After applying the following code I get output mapped image started from centre top point. How can I put the starting point to left bottom corner? So, the output image should be stretched from left bottom point(not from top centre as it is now)...
im = imread ('peppers.png');
im = rgb2gray(im);
[nZ,nX] = size(im);
theta = ((0:(nX-1))-nX/2)*(0.1*(pi/180)) - pi/2;
rr = (0:(nZ-1))*0.1e-3;
%% Plot image in rectangular coordinates
figure
imagesc(theta*(180/pi), rr*1e3, im)
xlabel('theta [deg]')
ylabel('r [mm]')
%% Create grids and convert polar coordinates to rectangular
[THETA,RR] = meshgrid(theta,rr);
[XX,YY] = pol2cart(THETA,RR);
%% Plot as surface, viewed from above
figure
surf(XX*1e3,YY*1e3,im,'edgecolor','none')
view(0,90)
xlabel('x [mm]')
ylabel('y [mm]')
Related
Given a image canvas I with 2 points (a_x, a_y) and (b_x, b_y). The plotted line on the image has the correct orientation.
However, when I plot the same coordinates (a and b) in a cartesian coordinate system, I get a line with the wrong orientation.
I would like to convert the image coordinates that they match with the cartesian system. Thanks.
% Create image canvas
canvas = zeros(320, 320);
I = uint8(canvas);
imshow(I)
a_x = 122.6544;
a_y = 234.9782;
b_x = 165.9290;
b_y = 126.9200;
hold on
plot([a_x, b_x], [a_y, b_y] )
% Plot cartesian coordinate system
figure()
plot([a_x, b_x], [a_y, b_y])
xlim([0 320])
ylim([0 320])
axis equal
grid on
Images generated by imshow have the (0,0) coordinate at the upper left corner. Normal plots at the lower left corner.
You can see this by switching on the axes of the image:
% Create image canvas
canvas = zeros(320, 320);
I = uint8(canvas);
imshow(I)
a_x = 122.6544;
a_y = 234.9782;
b_x = 165.9290;
b_y = 126.9200;
hold on
plot([a_x, b_x], [a_y, b_y] );
ax = gca;
ax.Visible = 'on';
To change the direction of the plot axes, use the YDir property of the axes:
figure()
plot([a_x, b_x], [a_y, b_y])
xlim([0 320])
ylim([0 320])
grid on
ax=gca;
ax.YDir = 'reverse';
I would like to know how to rotate a 2D image along its Z-axis using Matlab R2016b and to obtain an image after performing this procedure.
For example, let's take this 2D image:
Now, I rotate it of 45° roughly:
And now, of 90°:
Do you know if it is possible to perform the same operation in Matlab R2016b, please ?
Thank you very much for your help
Images Source: https://www.youtube.com/watch?v=m89mVexWQZ4
Yes it's possible. The easiest thing to do would be to map the image on the y = 0 plane in 3D, then rotate the camera to the desired azimuth or the angle with respect to the y axis. Once you do that, you can use the getframe / cdata idiom to actually capture the actual image data in a variable itself. The reason why you do this with respect to the y plane is because the method that I will be using to present the image is through the surf command that plots surface plots in 3D, but the y axis here is the axis that goes into and out of the screen. The x axis is the horizontal and the z axis would be the vertical when displaying data.
First read in your image using something like imread, then you need to define the 4 corners of the image that map to the 3D plane and then rotate the camera. You can use the view function to help you rotate the camera by adjusting the azimuthal angle (first parameter) and leaving the elevation angle as 0.
Something like this could work. I'll be using the peppers image that is part of the image processing toolbox:
im = imread('peppers.png'); % Read in the image
ang = 45; % Rotate clockwise by 45 degrees
% Define 4 corners of the image
X = [-0.5 0.5; -0.5 0.5];
Y = [0 0; 0 0];
Z = [0.5 0.5; -0.5 -0.5];
% Place the image on the y = 0 plane
% Turn off the axis and rotate the camera
figure;
surf(X, Y, Z, 'CData', im, 'FaceColor', 'texturemap');
axis('off');
view(ang, 0);
% Get the image data after rotation
h = getframe;
rot_im = h.cdata;
rot_im contains the rotated image. To appreciate the rotation of the image, we can loop through angles from 0 to 360 in real time. At each angle, we can use view to dynamically rotate the camera and use drawnow to update the figure. I've also updated the title of the figure to show you what the angle is at each update. The code for that is below as well as the output saved as an animated GIF:
for ang = 0 : 360
view(ang, 0);
pause(0.01);
drawnow;
title(sprintf('Angle: %d degrees', ang));
end
I am trying to find a way to crop from a circle object (Image A) the largest square that can fit inside it.
