python实现Gabor滤波器

article/2025/9/15 16:19:11

Gabor 函数表示

复数表示：

Gabor函数复数表示

实数部分：

Gabor函数实数部分

虚数部分：

Gabor函数虚数部分

其中：

Gabor函数 x' 、y' 计算公式

代码中参数和Gabor函数参数对应关系

代码实现了Gabor滤波器的实数部分。代码中参数和Gabor函数实数部分参数对应如下：
python实现Gabor的核心代码

对应

Gabor实数部分
看上去是不是感觉很容易实现Gabor滤波器，那我来实现一下，并使用Gabor滤波器来提取图像特征。

import cv2
import numpy as np
import matplotlib.pyplot as plt# Grayscale
def BGR2GRAY(img):# Grayscalegray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]return gray# Gabor Filter
def Gabor_filter(K_size=111, Sigma=10, Gamma=1.2, Lambda=10, Psi=0, angle=0):# get half sized = K_size // 2# prepare kernelgabor = np.zeros((K_size, K_size), dtype=np.float32)# each valuefor y in range(K_size):for x in range(K_size):# distance from centerpx = x - dpy = y - d# degree -> radiantheta = angle / 180. * np.pi# get kernel x_x = np.cos(theta) * px + np.sin(theta) * py# get kernel y_y = -np.sin(theta) * px + np.cos(theta) * py# fill kernelgabor[y, x] = np.exp(-(_x**2 + Gamma**2 * _y**2) / (2 * Sigma**2)) * np.cos(2*np.pi*_x/Lambda + Psi)# kernel normalizationgabor /= np.sum(np.abs(gabor))return gabor# 使用Gabor滤波器作用于图像上
def Gabor_filtering(gray, K_size=111, Sigma=10, Gamma=1.2, Lambda=10, Psi=0, angle=0):# get shapeH, W = gray.shape# paddinggray = np.pad(gray, (K_size//2, K_size//2), 'edge')# prepare out imageout = np.zeros((H, W), dtype=np.float32)# get gabor filtergabor = Gabor_filter(K_size=K_size, Sigma=Sigma, Gamma=Gamma, Lambda=Lambda, Psi=0, angle=angle)# filteringfor y in range(H):for x in range(W):out[y, x] = np.sum(gray[y : y + K_size, x : x + K_size] * gabor)out = np.clip(out, 0, 255)out = out.astype(np.uint8)return out# 使用6个不同角度的Gabor滤波器对图像进行特征提取
def Gabor_process(img):# get shapeH, W, _ = img.shape# gray scalegray = BGR2GRAY(img).astype(np.float32)# define angle#As = [0, 45, 90, 135]As = [0,30,60,90,120,150]# prepare pyplotplt.subplots_adjust(left=0, right=1, top=1, bottom=0, hspace=0, wspace=0.2)out = np.zeros([H, W], dtype=np.float32)# each anglefor i, A in enumerate(As):# gabor filtering_out = Gabor_filtering(gray, K_size=11, Sigma=1.5, Gamma=1.2, Lambda=3, angle=A)# add gabor filtered imageout += _out# scale normalizationout = out / out.max() * 255out = out.astype(np.uint8)return out# Read image
img = cv2.imread("../paojie.jpg").astype(np.float32)# gabor process
out = Gabor_process(img)cv2.imwrite("out.jpg", out)
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.destroyAllWindows()