def create_xs_ys(self):
self.files_list = \
lslR.get_files(directory=self.IMAGE_DIRECTORY,include=self.include,\
exclude = self.exclude,ftype=self.ftypes)
#print files_list
for file in self.files_list:
for item in self.keywords.iteritems():
#the 1st entry is the class number 0,1,2,3...
key = item[0]
#the second entry is the reg-exp identifying the class
value = item[1]
if file.rfind(value) != -1 :
#print key , file
self.xs.append(initial_processing.imageToVector(file))
self.ys.append(key)
def test(self,image_directory,classKeys,include=None,exclude=None):
"""
The method implements the testing part of the algorithm and returns
the successrate in fractions between 0 and 1
"""
#the following reassignment is done to keep the __init__
#method and the test method arguments consistent
test_path = image_directory
test_classes = classKeys
includ = include
exclud = exclude
print "Starting to Test"
#test_path = raw_input("Enter the Path containing test images:")
#test_pattern = list(raw_input("Enter the regexp identifying \
#test images in the directory above:"))
test_files = \
lslR.get_files(directory=test_path,ftype=self.ftypes,include=includ,exclude=exclud)
num = len(test_files)
i = 0
success = 0
failure = 0
for fil in test_files:
#if num - i > 0 :
# print "processing Image number: (%d) , Corresponding to File name: %s " % (i,fil)
xTest = \
numpy.matrix(initial_processing.imageToVector(fil))
xTest_class = -1
#----------------debug code------------
#print type(xTest)
#--------------------------------------
#project testing image on A
#yTest = numpy.transpose(self.A * xTest.transpose())
yTest = xTest * self.A.transpose()
#find the distance of yTest from each Yi
distAndYPair = [] #inf denotes infinity
#self.y_Transpose = numpy.transpose(self.y)
#for y in self.y_Transpose:
for y in self.y:
#print type(yTest),type(y),yTest.shape,y.shape
dist = spatial.distance.euclidean(yTest,y)
#if dist < minPair[0]:
# print "iterating to find the minimum match"
# print "y :",y
# print "yTest :",yTest
# minPair[0] = dist
# minPair[1] = tuple(y)
distAndYPair.append([dist,y])
#print "type of minPair[1]"+str(type(minPair[1]))
#print minPair[1] in self.y_Transpose
distAndYPair.sort()
#print minPair[1] in numpy.transpose(self.y)
#print type(self.y[0])
#print "Minimum distance: ",minPair[0]
recognized_class = self.yZipYs[distAndYPair[0][1]]
#because the class number starts from 0 and the numbering of
#of
for key in test_classes.keys():
if fil.find(key) != -1:
xTest_class = test_classes[key]
break
if recognized_class == xTest_class :
success += 1
else:
failure += 1
#print "Success %d , Failure %d" % (success,failure)
#print "Recognized Class: %d And xTest_Class: %d " % \
#(recognized_class,xTest_class)
i +=1
rate = (float(success)/float(success+failure))
#print "Success Rate is : %f" % rate
return rate