Gabor滤波器 pytorch实现

Gabor函数与人眼的生物作用相仿，尤其对于图片的方向和尺度特征可以很好的捕捉。所以，经常用于纹理、物体识别。对于Gabor的原理、公式不再赘述。
下面对Gabor滤波器用pytorch实现，并进行可视化：

import math
import torch
import numpy as np
from matplotlib import pyplot as plt

def getGaborFilterBank(nScale, M, h, w):#nscale表示波长，M表示方向，h表示图片的长，w表示图片的宽
    Kmax = math.pi / 2
    f = math.sqrt(2)
    sigma = math.pi
    sqsigma = sigma ** 2
    postmean = math.exp(-sqsigma / 2)
    if h != 1:
        gfilter_real = torch.zeros(M, h, w)
        for i in range(M):
            theta = i / M * math.pi
            k = Kmax / f ** (nScale - 1)
            xymax = -1e309
            xymin = 1e309
            for y in range(h):
                for x in range(w):
                    y1 = y + 1 - ((h + 1) / 2)
                    x1 = x + 1 - ((w + 1) / 2)
                    tmp1 = math.exp(-(k * k * (x1 * x1 + y1 * y1) / (2 * sqsigma)))
                    #tmp2 = math.cos(k * math.cos(theta) * x1 + k * math.sin(theta) * y1) - postmean # For real part
                    tmp2 = math.sin(k * math.cos(theta) * x1 + k * math.sin(theta) * y1) # For imaginary part
                    gfilter_real[i][y][x] = k * k * tmp1 * tmp2 / sqsigma			
                    xymax = max(xymax, gfilter_real[i][y][x])
                    xymin = min(xymin, gfilter_real[i][y][x])
            gfilter_real[i] = (gfilter_real[i] - xymin) / (xymax - xymin)
    else:
        gfilter_real = torch.ones(M, h, w)
    return gfilter_real

#可视化
def plots(ims, interp=False, titles=None):
    ims=np.array(ims)
    mn,mx=ims.min(),ims.max()
    f = plt.figure(figsize=(12,24))
    for i in range(len(ims)):
        sp=f.add_subplot(1, len(ims), i+1)
        if not titles is None: sp.set_title(titles[i], fontsize=18)
        plt.imshow(ims[i], interpolation=None if interp else 'none', vmin=mn,vmax=mx)

G=getGaborFilterBank(4,4,32,32)
plots(G)

结果如图所示：