def read_TFRecord(dataset_name="train"):
filename_queue = filter(lambda x: dataset_name in x,
tfrecord_auto_traversal())
filename_queue = list(
map(lambda x: os.path.join("flowers/", x), filename_queue))
print("the file queue is ", filename_queue)
dataset = tf.data.TFRecordDataset(filename_queue)
dataset = dataset.map(_parse_function)
if dataset_name == "train":
dataset = dataset.batch(batch_size=50)
dataset = dataset.shuffle(buffer_size=50)
else:
dataset = dataset.batch(batch_size=1)
iterator = dataset.make_initializable_iterator()
print("=======================================")
# print(iterator.get_next())
print("=======================================")
return iterator
current_image_object.label = tf.cast(features["image/class/label"],
tf.int32) # label of the raw image
return current_image_object
def generate_mini_batch(image, label, batch_size=50):
images, labels = tf.train.shuffle_batch(
[image, label],
batch_size=batch_size,
capacity=min_queue_examples + 3 * batch_size,
min_after_dequeue=min_queue_examples)
return images, labels
filename_queue = tf.train.string_input_producer(tfrecord_auto_traversal(),
shuffle=True)
current_image_object = read_and_decode(filename_queue)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
print("Write cropped and resized image to the folder './resized_image'")
for i in range(FLAGS.image_number): # number of examples in your tfrecord
pre_image, pre_label = sess.run(
[current_image_object.image, current_image_object.label])
img = Image.fromarray(pre_image, "RGB")
if not os.path.isdir("./resized_image/"):
os.mkdir("./resized_image")
return current_image_object
def generate_mini_batch(image, label, batch_size = 50):
images, labels = tf.train.shuffle_batch(
[image, label],
batch_size = batch_size,
capacity = min_queue_examples + 3 * batch_size,
min_after_dequeue = min_queue_examples
)
return images, labels
filename_queue = tf.train.string_input_producer(
tfrecord_auto_traversal(),
shuffle = True)
current_image_object = read_and_decode(filename_queue)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
print("Write cropped and resized image to the folder './resized_image'")
for i in range(FLAGS.image_number): # number of examples in your tfrecord
pre_image, pre_label = sess.run([current_image_object.image, current_image_object.label])
img = Image.fromarray(pre_image, "RGB")
if not os.path.isdir("./resized_image/"):
os.mkdir("./resized_image")
