addendum Re: working with images (PIL ?)

[Poppy]

Thank you and the other responders have given me something to consider, I 
understand the concept of the posterize idea and will be experimenting with 
that.

I wanted to respond to this statement below which is true, however I believe 
the histogram sums the values so both colors would be in bin 229.  I say 
that because all white is in histogram element 767, while black is in 
element 0. Anyone on this list know how to interpret the histogram list? 
Your point is still valid regardless of my interpretation.

> So, for example, colours (2, 27, 200) and (200, 27, 2) are both in
> the bin for G=27. But they are very different colours.

I will be checking out the SIG for PIL thanks for that pointer.



"Ken Starks" <straton at lampsacos.demon.co.uk> wrote in message 
news:g0sbns$a5i$1$8300dec7 at news.demon.co.uk...
>I would still be concerned that you are checking against the percentage
> of the 768 bins returned by the histogram method. Two pixels of
> widely different colour end up in the same bin, so long as just ONE
> of the Red, Green, or Blue components is equal.
>
> So, for example, colours (2, 27, 200) and (200, 27, 2) are both in
> the bin for G=27. But they are very different colours.
>
> There are actualy 256 * 256 * 256 colours, but I don't suppose
> you want that many bins!
>
> What you want is a much smaller number of bins, with pixels
> of 'close' colours (whatever that means) put into the same bin.
>
> What 'close' means for colours, is quite a difficult thing, and
> the consensus is that using the three RGB coordinates is not
> as good as certain other colour spaces.
>
> You could use the ImageOps.posterize method to reduce the number of 
> colours in the image, but whether 'close' colours end up together,
> I don't know.
>
> You might try the PIL special interest group (SIG) 'image-sig'
>
> http://mail.python.org/mailman/listinfo/image-sig
>
> (If you want to know exactly how many unique colours an image actually
> has, load the image into the 'GIMP' assuming you have it,
> and go to :
>
> Menubar --> Filters --> Colours --> Colourcube analysis...
>
> )
>
>
>
>
>
>
>
>
>
>
> Poppy wrote:
>> Thanks, since posting I  figured out how to interpret the histogram 
>> results, which seems to be the consensus in responses. I wrote a check 
>> image program and have been periodically calling it against a folder 
>> where I make a copy of our images used for production. My method right 
>> now is to check what we send for errors, but is not preventive.
>>
>> Also I determined whitespace is not the only issue, any color that 
>> dominates. I'm considering rewriting this code below to setup bins, so if 
>> combined neighboring colors exceeds the threshold then reject the image. 
>> I have examples where half the image appears black, but actually varies 
>> throughout.
>>
>> Since my image is RGB I'm looping through a 768 element list.
>>
>> Zach-
>>
>> import Image, os
>>
>>
>> def check_image(file):
>>
>>     try:
>>         im = Image.open(file)
>>     except:
>>         return "Can't open file %s " % file
>>
>>     imData = im.histogram()
>>     i = 0
>>     for ea in imData:
>>         if ea > ((im.size[0] * im.size[1]) / 4): ## 25% of image size
>>             return "bad image %s - %s element num is %s " % (file, ea, 
>> str(i))
>>         i = i + 1
>>
>>     return "good image %s, image size is %s" % (file, im.size)
>>
>>
>> def main(dir):
>>     data = ""
>>     try:
>>         files = os.listdir(dir)
>>         for ea in files:
>>             data = data + str(check_image(os.path.join(dir,ea))) + "\n"
>>     except:
>>         return "Can't get files in %s" % dir
>>     return data
>>
>> print main("\\\\host\\path\\to\\image_folder\\")
>>