上代码：
import tensorflow as tf import os import numpy as np import re from PIL import Image import matplotlib.pyplot as plt class NodeLookup(object): def __init__(self): label_lookup_path = \'inception_model/imagenet_2012_challenge_label_map_proto.pbtxt\' uid_lookup_path = \'inception_model/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): # 加载分类字符串n********对应分类名称的文件 proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines() uid_to_human = {} #一行一行读取数据 for line in proto_as_ascii_lines : #去掉换行符 line=line.strip(\'\n\') #按照\'\t\'分割 parsed_items = line.split(\'\t\') #获取分类编号 uid = parsed_items[0] #获取分类名称 human_string = parsed_items[1] #保存编号字符串n********与分类名称映射关系 uid_to_human[uid] = human_string # 加载分类字符串n********对应分类编号1-1000的文件 proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines() node_id_to_uid = {} for line in proto_as_ascii: if line.startswith(\' target_class:\'): #获取分类编号1-1000 target_class = int(line.split(\': \')[1]) if line.startswith(\' target_class_string:\'): #获取编号字符串n******** target_class_string = line.split(\': \')[1] #保存分类编号1-1000与编号字符串n********映射关系 node_id_to_uid[target_class] = target_class_string[1:-2] #建立分类编号1-1000对应分类名称的映射关系 node_id_to_name = {} for key, val in node_id_to_uid.items(): #获取分类名称 name = uid_to_human[val] #建立分类编号1-1000到分类名称的映射关系 node_id_to_name[key] = name return node_id_to_name #传入分类编号1-1000返回分类名称 def id_to_string(self, node_id): if node_id not in self.node_lookup: return \'\' return self.node_lookup[node_id] #创建一个图来存放google训练好的模型 with tf.gfile.FastGFile(\'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\') #遍历目录，如果images下没有图片，则没有任何识别 for root,dirs,files in os.walk(\'images/\'): for file in files: #载入图片 image_data = tf.gfile.FastGFile(os.path.join(root,file), \'rb\').read() predictions = sess.run(softmax_tensor,{\'DecodeJpeg/contents:0\': image_data})#图片格式是jpg格式 predictions = np.squeeze(predictions)#把结果转为1维数据 #打印图片路径及名称 image_path = os.path.join(root,file) print(image_path) #显示图片 img=Image.open(image_path) plt.imshow(img) plt.axis(\'off\') plt.show() #排序 top_k = predictions.argsort()[-5:][::-1] node_lookup = NodeLookup() for node_id in top_k: #获取分类名称 human_string = node_lookup.id_to_string(node_id) #获取该分类的置信度 score = predictions[node_id] print(\'%s (score = %.5f)\' % (human_string, score)) print()
识别结果：