def BuildFeatureVectorForTweet(tweet):
#print "BuildFeatureVectorForTweet Called"
global char_n_grams_index, word_n_grams_index
happy, sad, anger, fear, surprise, disgust, hashtags, usernames, \
urls, punctuations_marks_count, repetitive_words, char_n_grams, \
word_n_grams, upper_case_words, intensifiers, negations, score_vector = pre.PreProcessing(tweet)
feature_vector = []
#print char_n_grams_index
#print word_n_grams_index
feature_vector = AddEmoticonFeatures(feature_vector, happy, sad, disgust,
anger, fear, surprise)
feature_vector = AddCharNGramFeatures(feature_vector, char_n_grams_index,
char_n_grams)
feature_vector = AddWordNGramFeatures(feature_vector, word_n_grams_index,
word_n_grams)
feature_vector = AddRepetitiveWordsFeature(feature_vector,
repetitive_words)
feature_vector = AddPunctuationMarksFeature(feature_vector,
punctuations_marks_count)
feature_vector = AddUpperCaseWordsFeature(feature_vector, upper_case_words)
feature_vector = AddIntensifersFeature(feature_vector, intensifiers)
feature_vector = AddNegationsFeature(feature_vector, negations)
feature_vector = AddLexiconScoreFeature(feature_vector, score_vector)
return feature_vector
def _main():
data = datasets.open_subtitles(download=False)
processor = preprocessing.PreProcessing()
# s = list(data.values())[0]
token_vectors, dictionary = load_token_vectors(processor)
print('[train] creating network...')
input_dim = len(dictionary['words'])
input_length = MAX_LENGTH
output_length = MAX_LENGTH
output_dim = input_dim
n_hidden = 10
depth = 4
batch_size = 50
nb_epoch = 10
dialog = models.Dialog(input_dim=input_dim,
input_length=input_length,
hidden_dim=n_hidden,
output_length=output_length,
output_dim=output_dim,
depth=depth)
model = dialog.create_model()
print('[train] validating...')
for vectors in token_vectors:
print('epoch')
x_train, x_test, y_test, y_train = dialog.get_training_batch(vectors)
dialog.train(x_train,
y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
validation_data=(x_test, y_test),
save_model=True)
def __init__(self, image, N, M, L, tau1, tau2, tau3, tau4, tau5, counter, f_report):
self.start_time = time.time()
self.start_date = datetime.now()
self.N = N
self.M = M
self.L = L
self.counter = counter
self.image = image
self.image_preprocessed = np.array([])
self.sigOrig = bitarray()
self.sigGen = bitarray()
f = open("signature.bin", "rb")
self.f_report = f_report
self.sigOrig.fromfile(f)
self.pre_process = pre.PreProcessing(L, False)
self.extract_process = extract.SignatureExtraction(N, M, L)
self.matching_process = match.SignatureMatching(self.sigOrig[0:238], tau1, tau2, tau3, tau4, tau5)
table_to="stats_dim")
loader.insert_form_data(data=form_dim,
server=server,
database=database,
table_to="form_dim")
loader.insert_fact_data(data=fifa_fact,
server=server,
database=database,
table_to="fifa_fact")
### PRE-PROCESSING ###
training_data = extractor.query_data(server=server,
database=database,
table="fifa_fact")
preprocessor = preprocessing.PreProcessing()
X_train, x_test, Y_train, y_test = preprocessor.preprocess(
data=training_data,
test_size=0.25,
train_size=0.75,
random_state=69,
target_variable="Value" # Possibilites: "Value", "Wage", "Release_Clause"
)
### MODEL BUILDING ###
modeller = model.Model()
gbr_model, rfr_model, dtr_model = modeller.train_models(X_train,
Y_train,
n_estimators=1000,
from bitarray import bitarray
import time
import multiprocessing
import sys
from picamera import PiCamera
from picamera.array import PiRGBArray
from time import sleep
import gc
cv2.setUseOptimized(True)
gc.enable()
#Define objects
sigOrig = bitarray()
f = open("signature.bin", "rb")
sigOrig.fromfile(f)
pre_process = pre.PreProcessing(128, False)
extract_process = extract.SignatureExtraction(8, 4, 128)
matching_process = match.SignatureMatching(sigOrig[0:238], 24, 38, 4, 28, 22)
camera = PiCamera()
camera.resolution = (544, 400)
#camera.framerate = 30
##camera.led = False
rawCapture = PiRGBArray(camera, size=(544, 400))
counter = 0
true_counter = 0
camera.start_preview()
time.sleep(5)
f_report = open("Quality_Reports_Image/new_timing7.txt", "w")
def run_preprocessing(self, logger, io_config):
Prep = prep.PreProcessing(logger, io_config)
Prep.process_user_table()
import sys
pre.cv2.setUseOptimized(True)
cap = cv2.VideoCapture(0)
cap.set(3, 1024)
cap.set(4, 768)
counter = 0
while (counter < 60):
ret, image = cap.read()
cv2.imshow("deneme", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
pre_process = pre.PreProcessing(image, 128, False)
points = pre_process.get_contour(3)
check = pre_process.get_perspective(points)
if not check:
print "ERROR:Contour not detected"
else:
image2 = pre_process.get_scaled()
cv2.imshow("scaled", image2)
cv2.waitKey(0)
cv2.destroyAllWindows()
image3 = pre_process.get_cropped()