I have searched the internet for a solution to the question above but have had no luck up to now. I have been producing a number of 2D plots where the origin of (0,0 point) is represented by an image. I have made these by plotting the data on an image, where the image is all white apart from the desired symbol at the center point (so I can reshape it as needed). I then move the axis so they cross at the center point. This all works fine, but I now have to create a number of plots using ‘fill’ to place two shaded areas on the plot that overlap in the center. This causes the symbol to be difficult to see even using ‘alpha’.
I therefore have two options to get the desired effect, both requiring me to put an image on top of the figure after the data is plotted. These options are:
1) I place the image on top of the plot and apply alpha to it (May not look very good as it will mute the plot).
2) The better option would be to crop the image around the symbol and then position it on top of the plot so the image center is at the origin (I have no idea how to position the image this way).
Both methods need the image to be placed on top of the plot after the data is plotted. I have tried 'hold on' and calling 'figure(imagesc(Image))' neither work. I have Matlab 2012b but no toolboxes (so cannot use subimage etc.)
Thanks for any help you can give
You can set the transparency of individual pixels of an image using the 'AlphaData' option. So, you can plot the overlay as follows:
% plot your data normally
...
hold on
% assuming the overlay is a 11x11 image
image(-5:5,-5:5,image_matrix,'AlphaData',alpha_matrix);
image_matrix would obviously be the matrix with your image data stored in it, while alpha_matrix would be a matrix of the same size as image_matrix. Every pixel you want to show would have a value of 1, every pixel you want to hide (the white pixels) would be 0.
How do I separate the two connected circles in the image below, using MATLAB? I have tried using imerode, but this does not give good results. Eroding does not work, because in order to erode enough to separate the circles, the lines disappear or become mangled. In other starting pictures, a circle and a line overlap, so isolating the overlapping objects won't work either.
The image shows objects identified by bwboundaries, each object painted a different color. As you can see, the two light blue circles are joined, and I want to disjoin them, producing two separate circles. Thanks
I would recommend you use the Circular Hough Transform through imfindcircles. However, you need version 8 of the Image Processing Toolbox, which was available from version R2012a and onwards. If you don't have this, then unfortunately this won't work :(... but let's go with the assumption that you do have it. However, if you are using something older than R2012a, Dev-iL in his/her comment above linked to some code on MATLAB's File Exchange on an implementation of this, most likely created before the Circular Hough Transform was available: http://www.mathworks.com/matlabcentral/fileexchange/9168-detect-circles-with-various-radii-in-grayscale-image-via-hough-transform/
This is a special case of the Hough Transform where you are trying to find circles in your image rather than lines. The beauty with this is that you are able to find circles even when the circle is partially completed or overlapping.
I'm going to take the image that you provided above and do some post-processing on it. I'm going to convert the image to binary, and remove the border, which is white and contains the title. I'm also going to fill in any holes that result so that all of the objects are filled in with solid white. There is also some residual quantization noise after I do this step, so I'm going to a small opening with a 3 x 3 square element. After, I'm going to close the shapes with a 3 x 3 square element, as I see that there are noticeable gaps in the shapes. Therefore:
Therefore, directly reading in your image from where you've posted it:
im = imread('http://s29.postimg.org/spkab8oef/image.jpg'); %// Read in the image
im_gray = im2double(rgb2gray(im)); %// Convert to grayscale, then [0,1]
out = imclearborder(im_gray > 0.6); %// Threshold using 0.6, then clear the border
out = imfill(out, 'holes'); %// Fill in the holes
out = imopen(out, strel('square', 3));
out = imclose(out, strel('square', 3));
This is the image I get:
Now, apply the Circular Hough Transform. The general syntax for this is:
[centres, radii, metric] = imfindcircles(img, [start_radius, end_radius]);
img would be the binary image that contains your shapes, start_radius and end_radius would be the smallest and largest radius of the circles you want to find. The Circular Hough Transform is performed such that it will find any circles that are within this range (in pixels). The outputs are:
centres: Which returns the (x,y) positions of the centres of each circle detected
radii: The radius of each circle
metric: A measure of purity of the circle. Higher values mean that the shape is more probable to be a circle and vice-versa.
