Converting a numpy array to grayscale

[Johannes Schönberger]

Hi,

Could you provide us with both the grayscale and RGB version of your image?

Johannes Schönberger

Am 17.05.2013 um 19:15 schrieb Brickle Macho <bricklemacho at gmail.com>:

> I porting a 8 line Matlab script.    Basically I read in am image, down sample the image, perform some FFT operations and output a final smoothed image. I am porting this to a standalone function.   The problem is that I am getting different from my python script to what Matlab was outputs
> 
> After comparing the contents of the array/matrix in python/matlab I noticed the values were different after the image was converted to grayscale.  That is, when the image is read in, the values are the same, once converted they begin to differ around the first/second decimal place.
> 
> If I read in the file using ndimage.imread( flatten=true) then I get the same value as matlab correct to 5 decimal places, whereas using color.rgb2gray() is only correct to 2 decimal places.  In the final version of the code I will only have access to the numpy array after it has been loaded into memory, so using imread() was just for tracing/locating/identifying the problem.  Here is a snippet of code:
> 
> >>> gray = ndimage.imread('1.jpg',flatten=True)
> >>> gray /= gray.max()
> >>> gray
> array([[ 0.30133331,  0.2895686 ,  0.28172547, ...
> ...
> 
> >>> gray2 = color.rgb2gray(rgb)
> >>> gray2
> array([[ 0.31065608,  0.29889137,  0.29104824, ...,
> ...
> 
> I believe this difference is causing the problem.  Note if I convert the image to grayscale using a external tool and read this in then the values of the numpy array match similar/same to Matlab matrix.
> 
> So what is the difference between converting an array to gray scale verse reading it in as grayscale?  Have I done something wrong?  Is there another way to convert a numpy array to grayscale?
> 
> Any help appreciated.
> 
> Michael.
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