tensorflow 1.0 学习：用别人训练好的模型来进行图像分类

Posted 2020-09-21 denny的学习专栏

谷歌在大型图像数据库ImageNet上训练好了一个Inception-v3模型，这个模型我们可以直接用来进来图像分类。

下载地址：https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015.zip

下载完解压后，得到几个文件：

其中的classify_image_graph_def.pb 文件就是训练好的Inception-v3模型。

imagenet_synset_to_human_label_map.txt是类别文件。

随机找一张图片：如

对这张图片进行识别，看它属于什么类？

代码如下：先创建一个类NodeLookup来将softmax概率值映射到标签上。

然后创建一个函数create_graph()来读取模型。

最后读取图片进行分类识别：

# -*- coding: utf-8 -*-

import tensorflow as tf
import numpy as np
import re
import os

model_dir=\'D:/tf/model/\'
image=\'d:/cat.jpg\'


#将类别ID转换为人类易读的标签
class NodeLookup(object):
  def __init__(self,
               label_lookup_path=None,
               uid_lookup_path=None):
    if not label_lookup_path:
      label_lookup_path = os.path.join(
          model_dir, \'imagenet_2012_challenge_label_map_proto.pbtxt\')
    if not uid_lookup_path:
      uid_lookup_path = os.path.join(
          model_dir, \'imagenet_synset_to_human_label_map.txt\')
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)

  def load(self, label_lookup_path, uid_lookup_path):
    if not tf.gfile.Exists(uid_lookup_path):
      tf.logging.fatal(\'File does not exist %s\', uid_lookup_path)
    if not tf.gfile.Exists(label_lookup_path):
      tf.logging.fatal(\'File does not exist %s\', label_lookup_path)

    # Loads mapping from string UID to human-readable string
    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile(r\'[n\\d]*[ \\S,]*\')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      uid = parsed_items[0]
      human_string = parsed_items[2]
      uid_to_human[uid] = human_string

    # Loads mapping from string UID to integer node ID.
    node_id_to_uid = {}
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
    for line in proto_as_ascii:
      if line.startswith(\'  target_class:\'):
        target_class = int(line.split(\': \')[1])
      if line.startswith(\'  target_class_string:\'):
        target_class_string = line.split(\': \')[1]
        node_id_to_uid[target_class] = target_class_string[1:-2]

    # Loads the final mapping of integer node ID to human-readable string
    node_id_to_name = {}
    for key, val in node_id_to_uid.items():
      if val not in uid_to_human:
        tf.logging.fatal(\'Failed to locate: %s\', val)
      name = uid_to_human[val]
      node_id_to_name[key] = name

    return node_id_to_name

  def id_to_string(self, node_id):
    if node_id not in self.node_lookup:
      return \'\'
    return self.node_lookup[node_id]

#读取训练好的Inception-v3模型来创建graph
def create_graph():
  with tf.gfile.FastGFile(os.path.join(
      model_dir, \'classify_image_graph_def.pb\'), \'rb\') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name=\'\')


#读取图片
image_data = tf.gfile.FastGFile(image, \'rb\').read()

#创建graph
create_graph()

sess=tf.Session()
#Inception-v3模型的最后一层softmax的输出
softmax_tensor= sess.graph.get_tensor_by_name(\'softmax:0\')
#输入图像数据，得到softmax概率值（一个shape=(1,1008)的向量）
predictions = sess.run(softmax_tensor,{\'DecodeJpeg/contents:0\': image_data})
#(1,1008)->(1008,)
predictions = np.squeeze(predictions)

# ID --> English string label.
node_lookup = NodeLookup()
#取出前5个概率最大的值（top-5)
top_5 = predictions.argsort()[-5:][::-1]
for node_id in top_5:
  human_string = node_lookup.id_to_string(node_id)
  score = predictions[node_id]
  print(\'%s (score = %.5f)\' % (human_string, score))
  
sess.close()

最后输出：

tiger cat (score = 0.40316)
Egyptian cat (score = 0.21686)
tabby, tabby cat (score = 0.21348)
lynx, catamount (score = 0.01403)
Persian cat (score = 0.00394)