Question

我已经在我自己的数据上重新训练了一个InceptionV3模型，并尝试修改Tensorflow图像分类教程中的代码https://www.tensorflow.org/tutorials/image_recognition。

我尝试在目录中作为列表阅读并循环遍历它，但这不起作用：

  load_graph(FLAGS.graph)

filelist = os.listdir(FLAGS.image)

for i in filelist:
  # load image
  image_data = load_image(i)

我刚刚收到错误，说FLAGS尚未定义，所以我猜FLAGS必须与load_image函数一起使用？这是原始程序：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys
import os
import tensorflow as tf

parser = argparse.ArgumentParser()
parser.add_argument(
    '--image', required=True, type=str, help='Absolute path to image file.')
parser.add_argument(
    '--num_top_predictions',
    type=int,
    default=5,
    help='Display this many predictions.')
parser.add_argument(
    '--graph',
    required=True,
    type=str,
    help='Absolute path to graph file (.pb)')
parser.add_argument(
    '--labels',
    required=True,
    type=str,
    help='Absolute path to labels file (.txt)')
parser.add_argument(
    '--output_layer',
    type=str,
    default='final_result:0',
    help='Name of the result operation')
parser.add_argument(
    '--input_layer',
    type=str,
    default='DecodeJpeg/contents:0',
    help='Name of the input operation')


def load_image(filename):
  """Read in the image_data to be classified."""
  return tf.gfile.FastGFile(filename, 'rb').read()


def load_labels(filename):
  """Read in labels, one label per line."""
  return [line.rstrip() for line in tf.gfile.GFile(filename)]


def load_graph(filename):
  """Unpersists graph from file as default graph."""
  with tf.gfile.FastGFile(filename, 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='')


def run_graph(image_data, labels, input_layer_name, output_layer_name,
              num_top_predictions):
  with tf.Session() as sess:
    # Feed the image_data as input to the graph.
    #   predictions will contain a two-dimensional array, where one
    #   dimension represents the input image count, and the other has
    #   predictions per class
    softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
    predictions, = sess.run(softmax_tensor, {input_layer_name: image_data})

    # Sort to show labels in order of confidence
    top_k = predictions.argsort()[-num_top_predictions:][::-1]
    for node_id in top_k:
      human_string = labels[node_id]
      score = predictions[node_id]
      print('%s (score = %.5f)' % (human_string, score))

    return 0


def main(argv):
  """Runs inference on an image."""
  if argv[1:]:
    raise ValueError('Unused Command Line Args: %s' % argv[1:])

  if not tf.gfile.Exists(FLAGS.image):
    tf.logging.fatal('image file does not exist %s', FLAGS.image)

  if not tf.gfile.Exists(FLAGS.labels):
    tf.logging.fatal('labels file does not exist %s', FLAGS.labels)

  if not tf.gfile.Exists(FLAGS.graph):
    tf.logging.fatal('graph file does not exist %s', FLAGS.graph)


  # load image
  image_data = load_image(FLAGS.image)

  # load labels
  labels = load_labels(FLAGS.labels)

  # load graph, which is stored in the default session
  load_graph(FLAGS.graph)

  run_graph(image_data, labels, FLAGS.input_layer, FLAGS.output_layer,
            FLAGS.num_top_predictions)


if __name__ == '__main__':
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=sys.argv[:1]+unparsed)

Answer 1

尝试tf.flags.FLAGS，或在顶部from tf.flags import FLAGS

Answer 2

尝试以下方法，

import os
import tensorflow as tf

# Define this after your imports. This is similar to python argparse except more verbose
FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('image', '/Users/photos',
                           """
                           Define your 'image' folder here 
                           or as an argument to your script
                           for eg, test.py --image /Users/..
                           """)



# use listdir to list the images in the target folder
filelist = os.listdir(FLAGS.image)

# now iterate over the objects in the list
for i in filelist:
    # load image
    image_data = load_image(i)

这应该有效。希望它有所帮助。

Answer 3

感谢您给出的帮助，FLAGS来自argparser模块而不是TensorFlow标志模块，并且可能必须从函数内调用FLAGS。我最终通过制作一个单独的函数来解决这个问题，所以我认为这就是发生的事情：

def get_image_list(path):
    return glob.glob(path + '*.jpg')

然后进一步调用循环：

filelist = get_image_list(FLAGS.image)

  for i in filelist:
        image_data = load_image(i)

        run_graph(image_data, labels, FLAGS.input_layer, FLAGS.output_layer,
            FLAGS.num_top_predictions)

在TensorFlow教程中修改label_image.py以对多个图像进行分类

3 个答案: