I have an image with various odd shapes (such as circles and squares) which are coloured pure red (rgb(255, 0, 0) exactly). I want to draw boxes around these shapes, but to do that I need the coordinates of each corner from each box. This is the part I am having difficulty with.
I basically want to go from this:
To this:
I have tried many different ways to achieve this, including parsing the y-axis until I find a shape and boxing it that way, starting from the very corners of the image and moving towards the middle (both methods of which don't work well for multiple shapes) and using external packages such as an OpenCV.
I could use OpenCV to achieve this, but given the constraints I was hoping there was a way to do it which doesn't require an external package.
Can anyone with a bit more machine vision experience point me in the right direction please?
First, use the Hoshen-Kopelman algorithm to determine the connected clusters of pixels with the given criteria (being red), then all you have to do is find their min/max regions (on x and y axes) to wrap them with a rectangle.
Related
With a group of friend, we are trying to accomplish a computer vision task on Raspberry Pi, coding with C++ using OpenCV library.
Let me explain the task first.
There is a pattern consisting of 16 seperate squares with each square being red, yellow or blue colored. We are mounting rasperry pi on a quadcopter with its camera module and gathering video feed of the pattern.
We have to detect colors of squares which was easy to accomplish with a little research on web. Tricky part is we have to detect order of the squares as well in order to save the colors in an array in an order.
So far we have accomplished filtering desired colors (red, yellow, blue) to determine squares.
example pattern to recognize and our process so far
In the second image, we know the colors and center points of each square. What we need is a way to write them in an order to a file or on screen.
And to find the order, we tried several OpenCV methods that find corners. With corner points at hand, we compared each point and determined end points so we could draw a boundingrectangle and overcome little distortions.
But since quadcopter gets the video stream, there is always a chance of high distortion. That messes up our corner theory, resulting in wrong order of colors. For example it can capture an image like this:
highly distorted image
It is not right to find order of these squares by comparing their center points. It also won't work finding endpoints to draw a larger rectangle around them to flatten pattern. And then order...
What I ask for is algorithm suggestions. Are we totally going in the wrong direction trying to find corners? Is it possible to determine the order without taking distortion into consideration?
Thanks in advance.
Take the two centers that are the furthest apart and number them 1 and 16. Then find the two centers that are the furthest from the line 1-16, to the left (number 4) and to the right (number 13). Now you have the four corners.
Compute the affine transform that maps the coordinates of the corners 1, 4 and 13 to (0,0), (3,0) and (0,3). Apply this transform to the 16 centers and round to the nearest integers. If all goes well, you will obtain the "logical" coordinates of the squares, in range [0, 3] x [0, 3]. The mapping to the cell indexes is immediate.
Note that because of symmetry, a fourfold undeterminacy will remain, which you can probably lift by checking the color patterns.
This procedure will be very robust to deformations. If there is extreme perspective, you can even exploit the four corners to determine an homographic transform instead of affine. In your case, I doubt this will be useful. You can assess proper working by checking that all expected indexes have been assigned.
I have a semi circle shaped image that I am trying to make into a 120 degree fan shape. The image needs to be squished together between the two edges coming closer together.
After a lot of searching I've tried both affine and perspective transforms. Neither seemed to give me the results I was looking for.
I am using OpenCV2 and C++
How can I achieve this effect?
Edit:
Currently I map a rectangular image onto a semi circle. So, it would also be acceptable to map directly onto a 120 degree fan shape. Would that be a better approach? and if so how might I accomplish that?
I am trying to detect a ball in an filtered image.
In this image I've already removed the stuff that can't be part of the object.
Of course I tried the HoughCircle function, but I did not get the expected output.
Either it didn't find the ball or there were too many circles detected.
The problem is that the ball isn't completly round.
Screenshots:
I had the idea that it could work, if I identify single objects, calculate their center and check whether the radius is about the same in different directions.
But it would be nice if it detect the ball also if he isn't completely visible.
And with that method I can't detect semi-circles or something like that.
EDIT: These images are from a video stream (real time).
What other method could I try?
Looks like you've used difference imaging or something similar to obtain the images you have..? Instead of looking for circles, look for a more generic loop. Suggestions:
Separate all connected components.
For every connected component -
Walk around the contour and collect all contour pixels in a list
Suggestion 1: Use least squares to fit an ellipse to the contour points
Suggestion 2: Study the curvature of every contour pixel and check if it fits a circle or ellipse. This check may be done by computing a histogram of edge orientations for the contour pixels, or by checking the gradients of orienations from contour pixel to contour pixel. In the second case, for a circle or ellipse, the gradients should be almost uniform (ask me if this isn't very clear).
