I am working on the blind man navigation project. for this i need to detect right and left arrows using open cv and python. can anyone help with procedure or with sample code. i am pretty much new to open cv
I have to detect the following shape:
This will be in a live environment i.e. the arrow will be printed on a piece of A4 paper and hung up in a corridor. The camera which needs to detect the image will most likely be a bit shaky so there will be some deformation of the image I presume, also lighting might be an issue. Further the arrow only needs to be detected from the front e.g. not from the side where it will be deformed.
I am now wondering now what my best approach might be to correctly detect the arrow and as such its direction, left or right.
You can use template matching to detect the arrow, however if you using handheld camera, then to get the direction right, you need to make sure that the camera pose is correct, that is the camera is not rotated.
You can use feature based classifier such as HOG or Corners, Lines etc to build a detector and later predict the direction.
This Paper parents a road sign detection approach which is applicable in your case.
Related
I am trying to improve my webcam based OpenCV mouse controller for disabled people (MFC C++ application): https://preability.com/face-controlled-mouse/
The cursor moves, when a person moves her/his head, clicks when smile, etc.
Controller finds face area then use goodFeaturesToTrack, cornerSubPix and calcOpticalFlowPyrLK.
In general, I managed to stabilize cursor if lighting is good.
What I use now:
Evaluating and filtering the direction of the each corner point movement.
Spreading the corner points all over the face area for cv::goodFeaturesToTrack() helped a little too.
EWMA (or Kalman) filter for the cursor position.
I’ve included equalizeHist() for the face ROI. The detector performed much better in low light conditions.
In addition, I tried morphology operations of OpenCV without improvement.
However, the corner points still dance in uneven lighting.
I can see that similar old program eViacam has preprocessing module for webcam Creavision (old too) and the corner points are more stable.
Please advise what can be done with the input Mat? Or how can the video be processed with reasonable CPU loading?
Now I can answer my own question. Christoph Rackwitz gave me some good advice:
don’t track the whole head. track each feature. and don’t use those trackers, they’re too complex. use MOSSE. it’s dumb but very precise, as long as the object (which should be a tiny feature on the face) doesn’t change much.
MOSSE approaches optical flow. methods to calculate optical flow work like MOSSE, except they use simpler math and smaller regions, hence the result is noisier. MOSSE uses a larger area (for a single track/point of course) and more sophisticated math, for a more accurate result.
When MOSSE algorithm tracks “corner points”, cursor moves much more smoothly. There was a slight issue with discrete movement as the object rectangles moved the same number of pixels at the same time. Cursor moved in leaps. So, I had to use filter on each tracked point. Anyway, as you can see in the video, the CPU load did not increase comparing to Lukas-Kanade optical flow algorithm + filtration only cursor position. In good light, the difference is also very noticeable.
https://www.youtube.com/watch?v=WKwuas0GVkA
Lucas-Kanade optical flow:
goodFeaturesToTrack,
cornerSubPix,
calcOpticalFlowPyrLK,
cursor EWMA filter
MOSSE object tracking:
goodFeaturesToTrack,
cornerSubPix,
TrackerMOSSE,
all points EWMA filtration
And of course I had to remember to include the tracking453.lib to Linker when add legacy Tracker. I spent half a day googling the “unresolved external symbol LNK2001 error”. For some reason including a tracker from the core library (cv::Tracker) does not result such compilation error, so it is confusing.
How to recognize rain on camera vision using with OpenCV in C++?
Or if somebody stick a sticker on a camera how recognize it with OpenCV in C++?
Or if somebody throw color to the camera how can i detect it with OpenCV in C++?
Detect these on camera vision:
Rain
Sticker
Color
Here is an example video of sticker!
Camera Vision-Sticker
In case of a sticker, you're just looking for a large dark area that doesn't change in time.
In case of color, analyze image color stats - if somebody sprays some paint on a camera (is that what you mean by "throwing color"?), some color is going to be dominant over all the others.
You can also try to handle both cases by subtracting frames and detecting image areas that don't change in time that way.
You may want to use machine learning for finding threshold values (e.g. area size, its shape properties, such as width/length ratio, continuousness etc.) used to decide when to consider something to be a sticker/color or something else.
As for the rain, I guess there's no simple answer that can be given in a few sentences. There are some articles available in the web though. That said, I would guess it would be simpler and cheaper to detect rain by just installing external rain sensors (like the ones activating wipers in a car) rather than trying to do it by developing your own computer vision algorithm for that purpose.
This sounds like an interesting project, where a camera can automatically detect obstruction (paint, sticker, rain). It will most likely be necessary for the camera to be mounted without obstructions so that the expected image can be learned. If the usage scenario allows that, it won't be very hard.Both sticker and rain result in strong permanent deviations from the expected image, while rain will result in noisy images.
OpenCV with C++ or Python can help solve this kind of problems, because complicated computer vision algorithms are already implemented there. It takes some time to get started with, but after that OpenCV is not hard.
So I am fairly inexperienced with After Effects. But I am making a title for a video project and am having a hard time figuring something out. I have some text that slides over, and appears once it is in the masked area. However this is a very hard edge and doesn't look the best. How do I soften that edge? I tried making another shape and blurring it and then masking that over the other shape but that didn't work. I tried Googling but it was full of people saying you cannot feather a shapes edges in AE.