I searched for circles having a radius between 30 and 60 pixels. Therefore:
[centres, radii, metric] = imfindcircles(out, [30, 60]);
We can then demonstrate the detected circles, as well as the radii by a combination of plot and viscircles. Therefore:
imshow(out);
hold on;
plot(centres(:,1), centres(:,2), 'r*'); %// Plot centres
viscircles(centres, radii, 'EdgeColor', 'b'); %// Plot circles - Make edge blue
Here's the result:
As you can see, even with the overlapping circles towards the top, the Circular Hough Transform was able to detect two distinct circles in that shape.
Edit - November 16th, 2014
You wish to ensure that the objects are separated before you do bwboundaries. This is a bit tricky to do. The only way I can see you do this is if you don't even use bwboundaries at all and do this yourself. I'm assuming you'll want to analyze each shape's properties by themselves after all of this, so what I suggest you do is iterate through every circle you have, then place each circle on a new blank image, do a regionprops call on that shape, then append it to a separate array. You can also keep track of all of the circles by having a separate array that adds the circles one at a time to this array.
Once you've finished with all of the circles, you'll have a structure array that contains all of the measured properties for all of the measured circles you have found. You would use the array that contains only the circles from above, then use these and remove them from the original image so you get just the lines. You'd then call one more regionprops on this image to get the information for the lines and append this to your final structure array.
Here's the first part of the procedure I outlined above:
num_circles = numel(radii); %// Get number of circles
struct_reg = []; %// Save the shape analysis per circle / line here
%// For creating our circle in the temporary image
[X,Y] = meshgrid(1:size(out,2), 1:size(out,1));
%// Storing all of our circles in this image
circles_img = false(size(out));
for idx = 1 : num_circles %// For each circle we have...
%// Place our circle inside a temporary image
r = radii(idx);
cx = centres(idx,1); cy = centres(idx,2);
tmp = (X - cx).^2 + (Y - cy).^2 <= r^2;
% // Save in master circle image
circles_img(tmp) = true;
%// Do regionprops on this image and save
struct_reg = [struct_reg; regionprops(tmp)];
end
The above code may be a bit hard to swallow, but let's go through it slowly. I first figure out how many circles we have, which is simply looking at how many radii we have detected. I keep a separate array called struct_reg that will append a regionprops struct for each circle and line we have in our image. I use meshgrid to determine the (X,Y) co-ordinates with respect to the image containing our shapes so that I can draw one circle onto a blank image at each iteration. To do this, you simply need to find the Euclidean distance with respect to the centre of each circle, and set the pixels to true only if that location has its distance less than r. After doing this operation, you will have created only one circle and filtered all of them out. You would then use regionprops on this circle, add it to our circles_img array, which will only contain the circles, then continue with the rest of the circles.
At this point, we will have saved all of our circles. This is what circles_img looks like so far:
You'll notice that the circles drawn are clean, but the actual circles in the original image are a bit jagged. If we tried to remove the circles with this clean image, you will get some residual pixels along the border and you won't completely remove the circles themselves. To illustrate what I mean, this is what your image looks like if I tried to remove the circles with circles_img by itself:
... not good, right?
If you want to completely remove the circles, then do a morphological reconstruction through imreconstruct where you can use this image as the seed image, and specify the original image to be what we're working on. The job of morphological reconstruction is essentially a flood fill. You specify seed pixels, and an image you want to work on, and the job of imreconstruct is from these seeds, flood fill with white until we reach the boundaries of the objects that the seed pixels resided in. Therefore:
out_circles = imreconstruct(circles_img, out);
Therefore, we get this for our final reconstructed circles image:
Great! Now, use this and remove the circles from the original image. Once you do this, run regionprops again on this final image and append to your struct_reg variable. Obviously, save a copy of the original image before doing this:
out_copy = out;
out_copy(out_circles) = false;
struct_reg = [struct_reg; regionprops(out_copy)];
Just for sake of argument, this is what the image looks like with the circles removed:
Now, we have analyzed all of our shapes. Bear in mind I did the full regionprops call because I don't know exactly what you want in your analysis... so I just decided to give you everything.
