I'm working on a project right now in which a key part relies on a glass slide being clean and fully transparent after having a film adhered to it.
By eye it is obvious when the the film has been well applied, since there are no cloudy sections or bubbles. Is there a good way to measure this 'clarity' with openCV?
I'm in the fortuante position that imaging of the glass can be carried out in a controlled environment; For example I can control the focus and anything that might appear behind the glass. I wondered if I could use this to try to pick out a known pattern and from that somehow determine clarity. Additionally, it may be helpful to be able to detect cracks in the glass, and I can control the lighting which may assist in illuminating fractures.
Has anyone done anything similar before, and could point me towards some reading or examples?
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I am learning OpenCV applications by reading research papers and attempting to duplicate their tests and results. I may have jumped a bit too deep off the beaten path and am now curious the proper way to go about this investigation.
Goal: 1) Register these two images. 2) Stack the exposures (there are actually 20+ in this series). 3) Learn.
Attached below is an example image- shot with a cell phone, in low light, in burst mode. If one were to level stretch one would see there are very few hard edges (some sheets), but there are enough details to manually align portions of the images with each other. I ran this through the default OpenCV implementations of ORB and SIFT and, as expected, came back with poor matches.
I have not yet stumbled upon the right technique described to increase edge detection. As mentioned, no hard edges are present. However I thought I'd previously read that one could downsample the image using a max function and get a better 'edge' detection. That edge should be able to provide registration homography to the higher resolution image. But I can neither find the resource to do so nor any descriptions of similar activity. Help here would be appreciated.
In addition if there are any authored papers discussing this technique that I could be pointed to I'd appreciate it. I'm quite familiar with astrophotography and star stacking, and am looking forward to trying drizzle on a different type of image set.
Downsampling the image techniques I've tried to better indicate edges: Differences of Gaussians, Laplace, directional edge detection, and a few others.
I appreciate the time you've taken to help me learn how to expand my efforts for this.
Thank you.
Edit: Modifying the image's contrast, or brightness, or tonal response, has no effect on the correlation of the image content. At least in the limited set of tests I've been able to run. It makes them 'prettier' but, honestly, the algorithms don't care if they're in 'human visual space' or in 'linear digital counts'. I can post it as a pretty image but, without those sharp edges, most of the filters fail and matches don't succeed- which is the crux of my issues here.
I'm producing an ugv prototype. The goal is to perform the desired actions to the targets set within the maze. When I surf the Internet, the mere right to navigate in the labyrinth is usually made with a distance sensor. I want to consult more ideas than the question.
I want to navigate the labyrinth by analyzing the image from the 3d stereo camera. Is there a resource or successful method you can suggest for this? As a secondary problem, the car must start in front of the entrance of the labyrinth, see the entrance and go in, and then leave the labyrinth after it completes operations in the labyrinth.
I would be glad if you suggest a source for this problem. :)
The problem description is a bit vague, but i'll try to highlight some general ideas.
An useful assumption is that labyrinth is a 2D environment which you want to explore. You need to know, at every moment, which part of the map has been explored, which part of the map still needs exploring, and which part of the map is accessible in any way (in other words, where are the walls).
An easy initial data structure to help with this is a simple matrix, where each cell represents a square in the real world. Each cell can be then labelled according to its state, starting in an unexplored state. Then you start moving, and exploring. Based on the distances reported by the camera, you can estimate the state of each cell. The exploration can be guided by something such as A* or Q-learning.
Now, a rather subtle issue is that you will have to deal with uncertainty and noise. Sometimes you can ignore it, sometimes you don't. The finer the resolution you need, the bigger is the issue. A probabilistic framework is most likely the best solution.
There is an entire field of research of the so-called SLAM algorithms. SLAM stands for simultaneous localization and mapping. They build a map using some sort of input from various types of cameras or sensors, and they build a map. While building the map, they also solve the localization problem within the map. The algorithms are usually designed for 3d environments, and are more demanding than the simpler solution indicated above, but you can find ready to use implementations. For exploration, something like Q-learning still have to be used.
