def getSegmentationAccuracy(pathArray):
dataX = []
dataY = []
j = 0
for path in pathArray:
for f in os.listdir(path):
j+=1
if f[-5:] != "inkml": continue
# print f
traceList, symbolsList = gettrace.parseINKMLFile(path + f)
segmentIndices = segment.segmentSymbols(traceList)
for label, elem in symbolsList:
# if label in ['=', 'i', 'j','\\leq', '\\log', '\\sin', '\\cos', '\\lim', '\\geq', '\\righarrow', '\\div']: continue
# Correctly classified
if elem in segmentIndices:
strokes = [traceList[i] for i in elem]
pixels = itp.inkml_to_pixels(strokes)
chain = list(itertools.chain(*pixels))
chain.append(len(strokes))
dataX.append(chain)
dataY.append(label)
# cr.display(pixels)
print j
f = file("segmented_data_18", "w")
pickle.dump((dataX, dataY), f)
def recognize():
global svm
print request.form['info']
strokes = json.loads(request.form['info'])
symbolsIndices = segment.segmentSymbols(strokes)
returnstr = ""
for elem in symbolsIndices:
s = [strokes[i] for i in elem]
pixels = inkml_to_pixels.inkml_to_pixels(s)
chain = list(itertools.chain(*pixels))
chain.append(len(s))
prediction = svm.predict(chain)[0]
returnstr += prediction
return returnstr
def getSegmentationAccuracy(pathArray):
f = open('svm18px', "r")
svm = pickle.load(f)
j = 0
total = 0
total_correct = 0
off_by_one = 0
off_by_two = 0
for path in pathArray:
for f in os.listdir(path):
j+=1
if f[-5:] != "inkml": continue
# print f
traceList, symbolsList = gettrace.parseINKMLFile(path + f)
segmentIndices = segment.segmentSymbols(traceList)
correct = 0
for label, elem in symbolsList:
# Correctly classified
if elem in segmentIndices:
strokes = [traceList[i] for i in elem]
pixels = itp.inkml_to_pixels(strokes)
chain = list(itertools.chain(*pixels))
chain.append(len(strokes))
if svm.predict(chain)[0] == label:
correct +=1
if correct == len(symbolsList):
total_correct +=1
if correct + 1 >= len(symbolsList):
off_by_one +=1
if correct + 2 >= len(symbolsList):
off_by_two +=1
# cr.display(pixels)
print j
print total_correct
print off_by_one
print off_by_two
print float(total_correct)/836
print float(off_by_one)/836
print float(off_by_two)/836
def recognize_hmm():
global hmm_instance
strokes = json.loads(request.form['info'])
symbolsIndices = segment.segmentSymbols(strokes)
returnstr = ""
equation = []
for elem in symbolsIndices:
s = [strokes[i] for i in elem]
pixels = inkml_to_pixels.inkml_to_pixels(s)
chain = list(itertools.chain(*pixels))
chain.append(len(s))
equation.append((chain,))
prediction = hmm_instance.compute_best_sequence(equation)
for symbol in prediction:
returnstr += symbol
return returnstr
def getSegmentationAccuracy(pathArray):
trainData = []
from time import time
start = time()
completely_correct = 0
total_files = 0
correct = 0
total = 0
mistakes = {}
for path in pathArray:
for file in os.listdir(path):
if file[-5:] != "inkml": continue
# print file
traceList, symbolsList = gettrace.parseINKMLFile(path + file)
segmentIndices = segment.segmentSymbols(traceList)
correct_file = 0
total_file = 0
for label, elem in symbolsList:
# if label in ['=', 'i', 'j','\\leq', '\\log', '\\sin', '\\cos', '\\lim', '\\geq', '\\righarrow', '\\div']: continue
if elem in segmentIndices:
correct_file = correct_file + 1
else:
if label not in mistakes:
mistakes[label] = 0
mistakes[label] +=1
total_file = total_file + 1
if correct_file == total_file:
completely_correct +=1
correct += correct_file
total += total_file
total_files +=1
# cr.display(pixels)
print time()-start
print float(correct)/total
print float(completely_correct)/total_files
print mistakes
c = 0
for elem in mistakes:
c = c + mistakes[elem]
print c
# each element in the list corresponds to a trace.
# Each trace is represented as a list of (x,y) tuples.
def parseINKMLFile(filename):
with file(filename) as f:
s = f.read()
parser = MyINKMLParser()
parser.init()
parser.feed(s)
return parser.traceList, parser.symbolsList
# Given the path to an INKML file,
def parseSymbolOrder(filename):
orderedSymbolList = []
with file(filename) as f:
s = f.read()
parser = MyINKMLParser()
parser.init()
parser.feed(s)
for key in sorted(parser.symbolOrder):
orderedSymbolList.append(parser.symbolOrder[key])
return orderedSymbolList
if __name__ == "__main__":
# n = 'samples/test_sample_recognized.inkml'
# n = 'ICFHR_package/CROHME2012_data/testDataGT/001-equation001.inkml'
n = 'ICFHR_package/CROHME2012_data/trainData/trainData/algb09.inkml'
# n = 'samples/train_sample_with_GT.inkml'
traceList, symbolsList = parseINKMLFile(n)
segmentIndices = segment.segmentSymbols(traceList)