I'm trying to align two rectangles using the perspectiveTransform. In the image below there are the two orange rectangles (I know their dimensions and locations) and I want to warp the perspective so that they are of approximately the same size and in line (the green ones in the image). A perspectiveTransform that e.g. makes the small one equal in size with the big one doesn't really do the trick, as the size of the big one changes too. Any help much appreciated!
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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 have an image with only black and white pixels. The image contains edges (the black pixels) with the width of one pixel (each black pixel has exactly one or two black neighbourpixels). Now i want to group the edges into different shape classes (e.g. line, triangle, ellipse). Problem: the edges are not perfect lines, triangles or ellipses.
I think i can partially solve the problem by logical thinking. But i also have more complex geometries where this will be more difficult.
Does anyone know how to solve this kind of problem? Or can anyone give me some ideas?
A general way to find the shape of the edges will be to find the convex hull of the points. After that you can try to discard sides in the convex hull which are small than a certain threshold.
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 have a photograph containing multiple rectangles of various sizes and orientations. I am currently trying to find the distance from the camera to any rectangles present in the image. What is the best way to accomplish this?
For example, an example photograph might look like similar to this (although this is probably very out-of-proportion):
I can find the pixel coordinates of the corners of any of the rectangles in the image, along with the camera FOV and resolution. I also know beforehand the length and width of any rectangle that could be in the image (but not what angle they face the camera). The ratio of length to width of each rectangular target that could be in the image is guaranteed to be unique. The rectangles and the camera will always be parallel to the ground.
What I've tried:
I hacked out a solution based on some example code I found on the internet. I'm basically iterating through each rectangle and finding the average pixel length and height.
I then use this to find the ratio of length vs. height, and compare it against a list of
the ratios of all known rectangular targets so I can find the actual height of the target in inches. I then use this information to find the distance:
...where actual_height is the real height of the target in inches, the IMAGE_HEIGHT is how tall the image is (in pixels), the pixel_height is the average height of the rectangle on the image (in pixels), and the VERTICAL_FOV is the angle the camera sees along the vertical axis in degrees (about 39.75 degrees on my camera).
I found this formula on the internet, and while it seems to work somewhat ok, I don't really understand how it works, and it always seems to undershoot the actual distance by a bit.
In addition, I'm not sure how to go about modifying the formula so that it can deal with rectangles that are very skewed from viewing them along an angle. Since my algorithm works by finding the proportion of the length and height, it works ok for rectangles 1 and 2 (which aren't too skewed), but doesn't work for rectangle 3, since it's very skewed, throwing the ratios completely off.
I considered finding the ratio using the method outlined in this StackOverflow question regarding the proportions of a perspective-deformed rectangle, but I wasn't sure how well that would work with what I have, and was wondering if it's overkill or if there's a simpler solution I could try.
FWIW I once did something similar with triangles (full 6DoF pose, not just distance).
Let's say I take a picture of two hammers side-by-side (although they may be aligned differently, but always one on the right and one on the left), wherein each might look like this, and I want to calculate the ratio of the lengths of the handles of the hammers.
For example, the output from an input image would be the length of the red part of the one on the left (its handle) divided by the length of the handle of the one on the right.
How would I go about doing this?
If you know the handle color it doesn't sound hard. Just select those pixels and take the longer side of a minimum oriented bounding box.
Here are a couple of hints:
Make sure that the bounding boxes of the hammers don't overlap. If you can guarantee this, try this approach:
Scale the image to width=10%, height=10px. Find the largest amount of pixels in background color near the middle of the image. That allows you to separate the two hammers into individual images. Multiply the positions by 10 to transform them back into coordinates of the original image.
Create two images (one for each hammer)
Crop the border
Scale the image to width = 10px, height = 10%. Count all reddish pixels (save the image and examine the pixel values for red and non-red parts to get an idea what to look for)