I build an alpha shape from some points (example given in code) and want to export the shape to a raster graphics format. I need the shape only, not the plot markings (axis, scales ect).
I need only the resulting triangle on white ground as a bitmap.
Scale needs to be 1 unit = 1 pixel.
x = [0 10 20 30 30 30 15];
y = [0 0 0 0 15 30 15];
shape = alphaShape (x',y');
plot (shape, 'FaceColor', 'black');
I have not found anything on how to export shapes or how to rasterize them. Is there any way to do that?
Run the following code after yours.
imgwidth = max(1, ceil(max(x) - min(x)));
imgheight = max(1, ceil(max(y) - min(y)));
ax = gca;
ax.Visible = 'off';
ax.XTickMode = 'manual';
ax.YTickMode = 'manual';
ax.ZTickMode = 'manual';
ax.XLimMode = 'manual';
ax.YLimMode = 'manual';
ax.ZLimMode = 'manual';
ax.Position = ax.OuterPosition;
af = gcf;
figpos = getpixelposition(af);
resolution=get(0, 'ScreenPixelsPerInch');
set(af, 'paperunits','inches', ....
'papersize',[imgwidth imgheight]/resolution, ....
'paperposition',[0 0 [imgwidth imgheight]/resolution]);
print(af,'out.png','-dpng',['-r',num2str(resolution)],'-opengl')
Things done:
Fetch data range and convert to image dimensions.
Turn off axes and ticks.
Minimize/remove padding space surrounding the actual content.
Map 1 unit in data into 1 pixel in output image.
Things not done:
Guarantee aspect ratio. (should work, though)
This screenshot shows non-unity aspect ratio output:
References
Mathworks - Save Figure at Specific Size and Resolution
MATLAB Central - saving a figure at a set resolution
Mathworks - print
Mathworks - Save Figure with Minimal White Space
Related
I have the following code:
close all;
star = imread('/Users/name/Desktop/folder/pics/OnTheBeach.png');
blrtype = fspecial('average',[3 3]);
blurred = imfilter(star, blrtype);
[rows,cols,planes] = size(star);
R = star(:,:,1); G = star(:,:,2); B = star(:,:,3);
starS = zeros(rows,cols);
ind = find(R > 190 & R < 240 & G > 100 & G < 170 & B > 20 & B < 160);
starS(ind) = 1;
K = imfill(starS,'holes');
stats = regionprops(logical(K), 'Area', 'Solidity');
ind = ([stats.Area] > 250 & [stats.Solidity] > 0.1);
L = bwlabel(K);
result = ismember(L,find(ind));
Up to this point I load an image, blur to filter out some noise, do colour segmentation to find the specific objects which fall in that range, then create a binary image that has value 1 for the object's colour, and 0 for all other stuff. Finally I do region filtering to remove any clutter that was left in the image so I'm only left with the objects I'm looking for.
Now I want to recolour the original image based on the segmentation mask to change the colour of the starfish. I want to create Red,Green,Blue channels, assign value to them then lay the mask over the image. (To have red starfishes for example)
red = star;
red(starS) = starS(:,:,255);
green = star;
green(starS) = starS(:,:,0);
blue = star;
blue(starS) = star(:,:,0);
out = cat(3, red, green, blue);
imshow(out);
This gives me an error: Index exceeds matrix dimensions.
Error in Project4 (line 28)
red(starS) = starS(:,:,255);
What is wrong with my current approach?
Your code is kinda confusing... I don't understand whether the mask you want to use is starS or result since both look like 2d indexers. In your second code snippet you used starS, but the mask you posted in your question is result.
Anyway, no matter what your desired mask is, all you have to do is to use the imoverlay function. Here is a small example based on your code:
out = imoverlay(star,result,[1 0 0]);
imshow(out);
and here is the output:
If the opaque mask of imoverlay suggested by Tommaso is not what you're after, you can modify the RGB values of the input to cast a hue over the selected pixels without saturating them. It is only slightly more involved.
I = find(result);
gives you an index of the pixels in the 2D image. However, star is 3D. Those indices will point at the same pixels, but only at the first 2D slice. That is, if I points at pixel (x,y), it is equivalently pointing to pixel (x,y,1). That is the red component of the pixel. To index (x,y,2) and (x,y,2), the green and blue components, you need to increment I by numel(result) and 2*numel(result). That is, star(I) accesses the red component of the selected pixels, star(I+numel(result)) accesses the green component, and star(I+2*numel(result)) accesses the blue component.
Now that we can access these values, how do we modify their color?
