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自分のデータでInceptionV3モデルを再学習し、Tensorflow画像分類チュートリアルhttps://www.tensorflow.org/tutorials/image_recognitionからコードを修正しようとしています。複数の画像を分類するためのTensorFlowチュートリアルのlabel_image.pyの変更
は私がリストとしてディレクトリに読み込み、それをループしようとしましたが、これは動作しませんでした：
load_graph(FLAGS.graph)
filelist = os.listdir(FLAGS.image)
for i in filelist:
# load image
image_data = load_image(i)
私はエラーがFLAGSが定義されていないことを言ってもらうので、私はFLAGSが持っていると思いますload_image関数と一緒に行く？これは動作するはず
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)
、
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)