Opencv 使用Gabor滤波器进行特征提取

通过将上节使用Gabor滤波器进行边缘检测 中得到的4张图像加在一起，提取图像的特征。

观察得到的结果，图像的轮廓部分是白色的，获得了类似于边缘检测的输出。

深度学习中的卷积神经网络，最初已经具有提取图像的特征的功能，在不断重复特征提取的计算过程中，自动提取图像的特征。

输入 (imori.jpg)	输出

python实现：

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Grayscale
def BGR2GRAY(img):
    # Grayscale
    gray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]
    return gray

# Gabor
def Gabor_filter(K_size=111, Sigma=10, Gamma=1.2, Lambda=10, Psi=0, angle=0):
    # get half size
    d = K_size // 2

    # prepare kernel
    gabor = np.zeros((K_size, K_size), dtype=np.float32)

    # each value
    for y in range(K_size):
        for x in range(K_size):
            # distance from center
            px = x - d
            py = y - d

            # degree -> radian
            theta = 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 kernel
            gabor[y, x] = np.exp(-(_x**2 + Gamma**2 * _y**2) / (2 * Sigma**2)) * np.cos(2*np.pi*_x/Lambda + Psi)

    # kernel normalization
    gabor /= np.sum(np.abs(gabor))

    return gabor


def Gabor_filtering(gray, K_size=111, Sigma=10, Gamma=1.2, Lambda=10, Psi=0, angle=0):
    # get shape
    H, W = gray.shape

    # padding
    gray = np.pad(gray, (K_size//2, K_size//2), 'edge')

    # prepare out image
    out = np.zeros((H, W), dtype=np.float32)

    # get gabor filter
    gabor = Gabor_filter(K_size=K_size, Sigma=Sigma, Gamma=Gamma, Lambda=Lambda, Psi=0, angle=angle)

    # filtering
    for 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


def Gabor_process(img):
    # get shape
    H, W, _ = img.shape

    # gray scale
    gray = BGR2GRAY(img).astype(np.float32)

    # define angle
    As = [0, 45, 90, 135]

    # prepare pyplot
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0, hspace=0, wspace=0.2)

    out = np.zeros([H, W], dtype=np.float32)

    # each angle
    for 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 image
        out += _out

    # scale normalization
    out = out / out.max() * 255
    out = out.astype(np.uint8)

    return out


# Read image
img = cv2.imread("imori.jpg").astype(np.float32)

# gabor process
out = Gabor_process(img)


cv2.imwrite("out.jpg", out)
cv2.imshow("result", out)
cv2.waitKey(0)