Apply constraints on perimeter, area, lengths of major and minor axes, etc. of the ellipse or loop. Collect these properties as features.
You can either use hard-coded heuristics/thresholds to classify a set of features as ball/non-ball, or use a machine learning algorithm. I would first keep it simple and simply use thresholds obtained after studying some images.
Hope this helps.
I have written an algorithm to process a camera capture and extract a binary image of two features I'm interested in. I'm trying to find the best (fastest) way of detecting when the two features intersect and where the lowest (y coordinate is greatest) point is (this will be the intersection).
I do not want to use a findContours() based method as this is too slow and, in my opinion, unnecessary. I also think blob detection libraries are too bloated for this.
I have two sample images (sorry for low quality):
(not touching: http://i.imgur.com/7bQ9qMo.jpg)
(touching: http://i.imgur.com/tuSmKw7.jpg)
Due to the way these images are created, there is often noise in the top right corner which looks like pixelated lines but methods such as dilation and erosion lose resolution around the features I'm trying to find.
My initial thought would be to use direct pixel access to form a width filter and a height filter. The lowest point in the image is therefore the intersection.
I have no idea how to detect when they touch... logically I can see that a triangle is formed when they intersect and otherwise there is no enclosed black area. Can I fill the image starting from the corner with say, red, and then calculate how much of the image is still black?
Does anyone have any suggestions?
Thanks
Your suggestion is a way more slow than finding contours. For binary images, finding contour is very easy and quick because you just need to find a black pixel followed by a white pixel or vice versa.
Anyway, if you don't want to use it, you can use the vertical projection or vertical profile you will see it the objects intersect or not.
For example, in the following image check the the letter "n" which is little similar to non-intersecting object, and the letter "o" which is similar to intersecting objects :
By analyzing the histograms you can recognize which one is intersecting or not.
Finding Circle Edges :
Here are the two sample images that i have posted.
Need to find the edges of the circle:
Does it possible to develop one generic circle algorithm,that could find all possible circles in all scenarios ?? Like below
1. Circle may in different color ( White , Black , Gray , Red)
2. Background color may be different
3. Different in its size
http://postimage.org/image/tddhvs8c5/
http://postimage.org/image/8kdxqiiyb/
Please suggest some idea to write a algorithm that should work out on above circle
Sounds like a job for the Hough circle transform:
I have not used it myself so far, but it is included in OpenCV. Among other parameters, you can give it a minimum and maximum radius.
Here are links to documentation and a tutorial.
I'd imagine your second example picture will be very hard to detect though
You could apply an edge detection transformation to both images.
Here is what I did in Paint.NET using the outline effect:
You could test edge detect too but that requires more contrast in the images.
Another thing to take into consideration is what it exactly is that you want to detect; in the first image, do you want to detect the white ring or the disc inside. In the second image; do you want to detect the all the circles (there are many tiny ones) or just the big one(s). These requirement will influence what transformation to use and how to initialize these.
After transforming the images into versions that 'highlight' the circles you'll need an algorithm to find them.
Again, there are more options than just one. Here is a paper describing an algoritm
Searching the web for image processing circle recognition gives lots of results.
I think you will have to use a couple of different feature calculations that can be used for segmentation. I the first picture the circle is recognizeable by intensity alone so that one is easy. In the second picture it is mostly the texture that differentiates the circle edge, in that case a feature image based based on some kind of texture filter will be needed, calculating the local variance for instance will result in a scalar image that can segment out the circle. If there are other features that defines the circle in other scenarios (different colors for background foreground etc) you might need other explicit filters that give a scalar difference for those cases.
When you have scalar images where the circles stand out you can use the circular Hough transform to find the circle. Either run it for different circle sizes or modify it to detect a range of sizes.
If you know that there will be only one circle and you know the kind of noise that will be present (vertical/horizontal lines etc) an alternative approach is to design a more specific algorithm e.g. filter out the noise and find center of gravity etc.
Answer to comment:
The idea is to separate the algorithm into independent stages. I do not know how the specific algorithm you have works but presumably it could take a binary or grayscale image where high values means pixel part of circle and low values pixel not part of circle, the present algorithm also needs to give some kind of confidence value on the circle it finds. This present algorithm would then represent some stage(s) at the end of the complete algorithm. You will then have to add the first stage which is to generate feature images for all kind of input you want to handle. For the two examples it should suffice with one intensity image (simply grayscale) and one image where each pixel represents the local variance. In the color case do a color transform an use the hue value perhaps? For every input feed all feature images to the later stage, use the confidence value to select the most likely candidate. If you have other unknowns that your algorithm need as input parameters (circle size etc) just iterate over the possible values and make sure your later stages returns confidence values.