High Pass Filter for image processing in python by using scipy/numpy

"High pass filter" is a very generic term. There are an infinite number of different "highpass filters" that do very different things (e.g. an edge dectection filter, as mentioned earlier, is technically a highpass (most are actually a bandpass) filter, but has a very different effect from what you probably had in mind.)

At any rate, based on most of the questions you've been asking, you should probably look into scipy.ndimage instead of scipy.filter, especially if you're going to be working with large images (ndimage can preform operations in-place, conserving memory).

As a basic example, showing a few different ways of doing things:

import matplotlib.pyplot as pltimport numpy as npfrom scipy import ndimageimport Imagedef plot(data, title):    plot.i += 1    plt.subplot(2,2,plot.i)    plt.imshow(data)    plt.gray()    plt.title(title)plot.i = 0# Load the data...im = Image.open('lena.png')data = np.array(im, dtype=float)plot(data, 'Original')# A very simple and very narrow highpass filterkernel = np.array([[-1, -1, -1],                   [-1,  8, -1],                   [-1, -1, -1]])highpass_3x3 = ndimage.convolve(data, kernel)plot(highpass_3x3, 'Simple 3x3 Highpass')# A slightly "wider", but sill very simple highpass filter kernel = np.array([[-1, -1, -1, -1, -1],                   [-1,  1,  2,  1, -1],                   [-1,  2,  4,  2, -1],                   [-1,  1,  2,  1, -1],                   [-1, -1, -1, -1, -1]])highpass_5x5 = ndimage.convolve(data, kernel)plot(highpass_5x5, 'Simple 5x5 Highpass')# Another way of making a highpass filter is to simply subtract a lowpass# filtered image from the original. Here, we'll use a simple gaussian filter# to "blur" (i.e. a lowpass filter) the original.lowpass = ndimage.gaussian_filter(data, 3)gauss_highpass = data - lowpassplot(gauss_highpass, r'Gaussian Highpass, $\sigma = 3 pixels$')plt.show()

Here is how we can design a HPF with scipy fftpack

from skimage.io import imreadimport matplotlib.pyplot as pltimport scipy.fftpack as fpim = np.mean(imread('../images/lena.jpg'), axis=2) # assuming an RGB imageplt.figure(figsize=(10,10))plt.imshow(im, cmap=plt.cm.gray)plt.axis('off')plt.show()

Original Image

F1 = fftpack.fft2((im).astype(float))F2 = fftpack.fftshift(F1)plt.figure(figsize=(10,10))plt.imshow( (20*np.log10( 0.1 + F2)).astype(int), cmap=plt.cm.gray)plt.show()

Frequency Spectrum with FFT

(w, h) = im.shapehalf_w, half_h = int(w/2), int(h/2)# high pass filtern = 25F2[half_w-n:half_w+n+1,half_h-n:half_h+n+1] = 0 # select all but the first 50x50 (low) frequenciesplt.figure(figsize=(10,10))plt.imshow( (20*np.log10( 0.1 + F2)).astype(int))plt.show()

Block low Frequencies in the Spectrum

im1 = fp.ifft2(fftpack.ifftshift(F2)).realplt.figure(figsize=(10,10))plt.imshow(im1, cmap='gray')plt.axis('off')plt.show()

Output Image after applying the HPF

One simple high-pass filter is:

-1 -1 -1-1  8 -1-1 -1 -1

The Sobel operator is another simple example.

In image processing these sorts of filters are often called "edge-detectors" - the Wikipedia page was OK on this last time I checked.