I have the following image. I want to fit ellipse into each black region. Then I want to measure the major and minor axis length. I would be grateful if any one can give me any idea. I know how to fit an ellipse over the image object.
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I'm trying to blindly detect signals in a spectra.
one way that came to my mind is to detect rectangles in the waterfall (a 2D matrix that can be interpret as an image) .
Is there any fast way (in the order of 0.1 second) to find center and width of all of the horizontal rectangles in an image? (heights of rectangles are not considered for me).
an example image will be uploaded (Note I know that all rectangles are horizontal.
I would appreciate it if you give me any other suggestion for this purpose.
e.g. I want the algorithm to give me 9 center and 9 coordinates for the above image.
Since the rectangle are aligned, you can do that quite easily and efficiently (this is not the case with unaligned rectangles since they are not clearly separated). The idea is first to compute the average color of each line and for each column. You should get something like that:
Then, you can subtract the background color (blue), compute the luminance and then compute a threshold. You can remove some artefact using a median/blur before.
Then, you can just scan the resulting 1D array filled with binary values so to locate where each rectangle start/stop. The center of each rectangle is ((x_start+x_end)/2, (y_start+y_end)/2).
I would like to measure the horizontal lengths of multiple ROI. I tried Feret's diameter, but it only gives the longest distance between any two points along the selection boundary. I tried bounding rectangle, but I suppose the rectangles are tilted to obtain the minimum bounding rectangle.
Does anyone have another idea? Because clearly, the selection boundaries fit nicely to the ROI - so how could I extract that information, i.e. the xy-coordinates of the fits? Thanks in advance
PS: I did not write ROIs because 'Region of Interests' makes no sense
I want to fit an image of a clown like face into a contour of another face (a person).
I am detecting the persons face and getting a elliptical-like contour.
I can figure out the center, radius, highest, lowest, left-most and right-most points.
How do I fit the clown face (a square image which I can make elliptical by cutting the face out of the empty background of a png and then detecting the contour) into the persons face?
Or at the least, how do I fit a polygon into another polygon.
I can fit a rectangular image into a rectangular contour with ease, but faces aren't that shape.
Python preferable, but C++ is also manageable, thank you.
Edit: Visual representation as requested:
I have
and I want to make it like this:
but I want the clown face to stretch over the guys face and fit within the blue contour.
I think the keyword you are looking for is Active Appearance Models. First, you need to fit a model to first face (such as this one), which lays inside the contour. Then, you should fit the same model to the clown face. After that, since you have fitted same model to both faces, you can stretch it as you need.
I haven't use AAM myself and I'm not an expert about it, so my explanation might not be enough or might not be exactly correct, but I'm sure it will give you some insight.
A simple and good answer to this question is to find the extreme top, bottom, left, and right points on your contour (head) and then resize your mask to match the aspect ration and place it to cover the 4 points.
Because human heads are elliptical you can use fitEllipse() to give you those 4 points. This will automagically fix any problems with the person tilting their head because regardless of the angle you will know which point is top, bottom, left, and right.
The relevant code for finding the ellipse is:
vector<Point> contour;
// Do whatever you are doing to populate this vector
RotatedRect ellipse = fitEllipse(Mat(contour));
There is also an example as well as documentation for RotatedRect.
// Resize your mask with these sizes for optimum fit
ellipse.size.width
ellipse.size.height
You can rotate your image like this.
UPDATE:
You may also want to find the contour's extreme points to know how much you need to scale your image to ensure that all of the face is covered.
I'm going to find the most look-like rectangles among shapes. The first image is the original image with shapes which possibly be rectangles but they are not. The green rectangles in the second image is what I want. So is there a way to do this with opencv? I've tried hough lines but the result's not good
The source image:
And what I want is to find out the most look-like rectangle among these shapes, like the rectangles in green.
What I want:
A very simple approach is, after you have a rectangle bounding box around your shape, count the percentage of pixels inside the box which are white.
The higher the percentage of white pixels, the closest to a rectangle it is.
To get the bounding boxes you should take a look at either findContours from opencv, or some Blob extracting algorithm, you will find plenty of questions regarding those.
Edit:
Maybe you should first get the Minimum bounding rectangles of the shapes and then do this kind of heuristic:
Shrink the rectangle dimensions until the white-pixel percentage inside the rectangle reaches some threshold defined by you (like 90% of white pixels inside the rectangle).
To get the Minimum bounding rectangle (the smallest rectangle which contains the whole shape), you might check this tutorial:
http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html
One thing that might also help is doing the difference of sizes from the minimum bounding rectangle and the maximum inner rectangle (the biggest rectangle you can fit inside the white shape). The less difference there is between those rectangle's properties (width, height, area, center coordinates) the closest is the shape to a rectangle.
I am looking for an efficient way to calculate the position of an oject on a surface based on an image taken from a certain perspective.
Let me explain a little further.
There is an object on a rectangular flat surface.
I have a picture taken of this setup with the camera positioned at one of the corners of the surface area at a rather low angle.
On the picture I will thus see a somewhat distorted, diamond-shaped view of the surface area and somewhere on it the object.
Through some image processing I do have the coordinates of the object on the picture but now have to calculate the actual position of the object on the surface.
So I do know that the center of the object is at the pixel-coordinates (x/y) on the picture and I know the coordinates of the 4 reference points that represent the corners of the area.
How can I now calculate the "real world" position of the object most efficiently (x and y coordinates on the surface)?
Any input is highly appreciated since I have worked so hard on this I can't even think straight anymore.
Best regards,
Tom
You have to find a perspective transformation.
Here you may find an explanation and code in Matlab
HTH!
How good is your linear algebra? A perspective transformation can be described by a homography matrix. You can estimate that matrix using the four corner points, invert it and the calculate the world coordinates of every pixel in your image.
Or you can just let OpenCV do that for you.