Can someone please explain/show me how to find the biggest square fit parameters of the white space inside the circle (Image I) and based on them crop the square in the original image (Image A).
Script:
A = imread('E:/CirTest/Test.jpg');
%imshow(A)
level = graythresh(A);
BW = im2bw(A,level);
%imshow(BW)
I = imfill(BW, 'holes');
imshow(I)
d = imdistline;
[centers, radii, metric] = imfindcircles(A,[1 500]);
imageCrop=imcrop(A, [BoxBottomX BoxBottomY NewX NewY]);
I have a solution for you but it requires a bit of extra work. What I would do first is use imfill but directly on the grayscale image. This way, noisy pixels in uniform areas get inpainted with the same intensities so that thresholding is easier. You can still use graythresh or Otsu's thresholding and do this on the inpainted image.
Here's some code to get you started:
figure; % Open up a new figure
% Read in image and convert to grayscale
A = rgb2gray(imread('http://i.stack.imgur.com/vNECg.jpg'));
subplot(1,3,1); imshow(A);
title('Original Image');
% Find the optimum threshold via Otsu
level = graythresh(A);
% Inpaint noisy areas
I = imfill(A, 'holes');
subplot(1,3,2); imshow(I);
title('Inpainted image');
% Threshold the image
BW = im2bw(I, level);
subplot(1,3,3); imshow(BW);
title('Thresholded Image');
The above code does the three operations that I mentioned, and we see this figure:
Notice that the thresholded image has border pixels that need to be removed so we can concentrate on the circular object. You can use the imclearborder function to remove the border pixels. When we do that:
% Clear off the border pixels and leave only the circular object
BW2 = imclearborder(BW);
figure; imshow(BW2);
... we now get this image:
Unfortunately, there are some noisy pixels, but we can very easily use morphology, specifically the opening operation with a small circular disk structuring element to remove these noisy pixels. Using strel with the appropriate structuring element in addition to imopen should help do the trick:
% Clear out noisy pixels
SE = strel('disk', 3, 0);
out = imopen(BW2, SE);
figure; imshow(out);
We now get:
This mask contains the locations of the circular object we now need to use to crop our original image. The last part is to determine the row and column locations using this mask to locate the top left and bottom right corner of the original image and we thus crop it:
% Find row and column locations of circular object
[row,col] = find(out);
% Find top left and bottom right corners
top_row = min(row);
top_col = min(col);
bottom_row = max(row);
bottom_col = max(col);
% Crop the image
crop = A(top_row:bottom_row, top_col:bottom_col);
% Show the cropped image
figure; imshow(crop);
We now get:
It's not perfect, but it will of course get you started. If you want to copy and paste this in its entirety and run this on your computer, here we are:
figure; % Open up a new figure
% Read in image and convert to grayscale
A = rgb2gray(imread('http://i.stack.imgur.com/vNECg.jpg'));
subplot(2,3,1); imshow(A);
title('Original Image');
% Find the optimum threshold via Otsu
level = graythresh(A);
% Inpaint noisy areas
I = imfill(A, 'holes');
subplot(2,3,2); imshow(I);
title('Inpainted image');
% Threshold the image
BW = im2bw(I, level);
subplot(2,3,3); imshow(BW);
title('Thresholded Image');
% Clear off the border pixels and leave only the circular object
BW2 = imclearborder(BW);
subplot(2,3,4); imshow(BW2);
title('Cleared Border Pixels');
% Clear out noisy pixels
SE = strel('disk', 3, 0);
out = imopen(BW2, SE);
% Show the final mask
subplot(2,3,5); imshow(out);
title('Final Mask');
% Find row and column locations of circular object
[row,col] = find(out);
% Find top left and bottom right corners
top_row = min(row);
top_col = min(col);
bottom_row = max(row);
bottom_col = max(col);
% Crop the image
crop = A(top_row:bottom_row, top_col:bottom_col);
% Show the cropped image
subplot(2,3,6);
imshow(crop);
title('Cropped Image');
... and our final figure is:
You can use bwdist with L_inf distance (aka 'chessboard') to get the axis-aligned distance to the edges of the region, thus concluding the dimensions of the largest bounded box:
bw = imread('http://i.stack.imgur.com/7yCaD.png');
lb = bwlabel(bw);
reg = lb==2; %// pick largest area
d = bwdist(~reg,'chessboard'); %// compute the axis aligned distance from boundary inward
r = max(d(:)); %// find the largest distance to boundary
[cy cx] = find(d==r,1); %// find the location most distant
boundedBox = [cx-r, cy-r, 2*r, 2*r];
And the result is
figure;
imshow(bw,'border','tight');
hold on;
rectangle('Position', boundedBox, 'EdgeColor','r');
Once you have the bounding box, you can use imcrop to crop the original image
imageCrop = imcrop(A, boundedBox);
Alternatively, you can
imageCrop = A(cy + (-r:r-1), cx + (-r:r-1) );
I'd like to take in an RGB image, find the points in the image that are white, and get the cartesian coordinates of those points in the image. I've gotten most of the way there, but when I try to plot the cartesian coordinates, I get a vertically tiled image (i.e. 5 overlapped copies of what I should see). Anyone know what could be causing this?
