def ocr_images( images ):
model = models.load_model( opt.crnn )
maxWidth = max([i.shape[1] for i in images ])
images = [ np.pad( i, [(2, 1), (0, maxWidth - i.shape[1] ), (0,0)], mode='constant', constant_values=1) for i in images ]
out = model.predict( np.array(images) )
out = out.argmax(2)
textResults = [ decodeStr( i, raw=False ) for i in out ]
return textResults
def ocr_images(model, images):
# import pdb; pdb.set_trace();
# from IPython import embed; embed()
out = []
for img in images:
res = model.predict(np.array([img]))
res = res.argmax(2)
out.append(decodeStr(res[0], raw=False))
return out
def testEncoding(self, opt):
"""
function for self testing.
Encode each wrod, then decode it back
"""
goodWords = []
words = getTrainingTexts(self.WORD_LIST_FILE)
for w in words:
try:
enc, encSize = encodeStr(w)
goodWords.append(w)
except Exception as e:
print('Error encoding "%s"' % w, e)
continue
dec = decodeStr(enc)
if (w != dec):
raise ValueError('Encoding failed "%s" != "%s"' % (w, dec))
if (opt.update):
writeFile(self.WORD_LIST_FILE, '\n'.join(goodWords))