I would use the mask in my image directly with the feather option but then that mask moves with my image, and that's not what I want.
So I learned that you can just make an image in photoshop that does whatever you want it to do and just use that.
The masks feathering moves your image? Can you put up a screen recording of the problem?
currently i am having much difficulty thinking of a good method of removing the gradient from a image i received.
The image is a picture taken by a microscope camera that has a light glare in the middle. The image has a pattern that goes throughout the image. However i am supposed to remove the light glare on the image created by the camera light.
Unfortunately due to the nature of the camera it is not possible to take a picture on black background with the light to find the gradient distribution. Nor do i have a comparison image that is without the gradient. (note- the location of the light glare will always be consistant when the picture is taken)
In easier terms its like having a photo with a flash in it but i want to get rid of the flash. The only problem is i have no way to obtaining the image without flash to compare to or even obtaining a black image with just the flash on it.
My current thought is conduct edge detection and obtain samples in specific locations away from the edges (due to color difference) and use that to gauge the distribution of gradient since those areas are supposed to have relatively identical colors. However i was wondering if there was a easier and better way to do this.
If needed i will post a example of the image later.
At the moment i have a preferrence of solving this in c++ using opencv if that makes it easier.
thanks in advance for any possible ideas for this problem. If there is another link, tutorial, or post that may solve my problem i would greatly appreciate the post.
as you can tell there is a light thats being shinned on the img as you can tell from the white spot. and the top is lighter than the bottome due to the light the color inside the oval is actually different when the picture is taken in color. However the color between the box and the oval should be consistant. My original idea was to perhaps sample only those areas some how and build a profile that i can utilize to remove the light but i am unsure how effective that would be or if there is a better way
EDIT :
Well i tried out Roger's suggestion and the results were suprisngly good. Using 110 kernel gaussian blurr to find illumination and conducting CLAHE on top of that. (both done in opencv)
However my colleage told me that the image doesn't look perfectly uniform and pointed out that around the area where the light used to be is slightly brighter. He suggested trying a selective gaussian blur where the areas above certain threshold pixel values are not blurred while the rest of the image is blurred.
Does anyone have opinions regarding this and perhaps a link, tutorial, or an example of something like this being done? Most of the things i find tend to be selective blur for programs like photoshop and gimp
EDIT2 :
it is difficult to tell with just eyes but i believe i have achieved relatively close uniformization by using a simple plane fitting algorithm.((-A * x - B * y) / C) (x,y,z) where z is the pixel value. I think that this can be improved by utilizing perhaps a sine fitting function? i am unsure. But I am relatively happy with the results. Many thanks to Roger for the great ideas.
I believe using a bunch of pictures and getting the avg would've been another good method (suggested by roger) but Unofruntely i was not able to implement this since i was not supplied with various pictures and the machine is under modification so i was unable to use it.
I have done some work in this area previously and found that a large Gaussian blur kernel can produce a reasonable approximation to the background illumination. I will try to get something working on your example image but, in the meantime, here is an example of your image after Gaussian blur with radius 50 pixels, which may help you decide if it's worth progressing.
UPDATE
Just playing with this image, you can actually get a reasonable improvement using adaptive histogram equalisation (I used CLAHE) - see comparison below - any use?
I will update this answer with more details as I progress.
I would like to point you to this paper: http://www.cs.berkeley.edu/~ravir/dirtylens.pdf, but, in my opinion, without any sort of calibration/comparison image taken apriori, it is difficult to mine out the ground truth from the flared image.
However, if you are trying to just present the image minus the lens flare, disregarding the actual scientific data behind the flared part, then you switch into the domain of image inpainting. Criminsi's algorithm, as described in this paper: http://research.microsoft.com/pubs/67276/criminisi_tip2004.pdf and explained/simplified in these two links: http://cs.brown.edu/courses/csci1950-g/results/final/eboswort/ http://www.cc.gatech.edu/~sooraj/inpainting/, will do a very good job in restoring texture information to the flared up regions. (If you'd really like to pursue this approach, do mention that. More comprehensive help can be provided for this).
However, given the fact that we're dealing with microscopic data, I doubt if you'd like to lose the scientific data contained in a particular region of an image. In that case, I really think you need to find a workaround to determine the flare model of the flash/light source w.r.t the lens you're using.
I hope someone else can shed more light on this.
I am making this C++ program with rectangles on it that's needed to be drag whether horizontally or vertically by clicking on them and checking if other rectangles will collide onto it.
Now, in my situation, I have this case that if the user moves the mouse very fast. The collision detection won't work, I mean, the other coordinates are skipped out or jump out on a very large distance. I am assuming that adding mouse sensitivity on my program can change this unwanted behavior.
I use GLFW for windowing. I think glfwSetMousePos() can do what I want but I don't know what math should I apply to attain this. And if possible, I don't want to use other libraries. Can someone show some code how could I do this?
Thanks!
Update
Sadly to know, mouse sensitivity is not the issue in my case. I think I can work this around by expanding my code with lots of if. However, for those who are looking for this kind of question, I suggest this. You can use it as a separate program by just running that code.
Sounds like you need a collision detection algorithm that handles penetration at any speed.
Look into sweeping collision detection. It's pretty much how all physics engines work.