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This is a class of Bag-of-Features for OpenCV
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import cv2 | |
import numpy as np | |
class BagOfFeatures: | |
"""This is a class of Bag-of-Features by K-means for OpenCV""" | |
codebookSize=0 | |
classifier=None | |
def __init__(self, codebookSize): | |
self.codebookSize=codebookSize | |
self.classifier=cv2.KNearest() | |
def train(self,features,iterMax=100,term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )): | |
retval, bestLabels, codebook=cv2.kmeans(features,self.codebookSize,term_crit,iterMax,cv2.KMEANS_RANDOM_CENTERS) | |
self.classifier.train(codebook,np.array(range(self.codebookSize))) | |
def makeHistogram(self, feature): | |
histogram=np.zeros(self.codebookSize) | |
if self.classifier==None : | |
raise Exception("You need train this instance.") | |
retval, results, neighborResponses, dists=self.classifier.find_nearest(feature,1) | |
for idx in results: | |
idx=int(idx) | |
histogram[idx]=histogram[idx]+1 | |
histogram=cv2.normalize(histogram,norm_type=cv2.NORM_L2) | |
#transpose | |
histogram=np.reshape(histogram,(1,-1)) | |
return histogram | |
#see also: http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GMM.html | |
import numpy as np | |
from sklearn import mixture,preprocessing | |
class BagOfFeaturesGMM: | |
"""This is a class of Bag-of-Features by GMM """ | |
codebookSize=0 | |
classifier=None | |
def __init__(self, codebookSize): | |
self.codebookSize=codebookSize | |
def train(self,features,iterMax=100): | |
# construct a GMM classifier | |
gmm = mixture.GMM(n_components=self.codebookSize,n_iter=iterMax) | |
# train the classifier | |
self.classifier = gmm.fit(features) | |
def makeHistogram(self, feature): | |
histogram=np.zeros(self.codebookSize) | |
if self.classifier==None : | |
raise Exception("You need train this instance.") | |
results=self.classifier.predict(feature) | |
for idx in results: | |
idx=int(idx) | |
histogram[idx]=histogram[idx]+1 | |
histogram=preprocessing.normalize([histogram], norm='l2')[0] | |
return histogram |
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