def main(_):
checkpoint_dir = os.path.join(FLAGS.exp_dir, 'log')
# eval_dir = os.path.join(FLAGS.exp_dir, 'log/eval')
model_config, _, _ = get_configs_from_exp_dir()
model = model_builder.build(model_config, is_training=False)
input_image_str_tensor = tf.placeholder(dtype=tf.string, shape=[])
input_image_tensor = tf.image.decode_jpeg(
input_image_str_tensor,
channels=3,
)
resized_image_tensor = tf.image.resize_images(
tf.to_float(input_image_tensor), [64, 256])
predictions_dict = model.predict(tf.expand_dims(resized_image_tensor, 0))
recognitions = model.postprocess(predictions_dict)
recognition_text = recognitions['text'][0]
control_points = predictions_dict['control_points'],
rectified_images = predictions_dict['rectified_images']
saver = tf.train.Saver(tf.global_variables())
checkpoint = os.path.join(FLAGS.exp_dir, 'log/model.ckpt')
fetches = {
'original_image': input_image_tensor,
'recognition_text': recognition_text,
'control_points': predictions_dict['control_points'],
'rectified_images': predictions_dict['rectified_images'],
}
with open(FLAGS.input_image, 'rb') as f:
input_image_str = f.read()
with tf.Session() as sess:
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer(),
tf.tables_initializer()
])
saver.restore(sess, checkpoint)
sess_outputs = sess.run(
fetches, feed_dict={input_image_str_tensor: input_image_str})
print('Recognized text: {}'.format(
sess_outputs['recognition_text'].decode('utf-8')))
rectified_image = sess_outputs['rectified_images'][0]
rectified_image_pil = Image.fromarray(
(128 * (rectified_image + 1.0)).astype(np.uint8))
input_image_dir = os.path.dirname(FLAGS.input_image)
rectified_image_save_path = os.path.join(input_image_dir,
'rectified_image.jpg')
rectified_image_pil.save(rectified_image_save_path)
print('Rectified image saved to {}'.format(rectified_image_save_path))
def test_single_predictor_model_training(self):
model_proto = model_pb2.Model()
text_format.Merge(SINGLE_PREDICTOR_MODEL_TEXT_PROTO, model_proto)
model_object = model_builder.build(model_proto, True)
test_groundtruth_text_list = [
tf.constant(b'hello', dtype=tf.string),
tf.constant(b'world', dtype=tf.string)
]
model_object.provide_groundtruth(
{'groundtruth_text': test_groundtruth_text_list})
test_input_image = tf.random_uniform(shape=[2, 32, 100, 3],
minval=0,
maxval=255,
dtype=tf.float32,
seed=1)
prediction_dict = model_object.predict(
model_object.preprocess(test_input_image))
loss = model_object.loss(prediction_dict)
with self.test_session() as sess:
sess.run(
[tf.global_variables_initializer(),
tf.tables_initializer()])
outputs = sess.run({'loss': loss})
print(outputs['loss'])
def test_stn_multi_predictor_model_inference(self):
model_proto = model_pb2.Model()
text_format.Merge(STN_MULTIPLE_PREDICTOR_MODEL_TEXT_PROTO, model_proto)
model_object = model_builder.build(model_proto, False)
test_groundtruth_text_list = [
tf.constant(b'hello', dtype=tf.string),
tf.constant(b'world', dtype=tf.string)
]
model_object.provide_groundtruth(
{'groundtruth_text': test_groundtruth_text_list})
test_input_image = tf.random_uniform(shape=[2, 32, 100, 3],
minval=0,
maxval=255,
dtype=tf.float32,
seed=1)
prediction_dict = model_object.predict(
model_object.preprocess(test_input_image))
recognition_dict = model_object.postprocess(prediction_dict)
with self.test_session() as sess:
sess.run(
[tf.global_variables_initializer(),
tf.tables_initializer()])
outputs = sess.run(recognition_dict)
print(outputs)
def test_build_attention_model_single_branch(self):
model_text_proto = """
attention_recognition_model {
feature_extractor {
convnet {
crnn_net {
net_type: SINGLE_BRANCH
conv_hyperparams {
op: CONV
regularizer { l2_regularizer { weight: 1e-4 } }
initializer { variance_scaling_initializer { } }
batch_norm { }
}
summarize_activations: false
}
}
bidirectional_rnn {
fw_bw_rnn_cell {
lstm_cell {
num_units: 256
forget_bias: 1.0
initializer { orthogonal_initializer {} }
}
}
rnn_regularizer { l2_regularizer { weight: 1e-4 } }
num_output_units: 256
fc_hyperparams {
op: FC
activation: RELU
initializer { variance_scaling_initializer { } }
regularizer { l2_regularizer { weight: 1e-4 } }
}
}
summarize_activations: true
}
predictor {
name: "ForwardPredictor"
bahdanau_attention_predictor {
reverse: false
rnn_cell {
lstm_cell {
num_units: 256
forget_bias: 1.0
