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this should not be nessecary, since BOW is wrapped to python in 2.4.9.

import cv2
import numpy as np

#
# based on :
#  http://gilscvblog.wordpress.com/2013/08/23/bag-of-words-models-for-visual-categorization/
#
# for this sample, we'll use the left/right checkerboard shots from samples/cpp.
#  admittedly, there's not much sense in training an svm on left vs right checkerboards, 
#  but it shows the general flow nicely.
#
# please modify !
datapath="e:/code/opencv/samples/cpp"
def path(cls,i): # "./left03.jpg"
    return "%s/%s%02d.jpg"  % (datapath,cls,i+1)


detect = cv2.FeatureDetector_create("SIFT")
extract = cv2.DescriptorExtractor_create("SIFT")

flann_params = dict(algorithm = 1, trees = 5)      # flann enums are missing, FLANN_INDEX_KDTREE=1
matcher = cv2.FlannBasedMatcher(flann_params, {})  # need to pass empty dict (#1329)


## 1.a setup BOW
bow_train   = cv2.BOWKMeansTrainer(8) # toy world, you want more.
bow_extract = cv2.BOWImgDescriptorExtractor( extract, matcher )

## try those, please!
#help(bow_train) 
#help(bow_extract)

## 1.b add positives and negatives to the bowtrainer, use SIFT DescriptorExtractor 
def feature_sift(fn):
    im = cv2.imread(fn,0)
    return extract.compute(im, detect.detect(im))[1]

for i in range(8):
    bow_train.add(feature_sift(path("left", i)))
    bow_train.add(feature_sift(path("right",i)))


## 1.c kmeans cluster descriptors to vocabulary
voc = bow_train.cluster()
bow_extract.setVocabulary( voc )
print "bow vocab", np.shape(voc)

## 2.a gather svm training data, use BOWImgDescriptorExtractor
def feature_bow(fn):
    im = cv2.imread(fn,0)
    return bow_extract.compute(im, detect.detect(im))

traindata, trainlabels = [],[]
for i in range(6): # save first 2 for testing
    traindata.extend(feature_bow(path("left", i+2))); trainlabels.append(-1)
    traindata.extend(feature_bow(path("right",i+2))); trainlabels.append(1)

print "svm items", len(traindata), len(traindata[0])

## 2.b create & train the svm
svm = cv2.SVM()
#params = dict( kernel_type=cv2.SVM_RBF, svm_type=cv2.SVM_C_SVC, C=1, gamma=0.5 )
svm.train(np.array(traindata), np.array(trainlabels))#,np.array(),np.array(),params)


## 2.c predict the remaining 2*2 images, use BOWImgDescriptorExtractor again
def predict(fn):
    f = feature_bow(fn);    
    p = svm.predict(f)
    print fn, "\t", p 

for i in range(2): #  testing
    predict( path("left",i) )
    predict( path("right",i) )


"""
bow vocab (8, 128)
svm items 12 8
e:/code/opencv/samples/cpp/left01.jpg   -1.0
e:/code/opencv/samples/cpp/right01.jpg  1.0
e:/code/opencv/samples/cpp/left02.jpg   -1.0
e:/code/opencv/samples/cpp/right02.jpg  1.0
"""

this should not be nessecary, since BOW is is wrapped to python in 2.4.9.

import cv2
import numpy as np

#
# based on :
#  http://gilscvblog.wordpress.com/2013/08/23/bag-of-words-models-for-visual-categorization/
#
# for this sample, we'll use the left/right checkerboard shots from samples/cpp.
#  admittedly, there's not much sense in training an svm on left vs right checkerboards, 
#  but it shows the general flow nicely.
#
# please modify !
datapath="e:/code/opencv/samples/cpp"
def path(cls,i): # "./left03.jpg"
    return "%s/%s%02d.jpg"  % (datapath,cls,i+1)


detect = cv2.FeatureDetector_create("SIFT")
extract = cv2.DescriptorExtractor_create("SIFT")

flann_params = dict(algorithm = 1, trees = 5)      # flann enums are missing, FLANN_INDEX_KDTREE=1
matcher = cv2.FlannBasedMatcher(flann_params, {})  # need to pass empty dict (#1329)


## 1.a setup BOW
bow_train   = cv2.BOWKMeansTrainer(8) # toy world, you want more.
bow_extract = cv2.BOWImgDescriptorExtractor( extract, matcher )

## try those, please!
#help(bow_train) 
#help(bow_extract)

## 1.b add positives and negatives to the bowtrainer, use SIFT DescriptorExtractor 
def feature_sift(fn):
    im = cv2.imread(fn,0)
    return extract.compute(im, detect.detect(im))[1]

for i in range(8):
    bow_train.add(feature_sift(path("left", i)))
    bow_train.add(feature_sift(path("right",i)))


