The regex online tool for python and any programming languages.

Today I tested this online tool.
Is a tool for a regular expression (regex or regexp for short) for many programming languages.
These programming languages are php, javascript, golang and python.
The tool is easy to use it.
First, you need to select the programming language that is used for regular expression.
The next step is to put the regular expression into the edit box and add your text to be parsed by this regular expression.
For example, if you use this inputs for a regular expression:
([a-zA-Z]+) \d+
and this text example:
March 7 1976, June 1, August 9, Dec 25
the result output will be this:
March , June , August , Dec

Python 3.6.4 : Testing OpenCV default GrabCut algorithm.

The main goal for me was to test the new install of python 3.6.4 and python modules with Windows operating system version 8.1.
For this tutorial, I chose these python modules: cv2, numpy and matplotlib .
I have tested the GrabCut algorithm article from here.
The article comes with a python script that includes the modules I tested in this programming language.
They tell us:
User inputs the rectangle. Everything outside this rectangle will be taken as sure background (That is the reason it is mentioned before that your rectangle should include all the objects). Everything inside rectangle is unknown. Similarly any user input specifying foreground and background are considered as hard-labelling which means they won't change in the process.
From my point of view, it is not a very successful algorithm to crop off the background but is working well.

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('test_python_opencv.jpg')
mask = np.zeros(img.shape[:2],np.uint8)

bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)

rect = (57,58,476,741)
cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)

mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img = img*mask2[:,:,np.newaxis]

plt.imshow(img),plt.colorbar(),plt.show()

The intersection areas are eliminated exactly as in the documentation.
See my first test on an image taken from the internet.