Okay so i am trying to find homography of a soccer match. What i have till now is
Read images from a folder which is basically many cropped images of a template soccer field. Basically this has images for center circle and penalty lines etc.
Read video stream from a file and crop it into many smaller segments.
Loop inside the images in video stream and inside that another loop for images that i read from folder.
Now in the two images that i get through iteration , i applied a green filter because of my assumption that field is green
Use orb to find points and then find matches.
Now the Problem is that because of players and some noise from croud, i am unable to find proper matches for homography. Also removing them is a problem because that also tends to hide the soccer field lines that i need to calculate the homography on.
Any suggestions on this is greatly appreciated. Also below are some sample code and images that i am using.
"Code being used"
Sample images
Output that i am getting
The image on right of output is a frame from video and that on left is the same sample image that i uploaded after filterGreen function as can be seen from the code.
Finally what i want is for the image to properly map to center circle so i can draw a cube in center, Somewhat similar to "This example" . Thanks in advance for helping me out.
An interesting technique to throw at this problem is RASL. It computes homographies that align stacks of related images. It does not require that you specify corresponding points on the images, but operates directly on the image pixels. It is robust against image occlusions (eg, players moving in the foreground).
I've just released a Python implementation here: https://github.com/welch/rasl
(there are also links there to the original RASL paper, MATLAB implementation, and data).
I am unsure if you'd want to crop the input images to that center circle, or if the entire frames can be aligned. Try both and see.
Related
Forgive me but I'm new in OpenCV.
I would like to delete the common background in 3 images, where there is a landscape and a man.
I tried some subtraction codes but I can't solve the problem.
I would like output each image only with the man and without landscape
Are there in OpenCV Algorithms what do this do? (then without any manual operation so no markers or other)
I tried this python code CV - Extract differences between two images
but not works because in my case i don't have an image with only background (without man).
I thinks that good solution should to Compare all the images and save those "points" that are the same at least in an image.
In this way I can extrapolate a background (which we call "Result.jpg") and finally analyze each image and cut those portions that are also present in "Result.jpg".
You say it's a good idea? Do you have other simplest ideas?
Without semantic segmentation, you can't do that.
Because all you can compute is where two images differ, and this does not give you the silhouette of the person, but an overlapping of two silhouettes. You'll never know the exact outline.
I find many examples of passing a list of images, and returning a stitched image, but not much information about how these images have beeen stitched together.
In a project, we have a camera fixed still, pointing down, and coveyers pass underneath. The program detects objects and start recording images. However some objects do not enter completely in the image, so we need to capture multiple images and stich then together, but we need to know the position of the stitched image because there are other sensors synchronized with the captured image, and we need to also synchronize their readings within the stitched image (i.e. we know where the reading is within each single capture, but not if captures are stitched together).
In short, given a list of images, how can we find the coordinates of each images relative to each other?
Basically while stiching correspondence between two (or more) images are setup. This is done with some constant key points. After finding those key points the images are warped or transformed & put together, i.e. stitched.
Now those key points could be set/ noted as per a global coordinate system (containing all images). Then one can get the position after stitching too.
Most of the steps are similar to panaroma stitching. You can follow this link for your problem.
Basically, one/some image(s) will be your reference image(s), and you will find the transformation between it and the other ones. These transformations will be your 4*3 homography matrices. Any point in an image can be transformed into another image's coordinate system using these matrices homography. So, you can overcome the occlusion issue that you are trying to solve. Another source for homography.
I have a database of original images and for each original images there are various cropped versions.
This is an example of how the image look like:
Original
Horizontal Crop
Square Crop
This is a very simple example, but most images are like this, some might taken a smaller section of the original image than others.
I was looking at OpenCV in python but I'm very new to this kind of image processing.
The idea is to be able to save the cropping information separate from the image to save space and then generate all the cropping and different aspect ratio on the fly with a cache system instead.
The method you are looking for is called "template matching". You find examples here
https://docs.opencv.org/trunk/d4/dc6/tutorial_py_template_matching.html
For your problem, given the large images, it might be a good idea to constrain the search space by resizing both images by the same factor. So that searching a position that isn't as precise, but allows then to constrain the actual full pixel sized search to a smaller region around that point.
I am working on an application where I need feature like Cam Scanner where document is to be detected in an image. For that I am using Canny Edge detection followed by Hough Transform.
The results look promising but the text in the document is creating issues as explained via images below:
Original Image
After canny edge detection
After hough transform
My issue lies in the third image, the text in original mage near the bottom has forced hough transform to detect the horizontal line(2nd cluster from bottom).
I know I can take the largest quadrilateral and that would work fine in most cases, but still I want to know any other ways where in this processing I can ignore the effect of text on the edges.
Any help would be appreciated.
I solved the issue of text with the help of median filter of size 15(square) in an image of 500x700.
Median filter doesn't affect the boundaries of the paper, but can help eliminate the text completely.
Using that I was able to get much more effective boundaries.
Another approach you could try is to use thresholding to find the paper boundaries. This would create a binary image. You can then examine the blobs of white pixels and see if any are large enough to be the paper and have the right dimensions. If it fits the criteria, you can find the min/max points of this blob to represent the paper.
There are several ways to do the thresholding, including iterative, otsu, and adaptive.
Also, for best results you may have to dilate the binary image to close the black lines in the table as shown in your example.
I am getting video input from 2 separate cameras with some area of overlap between the output videos. I have tried out a code which combines the video output horizontally. Here is the link for that code:
https://github.com/rajatsaxena/NeuroscienceLab/blob/master/positiontracking/combinevid.py
To explain the problem visually:
The red part shows the overlap region between two image frame. I need the output to look like the second image, with first frame in blue and second frame in green (as shown in third illustration)
A solutions I can think of but unable to implement is, Using SIFT/SURF find out the maximum distance keypoints from both frames and then take the first video frame completely and just pick the non overlapping region from second video frame and horizontally combine them to get the stitched output.
Let me know of any other solutions possible as well. Thanks!
I read this post one hour ago. I tried some really easy approach. Not perfect but in some cases should work well. For example, if you have both cameras on one frame placed side by side.
I took 2 images from the phone like on a picture (color images). Program select Rectangles region from both source images and resize end extract this roi rectangles. The idea is to find the "best" overlapping Rect regions by normalized correlation.
M1 and M2 is mat roi to compare,
matchTemplate(M1, M2, res, TM_CCOEFF_NORMED);
After, I find this overlapping Rect use this to crop source images and combine by hconcat() function together.
My code is in C++ but is really simple to replicate this in python. It is not the best solution but one of the most simple solution. If your cameras are fixed in stable position between themselves. This is a good solution I think.
I hold my phone in hand :)
You can also use this simple approach on video. The speed depends only on the number of rectangle candidate you compare.
You can improve this by smart region to compare selection.
Also, I am thinking about another idea to use optical flow by putting your images from a camera at the same time to sequence behind each other. From the possible overlapping regions in one image extract good features to track and find them in the region of second images.
Surf and sift are great for this but this is the most simple idea on my mind.
Code is Here Code