def tester_char(uri):
img = cv2.imread(uri)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dataString_middle=""
dataString_lower=""
dataString_upper=""
lowerZoneLabels = fileIO.read_label_file('learner/lowerZoneLabels')
middleZoneLabels = fileIO.read_label_file('learner/middleZoneLabels')
upperZoneLabels = fileIO.read_label_file('learner/upperZoneLabels')
character = gray[0:64, 0:128]
character = normalize.get_mask(character, 5)
xMin, xMax, yMin, yMax= locate_character.char_location(character)
# print xMin, xMax, yMin, yMax
classifier_middle = pickle.load(open('learner/ANN_middle'))
classifier_lower = pickle.load(open('learner/ANN_lower'))
classifier_upper = pickle.load(open('learner/ANN_upper'))
dataArray_middle = feature_mapper.middle(character, 21, 43, xMin, xMax)
dataArray_middle.append('m1')
# print dataArray_middle
dataArray_lower = feature_mapper.lower(character, 42, 64, xMin, xMax)
dataArray_lower.append('l0')
# print dataArray_lower
dataArray_upper = feature_mapper.upper(character, 0, 22, xMin, xMax)
dataArray_upper.append('u0')
# MiddleZoneClasss = classifier_middle(dataArray_middle, Orange.classification.Classifier.GetBoth)
MiddleZoneProb = classifier_middle(dataArray_middle, Orange.classification.Classifier.GetProbabilities)
UpperZoneProb = classifier_upper(dataArray_upper, Orange.classification.Classifier.GetProbabilities)
LowerZoneProb = classifier_lower(dataArray_lower, Orange.classification.Classifier.GetProbabilities)
MiddleZoneClass=(classifier_middle(dataArray_middle, Orange.classification.Classifier.GetBoth))
UpperZoneClass=(classifier_upper(dataArray_upper, Orange.classification.Classifier.GetBoth))
LowerZoneClass=(classifier_lower(dataArray_lower, Orange.classification.Classifier.GetBoth))
char, lower, middle, upper = prob_match.probability_match(lower=[LowerZoneProb,lowerZoneLabels],
middle=[MiddleZoneProb, middleZoneLabels],
upper=[UpperZoneProb,upperZoneLabels])
# char_mapper.char_map()
print char+" "+lower+" "+middle+" "+upper
def classify(img):
lowerZoneLabels = fileIO.read_label_file('learner/lowerZoneLabels')
middleZoneLabels = fileIO.read_label_file('learner/middleZoneLabels')
upperZoneLabels = fileIO.read_label_file('learner/upperZoneLabels')
index = 0
#print img
# for i in range(1, 3)
if img is None:
# print "None"
return ""
if img.any() == np.array([0]).all():
# print "space"
return " "
else:
#im = np.array(img * 255, dtype = np.uint8)
#gray = np.array(img * 255, dtype = np.uint8)
dataString_middle = ""
dataString_lower = ""
dataString_upper = ""
character = img
# print character
character = normalize.get_mask(character, 5)
xMin, xMax, yMin, yMax = locate_character.char_location(character)
# print xMin, xMax, yMin, yMax
dataArray_middle = feature_mapper.middle(character, 21, 43, xMin, xMax)
dataArray_middle.append('m1')
# print dataArray_middle
dataArray_lower = feature_mapper.lower(character, 42, 64, xMin, xMax)
dataArray_lower.append('l0')
# print dataArray_lower
dataArray_upper = feature_mapper.upper(character, 0, 22, xMin, xMax)
dataArray_upper.append('u0')
# for i in range(1, 3):
# char1=""
# char2=""
# char3=""
MiddleZoneProb = classifier_middle(
dataArray_middle,
Orange.classification.Classifier.GetProbabilities)
UpperZoneProb = classifier_upper(
dataArray_upper, Orange.classification.Classifier.GetProbabilities)
LowerZoneProb = classifier_lower(
dataArray_lower, Orange.classification.Classifier.GetProbabilities)
# MiddleZoneClass=(classifier_middle(dataArray_middle, Orange.classification.Classifier.GetBoth))
# UpperZoneClass=(classifier_upper(dataArray_upper, Orange.classification.Classifier.GetBoth))
# LowerZoneClass=(classifier_lower(dataArray_lower, Orange.classification.Classifier.GetBoth))
###############################################
if classifier_lower(dataArray_lower,
Orange.classification.Classifier.GetValue) == 'l0':
for i in range(0, len(lowerZoneLabels)):
lowerZoneLabels[i] = 'l0'
for i in xrange(0, len(LowerZoneProb)):
LowerZoneProb[i] = 1
if classifier_upper(dataArray_upper,
Orange.classification.Classifier.GetValue) == 'u0':
for i in range(0, len(upperZoneLabels)):
upperZoneLabels[i] = 'u0'
for i in xrange(0, len(UpperZoneProb)):
UpperZoneProb[i] = 1
predicted_char, lower, middle, upper = prob_match.probability_match(
lower=[LowerZoneProb, lowerZoneLabels],
middle=[MiddleZoneProb, middleZoneLabels],
upper=[UpperZoneProb, upperZoneLabels])
# char_mapper.char_map()
# print char+" "+lower+" "+middle+" "+upper
return predicted_char
def validator_char(start, end):
# lowerZoneLabels, middleZoneLabels, upperZoneLabels, classifier_middle, classifier_lower, classifier_upper=loadfile()
img = cv2.imread(os.path.join(package_directory, 'data/chars - Copy.jpg'))
iMax = 109
# iMax=5
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
index = 0
dataString_middle = ""
dataString_lower = ""
dataString_upper = ""
char_matrix = {}
char_time_matrix = {}
lowerZoneLabels = fileIO.read_label_file('learner/lowerZoneLabels')
middleZoneLabels = fileIO.read_label_file('learner/middleZoneLabels')
upperZoneLabels = fileIO.read_label_file('learner/upperZoneLabels')
classifier_middle = pickle.load(
open(os.path.join(package_directory, 'learner/ANN_middle')))
classifier_lower = pickle.load(
open(os.path.join(package_directory, 'learner/ANN_lower')))
classifier_upper = pickle.load(
open(os.path.join(package_directory, 'learner/ANN_upper')))
for j in range(start, end):
for i in range(1, iMax):
time_start = time.time()
character = gray[64 * (j - 1):64 * j, 128 * (i - 1):128 * i]
character = normalize.get_mask(character, 5)
xMin, xMax, yMin, yMax = locate_character.char_location(character)
# cv2.imshow("sd",character)
# cv2.waitKey(0)
dataArray_middle = feature_mapper.middle(character, 21, 43, xMin,
xMax)
dataArray_middle.append('m1')
# print dataArray_middle
dataArray_lower = feature_mapper.lower(character, 42, 64, xMin,
xMax)
dataArray_lower.append('l0')
dataArray_upper = feature_mapper.upper(character, 0, 22, xMin,
xMax)
dataArray_upper.append('u0')
# MiddleZoneClasss = classifier_middle(dataArray_middle, Orange.classification.Classifier.GetBoth)
MiddleZoneProb = classifier_middle(
dataArray_middle,
Orange.classification.Classifier.GetProbabilities)
UpperZoneProb = classifier_upper(
dataArray_upper,
Orange.classification.Classifier.GetProbabilities)
LowerZoneProb = classifier_lower(
dataArray_lower,
Orange.classification.Classifier.GetProbabilities)
MiddleZoneClass = (classifier_middle(
dataArray_middle, Orange.classification.Classifier.GetBoth))
UpperZoneClass = (classifier_upper(
dataArray_upper, Orange.classification.Classifier.GetBoth))
LowerZoneClass = (classifier_lower(
dataArray_lower, Orange.classification.Classifier.GetBoth))
# print MiddleZoneClass
# char1=char_mapper.char_map(LowerZoneClass, MiddleZoneClass, UpperZoneClass)
# print char1
predicted_char, lower, middle, upper = prob_match.probability_match(
lower=[LowerZoneProb, lowerZoneLabels],
middle=[MiddleZoneProb, middleZoneLabels],
upper=[UpperZoneProb, upperZoneLabels])
# char_mapper.char_map()
input_char = validate_mapper.char_map(i)
time_end = time.time()
if input_char is not None:
print str(i) + "####################"
print str(
input_char
) + " : " + predicted_char + " " + lower + " " + middle + " " + upper + " time:" + str(
time_end - time_start)
# char_matrix[input_char]=predicted_cha
# #fdfdr
if input_char is not None:
char_matrix.setdefault(input_char, []).append(predicted_char)
char_time_matrix.setdefault(input_char,
[]).append(time_end - time_start)
performance(char_matrix, char_time_matrix)
def validator_char(start, end):
# lowerZoneLabels, middleZoneLabels, upperZoneLabels, classifier_middle, classifier_lower, classifier_upper=loadfile()
img = cv2.imread('C:/Users/Naleen/PycharmProjects/CharReco2/data/chars - Copy.jpg')
iMax = 109
# iMax=5
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dataString_middle=""
dataString_lower=""
dataString_upper=""
char_matrix = {}
char_time_matrix = {}
lowerZoneLabels = fileIO.read_label_file('learner/lowerZoneLabels')
middleZoneLabels = fileIO.read_label_file('learner/middleZoneLabels')
upperZoneLabels = fileIO.read_label_file('learner/upperZoneLabels')
# classifier_middle = pickle.load(open('learner/ANN_middle'))
# classifier_lower = pickle.load(open('learner/ANN_lower'))
# classifier_upper = pickle.load(open('learner/ANN_upper'))
classifier_middle = pickle.load(open(os.path.join(package_directory, 'learner/ANN_middle')))
classifier_lower = pickle.load(open(os.path.join(package_directory, 'learner/ANN_lower')))
classifier_upper = pickle.load(open(os.path.join(package_directory, 'learner/ANN_upper')))
for j in range (start, end):
for i in range (1, iMax):
time_start=time.time()
character = gray[64*(j-1):64*j, 128*(i-1):128*i]
character = normalize.get_mask(character, 5)
xMin, xMax, yMin, yMax= locate_character.char_location(character)
# cv2.imshow("sd",character)
# cv2.waitKey(0)
dataArray_middle = feature_mapper.middle(character, 21, 43, xMin, xMax)
dataArray_middle.append('m1')
# print dataArray_middle
dataArray_lower = feature_mapper.lower(character, 42, 64, xMin, xMax)
dataArray_lower.append('l0')
dataArray_upper = feature_mapper.upper(character, 0, 22, xMin, xMax)
dataArray_upper.append('u0')
# MiddleZoneClasss = classifier_middle(dataArray_middle, Orange.classification.Classifier.GetBoth)
MiddleZoneProb = classifier_middle(dataArray_middle, Orange.classification.Classifier.GetProbabilities)
UpperZoneProb = classifier_upper(dataArray_upper, Orange.classification.Classifier.GetProbabilities)
LowerZoneProb = classifier_lower(dataArray_lower, Orange.classification.Classifier.GetProbabilities)
MiddleZoneClass=(classifier_middle(dataArray_middle, Orange.classification.Classifier.GetBoth))
UpperZoneClass=(classifier_upper(dataArray_upper, Orange.classification.Classifier.GetBoth))
LowerZoneClass=(classifier_lower(dataArray_lower, Orange.classification.Classifier.GetBoth))
# print MiddleZoneClass
# char1=char_mapper.char_map(LowerZoneClass, MiddleZoneClass, UpperZoneClass)
# print char1
predicted_char, lower, middle, upper = prob_match.probability_match(lower=[LowerZoneProb,lowerZoneLabels],
middle=[MiddleZoneProb, middleZoneLabels],
upper=[UpperZoneProb,upperZoneLabels])
# char_mapper.char_map()
input_char= validate_mapper.char_map(i)
time_end=time.time()
if input_char is not None:
print str(i)+ "####################"
print str(input_char)+" : "+predicted_char+" "+lower+" "+middle+" "+upper+" time:"+str(time_end-time_start)
# char_matrix[input_char]=predicted_cha
# #fdfdr
if input_char is not None:
char_matrix.setdefault(input_char, []).append(predicted_char)
char_time_matrix.setdefault(input_char, []).append(time_end-time_start)
performance(char_matrix, char_time_matrix)