def progress_preprocess(infile, outfile, defines):
if debug > 1:
print " preprocess %s %s %s" % (defines, infile, outfile)
else:
echo(".")
if not isinstance(infile, (list)):
infile = [infile]
preprocess_outfile = open(outfile, "w")
preprocess(includes=infile, defines=defines, output=preprocess_outfile, line_endings="lf")
preprocess_outfile.close()
def feature_transformer2(data,emo, lexicon_feat, embed_feat):
stopWords = stopwords.words('english')
if type(data) == dict:
data = [preprocess(data['text']).encode('utf-8')]
elif type(data) == list:
data = [preprocess(d['text']).encode('utf-8') for d in data]
ngram_feat = CountVectorizer(ngram_range=(1,3), analyzer='word', binary=False, stop_words=stopWords, min_df=0.01, vocabulary=pickle.load(open("../vocab/vocab."+emo+".pkl", "rb")))
all_features = FeatureUnion([('lexicon_feature', lexicon_feat), ('embeddings', embed_feat), ('ngrams', ngram_feat)])
pipeline = Pipeline([('all_feature', all_features)])
feat = pipeline.fit_transform(data)
return feat
def feature_transformer2(data, emo, lexicon_feat, embed_feat):
stopWords = stopwords.words('english')
if type(data) == dict:
data = [preprocess(data['text']).encode('utf-8')]
elif type(data) == list:
data = [preprocess(d['text']).encode('utf-8') for d in data]
ngram_feat = CountVectorizer(ngram_range=(1, 3), analyzer='word', binary=False, stop_words=stopWords, min_df=0.01, vocabulary=pickle.load(open("../vocab/vocab."+emo+".pkl", "rb")))
all_features = FeatureUnion([('lexicon_feature', lexicon_feat), ('embeddings', embed_feat), ('ngrams', ngram_feat)])
pipeline = Pipeline([('all_feature', all_features)])
feat = pipeline.fit_transform(data)
return feat
def progress_preprocess(infile, outfile, defines):
if debug > 1:
print ' preprocess %s %s %s' % (defines, infile, outfile)
else:
echo('.')
if not isinstance(infile, (list)):
infile = [infile]
preprocess_outfile = open(outfile, 'w')
preprocess(
includes = infile,
defines = defines,
output = preprocess_outfile,
line_endings = 'lf',
)
preprocess_outfile.close()
def build_data(self):
self.imgs = np.zeros((self.n, self.img_h, self.img_w))
self.texts = []
for i, (img_filepath, text) in enumerate(self.samples):
img = preprocess(img_filepath, self.img_w, self.img_h)
self.imgs[i, :, :] = img
self.texts.append(text)
def getNext(self):
#load a new image
batchRange = range(self.currIdx, self.currIdx + self.batchSize)
gtTexts = [self.samples[i].gtText for i in batchRange]
imgs = [
preprocess(
cv2.imread(self.samples[i].filePath, cv2.IMREAD_GRAYSCALE),
self.imgSize, self.dataAugmentation) for i in batchRange
]
self.currIdx += self.batchSize
return Batch(gtTexts, imgs)
def predictTest(model, fnImg, charErrorRate, wordAccuracy):
img = preprocess(cv2.imread(fnImg, cv2.IMREAD_GRAYSCALE),
Model.imgSize) # preprocess the image
batch = Batch(None, [img]) # create a batch object
(recognized, probability) = model.makePrediction(batch, True)
print('Predicted word:', '"' + recognized[0] + '"')
now = datetime.now().time()
current_time = now.strftime("%H:%M:%S")
text_file = open("../predictions.txt", "a")
message = current_time + " >>> " + "prediction : " + str(
recognized[0]) + " | model accuracy : " + str(wordAccuracy) + '\n'
print(message, file=text_file)
text_file.close()
displayPlot(charErrorRate, wordAccuracy, recognized, probability, img)
def predict_image(model_predict, path, is_word):
if is_word:
width = word_cfg['img_w']
else:
width = line_cfg['img_w']
img = preprocess(path, width, 64)
img = img.T
if K.image_data_format() == 'channels_first':
img = np.expand_dims(img, 0)
else:
img = np.expand_dims(img, -1)
img = np.expand_dims(img, 0)
net_out_value = model_predict.predict(img)
pred_texts = decode_label(net_out_value)
return pred_texts
import TenCrossValidation as tcv from Preprocessor import preprocess import sys file = sys.argv[1] data = preprocess(file) result = tcv.validation(data)
def load_images_from_folder(folder):
images = []
for filename in os.listdir(folder):
img = cv2.imread(os.path.join(folder, filename))
if img is not None:
images.append(img)
return images
vals = [
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E',
'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
'U', 'V', 'W', 'X', 'Y', 'Z'
]
currdir = os.getcwd()
#for dir in vals:
os.chdir(currdir + '\\images_off_jetson')
images = load_images_from_folder(currdir + '\\images_off_jetson')
k = 0
os.chdir(currdir + '\\preprocessed')
#os.mkdir(os.getcwd() + '\\' +dir)
#os.chdir(os.getcwd() + '\\' +dir)
neg = np.zeros([28, 28, 3])
for i in images:
img = preprocess(i)
cv2.imwrite(str(k) + '.jpg', img)
k = k + 1
