def create_bottleneck(bottleneck_path, image_lists, label_name, index,
image_dir, category, sess, jpeg_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor):
tf.logging.info("Creating Bottleneck at {}".format(bottleneck_path))
image_path = utils.get_image_path(image_lists, label_name, index,
image_dir, category)
if not gfile.Exists(image_path):
tf.logging.fatal("File does not exist {}".format(image_path))
image_data = gfile.FastGFile(image_path, "rb").read()
try:
bottleneck_values = run_bottleneck_on_image(sess, image_data,
jpeg_data_tensor,
decoded_image_tensor,
resized_input_tensor,
bottleneck_tensor)
except Exception as e:
raise RuntimeError("Error bottlenecking {}\n{}".format(
image_path, str(e)))
bottleneck_string = ",".join(str(x) for x in bottleneck_values)
bottleneck_directory = "/".join(bottleneck_path.split("/")[:-1])
utils.create_directory(bottleneck_directory)
with open(bottleneck_path, "w") as bottleneck_file:
bottleneck_file.write(bottleneck_string)
def store_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
jpeg_data_tensor, decoded_image_tensor,
resized_input_tensor, bottleneck_tensor):
num_bottlenecks = 0
utils.create_directory(bottleneck_dir)
for label_name, label_lists in image_lists.items():
for category in ["training", "testing", "validation"]:
category_list = label_lists[category]
for index in range(len(category_list)):
get_bottleneck(sess, image_lists, label_name, index, image_dir,
category, bottleneck_dir, jpeg_data_tensor,
decoded_image_tensor, resized_input_tensor,
bottleneck_tensor)
num_bottlenecks += 1
if num_bottlenecks % 100 == 0:
tf.logging.info(
"{} bottleneck files created.".format(num_bottlenecks))