cp wrote:
image_array.reshape(-1,3).mean(axis=0)
what that does is reshape (without copying) the array into WxHx3 array
I have just one question considering importing the PIL image with asarray.
Suppose my initial image is an RGB image with 1600 pixels height and 1900 width.
img.size
(1900,1600)
arr=asarray(img)
arr.shape
(1600,1900,3)
In numpy syntax I would expect the last one to be (3,1900,1600). Now the
returned array seems to have 1600 layers and not one for each color channel,
leading to the reshape function you propose. Any idea why is that?
it looks like you've followed up on the lumpy list, but:
I misspoke a bit above:
image_array.reshape(-1,3)
means: "make this a 2-d array, making the first dimension whatever it
needs to be so that the second dimension is three". In this case, that
flattens out the width and height, while keeping the color separate,
which I think is what you wanted -- the mean of each of red, green and blue.
from the reshape doc string:
reshaped_array : disarrays
This will be a new view object if possible; otherwise, it will
be a copy.
as your array was created from a PEEL image, it may need to make a copy
to do this -- I'm not sure.
You might try the slice method I proposed -- if it can prevent a data
copy, the extra loop may be worth it.
IIUC, PIL and numpy don't share exactly the same data model, so you may
have to make a memory copy to go from one to the other -- that may the
source of your performance decrease.
If you really want to know, you could profile the code doing one step at
a time (not the mean, for instance) to see where the time is going.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
chris.bar...@noaa.gov
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