This is what imoverlay does:
I = find(result);
out = star;
out(I) = 255; % red channel
I = I + numel(result);
out(I) = 0; % green channel
I = I + numel(result);
out(I) = 0; % blue channel
Instead, you can increase the brightness of the red proportionally, and decrease the green and blue. This will change the hue, increase saturation, and preserve the changes in intensity within the stars. I suggest the gamma function, because it will not cause strong saturation artefacts:
I = find(result);
out = double(star)/255;
out(I) = out(I).^0.5; % red channel
I = I + numel(result);
out(I) = out(I).^1.5; % green channel
I = I + numel(result);
out(I) = out(I).^1.5; % blue channel
imshow(out)
By increasing the 1.5 and decreasing the 0.5 you can make the effect stronger.
I have binary images and they have semi or less circles. My aim is to find these circles, make them whole circles and remove all other objects . I found this but it is for MATLAB R2013a. I am using R2011b and it doesn't have the function centers = imfindcircles(A,radius).
How can I do that in MATLAB version R2011b?
Images:
Edit:
My aim is to get whole circle. I show this below for the last image.
Too bad about imfindcircles! One thing I can suggest is to invoke regionprops and specify the 'Area' and 'BoundingBox' flags. regionprops was available in MATLAB for as long as I can remember, so we can certainly use it here.
What this will do is that whatever distinct objects that are seen in the image that are connected, we will find both their areas and their bounding boxes that bound them. After you do this, threshold on the area so that any objects that have a very large area most likely contain circles of interest. Bear in mind that I'm only assuming that you have circles in your image. Should you have any objects that have a large area, this method will extract those out too.
As such, let's read in your image directly from Stack Overflow. When you uploaded the image, it's a RGB image, so I'll have to convert to binary:
im = imread('http://i.stack.imgur.com/wQLPi.jpg');
im_bw = im2bw(im);
Next, call regionprops:
s = regionprops(im_bw, 'Area', 'BoundingBox');
Now, collect all of the areas, and let's take a look at all of the unique areas of all objects seen in this image:
areas = [s.Area].';
unique(areas)
ans =
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
19
20
23
24
25
28
29
38
43
72
73
85
87
250
465
3127
If you take a look at the very end, you'll see that we have an object that has 3127 pixels in it. This probably contains our circle. As such, let's pick out that single element that contains this object:
s2 = s(areas == 3127);
In general, you'll probably have more than one circle in your image, so you should threshold the area to select those potential circles. Something like:
s2 = s(areas > 2000);
Now, let's create a new blank image that is the same size as the original image, then simply use the BoundingBox property to extract out the area that encompasses the circle in the original image and copy it over to the same location in the output image. The BoundingBox field is structured in the following way:
[x y w h]
x and y are the top-left corner of the bounding box. x would be the column and y would be the row. w and h are the width and height of the bounding box. As such, we can use this directly to access our image and copy those pixels over into the output image.
out = false(size(im_bw));
bb = floor(s2.BoundingBox); %// Could be floating point, so floor it
out(bb(2):bb(2)+bb(4)-1, bb(1):bb(1)+bb(3)-1) = im_bw(bb(2):bb(2)+bb(4)-1, bb(1):bb(1)+bb(3)-1);
This is what I get:
What you should probably do is loop over the circles in case we have more than one. The above code assumes that you detected just one circle. Therefore, do something like this:
out = false(size(im_bw));
for idx = 1 : numel(s2) %// For each potential circle we have...
bb = floor(s2(idx).BoundingBox); %// Could be floating point, so floor it
%// Copy over pixels from original bw image to output
out(bb(2):bb(2)+bb(4)-1, bb(1):bb(1)+bb(3)-1) = im_bw(bb(2):bb(2)+bb(4)-1, bb(1):bb(1)+bb(3)-1);
end
A small thing to note is that the bounding box encompasses the entire object, but there could also be some noisy pixels that are disconnected that are within that bounding box. You may have to apply some morphology to get rid of those pixels. A binary opening could suffice.
Here's what I get with your other images. I thresholded the area to search for those that have 2000 pixels or more (I did this above):
Just for self-containment and your copy-and-pasting pleasure, here's the code in one segment:
clear all;
close all;
%im = imread('http://i.stack.imgur.com/qychC.jpg');
%im = imread('http://i.stack.imgur.com/wQLPi.jpg');
im = imread('http://i.stack.imgur.com/mZMBA.jpg');
im_bw = im2bw(im);
s = regionprops(im_bw, 'Area', 'BoundingBox');
areas = [s.Area].';
s2 = s(areas > 2000);
out = false(size(im_bw));
for idx = 1 : numel(s2) %// For each potential circle we have...
bb = floor(s2(idx).BoundingBox); %// Could be floating point, so floor it
%// Copy over pixels from original bw image to output
out(bb(2):bb(2)+bb(4)-1, bb(1):bb(1)+bb(3)-1) = im_bw(bb(2):bb(2)+bb(4)-1, bb(1):bb(1)+bb(3)-1);
end
imshow(out);
All three images are there in the code. You just have to uncomment whichever one you want to use, comment out the rest, then run the code. It will display an image with all of your detected circles.
