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This is a class of Bag-of-Features for OpenCV
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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