def input_tensor(image_path, upsample_factor_for_whole_net=32):
image = Image.open(image_path)
image_float = tf.to_float(image, name='ToFloat')
mean_centered_image = _mean_image_subtraction(image_float,
[_R_MEAN, _G_MEAN, _B_MEAN])
original_shape = tf.shape(image_float)[0:2]
target_input_size_factor = tf.ceil(
tf.div(tf.to_float(original_shape),
tf.to_float(upsample_factor_for_whole_net)))
target_input_size = tf.to_int32(
tf.multiply(target_input_size_factor, upsample_factor_for_whole_net))
padding_size = (target_input_size - original_shape) // 2
mean_centered_image = tf.image.pad_to_bounding_box(mean_centered_image,
padding_size[0],
padding_size[1],
target_input_size[0],
target_input_size[1])
origin_image = tf.image.pad_to_bounding_box(image, padding_size[0],
padding_size[1],
target_input_size[0],
target_input_size[1])
images = tf.expand_dims(mean_centered_image, 0)
origin_images = tf.expand_dims(origin_image, 0)
return images, origin_images
def inputs(data_set, train=True, batch_size=1, num_epochs=1, upsample_factor_for_whole_net=32):
assert os.path.exists(data_set), '[{0}] not exist!!!'.format(data_set)
if not num_epochs:
num_epochs = None
with tf.name_scope('input') as scope:
filename_queue = tf.train.string_input_producer([data_set], num_epochs=num_epochs)
image, label = read_and_decode(filename_queue)
# Convert image to float32 before subtracting the
# mean pixel value
image_float = tf.to_float(image, name='ToFloat')
# Subtract the mean pixel value from each pixel
mean_centered_image = _mean_image_subtraction(image_float, [_R_MEAN, _G_MEAN, _B_MEAN])
if train:
seed = int(time.time())
img_corped = tf.random_crop(mean_centered_image, [224, 224, 3], seed=seed)
img_orig_corped = tf.random_crop(image, [224, 224, 3], seed=seed)
lbl_corped = tf.random_crop(label, [224, 224, 1], seed=seed)
images, origin_images, labels = tf.train.shuffle_batch([img_corped, img_orig_corped, lbl_corped],
batch_size=batch_size,
num_threads=4,
capacity=100 + 3 * batch_size,
min_after_dequeue=100)
else:
original_shape = tf.shape(image_float)[0:2]
target_input_size_factor = tf.ceil(
tf.div(tf.to_float(original_shape), tf.to_float(upsample_factor_for_whole_net)))
target_input_size = tf.to_int32(tf.multiply(target_input_size_factor, upsample_factor_for_whole_net))
padding_size = (target_input_size - original_shape) // 2
mean_centered_image = tf.image.pad_to_bounding_box(mean_centered_image,
padding_size[0],
padding_size[1],
target_input_size[0],
target_input_size[1])
annotation_tensor_paded = tf.image.pad_to_bounding_box(label,
padding_size[0],
padding_size[1],
target_input_size[0],
target_input_size[1])
origin_image = tf.image.pad_to_bounding_box(image,
padding_size[0],
padding_size[1],
target_input_size[0],
target_input_size[1])
images = tf.expand_dims(mean_centered_image, 0)
origin_images = tf.expand_dims(origin_image, 0)
labels = tf.expand_dims(annotation_tensor_paded, 0)
return images, origin_images, labels