通过使用scipy / numpy，在Python中进行图像处理的高通滤波器

scipy.ndimage

scipy.filter

import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage
import Image

def 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 filter
kernel = 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 - lowpass
plot(gauss_highpass, r\'Gaussian Highpass, $\\sigma = 3 pixels$\')

plt.show()

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

from skimage.io import imread
import matplotlib.pyplot as plt
import scipy.fftpack as fp

im = np.mean(imread(\'../images/lena.jpg\'), axis=2) # assuming an RGB image
plt.figure(figsize=(10,10))
plt.imshow(im, cmap=plt.cm.gray)
plt.axis(\'off\')
plt.show()

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()

(w, h) = im.shape
half_w, half_h = int(w/2), int(h/2)

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

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

import numpy as np
import cv2
from scipy import ndimage

class HPF(object):
    def __init__(self, kernel, image):
        self.kernel = np.array(kernel)
        self.image = image

    def process(self):
        return ndimage.convolve(self.image, self.kernel)


if __name__ == \"__main__\":
    #enter ur image location
    image = cv2.imread(\"images/test2.jpg\", 0)
    kernel3x3 = [[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]
    kernel5x5 = [[-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]]

    hpf1 = HPF(kernel3x3, image)
    hpfimage1 = hpf1.process()
    hpf2 = HPF(kernel5x5, image)
    hpfimage2 = hpf2.process()
    cv2.imshow(\"3x3\",hpfimage1)
    cv2.imshow(\"5x5\",hpfimage2)
    cv2.waitKey()
    cv2.destroyAllWindows()

blurred = cv2.GaussianBlur(image, (11, 11), 0)

g_hpf = image - blurred