convolution

I'm using zero padding around my image and convolution kernel, converting them to the Fourier domain, and inverting them back to get the convolved image, see code below. The result, however, is wrong. I was expecting a blurred image, but the output is four shifted quarters. Why is the output wrong, and how can I fix the code? Input image: Result of convolution: from PIL import Image,ImageDraw,ImageOps,ImageFilter import numpy as np from scipy import fftpack from copy import deepcopy import imageio ## STEP 1 ## im1=Image.open("pika.jpeg") im1=ImageOps.grayscale(im1) im1.show() print("s",im1

I have been trying to run the code below which I got from here and even though I have changed almost nothing other than the image size (350,350 instead of 150, 150) is still cannot get it to work. I am getting the above filter error (in title) which I do comprehend but I am not doing it wrong so I don't understand this. It basically says that I cannot have more nodes than inputs, correct? I was able to eventually hack my way to a solution by changing this line: model.add(Convolution2D(32, 5, 5, border_mode='valid', input_shape=(3, IMG_WIDTH, IMG_HEIGHT))) with this: model.add(Convolution2D(32,

I am new to Android Renderscript. I need to write a convolution multiplication in RenderScript since the final application is going to run on Android. Data stream is going to be an image. More specifically, I am not able to write the core logic using forEach functionality, though I can do it in Java, but speed it too slow! Please help! Steve During the rsForEach call (or other Renderscript function), you can access the neighbouring pixels of the original image (or whatever type of data you are using) by binding the original image allocation to a pointer within the Renderscript where it can

I'd like to improve the performance of convolution using python, and was hoping for some insight on how to best go about improving performance. I am currently using scipy to perform the convolution, using code somewhat like the snippet below: import numpy import scipy import scipy.signal import timeit a=numpy.array ( [ range(1000000) ] ) a.reshape(1000,1000) filt=numpy.array( [ [ 1, 1, 1 ], [1, -8, 1], [1,1,1] ] ) def convolve(): global a, filt scipy.signal.convolve2d ( a, filt, mode="same" ) t=timeit.Timer("convolve()", "from __main__ import convolve") print "%.2f sec/pass" % (10 * t.timeit