我想应用 Gabor 滤波器从图像中提取特征,然后在训练数据上应用 NN 或 SVM。虽然我没有应用批处理,但它会完成,或者如果你可以帮助我进行机器学习这对我来说很棒。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()
最佳答案
我可以通过改变theta,lamda(即频率和方向)等参数来定义更多的内核。我可以生成Gabor滤波器组,然后我将应用各种机器学习算法进行分类。
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)
关于python - 如何使用Gabor滤波器从图像中提取特征?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/51277949/