I am an undergraduate student working with detecting defects on a surface of an object, in a given digital image using image processing technique. I am planning on using OpenCV library to get image processing functions. Currently I am trying to decide on which defect detection algorithm to use, in order to detect defects. This is one of my very first projects related to this field, so it will be appreciated if I can get some help related to this issue. The reference image with a defect (missing teeth in the gear), which I am currently working with is uploaded as a link below ("defective gear image").
defective gear image
Get the convex hull of a gear (which is a polygon) and shrink is slightly so that it crosses the teeth. Make sure that the centroid of the gear is the fixed point.
Then sample the pixels along the hull, preferably using equidistant points (divide the perimeter by a multiple of the number of teeth). The unwrapped profile will be a dashed line, with missing dashes corresponding to missing teeth, and the problem is reduced to 1D.
You can also try a polar unwarping, making the outline straight, but you will need an accurate location of the center.
Related
I am doing some studies on eye vascularization - my project contains a machine which can detect the different blood vessels in the retinal membrane at the back of the eye. What I am looking for is a possibility to segment the picture and analyze each segmentation on it`s own. The Segmentation consist of six squares wich I want to analyze separately on the density of white pixels.
I would be very thankful for every kind of input, I am pretty new in the programming world an I actually just have a bare concept on how it should work.
Thanks and Cheerio
Sam
Concept DrawOCTA PICTURE
You could probably accomplish this by using numpy to load the image and split it into sections. You could then analyze the sections using scikit-image or opencv (though this could be difficult to get working. To view the image, you can either save it to a file using numpy, or use matplotlib to open it in a new window.
First of all, please note that in image processing "segmentation" describes the process of grouping neighbouring pixels by context.
https://en.wikipedia.org/wiki/Image_segmentation
What you want to do can be done in various ways.
The most common way is by using ROIs or AOIs (region/area of interest). That's basically some geometric shape like a rectangle, circle, polygon or similar defined in image coordinates.
The image processing is then restricted to only process pixels within that region. So you don't slice your image into pieces but you restrict your evaluation to specific areas.
Another way, like you suggested is to cut the image into pieces and process them one by one. Those sub-images are usually created using ROIs.
A third option which is rather limited but sufficient for simple tasks like yours is accessing pixels directly using coordinate offsets and several nested loops.
Just google "python image processing" in combination with "library" "roi" "cropping" "sliding window" "subimage" "tiles" "slicing" and you'll get tons of information...
I am new to meshlab and am trying to reconstruct an stl file which has a number of issues such as over 700 self-intersecting faces, non-manifold edges and flipped triangles. The part I am trying to fix is a sunglass frame just to give you some perspective. I was able to remove the flipped triangles using Netfabb, which reduced the number of self-intersecting faces. I attempted to fix the rest of the problems by using features within the "Cleaning and Repairing" tab in Meshlab such as remove non-manifold edges and intersecting faces; however, I was unable to fix all problems with the features in the "Cleaning and Repairing" tab alone. Thus I decided to convert the mesh into a point cloud, calculate normals from the "Sampling" tab and try surface reconstruction: poisson. This method gave me a mesh that looked like a big blob instead of the detailed part that I was trying to achieve.
Can anyone please give me a step by step outline of how I can convert the point cloud back into a mesh with surface reconstruction while maintaining the part's dimensional integrity and avoiding self-intersecting faces? Or if you have any other suggestions, I'd be more than happy to listen.
Thank you!
I am currently working on a program to detect coordinates of pool balls in an image of a pool table taken from an arbitrary point.
I first calculated the table corners and warped the perspective of the image to obtain a bird's eye view. Unfortunately, this made the spherical balls appear to be slightly elliptical as shown below.
In an attempt to detect the ellipses, I extracted all but the green felt area and used a Hough transform algorithm (HoughCircles) on the resulting image shown below. Unfortunately, none of the ellipses were detected (I can only assume because they are not circles).
Is there any better method of detecting the balls in this image? I am technically using JavaCV, but OpenCV solutions should be suitable. Thank you so much for reading.
The extracted BW image is good but it needs some morphological filters to eliminate noises then you can extract external contours of each object (by cvFindContours) and fit best ellipse to them (by cvFitEllipse2).
I am trying to create an application similar in UI to Sketchup. As a first step, I need to display the ground surface stretching out in all directions. What is the best way to do this?
Options:
Create a sufficiently large regular polygon stretching out in all directions from the origin. Here there is a possibility of the user hitting the edges and falling off the surface of the earth.
Model the surface of the earth as a sphere/spheroid. Here I will be limiting my vertex co-ordinates to very large values prone to rounding off errors. (Radius of earth is 6371000000 millimeter).
Same as 1 but dynamically extend the ends of the earth as the user gets close to them.
What is the usual practice?
I guess you would do neither of these, but instead use a virtual ground.
So you just find out, what portion of the ground is visible in the viewport and then create a plane large enough to fill that. With some reasonable maxiumum, which simulates the end of the line of sight aka horizon as we know it.
I would like to calculate the distance between my camera and a recognized "object".
The recognized "object" is a black rectangle sticker on a white board for example. I know the values of the rectangle (x,y).
Is there a method that I can use to calculate the distance with the values of my original rectangle, and the values of the picture of the rectangle I took with the camera?
I searched the forum for answeres, but none of the were specified to calculate the distance with these attributes.
I am working on a robot called Nao from Aldebaran Robotics, I am planing to use OpenCV to recognize the black rectangle.
If you could compute the angle taken up by the image of the target, then the distance to the target should be proportional to cot (i.e. 1/tan) of that angle. You should find that the number of pixels in the image corresponded roughly to the angles, but I doubt it is completely linear, especially up close.
The behaviour of your camera lens is likely to affect this measurement, so it will depend on your exact setup.
Why not measure the size of the target at several distances, and plot a scatter graph? You could then fit a curve to the data to get a size->distance function for your particular system. If your camera is close to an "ideal" camera, then you should find this graph looks like cot, and you should be able to find your values of a and b to match dist = a * cot (b * width).
If you try this experiment, why not post the answers here, for others to benefit from?
[Edit: a note about 'ideal' cameras]
For a camera image to look 'realistic' to us, the image should approximate projection onto a plane held infront of the eye (because camera images are viewed by us by holding a planar image in front of our eyes). Imagine holding a sheet of tracing paper up in front of your eye, and sketching the objects silhouette on that paper. The second diagram on this page shows sort of what I mean. You might describe a camera which achieves this as an "ideal" camera.
Of course, in real life, cameras don't work via tracing paper, but with lenses. Very complicated lenses. Have a look at the lens diagram on this page. For various reasons which you could spend a lifetime studying, it is very tricky to create a lens which works exactly like the tracing paper example would work under all conditions. Start with this wiki page and read on if you want to know more.
So you are unlikely to be able to compute an exact relationship between pixel length and distance: you should measure it and fit a curve.
It is a big topic. If you want to proceed from a single image, take a look at this old paper by A. Criminisi. For an in-depth view, read his Ph.D. thesis. Then start playing with the OpenCV routines in the "projective geometry" sectiop.
I have been working on Image/Object Recognition as well. I just released a python programmed android app (ported to android) that recognizes objects, people, cars, books, logos, trees, flowers... anything:) It also shows it's thought process as it "thinks" :)
I've put it out as a test for 99 cents on google play.
Here's the link if you're interested, there's also a video of it in action:
https://play.google.com/store/apps/details?id=com.davecote.androideyes
Enjoy!
:)