I have been trying very hard to plot graph using maxima. Sadly it is not working. I tried uninstalling and installing multiple times. This is the error I am getting pictures below.
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I'm trying to migrate an algorithm that uses a 2d histogram to run using the new G-API on OpenCV. I see that there is equalizeHist() available, but not calcHist(). Is there any way to calculate the histogram using the new Graph API?
Please open an issue at OpenCV GitHub so we take this to work. Meanwhile, the API is extensible so you can add a missing operation/kernel locally (within your app): https://docs.opencv.org/master/d0/d25/gapi_kernel_api.html
I'm working on my project for License Plate Recognition using OpenCV & Tesseract.
I use OpenCV to change original image to processed image so that Tesseract can read it well.
For example)
Original Image
Processed Image
But the result shows "38다9502"and it recognized 3 to 5.
These situation happens frequently especially when the number is 3 or 5.
Is there any suggestion or solution for it??
You can try retraining tesseract with some of your own data. It looks like a good candidate for simply fine-tuning the model. You may not even need much data, just give it several examples of the digits it is having trouble with.
Instructions for retraining are here: https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00
1)First it can be done with few image processing techniques which is mentioned in this link(https://cvisiondemy.com/license-plate-detection-with-opencv-and-python/)
2)Next if it doesn't show any improvement you can try image thresholding which you can go through in this link(https://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html)
3)If above steps didn't work ,then try to enlarge your image size.
I solved this question with using multiple models supported by Tesseract.
With Hangul model, I only received accurate information of Hangul word, not Numbers.
However, with English model, I can received accurate information of Numbers.
So I used these models in parallel and it resulted 99% accuracy of LPR.
Many days ago I saw a user using scipy function to get region of interest (RoI) in an image just by using different colors. I believe it was some sort of filter. Now I'm trying to do the same but I'm not able to find that content anymore.
I would grateful if you could help me with that problem.
Example of Semantic Segmentation:
I am observing different stitching results (on multiple runs for input images) using opencv.
As i know opencv uses RANSAC for getting inliers and calculate homography. So results might vary?
Is there any way to fix it to get same results consistently ?
If not, how people used to benchmark their results while using RANSAC?
I am new to Gephi, and I am computing a small world network. I want to plot reports from the statistical modules in gephi in log-log scale (otherwise it is hardly possible to see anything in the degree distribution).
I would like to obtain the raw data results to plot them on gnuplot, or just use the console in gephi to see them. I googled the issue and looked in the gephi forums but could not seem to find the answer.
Thanks in advance!
Go to the Data Laboratory and click on "export table". It will export a spreadsheet of the node attributes including the degree.