initializer { orthogonal_initializer { } }
}
}
rnn_regularizer { l2_regularizer { weight: 1e-4 } }
num_attention_units: 128
max_num_steps: 10
multi_attention: false
beam_width: 1
reverse: false
label_map {
character_set {
text_string: "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
delimiter: ""
}
label_offset: 2
}
loss {
sequence_cross_entropy_loss {
sequence_normalize: false
sample_normalize: true
}
}
}
}
}
"""
model_proto = model_pb2.Model()
text_format.Merge(model_text_proto, model_proto)
model_object = model_builder.build(model_proto, True)
test_groundtruth_text_list = [
tf.constant(b'hello', dtype=tf.string),
tf.constant(b'world', dtype=tf.string)
]
model_object.provide_groundtruth(test_groundtruth_text_list)
test_input_image = tf.random_uniform(shape=[2, 32, 100, 3],
minval=0,
maxval=255,
dtype=tf.float32,
seed=1)
prediction_dict = model_object.predict(
model_object.preprocess(test_input_image))
loss = model_object.loss(prediction_dict)
with self.test_session() as sess:
sess.run(
[tf.global_variables_initializer(),
tf.tables_initializer()])
outputs = sess.run({'loss': loss})
print(outputs['loss'])
def main(_):
checkpoint_dir = os.path.join(FLAGS.exp_dir, 'log')
# eval_dir = os.path.join(FLAGS.exp_dir, 'log/eval')
model_config, _, _ = get_configs_from_exp_dir()
model = model_builder.build(model_config, is_training=False)
input_image_str_tensor = tf.placeholder(dtype=tf.string, shape=[])
input_image_tensor = tf.image.decode_jpeg(
input_image_str_tensor,
channels=3,
)
resized_image_tensor = tf.image.resize_images(
tf.to_float(input_image_tensor), [64, 256])
predictions_dict = model.predict(tf.expand_dims(resized_image_tensor, 0))
recognitions = model.postprocess(predictions_dict)
recognition_text = recognitions['text'][0]
control_points = predictions_dict['control_points'],
rectified_images = predictions_dict['rectified_images']
saver = tf.train.Saver(tf.global_variables())
checkpoint = os.path.join(FLAGS.exp_dir, 'log/model.ckpt')
fetches = {
'original_image': input_image_tensor,
'recognition_text': recognition_text,
'control_points': predictions_dict['control_points'],
'rectified_images': predictions_dict['rectified_images'],
}
with tf.Session() as sess:
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer(),
tf.tables_initializer()
])
saver.restore(sess, checkpoint)
image_list = os.listdir(FLAGS.data_dir)
image_list.sort()
vid_num = '00000'
for file in image_list:
with open(os.path.join(FLAGS.data_dir, file), 'rb') as f:
input_image_str = f.read()
# Read .tsv file to get frame info
if vid_num != file.split('_')[0][4:]:
tsv_path = os.path.join(FLAGS.tsv_dir,
file.split('_')[0][4:] + '.tsv')
with open(tsv_path) as tsv_file:
tsv_reader = csv.reader(tsv_file, delimiter="\t")
frame_data = []
i = 0
for line in tsv_reader:
if i == 0:
pass
else:
frame_data.append(line)
i += 1
sess_outputs = sess.run(
fetches, feed_dict={input_image_str_tensor: input_image_str})
text = sess_outputs['recognition_text'].decode('utf-8')
print(
'Recognized text for ', file, ' : {}'.format(
sess_outputs['recognition_text'].decode('utf-8')))
# rectified_image = sess_outputs['rectified_images'][0]
# rectified_image_pil = Image.fromarray((128 * (rectified_image + 1.0)).astype(np.uint8))
# input_image_dir = os.path.dirname(FLAGS.input_image)
# rectified_image_save_path = os.path.join(FLAGS.data_dir, 'rectifed %s' %file)
# rectified_image_pil.save(rectified_image_save_path)
# print('Rectified image saved to {}'.format(rectified_image_save_path))
# print('Check Video Number : ', file.split('_')[0])
print('Check Image Name : ',
'_'.join([file.split('_')[0],
file.split('_')[1]]))
video_number = int(file.split('_')[0][4:])
key_frame_num = int(file.split('_')[1])
start_frame = int(frame_data[key_frame_num - 1][0])
end_frame = int(frame_data[key_frame_num - 1][2])
start_time = float(frame_data[key_frame_num - 1][1])
end_time = float(frame_data[key_frame_num - 1][3])
# frame_seg = frame_data[key_frame_num-1][0]+'-'+frame_data[key_frame_num-1][2]
# video_name_and_frame = '_'.join([file.split('_')[0][4:], frame_seg])
col.update({
"video": video_number,
"startFrame": start_frame
}, {
"$set": {
"video": video_number,
"startFrame": start_frame,
"endFrame": end_frame,
"startSecond": start_time,
"endSecond": end_time
},
"$addToSet": {
"text": text
}
},
upsert=True)
vid_num = file.split('_')[0][4:]