What are some methods to analyze image brightness using Python?

Using the techniques mentioned in the question, I came up with a few different versions.

Each method returns a value close, but not exactly the same as the others. Also, all methods run about the same speed except for the last one, which is much slower depending on the image size.

1. Convert image to greyscale, return average pixel brightness.

   def brightness( im_file ):
      im = Image.open(im_file).convert('L')
      stat = ImageStat.Stat(im)
      return stat.mean[0]
   

2. Convert image to greyscale, return RMS pixel brightness.

   def brightness( im_file ):
      im = Image.open(im_file).convert('L')
      stat = ImageStat.Stat(im)
      return stat.rms[0]
   

3. Average pixels, then transform to “perceived brightness”.

   def brightness( im_file ):
      im = Image.open(im_file)
      stat = ImageStat.Stat(im)
      r,g,b = stat.mean
      return math.sqrt(0.241*(r**2) + 0.691*(g**2) + 0.068*(b**2))
   

4. RMS of pixels, then transform to “perceived brightness”.

   def brightness( im_file ):
      im = Image.open(im_file)
      stat = ImageStat.Stat(im)
      r,g,b = stat.rms
      return math.sqrt(0.241*(r**2) + 0.691*(g**2) + 0.068*(b**2))
   

5. Calculate “perceived brightness” of pixels, then return average.

   def brightness( im_file ):
      im = Image.open(im_file)
      stat = ImageStat.Stat(im)
      gs = (math.sqrt(0.241*(r**2) + 0.691*(g**2) + 0.068*(b**2)) 
            for r,g,b in im.getdata())
      return sum(gs)/stat.count[0]
   

Update Test Results
I ran a simulation against 200 images. I found that methods #2, #4 gave almost identical results. Also methods #3, #5 were also nearly identical. Method #1 closely followed #3, #5 (with a few exceptions).