splitted_t_set = []
for t_set in glob.glob('./trainingdata/*.png'):
print("Reading image: " + t_set)
img = cv2.imread(t_set, cv2.IMREAD_GRAYSCALE)
preprocess = Preprocessing(img)
preprocess.binariseImg()
splitted_chars = preprocess.splitChars()
splitted_t_set.append(splitted_chars)
# Histogram
for i in range(len(char_values)):
for font in splitted_t_set:
histogram = Histogram(font[i])
database.add(char_values[i], 'histogram', histogram.rowWert2wert())
# Pixel Average
for i in range(len(char_values)):
for font in splitted_t_set:
pix_av = FeatureExtraction(font[i])
database.add(char_values[i], 'pixelAv', pix_av.getpixelaverage())
# PCA
pca = PCA()
for i in range(len(char_values)):
temp = []
for j in splitted_t_set:
temp.append(j[i])
pca.trainChar(char_values[i], temp)
temp.append(float(j))
featureVector = [pix_av_merkmale] + histogram_merkmale + temp
classify = Classify()
classify.crispKnn(featureVector, 3)
# print(featureVector)
characterCount = 4
## CLASSIFICATION
featureVectors = database.readFeatureVectors()
for char in featureVectors:
for char_vector_count in featureVectors[char]:
membershipvalue = 0 # Example value
database.add(
"featureMembership", char + str(char_vector_count), membershipvalue
) # Write to database. Don't forget to save database with database.saveDatabase()
membershipvalue = database.read(
"featureMembership",
char + str(char_vector_count)) # Read back from database.
# Testbild wird hier geladen und auf gleiche Weise durch Preprocessing gejagt
img = cv2.imread('trainingdata/trainingsdata.off/helloworld.png',
cv2.IMREAD_GRAYSCALE)
preprocess = Preprocessing(img)
preprocess.binariseImg()
splitted_chars = preprocess.splitChars()
for i in range(len(splitted_chars)):
#tools.writeImage(splitted_chars[i], "out" + str(i) + '.png')
