def prepare_features(coco_manager, image_source_dir, target):
image_manager = ImageManager()
processed = 0
storage = tf.python_io.TFRecordWriter(target)
image_batch = []
text_batch = []
len_batch = []
for img_id in coco_manager.img_ids():
logger().info('%d items processed', processed)
processed += 1
try:
img = coco_manager.load_images(img_id)[0]
raw_data = np.float32(PIL.Image.open(os.path.join(image_source_dir, img['file_name'])))
if raw_data.ndim == 2:
raw_data = raw_data.reshape(*(raw_data.shape + (1,))) + np.zeros((1, 1, 3), dtype=np.int32)
logger().warn('Found grayscale image, fixed')
text_data_batch = np.int64(coco_manager.sents(img_id))
text_buffer = np.zeros((config.sents_per_sample, config.max_len + 1), dtype=np.int64)
len_buffer = np.zeros(config.sents_per_sample, dtype=np.int64)
for idx, text_data in enumerate(text_data_batch[:config.sents_per_sample, :config.max_len]):
text_buffer[idx, 0] = config.words_count + 1
text_buffer[idx, 1 : 1 + text_data.shape[-1]] = text_data
len_buffer[idx] = text_data.shape[-1]
image_batch.append(raw_data)
text_batch.append(text_buffer)
len_batch.append(len_buffer)
except Exception as e:
logger().error('Failed processing image %s: %s', img['file_name'], str(e))
if (len(image_batch) == IMAGE_BATCH_SIZE):
image_data = image_manager.extract_features(image_batch)
for image, text, length in zip(image_data, text_batch, len_batch):
example = tf.train.Example()
example.features.feature['image'].float_list.value.extend([float(value) for value in image])
example.features.feature['text'].int64_list.value.extend(text[:, :-1].reshape(-1))
example.features.feature['result'].int64_list.value.extend(text[:, 1:].reshape(-1))
example.features.feature['len'].int64_list.value.extend(length)
storage.write(example.SerializeToString())
image_batch = []
text_batch = []
len_batch = []
from tensortalk import config
from tensortalk.image import ImageManager
from tensortalk.coco import CocoManager
from tensortalk.network import CaptionNetwork, UserInputPipeline
from tensortalk.sampler import BeamSearchSampler
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('images', metavar='image', type=str, nargs='+',
help='Images for captioning')
parser.add_argument('--model', dest='model_file', required=True,
help='Path to serialized model')
args = parser.parse_args()
image_manager = ImageManager()
coco_manager = CocoManager(config.train_annotations_file, config.words_count)
weights_file = args.model_file
session = tf.Session()
input_pipeline = UserInputPipeline()
model = CaptionNetwork(session, input_pipeline)
model.load(weights_file)
sampler = BeamSearchSampler(beam_size=5)
for img_name in args.images:
img = np.float32(PIL.Image.open(img_name))
img_features = image_manager.extract_features(img)