VideoPlayer();
maskPlayer = vision.VideoPlayer();
for ii = 1:nfiles
filenameii = [............]
frame= imread(filenameii);
mask = dOB(frame,BackgroundImg);
% some processing on the images
mask= bwareaopen(mask,27);
boundaries = bwboundaries(mask,'noholes');
B=boundaries{1};
Centroid = regionprops(mask,'centroid');
Centroids = cat(1, Centroid.Centroid);
plot(B(:,2),B(:,1),'g','LineWidth',3);
plot(Centroids(:,1), Centroids(:,2), 'r+', 'MarkerSize', 10); step(videoPlayer,frame);
step(maskPlayer, mask);
P.S: I know how to display it on a figure using hold on but I would like this done directly on the image before displaying it in the video player. Any guidance would be appreciated.
Simply paint the pixels on the mask first before displaying it in the video player. What you have does work, but it will plot the boundary inside the figure for the mask player. Therefore, take your boundaries that you detected from bwboundaries, create linear indices from these coordinates and set the values in your image to white. What may be even simpler is to take your mask that you detected and use bwperim to automatically produce a mask that contains the boundaries of the blobs. I also see that you are filling in the holes of the mask, so you can use imfill directly on the output of your post-processing so that it gives you an image instead of coordinates. You would then use this mask to directly index into your image and set the coordinates of the boundaries of the blob to your desired colour. If you desire to make the perimeter thicker, a simple image dilation with imdilate using the appropriately sized square structuring element will help. Simply define the size of the neighbourhood of this structuring element to be the thickness of the perimeter that you desire. Finally, if you want to insert the centroids into the mask and since you have the MATLAB Computer Vision System Toolbox, use the insertMarker function so that you can use a set of points for each centroid and put them directly in the image. However, you must be sure to change the mask from a logical to a data type more suitable for images. uint8 should work. Therefore, cast the image to this type then multiply all nonzero values by 255 to ensure the white colours are maintained in the mask. With insertMarker, you want to insert red pluses with a size of 10 so we need to make sure we call insertMarker to reflect that. Also, because you want to have a colour image you will have to make your mask artificially colour and to do this painting individually for each plane for the colour that you want. Since you want green, this corresponds to the RGB value of (0,255,0).
Therefore, I have modified your code so that it does this. In addition, I've calculated the centroids of the filled mask instead of the original. We wouldn't want to falsely report the centroids of objects with gaps... unless that's what you're aiming for, but let's assume you're not:
videoPlayer = vision.VideoPlayer();
maskPlayer = vision.VideoPlayer();
% New - Specify colour you want
clr = [0 255 0]; % Default is green
% New - Define thickness of the boundaries in pixels.
thickness = 3;
% New - Create structuring element
se = strel('square', thickness);
for ii = 1:nfiles
filenameii = [............]
frame = imread(filenameii);
mask = dOB(frame, BackgroundImg);
% some processing on the images
mask = bwareaopen(mask,27);
%boundaries = bwboundaries(mask,'noholes');
%B=boundaries{1};
% New code - fill in the holes
mask = imfill(mask, 'holes');
Centroid = regionprops(mask,'centroid');
% New code - Create a boundary mask
mask_p = bwperim(mask, 8);
% New code - Make the boundaries thicker
mask_p = imdilate(mask_p, se);
% New code - create a colour image out of the mask
[red, green, blue] = deal(255*uint8(mask));
% Paint the perimeter of the blobs in the desired colour
red(mask_p) = clr(1); green(mask_p) = clr(2); blue(mask_p) = clr(3);
Centroids = cat(1, Centroid.Centroid);
%plot(B(:,2),B(:,1),'g','LineWidth',3);
%plot(Centroids(:,1), Centroids(:,2), 'r+', 'MarkerSize', 10);
% New - Make mask into RGB image for marker painting and to
% show to the user
mask_p = cat(3, red, green, blue);
% New - Insert the centroids directly in the mask image
mask_p = insertMarker(mask_p, Centroids, '+', 'color', 'r', 'size', 10);
step(videoPlayer, frame);
% New - Show new mask in the player
step(maskPlayer, mask_p);
end
I'm using a Properly working Matlab code (The original code is from here) that uses Hough trnsform to detect basic shapes like round, square and triangle.Here below is the important code segment.
