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.
Related
I have been browsing the internet and stack overflow in order to find a solution to my problem, but to no avail.
So here is my problem:
Problem
I have a series of images with specific ROIs, where I detect a signal change. In order to extract the signal I need subtract the background of the image from the actual signal. Unfortunately I can't just subtract the images, as this doesn't delete the background noise sufficiently.
Solution Idea
What I want to do is to cut out (black out) my ROIs and then do an interpolation across the entire "reduced" image. Then I want to fill in the blacked out ROIs again via interpolation. This way I can get an idea of what the background below my signal is actually doing. I have been playing around with griddata, RectBivariateSpline, but I haven't found a way that works.
So far I have been doing this in MATLAB with the function scatteredInterpolant, but I would like to do it in python.
Below an image series, that describes the concept. One can see the third image being slightly blurry in the before blacked out ROIs.
Imageprocessing concept
So, does python provide a solution or way, which is similar to MATLABs scatteredInterpolant or how could I best tackle this problem?
Thank you.
I have a fixed camera and I need to check if its position or orientation has been changed. I am trying to use OpenCV (calculating diiferencies between a reference image and a new one) for this, but I am pretty new to OpenCV (and image processing in general) but I am not really sure what specific algorithm would be the best to use for this, or how to interpret the results to find if the camera has been moved/rotated. Any ideas?
Please help,
One way to do it would be to register the two frames to each other using affine image registration from openCV. From this you can extract the rotation and displacement difference between the two frames. Unfortunately this will only work well for in-plane rotations but I still think it is your best bet.
If you post some sample code and data I would be happy to take a look.
You can use Canny or HoughLinesP to find lines,From this you can get two lines,compare it.Maybe this will be effective in some simple background.if some object in your picture,try sift or other feature extractor,you can take features to find the relationship from two frames.
I have an image captured by android camera. Is it possible to calculate depth of object in the image ? Image contains object and background only. Any suggestion, explanation or links that you think can help me will be appreciated.
OpenCV is the library you need.
I did some depth identification of water levels in pure white background a few days ago. Generally, if you want to identify the depth, you can convert the question to identify the edge of the changing colors. In this case, you can convert the colorful pictures to grey and identify the changing of while-black-grey interface. OpenCV is capable of doing the job at high speed.
Hope it helps. Let me know if you need further help.
Edits:
If you want to find the actual depths, you need to project the coordinate system of your pictures to the real world, or vice versa. To do it, you have to know a fix location as your reference and the relationship between pixels and real distances.
What I did is find the fixed location and set it as zero. Afterwards, I measured a length of an object in the picture, and also calculated the pixel amount of the object. Therefore I obtained the relationship between pixels and real distances.
Note that these procedures may involve errors in the identification. I did it very carefully and the error was acceptable in my case.
With only one image, accurate depth estimation is near impossible. However, there are various methods of estimating depth under certain assumptions or the availability of the camera calibration matrix. As mentioned by #WenlongLiu, OpenCV is a very good place to start with.
My question is rather about feasibility of a task.
Note that I have read the solution of this question, however you can guess I am not dealing with rectangles and cameras here.
Situation:
I need to save lot of pictures in a folder all of them obeying to these rules:
In each picture, there is ONLY one object.
The object can be anything (car, horse, human hand ...)
The size and the format of the picture belong to certain set.
The background of the object is ALWAYS white.
The color of the object itself can be anything else (including, why not, areas of white pixels)
Goal:
I want to detect if the object of each image is CENTERED.
Development environment:
Python
OpenCV
Do you think this is feasible ?
I hope my question is not too broad. I just ask if this can be done automatically without human intervention on the pictures. I have thousands of them. The program will save in a separate folder pictures in which the object is not centered.
EDIT:
Following the comments and answer above: for me, a centered object is the one if I draw a square or rectangle around it, the edges of the square/rectangle must be equivalently distant from let and right sides of the image, whereas the top and the bottom of the object must be equivalently distant from the top and bottom of the picture.
Yep this is very feasible. However, depending on the type of objects the images contain, they are different ways to accomplish this. Assuming the objects in the images all have a uniform color you can easily perform a color detection algorithm, find the centre point of the object in terms of pixels and find it's position using the image resolution as the reference.
As the background is always white as specified, this is probably your best method as you can just extract all the non white (Or different shade of white) objects within the image.
if you do decide to go with this approach, i should be able to point you to some relevant code
Although writing in c++, more information on this can be found in the link below.
http://opencv-srf.blogspot.co.uk/2010/09/object-detection-using-color-seperation.html
the link is based on object detection in a video but as a video is just a series images the same concept can be used on images
I would like to eliminate the keypoints detected around the frame of an image (an artwork of a museum gallery ). In other words I want to separate out the actual artwork from its frame. Each artwork consist of different types of frames.
![Keypoints detected using sift][1]
I have already written a Python wrapper for David Lowe's SIFT implementation to detect keypoints as well as to compute descriptors.
However my question is what is the best approach to solve this problem? any of the following or something else?
Using Hough transformation (using Python Image Library)
Template matching
Your help is highly appreciated
Thanks again
I'd go with Hough transform and try to detect lines which form a quadrilateral.
You might get into trouble if the painting actually does contain a square or something. I'd look for some assumptions like: acceptable aspect ratio, acceptable size. Also find the outermost quadrilateral, and work your way towards the center of the image picking up inner quadrilaterals, if applicable. This would give you the frame and its thickness, so you can disregard any keypoints here or beyond the frame.
P.S. If you got some random replies from me, it's because I accidentally replied to another post in your thread... ^^
For each artwork, do you have a clean, properly framed reference image?
If so another solution to remove the background clutter is:
to use the ratio test algorithm to compute keypoints correspondences between your frame and the reference image,
to perform a geometric consistency check to filter out false matches.
In addition the geometric check will provide you with the homography matrix that you can use to warp your input frame or alternatively to project the corners of the reference images.
That way you will natively obtain the artwork area within your frame.
Here's an example about how you can do that with opensift's match tool - below is an illustration.