I have tried to use some denoising logic but it's affecting other images which don't have the noise. Can anyone help me to get rid of this. Thanks in advance for the help.
Code i have tried:
Remove wavy noise from image background using OpenCV
The above link was not working on the images then i have tried the below link which is working for noisy images but affecting the images which don't have noise. Any suggestion or approaches which can be implemented will be appreciated.
Related
My goal is to transform an image captured by a camera and transform that image to orthographical image without effects of perspective.
I have a few objects of known size on a surface. I have a camera, placed above and directed to those objects, as exemplified in the scene. The camera is capturing images as in image captured by the camera. I want to get an orthographical image of the environment as in orthographical image I want to get.
I have read few posts, but did not really understand their relevance to my problem, as I am not expert on these transforms. The answer from this question made me think it is possible, although I did not get how.
I would appreciate a clear explanation or pointing a clear tutorial, using Python or Lua if possible.
Any help is appreciated.
This was not possible without distorting the image. A straightforward explanation is that the perspective causes some parts of the image to be not visible, for example the white line in the marked area is not visible, and there could be something small that we are not able to observe. For those parts, the algorithm is supposed to produce some kind of prediction based on heuristics.
Please hold before downgrading the Question. I am not looking for a segmentation or detection algorithm/library.
I have also seen this Post
Remove background of the image using opencv Python
But the solution marked correct is again what I don't want. I want exactly
What are the ways to Subtract the BACKGROUND image from FOREGROUND?
Input:
Background Image without vehicle like the above post
Foreground Image with the vehicle
Output:
Vehicle
Be kind and thanks for the help.
Link to the Input Images Input
Link to the Output Output
OpenCV background Subtractor models Documentation
After a lot of testing, Videos based MOG/MOG2 works better than single Image based background subtraction with KNN.
This article solved my problem
Is it possible to dash the blurred part of the image?
Right now I am using python with OpenCV. I know only how to load images and display if the image is blurred.
My input is a blurred image:
I would like to get:
I do not have:
original/unblurred image.
Output can have still blurred parts but dashed.
Thanks a lot for help!
You could try by computing the "Variance of the Laplacian" on parts of the image to detect the regions that have a low variation in greyscales (= assumed blurry) and which regions have a high variation in greyscale (= assumed non-blurry).
There is a nice tutorial on how to check if an image is blurry, it can be found here
There is also a post here that explains the theory behind it.
It ain't a complete solution, but it might be a way to start.
So, I have the following Image
I want to detect the white lump in that image ignoring the thin white line.
I need help with the approach that I can use to detect it with OpenCV python.
Please help, I don't want the code but need a direction on how to go about it.
Thank you
I suggest the following solution:
Make it binary.
Perform opening morphological transformation.
I've been trying to identify ellipses in these pictures for a long time now for a project I'm working on. At the moment I'm trying a new method with a bit of success. I blur the image then subtract the original from it. After that I threshold that image which is how I get this: http://imgur.com/gIkv30A
I've been trying a few methods but have had pretty much no success with any of them. I can't get any more of the noise removed without compromising the quality of the ellipses I have found, but the ellipses I want to find seem to be decently defined.
If anyone has an idea on where I can go now I'd love to hear it.
Thanks,
Andy
edit:
Original Image: http://imgur.com/3ttIFiz
The main method I've tried so far using an adaptive threshold on the image then fitting an ellipse around each of the contours I find after that. It works quite well in one set of images, but performs very poorly in this set. I can see my current method working well in both I get it right.
How well it works with old images: http://imgur.com/eUYiYNa
How well it works with the new (more relevant to the program) images: http://imgur.com/1UXxXAp