Opencv 使用Gabor滤波器进行边缘检测

将imori.jpg灰度化之后，分别使用A=0,45,90,135的Gabor滤波器进行滤波。其它参数取为：K=111，\sigma=10，\gamma = 1.2，\lambda =10，p=0。

如在下面python实现看到的那样， Gabor滤波器提取了指定的方向上的边缘。因此，Gabor滤波器在边缘特征提取方面非常出色。

一般认为 Gabor 滤波器接近生物大脑视皮层中的初级简单细胞（V1 区）。也就是说，当生物看见眼前的图像时也进行了特征提取。

一般认为深度学习的卷积层接近 Gabor 滤波器的功能。然而，在深度学习中，滤波器的系数通过机器学习自动确定。作为机器学习的结果，据说将发生类似于Gabor滤波器的过程。

输入 (imori.jpg)	输出(answers/answer_79.png)

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):
    # 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)

    # 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)

        plt.subplot(1, 4, i+1)
        plt.imshow(out, cmap='gray')
        plt.axis('off')
        plt.title("Angle "+str(A))

    plt.savefig("out.png")
    plt.show()

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

# gabor process
Gabor_process(img)