Edit
You would like to draw complete circles, instead of extracting the shape themselves. That isn't a problem to do. All you need to do is determine the best "radii" that can be enclosed inside each of the bounding boxes. This is simply the maximum of the width and height of each bounding box, then divide these quantities by 2.
After, create a 2D grid of co-ordinates through meshgrid that is the same size as the original image itself, then create a binary image such that the Euclidean distance between the centre of this bounding box with any point in this 2D grid less than the radius is set to logical true while the other positions are set to logical false.
In other words, do this:
clear all;
close all;
im = imread('http://i.stack.imgur.com/qychC.jpg');
%im = imread('http://i.stack.imgur.com/wQLPi.jpg');
%im = imread('http://i.stack.imgur.com/mZMBA.jpg');
im_bw = im2bw(im);
s = regionprops(im_bw, 'Area', 'BoundingBox');
areas = [s.Area].';
s2 = s(areas > 2000);
out = false(size(im_bw));
for idx = 1 : numel(s2) %// For each potential circle we have...
bb = floor(s2(idx).BoundingBox); %// Could be floating point, so floor it
%// Copy over pixels from original bw image to output
out(bb(2):bb(2)+bb(4)-1, bb(1):bb(1)+bb(3)-1) = im_bw(bb(2):bb(2)+bb(4)-1, bb(1):bb(1)+bb(3)-1);
end
figure;
imshow(out);
%// Image that contains all of our final circles
out2 = false(size(im_bw));
[X,Y] = meshgrid(1:size(im_bw,2), 1:size(im_bw,1)); %// Find a 2D grid of co-ordinates
for idx = 1 : numel(s2) %// For each circle we have...
bb = floor(s2(idx).BoundingBox); %// Could be floating point, so floor it
cenx = bb(1) + (bb(3) / 2.0); %// Get the centre of the bounding box
ceny = bb(2) + (bb(4) / 2.0);
radi = max(bb(3), bb(4)) / 2; %// Find the best radius
tmp = ((X - cenx).^2 + (Y - ceny).^2) <= radi^2; %// Draw our circle and place in a temp. image
out2 = out2 | tmp; %// Add this circle on top of our output image
end
figure;
imshow(out2);
This script now shows you the original extracted shapes, and the best "circles" that describes these shapes in two separate figures. Bear in mind that this is a bit different than what I showed you previously with one circle. What I have to do now is allocate a blank image, then incrementally add each circle to this new image. For each circle, I create a temporary binary image that has just a circle I'm looking for, then I add this on top of the new image. At the end, we will show all of the circles in the image that are fully drawn as you desire.
This is what I get for the best circle for each of your images:
Good luck!
Say I have an image. How can I colour some specific pixels in that image using MATLAB?
Thanks.
RGB Pixels
I'd suggest working with an RGB image, so that you can easily represent color and gray pixels. Here's an example of making two red blocks on an image:
img = imread('moon.tif');
imgRGB = repmat(img,[1 1 3]);
% get a mask of the pixels you want and set an RGB vector to those pixels...
colorMask = false(size(imgRGB,1),size(imgRGB,2));
colorMask(251:300,151:200,:) = true; % two discontiguous blocks
colorMask(50:100,50:100,:) = true;
redPix = permute([255 0 0],[1 3 2]);
imgRGB(repmat(colorMask,[1 1 3])) = repmat(redPix, numel(find(colorMask)),1);
AlphaData image property
Another cool way of doing this is with an image's AlphaData property. See this example on a MathWorks blog. This essentially turns color on or off in certain parts of the image by making the gray image covering the color image transparent. To work with a gray image, do like the following:
img = imread('moon.tif');
influenceImg = abs(randn(size(img)));
influenceImg = influenceImg / (2*max(influenceImg(:)));
imshow(img, 'InitialMag', 'fit'); hold on
green = cat(3, zeros(size(img)), ones(size(img)), zeros(size(img)));
h = imshow(green); hold off
set(h, 'AlphaData', influenceImg)
See the second example at the MathWorks link.
