How to change the colour of pixels in an image depending on their initial luminosity?

There are many ways to do this, but the simplest and probably fastest is with Numpy, which you should get accustomed to using with image processing in Python:

from PIL import Image
import numpy as np

# Load image and ensure RGB, not palette image
im = Image.open('start.png').convert('RGB')

# Make into Numpy array
na = np.array(im)

# Make all pixels of "na" where the mean of the R,G,B channels is less than 50 into black (0)
na[np.mean(na, axis=-1)<50] = 0

# Convert back to PIL Image to save or display
result = Image.fromarray(na)
result.show()

That turns this:

Into this:

Another slightly different way would be to convert the image to a more conventional greyscale, rather than averaging for the luminosity:

# Load image and ensure RGB
im = Image.open('start.png').convert('RGB')

# Calculate greyscale version
grey = im.convert('L')

# Point process over pixels to make mask of darker ones
mask = grey.point(lambda p: 255 if p<50 else 0)

# Paste black (i.e. 0) into image where mask indicates it is dark
im.paste(0, mask=mask)

Notice that the blue channel is given considerably less significance in the ITU-R 601-2 luma transform that PIL uses (see the lower 114 weighting for Blue versus 299 for Red and 587 for Green) in the formula:

L = R * 299/1000 + G * 587/1000 + B * 114/1000

so the blue shades are considered darker and become black.

Another way would be to make a greyscale and a mask as above. but then choose the darker pixel at each location when comparing the original and the mask:

from PIL import Image, ImageChops
im = Image.open('start.png').convert('RGB')
grey = im.convert('L')
mask = grey.point(lambda p: 0 if p<50 else 255)
res = ImageChops.darker(im, mask.convert('RGB'))

That gives the same result as above.

Another way, pure PIL and probably closest to what you actually asked, would be to derive a luminosity value by averaging the channels:

# Load image and ensure RGB
im = Image.open('start.png').convert('RGB')

# Calculate greyscale version by averaging R,G and B
grey = im.convert('L', matrix=(0.333, 0.333, 0.333, 0))

# Point process over pixels to make mask of darker ones
mask = grey.point(lambda p: 255 if p<50 else 0)

# Paste black (i.e. 0) into image where mask indicates it is dark
im.paste(0, mask=mask)

Another approach could be to split the image into its constituent RGB channels, evaluate a mathematical function over the channels and mask with the result:

from PIL import Image, ImageMath

# Load image and ensure RGB
im = Image.open('start.png').convert('RGB')

# Split into RGB channels
(R, G, B) = im.split()

# Evaluate mathematical function over channels
dark = ImageMath.eval('(((R+G+B)/3) <= 50) * 255', R=R, G=G, B=B)

# Paste black (i.e. 0) into image where mask indicates it is dark
im.paste(0, mask=dark)

I created a function that returns a list with True if the pixel has a luminosity of less than a parameter, and False if it doesn't. It includes an RGB or RGBA option (True or False)

def get_avg_lum(pic,avg=50,RGBA=False):
  num=3
  numd=4
  if RGBA==False:
    num=2
    numd=3
  li=[[[0]for y in range(0,pic.size[1])] for x in range(0,pic.size[0])]
  for x in range(0,pic.size[0]):
    for y in range(0,pic.size[1]):
      if sum(pic.getpixel((x,y))[:num])/numd<avg:
        li[x][y]=True
      else:
        li[x][y]=False
  return(li)
a=get_avg_lum(im)

The pixels match in the list, so (0,10) on the image is [0][10] in the list.

Hopefully this helps. My module is for standard PIL objects.