Question

我正在使用Tensorflow图像分类示例（https://www.tensorflow.org/versions/r0.9/tutorials/image_recognition/index.html）。我怎么能一次分类多个图像？

编辑：理想情况下，我只会传入一个图像和一个数字（nb）作为参数，然后对该图像进行输入待分类的nb迭代

文件为classify_image.py，重要部分为：

def run_inference_on_image(image):
"""Runs inference on an image.

Args:
image: Image file name.

Returns:
Nothing
"""
if not tf.gfile.Exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = tf.gfile.FastGFile(image, 'rb').read()

# Creates graph from saved GraphDef.
create_graph()

with tf.Session() as sess:
# Some useful tensors:
# 'softmax:0': A tensor containing the normalized prediction across
#   1000 labels.
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
#   float description of the image.
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
#   encoding of the image.
# Runs the softmax tensor by feeding the image_data as input to the graph.
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
predictions = sess.run(softmax_tensor,
                       {'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)

# Creates node ID --> English string lookup.
node_lookup = NodeLookup()

top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
for node_id in top_k:
  human_string = node_lookup.id_to_string(node_id)
  score = predictions[node_id]
  print('%s (score = %.5f)' % (human_string, score))

def main(_):
maybe_download_and_extract()
image = (FLAGS.image_file if FLAGS.image_file else
       os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
run_inference_on_image(image)

Answer 1

与您相关的代码将是此部分：

def main(_):
  maybe_download_and_extract()
  image = (FLAGS.image_file if FLAGS.image_file else
           os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
  run_inference_on_image(image)

为了预测＆＃34;图像中的所有png，jpeg或jpg文件＆＃34;文件夹，你可以这样做：

def main(_):
  maybe_download_and_extract()

  # search for files in 'images' dir
  files_dir = os.getcwd() + '/images'
  files = os.listdir(files_dir)

  # loop over files, print prediction if it is an image
  for f in files:
    if f.lower().endswith(('.png', '.jpg', '.jpeg')):
      image_path = files_dir + '/' + f
      print run_inference_on_image(image_path)

这应该打印出该文件夹中所有图像的预测

tensorflow对多个图像进行分类

1 个答案: