class MatchTests:
def __init__(self, samStart=11, samEnd=36):
self.data = GetData(sampleStart=samStart, sampleEnd=samEnd)
self.avgs = self.data.GetAveraged()
self.samStart = samStart
def ClassifyTests(
self,
testDir="/home/brian/PycharmProjects/firstAttempt/HandLetters/"):
matches = []
testImages = [
(self.data.NormalizeImage(cv2.imread(testDir + "/" + j, 0)), j)
for j in os.listdir(testDir)
] #tuble(img,name)
for i in testImages:
name = i[1]
img = i[0]
bestMatch = 0
print type(self.avgs[0])
errorArray = self.avgs[0].astype(np.float_) - img.astype(np.float_)
errorArray = errorArray * errorArray
minError = sum(sum(errorArray))
for j in xrange(len(self.avgs)):
errorArray = self.avgs[j].astype(np.float_) - img.astype(
np.float_)
errorArray = errorArray * errorArray
error = sum(sum(errorArray))
if error < minError:
minError = error
bestMatch = j
matches.append(name + "was identified to be: " +
self.data.GetLetters()[bestMatch + self.samStart])
return matches
def __init__(self, sampStart=11, sampEnd=36):
data = GetData(sampleStart=sampStart, sampleEnd=sampEnd)
self.sampStart = sampStart
self.sampEnd = sampEnd
self.imgs = data.GetListImgs()
self.letters = data.GetLetters()
avgs = data.GetAveraged()
self.avgs = avgs[sampStart - 1:sampEnd]
self.kguesses = np.array([list(j.flatten()) for j in avgs])
self.k = sampEnd - sampStart + 1
self.data = data