I have an image with many shapes and I need to write some Matlab code which remove all the shapes except the rectangle.. Does it availabe to do it using only with strel,imclose and bwareaopen? if you think yes i will be very happy to hear your opinion.
Image:
If I understood right from your comment, rectangle may have any size. I think this can be asked only if the other shapes have fixed size since you are asked to use strel, imclose and bwareaopen. To briefly explain,
strel function creates a structuring element with given size for rectangle, disk or any other shape on the picture you added.
imclose should be used to connect the similar shapes you give as input(basically the structuring element you should find from the strel function).
bwareaopen will be used to delete the objects which has less than P -given as input- pixels.
So, if the rectangle can be given in any size for this image, the other shapes should stay the same in order to be able to define them with strel function, and connect by using imclose function. In this way, you may connect them all, take inverse, remove using bwareaopen and take inverse once again to end up with rectangle.
I could not think about any other solution, hope it helps!
Related
I'm aware my question is maybe somewhat lazy. But I hope someone could maybe give me head start with my idea, or can provide me with an existing code example that points me in the right direction.
I want to create an organic shape/blob that more or less fills up existing space, but wraps around typographical elements. Whenever these elements move around, the shape should adjust itself accordingly. I was looking at Paper.js where examples like http://paperjs.org/examples/candy-crash/ and http://paperjs.org/examples/voronoi/ make it seem like this should be possible.
You can use the path.subtract() boolean operation, along with the path.smooth() function to smooth your shape with the type of smoothing of your choice.
Here is a demo sketch. You can also try to smooth the rectangles ; and maybe randomly add points on your curves or randomly displace all segment handles.
I have an image with many shapes and I need to write some Matlab code which remove all the shapes except the rectangle.. Does it availabe to do it using only with strel,imclose and bwareaopen? if you think yes i will be very happy to hear your opinion.
Image:
If I understood right from your comment, rectangle may have any size. I think this can be asked only if the other shapes have fixed size since you are asked to use strel, imclose and bwareaopen. To briefly explain,
strel function creates a structuring element with given size for rectangle, disk or any other shape on the picture you added.
imclose should be used to connect the similar shapes you give as input(basically the structuring element you should find from the strel function).
bwareaopen will be used to delete the objects which has less than P -given as input- pixels.
So, if the rectangle can be given in any size for this image, the other shapes should stay the same in order to be able to define them with strel function, and connect by using imclose function. In this way, you may connect them all, take inverse, remove using bwareaopen and take inverse once again to end up with rectangle.
I could not think about any other solution, hope it helps!
I'm writing an application which measures boxes from pictures. A sample picture after manipulation is shown below:
My application has identified pixels that are part of the box and changed the color to red. You can see that the image is pretty noisy and therefore creates pretty rough looking edges on the rectangle.
I've been reading about edge/corner detection algorithms, but before I pursue one of them I wanted to step back and see if such a complicated algorithm is really necessary. It seems like there probably is a simpler way to go about this, considering I have a few conditions that simplify things:
The image only contains a rectangle, not any other shape.
Each image only has 1 rectangle.
I do not need to be exact, though I'd like to achieve as best fit as I can.
My first go at a simple algorithm involved finding the top most, bottom most, left most and right most points. Those are the 4 corners. That works OK, but isn't super accurate for noisy edges like this. It is easy to eye ball a much better point as the corner.
Can anyone point me towards an algorithm for this?
You have already identified the region of the image that you are interested in(red region).
Using this same logic you should be able to binarize the image. Say the red region then results in white pixels and the rest is black.
Then trace the external contour of the white region using a contour tracing algorithm.
Now you have a point set that represents the external contour of the region.
Find the minimum-area-rectangle that bounds this point set.
You can easily do this using the OpenCV library. Take a look at threshold, findContours, and minAreaRect if you are planning to use OpenCV. Hope this information helps.
