def chart(self, query):
result = detect.process(query)
airlines = {}
for row in result:
airline = row[4]
if airline not in airlines:
airlines[airline] = 0
airlines[airline] += 1
out = {
'name': 'airports',
'children': []
}
for airline in airlines:
group = {
'name': 'flights-for-' + airline,
'children': [
{ 'name': airline, 'size': 100*airlines[airline]/len(result) }
]
}
out['children'].append(group)
return json.dumps(out)
def chart_large(self, query):
result = detect.process(query)
out = 'price,price2,airline\n'
for row in result:
out += row[3] + ',' + str(float(row[3]) + random.random()*250) + ',' + row[4] + '\n'
return out
def chart_large(self, query):
result = detect.process(query)
out = 'price,price2,airline\n'
for row in result:
out += row[3] + ',' + str(float(row[3]) + random.random() *
250) + ',' + row[4] + '\n'
return out
def chart(self, query):
result = detect.process(query)
airlines = {}
for row in result:
airline = row[4]
if airline not in airlines:
airlines[airline] = 0
airlines[airline] += 1
out = {'name': 'airports', 'children': []}
for airline in airlines:
group = {
'name':
'flights-for-' + airline,
'children': [{
'name': airline,
'size': 100 * airlines[airline] / len(result)
}]
}
out['children'].append(group)
return json.dumps(out)
filesNames = listDirectory(options.dirname)
class_dict = eval(open("../bayes/dictionnary.txt").read())
haarc = haar.haarInit(os.path.dirname(os.path.realpath(__file__)) + '/../haar/cascade.xml')
trainData = []
responses = []
total = len(filesNames)
i = 1
for fileName in filesNames:
print "{0} / {1}".format(i,total)
i = i+1
_,_,densityVect = detect.process(detect.loadSample(fileName),haarc,True)
if densityVect is None:
continue
trainData.append(densityVect)
responses.append(class_dict[fileName])
matrixData = np.matrix(trainData).astype('float32')
matrixResp = np.matrix(responses)
classifier = cv2.NormalBayesClassifier()
classifier.train(matrixData,matrixResp)
def process_query(self, query):
## do something with the query
result = detect.process(query)
return json.dumps(result)
def process_query(self, query):
## do something with the query
result = detect.process(query)
return json.dumps(result)
