python - Gabor 特征提取和 SVM
问题描述
我想从 Gabor 过滤后的图像中提取 Gabor 特征,然后想应用 SVM 进行分类。我的最终目标是我想分离文本和图形。我想使用 Gabor 特征向量,例如 Local Energy、Mean、Amplitude 或 Phase Amplitude ,方差等。虽然在这里我使用 Haralick 特征进行分类。基本上“clf_svm.fit(train_features,train_labels)”显示错误。这是我的代码:
import cv2
import os
import glob
import numpy as np
from skimage import io
from sklearn.svm import LinearSVC
import mahotas as mt
img_dir = "C://Users//USER//Pictures//Saved Pictures//testing"
data_path = os.path.join(img_dir,'*g')
files = glob.glob(data_path)
data = []
num=0
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()
path = "H://summerinternship//train"
path=os.path.normpath(path)
path=os.path.join(path,'pic'+str(num)+'.png')
print (path)
cv2.imwrite(path,res1)
num=num+1
def extract_features(image):
# calculate haralick texture features for 4 types of adjacency
textures = mt.features.haralick(image)
# take the mean of it and return it
ht_mean = textures.mean(axis=0)
return ht_mean
train_path = "H://summerinternship//train"
train_names = os.listdir(train_path)
train_features = [[]]
train_labels = [[]]
i = 1
print ("[STATUS] Started extracting haralick textures..")
for train_name in train_names:
cur_path = train_path + "/" + train_name
cur_label = train_name
i = 1
for file in glob.glob(cur_path + "/*.png"):
print ("Processing Image - {} in {}".format(i, cur_label))
# read the training image
image = cv2.imread(file)
# convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# extract haralick texture from the image
features = extract_features(gray)
# append the feature vector and label
train_features.append(features)
train_labels.append(cur_label)
# show loop update
i += 1
# have a look at the size of our feature vector and labels
print ("Training features: {}".format(np.array(train_features).shape))
print ("Training labels: {}".format(np.array(train_labels).shape))
# create the classifier
print ("[STATUS] Creating the classifier..")
clf_svm = LinearSVC(random_state=9)
# fit the training data and labels
print ("[STATUS] Fitting data/label to model..")
clf_svm.fit(train_features, train_labels)
# loop over the test images
test_path = "H://summerinternship//test"
for file in glob.glob(test_path + "/*.png"):
# read the input image
image = cv2.imread(file)
# convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# extract haralick texture from the image
features = extract_features(gray)
# evaluate the model and predict label
prediction = clf_svm.predict(features.reshape(1, -1))[0]
# show the label
cv2.putText(image, prediction, (20,30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,255), 3)
print ("Prediction - {}".format(prediction))
# display the output image
cv2.imshow("Test_Image", image)
cv2.waitKey(0)
但我有这样的错误:
ValueError: Found array with 0 feature(s) (shape=(1, 0)) while a minimum of 1 is required.
解决方案
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