I have load an obj file to render my opengl model using pyopengl and pygame. The 3D model show successfully.
Below is the 3D model i render with obj file, Now i cut my model into ten pieces through y axis , my question is how to get the sectional drawing in each piece?
I'm really very new to openGL, Is there any way can do that?
There are two ways to do this and both use clipping to "slice" the object.
In older versions of OpenGL you can use user clip planes to "isolate" the slices you desire. You probably want to rotate the object before you clip it, but it's unclear from your question. You will need to call glClipPlane() and you will need to enable it using glEnable with the argument GL_CLIP_PLANE0, GL_CLIP_PLANE1, ...
If you don't understand what a plane equation is you will have to read up on that.
In theory you should check to see how many user clip planes exist on your GPU by calling glGetIntegerv with argument GL_MAX_CLIP_PLANES but all GPUs support at least 6.
Since user clip planes are deprecated in modern Core OpenGL you will need to use a shader to get the same effect. See gl_ClipDistance[]
Searching around on Google should get you plenty of examples for either of these.
Sorry not to provide source code but I don't like to post code unless I am 100% sure it works and I don't have the time right now to check it. However I am 100% sure you can easily find some great examples on the internet.
Finally, if you can't make it work with clip planes and some hacks to make the cross sections visible then this may indeed be complicated because creating closed cross sections from an existing model is a hard problem.
You would need to split the object, and then rotate the pieces so that they are seen from the side. (Or move the camera. The two ideas are equivalent. But if you're coding this from scratch, you don't really have the abstraction of a 'camera'.) At that point, you can just render all the slices.
This is complicated to do in raw OpenGL and python, essentially because objects in OpenGL are not solid. I would highly recommend that you slice the object into pieces ahead of time in a modeling program. If you need to drive those operations with scripting, perhaps look into Blender's python scripting system.
Now, to explain why:
When you slice a real-life orange, you expect to get cross sections. You expect to be able to see the flesh of the fruit inside, with all those triangular pieces.
There is nothing inside a standard polygonal 3D model.
Additionally, as the rind of a real orange has thickness, it is possible to view the rind from the side. In contrast, one face of a 3D model is infinitely thin, so when you view it from the side, you will see nothing at all. So if you were to render the slices of this simple model, from the side, each render would be completely blank.
(Well, the bits at the end will have 'caps', like the ends of a loaf a bread, but the middle sections will be totally invisible.)
Without a programming library that has a conception of what a cut is, this will get very complicated, very fast. Simply making the cuts is not enough. You must seal up the holes created by slicing into the original shape, if you want to see the cross-sections. However, filling up the cross sections has to be done intelligently, otherwise you'll wind up with all sorts of weird shading artifacts (fyi: this is caused by n-gons, if you want to go discover more about those issues).
To return to the original statement:
Modeling programs are designed to address problems such as these, so I would suggest you leverage their power if possible. Or at least, you can examine how Blender implements this functionality, as it is open source.
In Blender, you could make these cuts with the knife tool*, and then fill up the holes with the 'make face' command (just hit F). Very simple, even for those who are not great at art. I encourage you to learn a little bit about 3D modeling before doing too much 3D programming. It personally helped me a lot.
*(The loop cut tool may do the job as well, but it's hard to tell without understanding the topology of your model. You probably don't want to get into understanding topology right now, so just use the knife)
I have a small 12 volt board camera that is placed inside a bee hive. It is lit with infrared LEDs (bees can't see infrared). It sends a simple NTSC signal along a wire to a little TV monitor I have. This allows me to see the inside of the hive, without disturbing the bees.
The queen has a dot on her back such that it is very obvious when she's in the frame.
I would like to have something processing the signal such that it registers when the queen is in the frame. This doesn't have to be a very accurate count. Instead of processing the video, it would be just as fine to take an image every 10 seconds and see if there is a certain amount of brightness (indicating that the queen is in frame).
This is useful since it helps bee keepers know if the queen is alive (if she didn't appear for a number of days it could mean something is wrong).