[H, theta,rho]=hough(S);
Above H is the Hough transform matrix and S is the Black and White image of the shape.
for cnt = 1:max(max(H))
data(cnt) = sum(sum(H == cnt));
end
Here is the shape detection part.
[maxval,maxind] = max(data);
medval = median(data);
[p]=polyfit(1:maxind-5,data(1:maxind-5),2);
if maxval<3*medval
set(handles.txtResult,'string','Triangle');
elseif p(3)>100
set(handles.txtResult,'string','Square');
else
set(handles.txtResult,'string','Round');
end
I can understand the "data"(which stores Hough Matrix intensity frequencies). I just can't understand the logic it uses to detect the shape. maxval<3*medval and p(3)>100
By default, MATLAB function imrotate rotate image with black color filled in rotated portion. See this, http://in.mathworks.com/help/examples/images_product/RotationFitgeotransExample_02.png
We can have rotated image with white background also.
Question is, Can we rotate an image (with or without using imrotate) filled with background of original image?
Specific to my problem: Colored image with very small angle of rotation (<=5 deg.)
Here's a naive approach, where we simply apply the same rotation to a mask and take only the parts of the rotated image, that correspond to the transformed mask. Then we just superimpose these pixels on the original image.
I ignore possible blending on the boundary.
A = imread('cameraman.tif');
angle = 10;
T = #(I) imrotate(I,angle,'bilinear','crop');
%// Apply transformation
TA = T(A);
mask = T(ones(size(A)))==1;
A(mask) = TA(mask);
%%// Show image
imshow(A);
You can use padarray() function with 'replicate' and 'both' option to interpolate your image. Then you can use imrotate() function.
In the code below, I've used ceil(size(im)/2) as pad size; but you may want bigger pad size to eliminate the black part. Also I've used s and S( writing imR(S(1)-s(1):S(1)+s(1), S(2)-s(2):S(2)+s(2), :)) to crop the image where you can extract bigger part of image just expanding boundary of index I used below for imR.
Try this:
im = imread('cameraman.tif'); %// You can also read a color image
s = ceil(size(im)/2);
imP = padarray(im, s(1:2), 'replicate', 'both');
imR = imrotate(imP, 45);
S = ceil(size(imR)/2);
imF = imR(S(1)-s(1):S(1)+s(1)-1, S(2)-s(2):S(2)+s(2)-1, :); %// Final form
figure,
subplot(1, 2, 1)
imshow(im);
title('Original Image')
subplot(1, 2, 2)
imshow(imF);
title('Rotated Image')
This gives the output below:
Not so good but better than black thing..
I'm new to matlab!
I'm trying to detect pupils from a frame sequence... I need to use this approach to detect whether eyes are open or not.
imfindcircles is working when I don't use erode and dilate, but that steps of erode and dilate are bringing me a really cool image, it's a very clean black circle on white background, Hough will work like a charm, but it doesn't and I cannot really find a way out.
It doesn't depend on radii range, because if I comment erode and dilate it works. Erode and dilate produce a perfect image as I just said.
Any tips?
rightEye = imcrop(videoFrame, rightEyeBox);
leftEye = imcrop(videoFrame, leftEyeBox);
% to grayscale
grayRightEye = rgb2gray(rightEye);
grayLeftEye = rgb2gray(leftEye);
% binarize
grayRightEye = im2bw(grayRightEye, 0.45);
% erode and dilate
finalRight = imdilate(grayRightEye, strel('disk', 7));
finalRight = imerode(finalRight, strel('disk', 11));
imshow(grayRightEye);
rightCircle = max(rightRows, rightCols);
[centers, radii] = imfindcircles(grayRightEye,[5 25])
I've solved by typecasting finalRight to double!