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ways to reduce noise + smooth the image to create a mask for the largest object

I need your advice, guys! So I am trying to create a mask for a football (i.e. soccer) pitch, and if I only use filtering by the most common hue/saturation/value values, I get the following image
As you can see, the desired part is inside the grey boundary I drawn and there is a lot of noise here - trees, lines, external objects, and of course I would like to get rid of it. The desired outcome is something similar to this:
I thought about an algorithm that would transform the first image into another by analyzing each pixel's surrounding and color it white if more than threshold% of pixels into a (x, x) square is white, otherwise black.
Do you know if there is an implementation on openCV or similar libraries for this or I should build it from scratch?
Also, maybe you can propose other way to deal with the noise and external objects? I already tried the morphological transform and blurring techniques, but either I don't do it right or it doesn't work well for my problem.
Thank you in advance for your advice!

I actually found an easy implementation of the algo I proposed - I simply use cv2.blur on the image and then filter with cv2.inRange, so it does exactly what I wanted it to do.

Extract text from light text on withe background image

I have an image like the following:
and I would want to extract the text from it, that should be ws35, I've tried with pytesseract library using the method :
pytesseract.image_to_string(Image.open(path))
but it returns nothing... Am I doing something wrong? How can I get back the text using the OCR ? Do I need to apply some filter on it ?

You can try the following approach:
Binarize the image with a method of your choice (Thresholding with 127 seems to be sufficient in this case)
Use a minimum filter to connect the lose dots to form characters. Thereby, a filter with r=4 seems to work quite good:
If necessary the result can be further improved via application of a median blur (r=4):
Because i personally do not use tesseract i am not able to try this picture, but online ocr tools seem to be able to identify the sequence correctly (especially if you use the blurred version).

Similar to #SilverMonkey's suggestion: Gaussian blur followed by Otsu thresholding.

The problem is that this picture is low quality and very noisy!
even proffesional and enterprisal programs are struggling with this
you have most likely seen a capatcha before and the reason for those is because its sent back to a database with your answer and the image and then used to train computers to read images like these.
short answer is: pytesseract cant read the text inside this image and most likely no module or proffesional programs can read it either.

You may need apply some image processing/enhancement on it. Look at this post read suggestions and try to apply.

PNG to PGM conversion without quality loss

So, I have a PNG image file like the following example, and I need it to be converted into PGM format.
I'm using Ubuntu and Python, so any of terminal or Python tools would suit just fine. And there sure is a plenty of ways to do this: using ImageMagick convert command or pngtopam package or Python PIL library, etc.
But the point is, the quality of the image is essential in my case, and all of those failed in keeping it, always ending up with:
No need to mention this is totally not what I want to see. And the interesting thing is that when I tried to convert the same image into PGM manually using GIMP, it turned out quite well, looking exactly the way I'd like it to, i.e. the same as the PNG one.
So, that means it is possible to get a PGM image in fine quality after all, and now I'd really appreciate if someone can tell me how do I do that using terminal/Python tools. I guess, there should be some ImageMagick option that does the trick, it's just that I'm not aware of any.

You lost the antialiasing, which is conveyed via the alpha channel. To preserve it, use:
convert in.png -flatten out.pgm
Without -flatten, convert simply deletes the alpha channel; with -flatten it composites the input image against the background color, which is white by default.
Here are the results, magnified 10x so you can see what's going on:
Not flattened:
Flattened:

Exaggerating colors in GIMP/Python

I have images such as the one below from which I need to count the prominent white spots. Unfortunately my object counting algorithm is becoming confused due to those "fuzzy" white areas. It can sometimes see hundreds of objects there.
So what I'm wondering is whether there's some way to perhaps exaggerate the white spots and suppress the "fuzzy" areas either using filters in GIMP or Python libraries.
Thank you!

Increase the contrast in GIMP.

You probably want an adaptive threshold.
The modules that I know have this in Python are scikit-image and OpenCV.

I ended up using G'MIC's Bilateral Filtering, it was the perfect tool for the job.

Image color recognition, the general color

I want to get the most prominent color of an image, and the language can be in either python or ruby.
Is this easily done?

I don't know if this is what you mean, but maybe it will be helpful:
require 'rubygems'
require 'RMagick'
include Magick
image = Image.read("stack.png")[0]
hash = image.color_histogram
color, number = hash.max{|a,b| a[1] <=> b[1]}
puts color.to_color
This worked like a charm for very simple image (only 5 colors), but should work for more complex images too (I did not tested that; returned hash will be quite big in that case, so you might want to use quantize on your image before using color_histogram).
Some resources :
color_histogram
quantize
I hope this was useful to you. :)

OK. Let me introduce the library for Ruby.
Using Camellia, http://camellia.sourceforge.net/examples.html, you can label the area with the most prominent color.

Not sure if this is what you mean, but the Python PIL has im.histogram() and im.getcolors() functions. http://effbot.org/imagingbook/image.htm