,
Code: (JPG comes in as 2448 x x3264 x 3 uint8)
I = imread('IMG_0245.JPG');
imshow(I); % display unaltered image
% Convert image to grayscale
I = rgb2gray(I);
% Convert image to binary (black/white)
I = im2bw(I, 0.9);
% Generate cartesian coordinates of image
imageSize = size(I);
[x, y] = meshgrid( 1:imageSize(1), 1:imageSize(2) );
PerspectiveImage = [x(:), y(:), I(:)];
% Get indices of white points only
whiteIndices = find(PerspectiveImage(:,3));
figure; plot( PerspectiveImage(whiteIndices, 1), PerspectiveImage(whiteIndices, 2),'.');
% Flip vertically to correct indexing vs. plotting issue
axis ij
Very simple. You're declaring your meshgrid wrong. It should be:
[x, y] = meshgrid( 1:imageSize(2), 1:imageSize(1) );
The first parameter denotes the horizontal extents of the 2D grid, and so you want to make this vary for as many columns as you have. Similarly, the second parameter denotes the vertical extents of the 2D grid, and so you want to make this for as many rows as you have.
I had to pre-process some of your image to get some good results because your original image had a large white border surrounding the image. I had to remove this border by removing all pure white pixels. I also read in the image directly from StackOverflow:
I = imread('http://s7.postimg.org/ovb53w4ff/Track_example.jpg');
mask = all(I == 255, 3);
I = bsxfun(#times, I, uint8(~mask));
This was the image I get after doing my pre-processing:
Once I do this and change your meshgrid call, I get this:
Im looking to create an image in Matlab of a large black rectangle with 9 small circles arranged as a a 3x3 array aligned in the centre of the rectangle, i.e. the centre circle will have its midpoint in the centre of the square.
I need the circles evenly spaced apart with some distance between each circle and between the outer circles and the border of the rectangle (think of a square piece of paper with 9 holes placed in it by stabbing it with a pen). I need this so that i can see how image convolution using a 2D gaussian will distort this image.
However I’m relatively new to Matlab and have been trying to create this image. I have successfully made a black/white square and a white circle in a black square which takes up most of the square itself but I cant seem to make a small white circle in any desired location in a black square let alone multiple small circles in a specific alignment.
This is what I have used to create the black square with a large circle:
X = ones([100,1])*([-50:49]);
Y = ([-50:49]')*(ones([1,100]));
Z = (X.^2)+(Y.^2);
image = zeros([100 100]);
image(find(Z<=50^2)) = 1;
imshow(image)
If I understood correctly, try this:
% size of each small box. Final image will be 3Nx3N
N = 100;
% create a circle mask
t = linspace(0,2*pi,50); % approximated by 100 lines
r = (N-10)/2; % circles will be separated by a 10 pixels border
circle = poly2mask(r*cos(t)+N/2+0.5, r*sin(t)+N/2+0.5, N, N);
% replicate to build image
img = repmat(circle, 3,3);
subplot(121), imshow(img)
% after applying Gaussian filter
h = fspecial('gaussian', [15 15], 2.5);
img2 = imfilter(im2double(img), h);
subplot(122), imshow(img2)