I would love to hear suggestions for inexpensive ways of processing this video, especially with low power consumption. Raspberry pi? Arduino?
Camera example:
here
Sample video (no queen in frame):
here
First off, great project. I wish I was working on something this fun.
The obvious solution here is OpenCV, which will run on both Raspberry Pi (Linux) and the Android platform but not on an Arduino as far as I know. (Of the two, I'd go with Raspberry Pi to start with, since it will be less particular in how you do the programming.)
As you describe it, you may be able to get away with less robust image processing tools, but these problems are rarely as easy as they seem at first. For example, it seems to me that the brightest spot in the video is (what I guess to be) the illuminating diode reflecting off the glass. But if it's not this it will be something else, so don't start the project with your hands tied behind your back. And if this can't done with OpenCV, it probably can't be done at all.
Raspberry Pi computers are about $50, OpenCV is free, so I doubt you'll get much cheaper than this.
In case you haven't done something like this before, I'd recommend not programming OpenCV directly in C++ for something that's exploratory like this, and not very demanding either. Instead, use, for example, the Python bindings so you can explore the images interactively.
You also asked about Arduino, and I don't think this is such a good choice for this type of project. First, you'd need extra hardware, like a video shield (e.g., http://nootropicdesign.com/ve/), adding to the expense. Second, there aren't good image processing libraries for the Arduino, so you'd be doing everything from scratch. Third, generally speaking, debugging a microcontroller program is more difficult.
I don't have a good answer about image processing, but I know how to make it much easier. When you mark the queen, throw some retro-reflecting beads on the paint to get a much higher light return.
I think you can simply mix the beads in with your paint -- use 1 part beads to 3 parts paint by volume. That said, I think you'll get better results if you pour beads onto the surface of the wet paint when marking the queen. I'd pour a lot of beads on to ensure some stick (you can do it over a bowl or bag to catch all the extra beads.
I suggest doing some tests before marking the queen -- I've never applied beads before, but I've worked with retroreflective tape and paint, and it will give you a significantly higher light return. How much higher strongly depends (i.e. I don't have a number) but I'm guessing at least 2-5 times more light -- enough that your camera will saturate when it sees the queen with current exposure settings. If you set a trigger on saturation of some threshold number of pixels (making sure few pixels saturate normally) this should give you a very good signal to noise ratio that will vastly simplify image processing.to
[EDIT]
I did a little more digging, and there are some important parameters to consider. First, at an index of 1.5 (the beads I'd linked before) the beads won't focus light on the back surface and retro-reflect, they'll just act like lenses. They'll probably sparkle and reflect a bit, but you might be better off just adding glitter to the paint.
You can get VERY highly reflective tape that has the right kind of beads AND has a reflective coating on the back of the beads to reflect vastly more light! You'll have to figure out how to glue a bit of tape to a queen to use it, but it might be the best reflection you can get.
http://www.amazon.com/3M-198-Scotch-Reflective-Silver/dp/B00004Z49Q
You can also try the beads I recommended earlier with an index of refraction of 1.5. I'd be sure to test it on paper against glitter to make sure you're not wasting your time.
http://www.colesafety.com/Reflective-Powder-Glass-Beads-GSB10Powder.htm
I'm having trouble finding a source for 1lb or less glass beads with 1.9+ refractive index. I'll do more searching and I'll let you know if I find a decent source of small quantities.
Is there a simple way to track the motions of a single entity in a webcam feed? For example, I imagine a "hello world" app with an index finger used as mouse pointer.
I realize there's still a lot of basic research in this area, so it might be too early to expect an easy to use, generic abstraction.
For the sake of completeness, I've seen some related but lower-level (and non-Python) projects being mentioned, including AForge, WiimoteLib and an article on motion detection algorithms.
You might want to take a look at http://opencv.willowgarage.com/wiki/PythonInterface. I'm not sure how hard it would be to do arbitrary motion tracking, but it was fairly simple to implement face tracking.