Hope this helps!
erosion is the way to go. You should probably use a larger structuring element.
How about
1 erode
2 detect your objects
3 dilate each object for itself using the same structuring element
My image is a 2D surface of a protein, and I use matlab function "scatter" to display the image, so there are some white empty spaces in it.
I want to fill them with colors，but the question is that the points have different colors, some are red and some are orange(point color is decided by its RGB value).
So I wanna assign the color of the white space similar to their corresponding neighbors.
the original work i did is to extract the edge of the polygon first，which helps me detect if the point is inside the polygon or not, because I am not assigning colors to white spaces that are outside the polygon.
And then simply scan the whole image pixels one by one to check if the pixel is the white， if so, I just assign the neighbor color to it,like what i said, I have to check if the pixel is inside the polygon or not every time.
But the speed is really slow, and the result is not good enough,could anybody give me some idea on it ?
I have the 2D scatter points image and also the 3D structure.Each point in 2D can find one
counterpart in 3D, I don't know if this information would help.
After an erosion with a disk kernel(7x7) such as and then a bilateral filter:
PS: if you have the 3D points structure, upload it somewhere and post a link
I am using regionprop function in matlab to get MajorAxisLength of an image. I think logically this number should not be greater than sqrt(a^2+b^2) in wich a abd b are the width and heigth of the image. but for my image it is. My black and white image contains a black circle in the center of the image. I think this is strange. Can anybody help me?
Thanks.
If you look at the code of regionprops (subfunction ComputeEllipseParams), you see that they use the second moment to estimate the ellipsoid radius. This works very well for ellipsoid-shaped features, but not very well for features with holes. The second moment increases if you remove pixels from around the centroid (which is, btw, why they make I-beams). Thus, the bigger the 'hole' in the middle of your image, the bigger the apparent ellipsoid radius.
In your case, you may be better off using the extrema property of regionprops, and to calculate the largest radius from there.
please help with Matlab beginner challenge
i need to create an image with few geometrical objects (circles,
ellipses) and then to apply some projective transforms
my problem is that i cant understand how to actually "draw" on image
image is AFAIU generally defined as [X;Y;3] matrix,
functions as SCIRCLE1 can compute/return collection of points
representing circle, but the problem is that points are not discrete ,
coordinates are real numbers and not pixels
how can i recompute the scircle output to be valid in image
coordinates system ? i.e. how can i "pixelize" it?
thanks for your attention, i really missing some basic concept and
will appreciate your help
John
well, below is an answer i received on Matlab newsgroups
BOTTOM LINE-no built-in way in Matlab
======================================
'getframe' can be used to merge axes even though it is more commonly used to create movie frames.
MATLAB is really weak in this area. There are some primitive
functions for drawing into the overlay (such as rectangle() if you
want to draw a circle, and line() if you want to draw a line) but no
real way that I know of to draw right into the underlying image. So
you have to use "tricks" such as getframe and then apply logical
operations. And you have to be careful with that since I think when
it gives you the rasterized version of the overlay it might be the
size of the image on the screen, not the true original matrix size of
the underlying image (I'd have to recheck this).
full thread here : http://www.mathworks.com.au/matlabcentral/newsreader/view_thread/261232
I found this example which give you a easy way to put simple geometrical object onto pictures.
Read the input image.
I = imread('cameraman.tif');
Define the rectangle dimensions as [x y width height].
rectangle = int32([10 10 30 30]);
Draw the rectangle and display the result.
J = step(shapeInserter, I, rectangle);
imshow(J);
see this link
by the way..
I didn't get you point about points not being discrete and images being a matrix. The way I see it. It much the same. you could try to explain it more in depth ?
The insertShape function in the Computer Vision System Toolbox is what you need. It lets you draw rectangles, circles, and polygons into the image.
There is also insertText, insertMarker, and insertObjectAnnotation.