I want to stretch an elliptical object in an image until it forms a circle. My program currently inputs an image with an elliptical object (eg. coin at an angle), thresholds and binarizes it, isolates the region of interest using edge-detect/bwboundaries(), and performs regionprops() to calculate major/minor axis lengths.
Essentially, I want to use the 'MajorAxisLength' as the diameter and stretch the object on the minor axis to form a circle. Any suggestions on how I should approach this would be greatly appreciated. I have appended some code for your perusal (unfortunately I don't have enough reputation to upload an image, the binarized image looks like a white ellipse on a black background).
EDIT: I'd also like to apply this technique to the gray-scale version of the image, to examine what the stretch looks like.
code snippet:
rgbImage = imread(fullFileName);
redChannel = rgbImage(:, :, 1);
binaryImage = redChannel < 90;
labeledImage = bwlabel(binaryImage);
area_measurements = regionprops(labeledImage,'Area');
allAreas = [area_measurements.Area];
biggestBlobIndex = find(allAreas == max(allAreas));
keeperBlobsImage = ismember(labeledImage, biggestBlobIndex);
measurements = regionprops(keeperBlobsImage,'Area','MajorAxisLength','MinorAxisLength')
You know the diameter of the circle and you know the center is the location where the major and minor axes intersect. Thus, just compute the radius r from the diameter, and for every pixel in your image, check to see if that pixel's Euclidean distance from the cirlce's center is less than r. If so, color the pixel white. Otherwise, leave it alone.
[M,N] = size(redChannel);
new_image = zeros(M,N);
for ii=1:M
for jj=1:N
if( sqrt((jj-center_x)^2 + (ii-center_y)^2) <= radius )
new_image(ii,jj) = 1.0;
end
end
end
This can probably be optimzed by using the meshgrid function combined with logical indices to avoid the loops.
I finally managed to figure out the transform required thanks to a lot of help on the matlab forums. I thought I'd post it here, in case anyone else needed it.
stats = regionprops(keeperBlobsImage, 'MajorAxisLength','MinorAxisLength','Centroid','Orientation');
alpha = pi/180 * stats(1).Orientation;
Q = [cos(alpha), -sin(alpha); sin(alpha), cos(alpha)];
x0 = stats(1).Centroid.';
a = stats(1).MajorAxisLength;
b = stats(1).MinorAxisLength;
S = diag([1, a/b]);
C = Q*S*Q';
d = (eye(2) - C)*x0;
tform = maketform('affine', [C d; 0 0 1]');
Im2 = imtransform(redChannel, tform);
subplot(2, 3, 5);
imshow(Im2);
I have a movie file, in which I am interested in recording the movement of a point; center of a circular feature to be specific. I am trying to perform this using edge detection and corner detection techniques in Matlab.
To perform this, how do I specify a region of interest in the video? Is subplot a good idea?
I was trying to perform this using the binary masks as below,
hVideoSrc = vision.VideoFileReader('video.avi','ImageColorSpace', 'Intensity');
hEdge = vision.EdgeDetector('Method', 'Prewitt','ThresholdSource', 'Property','Threshold', 15/256, 'EdgeThinning', true);
hAB = vision.AlphaBlender('Operation', 'Highlight selected pixels');
WindowSize = [190 150];
hVideoOrig = vision.VideoPlayer('Name', 'Original');
hVideoOrig.Position = [10 hVideoOrig.Position(2) WindowSize];
hVideoEdges = vision.VideoPlayer('Name', 'Edges');
hVideoEdges.Position = [210 hVideoOrig.Position(2) WindowSize];
hVideoOverlay = vision.VideoPlayer('Name', 'Overlay');
hVideoOverlay.Position = [410 hVideoOrig.Position(2) WindowSize];
c = [123 123 170 170];
r = [160 210 210 160];
m = 480; % height of pout image
n = 720; % width of pout image
BW = ~poly2mask(c,r,m,n);
while ~isDone(hVideoSrc)
dummy_frame = step(hVideoSrc) > 0.5; % Read input video
frame = dummy_frame-BW;
edges = step(hEdge, frame);
composite = step(hAB, frame, edges); % AlphaBlender
step(hVideoOrig, frame); % Display original
step(hVideoEdges, edges); % Display edges
step(hVideoOverlay, composite); % Display edges overlayed
end
release(hVideoSrc);
but it turns out that the mask applied on frame is good only for the original video. The edge detection algorithm detects the edges those are masked by binary mask. How can I mask other features permanently and perform edge detection?
Is this what you mean?
BW = poly2mask(c,r,m,n);
frame = dummy_frame .* BW;