Good day everyone,
I would like to have your suggestions about how it could be possible to improve
process speed of my following script. The aim of the script is to scan each
pixel of an image, and to look its validity with its neighborhood. I use a
function that return the values of mask of 9x9 pixels around the scanned pixel
and calculate the interval confidence of it. If the pixel is valid with the 9x9
mask its stay unchanged, if it is outside the interval confidence, the pixel
receive the mean of the mask.
The problem is that this pixel by pixel processing is really slow. It could
take many hours for image not bigger than 4-5 Mo. Anybody knows how I could
increase the performance of such a script? I use PIL and numpy.
Thank youMaxime
Demers--------------------------------------------------------------------------------------------------------------------------
import PILimport ImageOpsimport Imageimport numpyfrom OuvrirImg import *
#function that open an imagefrom CreateSide import *
from RebuildImg import *from IntervalConfiance import *
def CreateMask(dimensions,image,i,j): 'create a mask of N dimensions at the
position i,j of a selected image and return the values of DN of each pixel in
the mask in a list' img = numpy.array(image) dim = dimensions/2 cpt =
1 mask = [] y = j x = i while cpt <= dimensions: dim2 =
dimensions/2 cpt2 = 1 while cptr2 <= dimensions:
mask.append(img[x-dim][y-dim2]) dim2 = dim2-1 cpt2 = cpt2
+ 1 dim = dim-1 cpt = cpt + 1 return mask
#This script scan each pixel of an image with a N dimension mask#If the the
scanned pixel is between the interval confidence of the pixels in the mask#that
pixel stay unchanged, if its outside the interval confidence, the pixel take
the mean#of the pixels in the mask
test=OuvrirImg('f:\\mapaq_petit.tif')img=CreateSide(9,test)
#create an image with side for a 9x9
maskNewImage=[0]*(img.shape[1]-8)*(img.shape[0]-8)h=0for i in
range(4,img.shape[0]-4): for j in range(4,img.shape[1]-4): mask2 =
CreateMask(9,img,i,j) #return a list of DN of the 9X9 mask at the
position i,j mean = IntervalConfiance(mask)[0] interval =
IntervalConfiance(mask)[1] if img[i][j] >= mean+interval or img[i][j] <=
mean-interval: NewImage[h] = mean else:
NewImage[h]=img[i][j] h = h+1
print "process completed, the new image is
created"Dilate=numpy.array(Dilate)Dilate=numpy.reshape(Dilate,(img.shape[0]-8,img.shape[1]-8))RebuildImg(Dilate,"f:\\newImage.tif")
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