Question

我想应用Gabor过滤器从图像中提取特征，然后在训练后的数据上应用NN或SVM。虽然我没有应用批处理，但是可以做到这一点，或者如果您可以帮助我进行机器学习部分对我来说太好了。谢谢。这是我的代码：

import cv2
import numpy as np
import glob

img=glob.glob("C://Users//USER//Pictures//Saved Pictures//tuhin.jpg")

img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]  
ret, labels = cv2.connectedComponents(img)
label_hue = np.uint8(179*labels/np.max(labels))
blank_ch = 255*np.ones_like(label_hue)
labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])
labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)
labeled_img[label_hue==0] = 0
cv2.imshow('labeled.png', labeled_img)
cv2.waitKey()

def build_filters():
filters = []
ksize = 31
for theta in np.arange(0, np.pi, np.pi / 16):
    kern = cv2.getGaborKernel((ksize, ksize), 4.0, theta, 10.0, 0.5, 0, 
              ktype=cv2.CV_32F)
    kern /= 1.5*kern.sum()
    filters.append(kern)
    return filters

def process(img, filters):
    accum = np.zeros_like(img)
    for kern in filters:
        fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)
        np.maximum(accum, fimg, accum)
        return accum

filters=build_filters()
res1=process(img,filters)
cv2.imshow('result',res1)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

这是有关使用gabor过滤器和scikit-image：http://scikit-image.org/docs/0.11.x/auto_examples/plot_gabor.html进行纹理提取的不错的教程。您可能想看看它。

您可能想使用深度学习/转移学习（取决于您拥有的数据量）来自动提取功能，而不是手工制作的功能。

Answer 2

仅通过更改频率，方向等theta，lamda等参数即可定义更多内核。生成Gabor滤波器组，然后将各种机器学习算法应用于分类。

批处理后的

代码：

import cv2
import os
import glob
import numpy as np


img_dir = "C://Users//USER//Pictures//Saved Pictures" 
data_path = os.path.join(img_dir,'*g')
files = glob.glob(data_path)
data = []
for f1 in files:
    img = cv2.imread(f1,0)
    data.append(img)
    img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]  
    ret, labels = cv2.connectedComponents(img)
    label_hue = np.uint8(179*labels/np.max(labels))
    blank_ch = 255*np.ones_like(label_hue)
    labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])
    labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)
    labeled_img[label_hue==0] = 0
    cv2.imshow('labeled.png', labeled_img)
    cv2.waitKey()

    def build_filters():
        filters = []
        ksize = 31
        for theta in np.arange(0, np.pi, np.pi / 16):
            kern = cv2.getGaborKernel((ksize, ksize), 4.0, theta, 10.0, 0.5, 0, ktype=cv2.CV_32F)
            kern /= 1.5*kern.sum()
            filters.append(kern)
            return filters

    def process(img, filters):
        accum = np.zeros_like(img)
        for kern in filters:
            fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)
            np.maximum(accum, fimg, accum)
            return accum

    filters=build_filters()
    res1=process(img,filters)
    cv2.imshow('result',res1)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imwrite("checking.tif",res1)

如何使用Gabor滤镜从图像中提取特征？

2 个答案: