Does anyone have a relatively fast gaussian blur implemented in pure
python? Below is my attempt but it takes 2.9 seconds for a 320x240
image. Image comes from byte string: self.array =
array.array('B',srcstring). Would some sort of matrix multiplication
be faster? I don't have experience in that.I don't want to use PIL or http://filters.sourceforge.net/ to avoid the 300kb dll just for a simple blur. def blur(self): # Convolution Kernels blurFilter = [[0,1,2,1,0], [1,2,4,2,1], [2,4,8,4,2], [1,2,4,2,1], [0,1,2,1,0]] blurDiv = 48 blurFilterSize = len(blurFilter) #5 blurFilterSize2 = blurFilterSize * blurFilterSize pixels = self.array stride = self.width * 4 w = self.width h = self.height red = 0 green = 0 blue = 0 sx = 0 ex = 0 sy = 0 ey = 0 ss = 0 f1 = 0 f2 = 0 ff = 0 idx = 0 samplesize = 0 offset1 = blurFilterSize/2 offset2 = blurFilterSize/2+2 clr = array.array('B',[255,255,255,255]) px = array.array('B',[255,255,255,255]) for x in xrange(0,w): #w sx = x - offset1 ex = x + offset2 if sx < 0: sx = x if ex > w: ex = w for y in xrange(0,h): sy = y - offset1 ey = y + offset2 if sy < 0: sy = y if ey > h: ey = h samplesize = (ex-sx) * (ey-sy) if samplesize > 0: ss = blurFilterSize2 / samplesize if ss > blurFilterSize - 2: ss = blurFilterSize - 2 else: ss = 0 idx = 0 ffx = 0 red = 0 green = 0 blue = 0 for vx in xrange(sx,ex): for vy in xrange(sy,ey): offset = vy * stride + vx * 4 px = pixels[offset:offset +4] f1 = idx / 5 f2 = (idx - f1) % 5 f1 += ss if f1 > 4: f1 = 4 ff = blurFilter[f1] [f2] #ff = 1 ffx += ff red += px[0] * ff green += px[1] * ff blue += px[2] * ff idx += 1 if samplesize > 0: red = red / ffx green = green / ffx blue = blue / ffx if red > 255: red = 255 if green > 255: green = 255 if blue > 255: blue = 255 clr[0] = red clr[1] = green clr[2] = blue self.setPixel(x, y, clr) -- http://mail.python.org/mailman/listinfo/python-list