I'm searching for an certain object in my photograph:
Object: Outline of a rectangle with an X in the middle. It looks like a rectangular checkbox. That's all. So, no fill, just lines. The rectangle will have the same ratios of length to width but it could be any size or any rotation in the photograph.
I've looked a whole bunch of image recognition approaches. But I'm trying to determine the best for this specific task. Most importantly, the object is made of lines and is not a filled shape. Also, there is no perspective distortion, so the rectangular object will always have right angles in the photograph.
Any ideas? I'm hoping for something that I can implement fairly easily.
Thanks all.
You could try using a corner detector (e.g. Harris) to find the corners of the box, the ends and the intersection of the X. That simplifies the problem to finding points in the right configuration.
Edit (response to comment):
I'm assuming you can find the corner points in your image, the 4 corners of the rectangle, the 4 line endings of the X and the center of the X, plus a few other corners in the image due to noise or objects in the background. That simplifies the problem to finding a set of 9 points in the right configuration, out of a given set of points.
My first try would be to look at each corner point A. Then I'd iterate over the points B close to A. Now if I assume that (e.g.) A is the upper left corner of the rectangle and B is the lower right corner, I can easily calculate, where I would expect the other corner points to be in the image. I'd use some nearest-neighbor search (or a library like FLANN) to see if there are corners where I'd expect them. If I can find a set of points that matches these expected positions, I know where the symbol would be, if it is present in the image.
You have to try if that is good enough for your application. If you have too many false positives (sets of corners of other objects that accidentially form a rectangle + X), you could check if there are lines (i.e. high contrast in the right direction) where you would expect them. And you could check if there is low contrast where there are no lines in the pattern. This should be relatively straightforward once you know the points in the image that correspond to the corners/line endings in the object you're looking for.
I'd suggest the Generalized Hough Transform. It seems you have a fairly simple, fixed shape. The generalized Hough transform should be able to detect that shape at any rotation or scale in the image. You many need to threshold the original image, or pre-process it in some way for this method to be useful though.
You can use local features to identify the object in image. Feature detection wiki
For example, you can calculate features on some referent image which contains only the object you're looking for and save the results, let's say, to a plain text file. After that you can search for the object just by comparing newly calculated features (on images with some complex scenes containing the object) with the referent ones.
Here's some good resource on local features:
Local Invariant Feature Detectors: A Survey
I'm trying to build something like the Liquify filter in Photoshop. I've been reading through image distortion code but I'm struggling with finding out what will create similar effects. The closest reference I could find was the iWarp filter in Gimp but the code for that isn't commented at all.
I've also looked at places like ImageMagick but they don't have anything in this area
Any pointers or a description of algorithms would be greatly appreciated.
Excuse me if I make this sound a little simplistic, I'm not sure how much you know about gfx programming or even what techniques you're using (I'd do it with HLSL myself).
The way I would approach this problem is to generate a texture which contains offsets of x/y coordinates in the r/g channels. Then the output colour of a pixel would be:
Texture inputImage
Texture distortionMap
colour(x,y) = inputImage(x + distortionMap(x, y).R, y + distortionMap(x, y).G)
(To tell the truth this isn't quite right, using the colours as offsets directly means you can only represent positive vectors, it's simple enough to subtract 0.5 so that you can represent negative vectors)
Now the only problem that remains is how to generate this distortion map, which is a different question altogether (any image would generate a distortion of some kind, obviously, working on a proper liquify effect is quite complex and I'll leave it to someone more qualified).
I think liquefy works by altering a grid.
Imagine each pixel is defined by its location on the grid.
Now when the user clicks on a location and move the mouse he's changing the grid location.
The new grid is again projected into the 2D view able space of the user.
Check this tutorial about a way to implement the liquify filter with Javascript. Basically, in the tutorial, the effect is done transforming the pixel Cartesian coordinates (x, y) to Polar coordinates (r, α) and then applying Math.sqrt on r.