| """A script to run inference on a set of image files.
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|
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| NOTE #1: The Attention OCR model was trained only using FSNS train dataset and
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| it will work only for images which look more or less similar to french street
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| names. In order to apply it to images from a different distribution you need
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| to retrain (or at least fine-tune) it using images from that distribution.
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|
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| NOTE #2: This script exists for demo purposes only. It is highly recommended
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| to use tools and mechanisms provided by the TensorFlow Serving system to run
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| inference on TensorFlow models in production:
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| https://www.tensorflow.org/serving/serving_basic
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|
|
| Usage:
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| python demo_inference.py --batch_size=32 \
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| --checkpoint=model.ckpt-399731\
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| --image_path_pattern=./datasets/data/fsns/temp/fsns_train_%02d.png
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| """
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| import numpy as np
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| import PIL.Image
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|
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| import tensorflow as tf
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| from tensorflow.compat.v1 import flags
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| from tensorflow.python.training import monitored_session
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|
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| import common_flags
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| import datasets
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| import data_provider
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|
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| FLAGS = flags.FLAGS
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| common_flags.define()
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|
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|
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| flags.DEFINE_string('image_path_pattern', '',
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| 'A file pattern with a placeholder for the image index.')
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|
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|
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| def get_dataset_image_size(dataset_name):
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|
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| ds_module = getattr(datasets, dataset_name)
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| height, width, _ = ds_module.DEFAULT_CONFIG['image_shape']
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| return width, height
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|
|
|
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| def load_images(file_pattern, batch_size, dataset_name):
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| width, height = get_dataset_image_size(dataset_name)
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| images_actual_data = np.ndarray(shape=(batch_size, height, width, 3),
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| dtype='uint8')
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| for i in range(batch_size):
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| path = file_pattern % i
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| print("Reading %s" % path)
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| pil_image = PIL.Image.open(tf.io.gfile.GFile(path, 'rb'))
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| images_actual_data[i, ...] = np.asarray(pil_image)
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| return images_actual_data
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|
|
|
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| def create_model(batch_size, dataset_name):
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| width, height = get_dataset_image_size(dataset_name)
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| dataset = common_flags.create_dataset(split_name=FLAGS.split_name)
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| model = common_flags.create_model(
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| num_char_classes=dataset.num_char_classes,
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| seq_length=dataset.max_sequence_length,
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| num_views=dataset.num_of_views,
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| null_code=dataset.null_code,
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| charset=dataset.charset)
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| raw_images = tf.compat.v1.placeholder(
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| tf.uint8, shape=[batch_size, height, width, 3])
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| images = tf.map_fn(data_provider.preprocess_image, raw_images,
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| dtype=tf.float32)
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| endpoints = model.create_base(images, labels_one_hot=None)
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| return raw_images, endpoints
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|
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|
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| def run(checkpoint, batch_size, dataset_name, image_path_pattern):
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| images_placeholder, endpoints = create_model(batch_size,
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| dataset_name)
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| images_data = load_images(image_path_pattern, batch_size,
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| dataset_name)
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| session_creator = monitored_session.ChiefSessionCreator(
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| checkpoint_filename_with_path=checkpoint)
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| with monitored_session.MonitoredSession(
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| session_creator=session_creator) as sess:
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| predictions = sess.run(endpoints.predicted_text,
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| feed_dict={images_placeholder: images_data})
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| return [pr_bytes.decode('utf-8') for pr_bytes in predictions.tolist()]
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|
|
|
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| def main(_):
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| print("Predicted strings:")
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| predictions = run(FLAGS.checkpoint, FLAGS.batch_size, FLAGS.dataset_name,
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| FLAGS.image_path_pattern)
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| for line in predictions:
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| print(line)
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|
|
|
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| if __name__ == '__main__':
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| tf.compat.v1.app.run()
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|
|
