def naiveBayes(trainingData, trainingClasses, testData, testClasses):
nbClassifier = cv2.NormalBayesClassifier()
nbClassifier.train(trainingData, trainingClasses)
ret, results = nbClassifier.predict(testData)
print results
return calculateAccuracy(testClasses,
results), calcSensitivity(testClasses, results)
def CallNaiveBayes(trainData, trainLabels, testData):
## Create a Naive (normal) Bayes Classifier
classifier = cv2.NormalBayesClassifier()
classifier.train(trainData, trainLabels)
## Use classifier to predict the test data
## TODO: (Overwrite the following line with your answer.)
retval, predictions = classifier.predict(testData)
#predictions = np.zeros((testData.shape[0], 1), np.float32)
return predictions
def supervised_classification_opencv(input_band_list,sample_matrix,train_matrix,classification_type):
'''Supervised classification using OpenCV library
Function used to recall the supervised classification from the OpenCV library. Train and samples matrix can be extracted directly from a shapefile with the generate_training function
or from a text file.
Available algorithms: Support Vector Machine, Decision Tree, Gradient Boosted Tree, Normal Bayes, Random Forest, K-Nearest-Neighbors.
Example: supervised_classification_opencv(input_band_list=(band1,band2,band3,band4),sample_matrix=samples_mat,train_matrix=training_mat,classification_type='svm')
:param input_band_list: list of 2darrays corresponding to bands (band 1: blue) (list of numpy arrays)
:param sample_matrix: 2darray with the samples to add
:param train_matrix: 1darray with the corresponding classes
:param classification type: definition of the desired classification algorithm ('svm','dt','gbt','bayes','rf','knn') (string)
:returns: 2darray with predicted classes
:raises: AttributeError, KeyError
Author: Daniele De Vecchi - Daniel Aurelio Galeazzo - Mostapha Harb
Last modified: 01/04/2014
'''
stack_new = np.dstack((input_band_list[0],input_band_list[1],input_band_list[2],input_band_list[3])) #stack with the original bands
samples = stack_new.reshape((-1,4)) #input image as matrix with rows = (rows_original * cols_original) and columns = number of bands
print classification_type
if classification_type == "svm":
params = dict( kernel_type = cv2.SVM_LINEAR, svm_type = cv2.SVM_C_SVC,C = 10000 ) #definition of the SVM parameters (kernel, type of algorithm and parameter related to the chosen algorithm
cl = cv2.SVM()
cl.train_auto(sample_matrix,train_matrix,None,None,params,100) #creation of the training set forcing the parameters optimization
y_val = cl.predict_all(samples) #classification of the input image
output = y_val.reshape(input_band_list[0].shape).astype(np.uint16) #reshape to the original rows and columns
if classification_type == "dt":
cl = tree.DecisionTreeClassifier()
cl = cl.fit(sample_matrix, train_matrix)
y_val = cl.predict(samples)
output = y_val.reshape(input_band_list[0].shape).astype(np.uint16)
if classification_type == "gbt":
cl = GradientBoostingClassifier()
cl = cl.fit(sample_matrix, train_matrix)
y_val = cl.predict(samples)
output = y_val.reshape(input_band_list[0].shape).astype(np.uint16)
if classification_type == "bayes":
cl = cv2.NormalBayesClassifier(sample_matrix, train_matrix)
y_val = cl.predict(samples)
y_val = np.array(y_val[1])
output = y_val.reshape(input_band_list[0].shape).astype(np.uint16)
if classification_type == "rf":
cl = cv2.RTrees()
cl.train(sample_matrix, cv2.CV_ROW_SAMPLE, train_matrix)
y_val = np.float32( [cl.predict(s) for s in samples] )
output = y_val.reshape(input_band_list[0].shape).astype(np.uint16)
if classification_type == "knn":
cl = cv2.KNearest()
cl.train(sample_matrix, train_matrix)
retval, results, neigh_resp, dists = cl.find_nearest(samples, k = 10)
y_val = results.ravel()
output = y_val.reshape(input_band_list[0].shape).astype(np.uint16)
return output
class NormalBayesClassifier(AbstractClassifier):
classifier = cv2.NormalBayesClassifier()
def predict(self, sample):
return self.classifier.predict(np.matrix(sample))[0]
print "Perform K-Means Training..."
for (i, fvec) in enumerate(fvec_train):
print "Image: " + str(i)
ret, labels, centers = cv2.kmeans(fvec, K_TRAIN, criteria, K_ATTEMPTS,
cv2.KMEANS_RANDOM_CENTERS) #Get K-Means
#Reshape and save labels
labels = labels.flatten()
labels = labels.reshape((img_train[i].shape[0], img_train[i].shape[1]))
kmean_train.append(labels)
#Save centers for use during training
for c in centers:
center_train.append(c)
# CREATE MODEL
if DO_BAYES:
bayes_model = cv2.NormalBayesClassifier()
else:
rtrees_model = cv2.RTrees()
# PREPARE FOR CLASSIFICATION (CHECK FOR 50% OVERLAP)
for i in range(len(kmean_train)):
# Count how many of each cluster are in image
region_count = [0] * K_TRAIN
overlap_count = [0] * K_TRAIN
for x in range(kmean_train[i].shape[0]):
for y in range(kmean_train[i].shape[1]):
region_count[kmean_train[i][x, y]] += 1
if img_train_gt[i][x, y] == 255:
overlap_count[kmean_train[i][x, y]] += 1
#Check Strength of match ratio:
def naiveBayes(trainingData, trainingClasses, testData, testClasses):
nbClassifier = cv2.NormalBayesClassifier()
nbClassifier.train(trainingData, trainingClasses)
ret, results = nbClassifier.predict(testData)
return results