Am 04.07.15 um 03:17 schrieb telmo bacile:
Hi list, I found a code that calculates entropy of images with
python that can be used for classifying interesting images from
uninteresting ones. Interesting images has more structured patterns
while uninsteresting are more noisy or completely homogeneous.
I was thinking this code (entropy of image) can be used for measuring
the level of disorder of a group of points in the image.
For example:
Imagine that we have 3 images, each image has 6 dots, the first one
has very ordered dots , the second one have dots a little bit
disordered and the third one has very dissordered dots.
Then entropy of each image should measure the level of
dissorganization of the dots.
Your question is not a Python question, it is about image processing.
Therefore x-post and fup to comp.dsp. Since you seem to be using the
mailing list, you can access comp.dsp via Google Groups:
https://groups.google.com/forum/#!forum/comp.dsp
But the wierd thing is that when i experimented with this i got resuts
without sense.
The result i get is that the image with intermedium dissorder has
less entropy that the very ordered image . Do anybody have an idea
why im getting this result?
Your model is too simple. Entropy by itself is not defined on bytes, but
on a stream of symbols. Your program simply takes each pixel as a
symbol. Therefore, the order is not considered at all! You could
randomly shuffle all pixels, and still get the exact same result. Only
the relative frequencies of the color is respected. In order to improve
on that, and to take into account the distribution you would need to
build a model. For example, you could predict each pixel value from the
previous line and then send it to the encoder resp. the entropy
calculation. This is what PNG does, for instance. As a rough estimate,
you could compress your image using PNG and compare the file size.
Maybe im misunderstanding something. Is it possible to use entropy of
image to measure the level of dissorganization of a group of points in
an image? If not , is there is another method for doing this?
In principle yes....
Christian
thanks in advance
T.
here is the code:
import math
from PIL import Image
imageFile = 'int2.jpg'
print imageFile
im = Image.open(imageFile)
rgbHistogram = im.histogram()
print 'Snannon Entropy for Red, Green, Blue:'
for rgb in range(3):
totalPixels = sum(rgbHistogram[rgb * 256 : (rgb + 1) * 256])
ent = 0.0
for col in range(rgb * 256, (rgb + 1) * 256):
freq = float(rgbHistogram[col]) / totalPixels
if freq > 0:
ent = ent + freq * math.log(freq, 2)
ent = -ent
print ent
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