def predict(self, img_file, out_dir=None):
#
# img_data
img = Image.open(img_file)
img_data = np.array(img, dtype=np.float32) / 255
# height, width, channel
#
img_data = [img_data[:, :, 0:3]] # rgba
img_size = img.size # (width, height)
w_arr = np.array([img_size[0]], dtype=np.int32)
#
with self.graph.as_default():
#
feed_dict = {self.x: img_data, self.w: w_arr}
#
r_cls, r_ver, r_hor = self.sess.run(
[self.rnn_cls, self.rnn_ver, self.rnn_hor], feed_dict)
#
# trans
text_bbox, conf_bbox = model_data.trans_results(r_cls, r_ver, r_hor, \
meta.anchor_heights, meta.threshold)
#
conn_bbox = model_data.do_nms_and_connection(text_bbox, conf_bbox)
#
if out_dir == None: return conn_bbox, text_bbox, conf_bbox
#
#
# predication_result save-path
if not os.path.exists(out_dir): os.mkdir(out_dir)
#
filename = os.path.basename(img_file)
#
# image
#
file_target = os.path.join(out_dir, 'predicted_' + filename)
img_target = Image.fromarray(np.uint8(img_data[0] *
255)) #.convert('RGB')
img_target.save(file_target)
model_data.draw_text_boxes(file_target, text_bbox)
#
file_target = os.path.join(out_dir, 'connected_' + filename)
img_target = Image.fromarray(np.uint8(img_data[0] *
255)) #.convert('RGB')
img_target.save(file_target)
model_data.draw_text_boxes(file_target, conn_bbox)
#
return conn_bbox, text_bbox, conf_bbox
def validate(self, data_valid, step):
#
# valid_result save-path
if not os.path.exists(meta.dir_results_valid): os.mkdir(meta.dir_results_valid)
#
self.create_graph_all(training = False)
#
with self.graph.as_default():
#
saver = tf.train.Saver()
with tf.Session(config = self.sess_config) as sess:
#
tf.global_variables_initializer().run()
#sess.run(tf.assign(self.is_train, tf.constant(False, dtype=tf.bool)))
#
# restore with saved data
ckpt = tf.train.get_checkpoint_state(meta.model_detect_dir)
#
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
#
# pb
constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, output_node_names = \
['rnn_cls','rnn_ver','rnn_hor'])
with tf.gfile.FastGFile(self.pb_file, mode='wb') as f:
f.write(constant_graph.SerializeToString())
#
# test
NumImages = len(data_valid)
curr = 0
for img_file in data_valid:
#
print(img_file)
#
txt_file = model_data.get_target_txt_file(img_file)
#
# input data
img_data, feat_size, target_cls, target_ver, target_hor = \
model_data.get_image_and_targets(img_file, txt_file, meta.anchor_heights)
#
img_size = img_data[0].shape # height, width, channel
#
w_arr = np.array([ img_size[1] ], dtype = np.int32)
#
#
feed_dict = {self.x: img_data, self.w: w_arr, \
self.t_cls: target_cls, self.t_ver: target_ver, self.t_hor: target_hor}
#
r_cls, r_ver, r_hor, loss_value = sess.run([self.rnn_cls, self.rnn_ver, self.rnn_hor, self.loss], feed_dict)
#
#
curr += 1
print('curr: %d / %d, loss: %f' % (curr, NumImages, loss_value))
#
# trans
text_bbox, conf_bbox = model_data.trans_results(r_cls, r_ver, r_hor, \
meta.anchor_heights, meta.threshold)
# conn_bbox = model_data.do_nms_and_connection(text_bbox, conf_bbox)
#
# image
#
filename = os.path.basename(img_file)
file_target = os.path.join(meta.dir_results_valid, str(step) + '_predicted_' + filename)
img_target = Image.fromarray(np.uint8(img_data[0] *255) ) #.convert('RGB')
img_target.save(file_target)
model_data.draw_text_boxes(file_target, text_bbox)
#
id_remove = step - self.valid_freq * self.keep_near
if id_remove % self.keep_freq:
file_temp = os.path.join(meta.dir_results_valid, str(id_remove) + '_predicted_' + filename)
if os.path.exists(file_temp): os.remove(file_temp)
#
#
print('validation finished')