def algoANN(self, trainingPath, testPath, featureExtraType, epochs):
'''
Using Artificial Neural Network algorithm to classify insect images
:param trainingPath: the path of training images
:param testPath: the path of testing images
:param featureExtraType: the feature type:sift or surf
:param epochs: the numbre of training for neural network
:return: the classification results
'''
loadImage = LoadImage()
featureExtra = FeatureExtractor()
bow = BOW()
# get the species,the name of all insects, the path of all insect images
insectNames, names, trainingPaths = loadImage.loadTrainImage(trainingPath)
insectSpecies = len(insectNames)
print "insect species:", insectSpecies
#get all descriptos of training images
trainDescriptors = bow.getDescriptors(trainingPaths, featureExtraType)
#get the BoW dictionary of trianing images
trainBowDictionary = bow.getBowDictionary(insectSpecies, trainDescriptors, featureExtraType)
#initialize a Neural Network
net = buildNetwork(insectSpecies, 100, 100, insectSpecies)
#initialize a data set
ds = SupervisedDataSet(insectSpecies, insectSpecies)
species = 0
#add all datas in data set
for p in insectNames:
trainingPaths = os.listdir(trainingPath + "\\" + p)
for j in trainingPaths:
#add data
ds.addSample(featureExtra.getSingleFeature(trainingPath + "\\" + p + "\\" + j,
trainBowDictionary, featureExtraType)[0], (species,))
species += 1
#initialize a trainer
trainer = BackpropTrainer(net, ds, learningrate=0.01, momentum=0.1, weightdecay=0.01)
#training
for i in range(1, epochs):
traError = trainer.train()
print 'after %d epochs,train error:%f' % (i, traError)
testInsectNames, testNames, testingPaths = loadImage.loadTrainImage(testPath)
testDescriptors = bow.getDescriptors(testingPaths, featureExtraType)
testBowDictionary = bow.getBowDictionary(insectSpecies, testDescriptors, featureExtraType)
# Initializes a zero matrix to save the classification results
result = np.zeros((insectSpecies, insectSpecies))
count = 0
# classify all the test immages
for m in testInsectNames:
testPaths = os.listdir(testPath + "\\" + m)
for n in testPaths:
test = net.activate(featureExtra.getSingleFeature(testPath + "\\" + m + "\\" + n,
testBowDictionary, featureExtraType)[0])
target = map(lambda x: (x), test) # numpy.array to list
result[count, target.index(max(target))] += 1
count += 1
return result
def algoSVM(self,trainingPath, testPath, featureExtraType):
'''
Using Support Vector Machine algorithm to classify insect images
:param trainingPath: the path of training images
:param testPath: he path of testing images
:param featureExtraType: the feature type : sift or surf
:return:
'''
loadImage = LoadImage()
featureExtra = FeatureExtractor()
bow = BOW()
#get the species,the name of all insects, the path of all insect images
insectSpecies, names, trainingPaths = loadImage.loadTrainImage(trainingPath)
print insectSpecies
print "Le bombre d'espece :", len(insectSpecies)
dictionarySize = len(insectSpecies)
insect = {}
num = 1
for name in insectSpecies:
insect[name] = num
num += 1
#get the descriptors of all training images
descriptors = bow.getDescriptors(trainingPaths, featureExtraType)
#get Bag of Words dictionary
bowDictionary = bow.getBowDictionary(dictionarySize, descriptors, featureExtraType)
print "bow dictionary"
#train data
trainDesc = []
#train response
trainLabels = []
i = 0
#initialize train datas and train responses
for p in trainingPaths:
trainDesc.extend(featureExtra.getSingleFeature(p, bowDictionary, featureExtraType))
trainLabels.append(insect[names[i]])
i = i + 1
svm = cv2.SVM()
#training
svm.train(np.array(trainDesc), np.array(trainLabels))
testInsectNames = os.listdir(testPath)
# Initialize a zero matrix to save the classification results
result = np.zeros((dictionarySize, dictionarySize))
print "result zero"
count = 0
#classify all the test immages
for test in testInsectNames:
testingImage = os.listdir(testPath + "\\" + test)
for p in testingImage:
#get feature from a test image
feature = featureExtra.getSingleFeature(testPath + "\\" + test + "\\" + p, bowDictionary, featureExtraType)
#predict
p = svm.predict(feature)
#save the result in the result matrix
result[count, p - 1] += 1
count += 1
return result
