Identify Table Cells Individually (Separately) using Python

I have a table, a clear table with vertical and horizontal grid lines (grid lines are sometimes black and sometimes white, it is possible to know this in advance).

I'm trying to find a way to locate each cell on the table photo individually, each cell got different properties (text, color, number, link, etc...) and I want to allow the user to perform some analysis on each cell before submitting.

When I'll show the user a given cell I will also show him the first cell from that row and the header cell for that column.

I've been searching the internet for the past 2-3 hours and found nothing, my code got me nowhere yet so there is no point in pasting it.

Some links I've tried:

1. Generating tables with images in cells using Python
2. Split cells from an image of a table
3. Recognize cells from a table with Open CV and display the recognition result in QGraphicsView of PyQt5
4. How to get individual cell in table using Pandas?
5. Detect columns from a table with opencv
6. detect a table part from entire image in python

Most of them usually work for only extracting textual data from an image, but I do not want to extract any data, I simply want as a start to detect all of the cells in the table and display the user each of the cells (for example in a loop, show him each cell [as is, without modification] with the corresponding header and row index, meaning each iteration will show the user 3 things: {1} the cell {2} the header {3} the row index)

Example Image (Actual data is classified so I found a Google image to show the principle Im looking for):

Image Link

> I know I didn't paste any code, it's because none of the tries I did worked even a little bit, I really have no idea what to do...
>
> If you have anything you think I can do better in order to improve my question, please tell me

(I've edited my answer to account for the fact that you can't remove the background colors)

The following code gives you the cells:

img = cv.imread("your_img.png",cv.IMREAD_GRAYSCALE)
## detect edges in the image. Will get the table cells and the text
## the text will be removed later
edges = cv.Canny(img,10,20)
## make the edges a bit thicker
edges = cv.dilate(edges, np.ones((3,3))) 

## invert image
_, edges = cv.threshold(edges,127,255,cv.THRESH_BINARY_INV)
## find the contours of the cells
conts,_  = cv.findContours(edges, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
## add a convex hull around to remove the text
conts = [cv.convexHull(cont) for cont in conts]
## filter out noise
conts = [cont for cont in conts if cv.contourArea(cont) > 100]
## draw on an image
edges = cv.drawContours(edges*0, conts, -1,(1,),1)

's the final image