## 1.c kmeans cluster descriptors to vocabulary
voc = bow_train.cluster()
bow_extract.setVocabulary( voc )
print "bow vocab", np.shape(voc)

## 2.a gather svm training data, use BOWImgDescriptorExtractor
def feature_bow(fn):
    im = cv2.imread(fn,0)
    return bow_extract.compute(im, detect.detect(im))

traindata, trainlabels = [],[]
for i in range(6): # save first 2 for testing
    traindata.extend(feature_bow(path("left", i+2))); trainlabels.append(-1)
    traindata.extend(feature_bow(path("right",i+2))); trainlabels.append(1)

print "svm items", len(traindata), len(traindata[0])

## 2.b create & train the svm
svm = cv2.SVM()
#params = dict( kernel_type=cv2.SVM_RBF, svm_type=cv2.SVM_C_SVC, C=1, gamma=0.5 )
svm.train(np.array(traindata), np.array(trainlabels))#,np.array(),np.array(),params)


## 2.c predict the remaining 2*2 images, use BOWImgDescriptorExtractor again
def predict(fn):
    f = feature_bow(fn);    
    p = svm.predict(f)
    print fn, "\t", p 

for i in range(2): #  testing
    predict( path("left",i) )
    predict( path("right",i) )


"""
bow vocab (8, 128)
svm items 12 8
e:/code/opencv/samples/cpp/left01.jpg   -1.0
e:/code/opencv/samples/cpp/right01.jpg  1.0
e:/code/opencv/samples/cpp/left02.jpg   -1.0
e:/code/opencv/samples/cpp/right02.jpg  1.0
"""

none of this should not be nessecary, since BOW is wrapped to python in 2.4.9.

import cv2
import numpy as np

#
# based on :
#  http://gilscvblog.wordpress.com/2013/08/23/bag-of-words-models-for-visual-categorization/
#
# for this sample, we'll use the left/right checkerboard shots from samples/cpp.
#  admittedly, there's not much sense in training an svm on left vs right checkerboards, 
#  but it shows the general flow nicely.
#
# please modify !
datapath="e:/code/opencv/samples/cpp"
def path(cls,i): # "./left03.jpg"
    return "%s/%s%02d.jpg"  % (datapath,cls,i+1)


detect = cv2.FeatureDetector_create("SIFT")
extract = cv2.DescriptorExtractor_create("SIFT")

flann_params = dict(algorithm = 1, trees = 5)      # flann enums are missing, FLANN_INDEX_KDTREE=1
matcher = cv2.FlannBasedMatcher(flann_params, {})  # need to pass empty dict (#1329)


## 1.a setup BOW
bow_train   = cv2.BOWKMeansTrainer(8) # toy world, you want more.
bow_extract = cv2.BOWImgDescriptorExtractor( extract, matcher )

## try those, please!
#help(bow_train) 
#help(bow_extract)

## 1.b add positives and negatives to the bowtrainer, use SIFT DescriptorExtractor 
def feature_sift(fn):
    im = cv2.imread(fn,0)
    return extract.compute(im, detect.detect(im))[1]

for i in range(8):
    bow_train.add(feature_sift(path("left", i)))
    bow_train.add(feature_sift(path("right",i)))


## 1.c kmeans cluster descriptors to vocabulary
voc = bow_train.cluster()
bow_extract.setVocabulary( voc )
print "bow vocab", np.shape(voc)

## 2.a gather svm training data, use BOWImgDescriptorExtractor
def feature_bow(fn):
    im = cv2.imread(fn,0)
    return bow_extract.compute(im, detect.detect(im))

traindata, trainlabels = [],[]
for i in range(6): # save first 2 for testing
    traindata.extend(feature_bow(path("left", i+2))); trainlabels.append(-1)
    traindata.extend(feature_bow(path("right",i+2))); trainlabels.append(1)

print "svm items", len(traindata), len(traindata[0])

## 2.b create & train the svm
svm = cv2.SVM()
#params = dict( kernel_type=cv2.SVM_RBF, svm_type=cv2.SVM_C_SVC, C=1, gamma=0.5 )
svm.train(np.array(traindata), np.array(trainlabels))#,np.array(),np.array(),params)


## 2.c predict the remaining 2*2 images, use BOWImgDescriptorExtractor again
def predict(fn):
    f = feature_bow(fn);    
    p = svm.predict(f)
    print fn, "\t", p 

for i in range(2): #  testing
    predict( path("left",i) )
    predict( path("right",i) )


"""
bow vocab (8, 128)
svm items 12 8
e:/code/opencv/samples/cpp/left01.jpg   -1.0
e:/code/opencv/samples/cpp/right01.jpg  1.0
e:/code/opencv/samples/cpp/left02.jpg   -1.0
e:/code/opencv/samples/cpp/right02.jpg  1.0
"""