使用Inception v3实现图像识别-蒲公英云

官方相关模型及文件下载：http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
下载的文件列表：
在这里插入图片描述
里面有训练好的pb模型，以及标签特征对应文件

载入相关库：

import tensorflow as tf
import os
import numpy as np
import re
from PIL import Image
import matplotlib.pyplot as plt

code：

class NodeLookup(object):
    def __init__(self):
    #加载对应的分类文件
        label_path = "inception_model/imagenet_2012_challenge_label_map_proto.pbtxt"
        uid_path = "inception_model/imagenet_synset_to_human_label_map.txt"
        self.node_lookup = self.load(label_path, uid_path)
    def load(self, label_path, uid_path):
    #读取pbtxt文件，按行读取
        proto_label_line = tf.gfile.GFile(label_path).readlines()
        #将读取的内容以字典的方式存储，key-value
        node_to_label_uid = { }
        #遍历每一行
        for line in proto_label_line:
        #去掉换行符
            line = line.strip('\n')
            #如果每一行以target_class开头：则对应的内容为 target_class: 442
            if line.startswith(" target_class:"):
                target_class = int(line.split(": ")[1])#442
            if line.startswith(" target_class_string:"):
            #包含引号
                target_class_string = line.split(": ")[1]
                # target_class_string: "n01494475"，取中间的字母数字部分，不包含引号
                node_to_label_uid[target_class] = target_class_string[1:-1]
        proto_uid_line = tf.gfile.GFile(uid_path).readlines()
        uid_to_label_human = { }
        for line in proto_uid_line:
            line = line.strip('\n')
            #分割
            parse_items = line.split("\t")
            uid = parse_items[0]
            target = parse_items[1]
            uid_to_label_human[uid] = target
            #uid_to_label_human[parse_items[0]] = parse_items[1]
        #重新对应如种类编号442对应类别cat（假设）
        node_id_to_name = { }
        for key, val in node_to_label_uid.items():
            name = uid_to_label_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]
#读取模型，重新创建相应的图，固定书写模式
with tf.gfile.GFile("inception_model/classify_image_graph_def.pb",'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name="")
with tf.Session() as sess:
    softmax_tensor = sess.graph.get_tensor_by_name("softmax:0")
    for root, dirs, files in os.walk("images/"):
        for file in files:
            image_data  = tf.gfile.GFile(os.path.join(root, file), 'rb').read()
            predict = sess.run(softmax_tensor, { "DecodeJpeg/contents:0":image_data})#图片是jpg格式
            predict = np.squeeze(predict)#将结果转为一维数据
            print(predict.shape)
            image_path = os.path.join(root, file)
            print(image_path)
            img = Image.open(image_path)
            plt.imshow(img)
            plt.axis("off")
            plt.show()
            top_pre = predict.argsort()[-3:][::-1]#升序排列，选择最后三个数据，再倒叙，即降序
            node_lookup = NodeLookup()
            for node_id in top_pre:
                human_string = node_lookup.id_to_string(node_id)
                score = predict[node_id]
                print("%s (score=%.5f)" %(human_string, score))
            print("*************************")

结果：
在这里插入图片描述

bug

1、readline()&readlines(),这里使用readlines(),否则只读取一行数据

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-21-74081abf60d9> in <module>
     74             #对预测结果排序，从小到大排序，取最后几个，[::-1]：对最后的五个概率值取倒叙
     75             top_k = predictions.argsort()[-5:][: :-1]
---> 76             node_lookup = NodeLookUp()
     77             for node_id in top_k:
     78                 #获取分类名称
<ipython-input-21-74081abf60d9> in __init__(self)
      4         label_lookup_path = "inception_model/imagenet_2012_challenge_label_map_proto.pbtxt"
      5         uid_lookup_path = "inception_model/imagenet_synset_to_human_label_map.txt"
----> 6         self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
      7 
      8     #载入数据进行处理
<ipython-input-21-74081abf60d9> in load(self, label_lookup_path, uid_lookup_path)
     19             #用键值对存储数据
     20             uid = parsed_items[0]
---> 21             human_string = parsed_items[1]
     22             #保存id和字符串为对应的映射关系
     23             uid_to_human[uid] = human_string
IndexError: list index out of range

2、载入模型时没有对其采用二进制读取，‘rb’

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-27-052cb1c2f17f> in <module>
     40 with tf.gfile.GFile("inception_model/classify_image_graph_def.pb") as f:
     41     graph_def = tf.GraphDef()
---> 42     graph_def.ParseFromString(f.read())
     43     tf.import_graph_def(graph_def, name="")
    UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbb in position 1: invalid start byte

‘r’:默认值，表示从文件读取数据。
‘w’:表示要向文件写入数据，并截断以前的内容
‘a’:表示要向文件写入数据，添加到当前内容尾部
‘r+’:表示对文件进行可读写操作（删除以前的所有数据）
‘r+a’：表示对文件可进行读写操作（添加到当前文件尾部）
‘b’:表示要读写二进制数据

3、未定义，tab，代码格式问题

if line.startswith(" target_class_string:"):
                target_class_string = line.split(": ")[1]
node_to_label_uid[target_class] = target_class_string[1:-1]
<ipython-input-28-ecfdf82b86d1> in load(self, label_path, uid_path)
     14             if line.startswith(" target_class_string:"):
     15                 target_class_string = line.split(": ")[1]
---> 16             node_to_label_uid[target_class] = target_class_string
     17 
     18         proto_uid_line = tf.gfile.GFile(uid_path).readlines()
UnboundLocalError: local variable 'target_class_string' referenced before assignment