def __init__(self, tf_model_path, conf_file, ks_model_path):
cfg_from_file(conf_file)
self.tf_model_path = tf_model_path
self.net_name = 'VGGnet_test'
self.sess, self.net = self.load_tf_model()
from keras.layers import Input
from keras.models import Model
from densenet import keys
from densenet import densenet
self.characters = keys.alphabet[:]
self.characters = self.characters[1:] + u'卍'
self.nclass = len(self.characters)
input = Input(shape=(32, None, 1), name='the_input')
y_pred = densenet.dense_cnn(input, self.nclass)
self.basemodel = Model(inputs=input, outputs=y_pred)
modelPath = os.path.join(os.getcwd(), ks_model_path)
if os.path.exists(modelPath):
self.basemodel.load_weights(modelPath)
global graph
graph = tf.get_default_graph()
'''
import tensorflow as tf
import os
from imp import reload
from keras.layers import Input
from keras.models import Model
# import keras.backend as K
from densenet import keys
from densenet import densenet
global basemodel,graph,nclass
graph = graph = tf.get_default_graph()
reload(densenet)
characters = keys.alphabet[:]
characters = characters[1:] + u'卍'
nclass = len(characters)
input = Input(shape=(32, None, 1), name='the_input')
y_pred= densenet.dense_cnn(input, nclass)
basemodel = Model(inputs=input, outputs=y_pred)
modelPath = os.path.join(os.getcwd(), './densenet/models/weights_densenet.h5')
if os.path.exists(modelPath):
basemodel.load_weights(modelPath)
def densenet_cnn_model(height=32,nClass=len(characters)):
input_tensor = Input(shape=(height,None,1),name='the_input')
y_pred = densenet.dense_cnn(input_tensor, nClass)
basemodel = Model(inputs=input_tensor,outputs=y_pred)
return basemodel
def __init__(self, text_process=False):
self._text_detector = TextDetector()
self._ocr_model = dense_cnn(len(char))
self._ocr_model.load_weights('./weights/weights-densenet.h5')
self.rec_results = None
self._text_process = text_process