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Reweighting (rake) in Python

I'm looking for a python library for replace the rake function from "Survey", an R library (https://www.rdocumentation.org/packages/survey/versions/4.0/topics/rake)
I have found and try Quantipy, but the weights quality is poor compared to the weights generate with R on the same dataset.
I have found PandaSurvey, but seems to not working correctly (and the documentation is very poor)
I am surprised not to find much on google on this subject. However, it is an essential function if you are working with polls. Python being a datascience language, it's surprising. But maybe I missed it.
Thank you very much!

Symbol detection in scheme using Computer Vision

I am trying to detect electrical symbol in electrical scheme.
Here I think 2 ways could be use:
classical way with OpenCV, I tried re to recognise shape with opencv and python but some symbole are too complexe
deep learning way: I tried with Mask-RCNN using a handmade dataset of symbol but nothing get really successful
Here is a really simple example of what I would like to do:
I think it could be easy to make a dataset of symbol but all symbol would be the same form and context of the image would not be represented.
How do you think I could handle this problem ?

QATM:Quality-Aware Template Matching For Deep Learning might be what you are looking for.
Original paper : https://arxiv.org/abs/1903.07254
And the following github contain an example with electric scheme:
https://github.com/kamata1729/QATM_pytorch

Since the components of electrical scheme are always the same, I would try first Template Matching with OpenCV. I guess you will have to cut the components and make rotated copies to find all of them. It would be also nice to have better resolution of the images.
Next idea, I would say is making convolution with kernel which would be basicly the component you are expecting to be in the image.
Lastly, idea, which will give you definitely more certain results, but its pretty much overkill, is to use google image recognition, which you can use with python and train it on your images
https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_template_matching/py_template_matching.html

How would I go about image labeling/Classification?

Let's say I have a set of images of passports. I am working on a project where I have to identify the name on each passport and eventually transform that object into text.
For the very first part of labeling (or classification (I think. beginner here)) where the name is on each passport, how would I go about that?
What techniques / software can I use to accomplish this?
in great detail or any links would be great. I'm trying to figure out how this is done exactly so I can began coding
I know training a model is involved possibly but I'm just not sure
I'm using Python if that matters.
thanks

There's two routes you can take, one where you have labeled data (or you want to label data yourseld), and one where you don't have that.
Let's start with the latter. Say you have an image of a passport. You want to detect where the text in the image is, and what that text says. You can achieve this using a library called pytessaract. It's an AI that does exactly this for you. It works well because it has been trained on a lot of other images, so it's good in detecting text in any image.
If you have labels you might be able to improve your model you could make with pytessaract, but this is a lot harder. If you want to learn it anyway, I would recommend with learning ŧensorflow, and use "transfer learning" to improve your model.

How to create a neuronal network in machine learning to make statistics?

I'm actually working on a machine learning project and I need to make some "previsions".
I have this datas (solar panel and kind of weather)
I need to make a prevision of the energy efficiency of the following days.
I search a little bit on this, there is some informations about neuronal network like Keras. I install it but I just don't know how to make it works in my situation. I am beginer in machine learning, I learn a lot about this but it's a lot of theory and little practice so I'm really lost.
If there is someone who can just says me how can I do or give me something like a search trail !
Thanks a lot for the support !

To use keras/tensorflow or other libraries you need to know how to code in python at least and should have the understanding of neural networks. To begin with, you can have a look at knime (https://www.knime.org/) this provides similar functionalities but you need not do any coding. This might help you in understanding what's happening when you apply any kind of algorithm. Once you have a fair idea you might want to try to use keras/tensorflow.

how to make semantic label image?

Now I want to train my own image data in caffe using SegNet.
But at the first step we need label our own image like these:
I have tried to search github but cannot find anything. So my question is anyone know which tool can make semantic label image?

Check out a tool called sloth: https://github.com/cvhciKIT/sloth, which is an open-source tool written in Python with PyQt for creating ground truth computer vision datasets for a wide array of applications, such as semantically creating data like you have above.
If you don't like sloth, you can use any image editing software, like GIMP where you would make one layer per label and use polygons and flood fill of different hues to create your data. You would then merge all of the layers together to make a final image that you would use for your purposes.
However, as user Miki mentioned (see discussion thread below), creating new datasets from the beginning will take a considerable amount of effort. It is highly advisable that you don't create this on your own as you need a lot of data to ensure your algorithms are performing correctly. You'll need the help of other (hopefully willing) PhD students, preferably those you know personally or work with you in your lab or workplace to help manually curate this data for you.
If this isn't an option, you can use crowd sourced funded places like Amazon Mechanical Turk where you can outsource the work to willing individuals where you inform them of the task at hand and you pay a small amount per image. This would be something to consider if you can't find many people to help you.
All in all, this will take a considerable amount of effort, not only in terms of time but in terms of people if you want to create a large data set within a short span of time. I would recommend you simply use established datasets, such as what you have referenced from Cambridge, or Miki suggested LabelMe by Antonio Torralba which not only is a toolbox for annotating images from his LabelMe dataset but it also allows you to do the same for your own images.
Good luck!

As answer by #rayryeng a tool called sloth is great to finish these task in simple way. However, if I have more than 20 object waiting for me to classify, sloth is not a ideal tools. Thus I develop a simple tool which call IsLabel to finish these problem with few algorithms.
And the result look like these while using IsLabel just took me 40s:
INPUT:
OUTPUT:
I know its not perfect but it work fine for me.

I would recommend using https://www.labelbox.io/. They open sourced a lot of their code and have a hosting platform to manage the whole labeling process end to end.
Here is an example of segmentation
And you can export labels with a mask.