写一下工件gabor特征提取的Python代码

以下是用Python实现的工件Gabor特征提取代码：

import numpy as np
import cv2

def get_gabor_kernel(ksize, theta, sigma, lambd, gamma):
    """
    function to generate Gabor kernel
    """
    sigma_x = sigma
    sigma_y = float(sigma) / gamma

    # Bounding box
    nstds = 3  # Number of standard deviation sigma
    xmax = max(abs(nstds * sigma_x * np.cos(theta)), abs(nstds * sigma_y * np.sin(theta)))
    xmax = np.ceil(max(1, xmax))
    ymax = max(abs(nstds * sigma_x * np.sin(theta)), abs(nstds * sigma_y * np.cos(theta)))
    ymax = np.ceil(max(1, ymax))
    xmin = -xmax
    ymin = -ymax
    (y, x) = np.meshgrid(np.arange(ymin, ymax + 1), np.arange(xmin, xmax + 1))

    # Rotation
    x_theta = x * np.cos(theta) + y * np.sin(theta)
    y_theta = - x * np.sin(theta) + y * np.cos(theta)

    # Gaussian component
    gb = np.exp(-0.5 * (x_theta ** 2 / sigma_x ** 2 + y_theta ** 2 / sigma_y ** 2))

    # Sinusoidal component
    gb *= np.cos(2 * np.pi / lambd * x_theta)

    return gb

def get_gabor_features(img, ksize, thetas, sigmas, lambds, gammas):
    """
    function to extract Gabor features from an image
    """
    features = []
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    for theta in thetas:
        for sigma in sigmas:
            for lambd in lambds:
                for gamma in gammas:
                    kernel = get_gabor_kernel(ksize, theta, sigma, lambd, gamma)
                    filtered = cv2.filter2D(gray_img, cv2.CV_8UC3, kernel)
                    features.append(filtered)
    return features

其中，`ksize`是Gabor核的大小，`thetas`是角度值，`sigmas`是Gabor核的尺度，`lambds`是Gabor函数的波长，`gammas`是Gabor核的椭圆率系数（aspect ratio）。这段代码会对输入图像进行Gabor滤波，并返回滤波后的特征。