def recognize(image_path: str,
weights_path: str,
config: GlobalConfig,
is_vis=True):
logger = LogFactory.get_logger()
image = load_and_resize_image(image_path)
inputdata = tf.placeholder(dtype=tf.float32,
shape=[1, 32, 100, 3],
name='input')
net = CRNN(phase='Test', hidden_nums=256, seq_length=25, num_classes=37)
with tf.variable_scope('shadow'):
net_out = net.build(inputdata=inputdata)
decodes, _ = tf.nn.ctc_beam_search_decoder(inputs=net_out,
sequence_length=25 * np.ones(1),
merge_repeated=False)
decoder = TextFeatureIO()
# config tf session
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.get_gpu_config(
).memory_fraction
sess_config.gpu_options.allow_growth = config.get_gpu_config(
).is_tf_growth_allowed()
# config tf saver
saver = tf.train.Saver()
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
preds = sess.run(decodes, feed_dict={inputdata: image})
preds = decoder.writer.sparse_tensor_to_str(preds[0])
logger.info('Predict image {:s} label {:s}'.format(
ops.split(image_path)[1], preds[0]))
if is_vis:
plt.figure('CRNN Model Demo')
plt.imshow(
cv2.imread(image_path, cv2.IMREAD_COLOR)[:, :, (2, 1, 0)])
plt.show()
sess.close()
def __init__(self, tfrecords_path: str, weights_path: str,
config: GlobalConfig):
self._log = LogFactory.get_logger()
self._tfrecords_path = tfrecords_path
self._weights_path = weights_path
self._batch_size = config.get_test_config().batch_size
self._merge_repeated = config.get_test_config().merge_repeated_chars
self._gpu_config = config.get_gpu_config()
self._decoder = TextFeatureIO().reader
self._recognition_time = None
