【先說一下自己想說的】:昨晚上找了很久才搞定,代碼和給的文件根本不匹配,轉載也不驗證一下就轉。弄得我花了一整天!(我就爲了加個單擊圖片顯示可能的標籤這麼個功能我……我容易嗎……555)
原帖:http://www.cnblogs.com/denny402/p/6942580.html(感謝此源貼的下方評論指引我找到了配套的庫)
然後我鄙視一下這些轉載不發源鏈接的↓(╬▔皿▔)凸(還有就是不驗證就敢轉發):
https://blog.csdn.net/u011600477/article/details/78607883
https://blog.csdn.net/m0_37167788/article/details/79084288
與原帖配套的模型和其他文件在:(不知道是不是源博主搞錯了,博主給的雲盤裏的東西完全是不着邊,這幫轉貼的也不自己驗證以下,像是傳下去的謊言——真是荒謬又可笑)
“看到這個鏈接了,裏面有博主提到的模型和pbtxt文件 https://github.com/taey16/tf/tree/master/imagenet”
以下是原帖,上邊該補充的都說了=================分割線=============
谷歌在大型圖像數據庫ImageNet上訓練好了一個Inception-v3模型,這個模型我們可以直接用來進來圖像分類。
下載地址:https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015.zip
下載完解壓後,得到幾個文件:
其中的classify_image_graph_def.pb 文件就是訓練好的Inception-v3模型。
imagenet_synset_to_human_label_map.txt是類別文件。
隨機找一張圖片:如
對這張圖片進行識別,看它屬於什麼類?
代碼如下:先創建一個類NodeLookup來將softmax概率值映射到標籤上。
然後創建一個函數create_graph()來讀取模型。
最後讀取圖片進行分類識別:
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import re
import os
model_dir='D:/tf/model/'
image='d:/cat.jpg'
#將類別ID轉換爲人類易讀的標籤
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]
#讀取訓練好的Inception-v3模型來創建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='')
#讀取圖片
image_data = tf.gfile.FastGFile(image, 'rb').read()
#創建graph
create_graph()
sess=tf.Session()
#Inception-v3模型的最後一層softmax的輸出
softmax_tensor= sess.graph.get_tensor_by_name('softmax:0')
#輸入圖像數據,得到softmax概率值(一個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個概率最大的值(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()
最後輸出:
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)
以下是親自驗證,上圖====================分割線====================================
上面這張圖,識別成seashore,還是挺準的。
注意,我是windows環境運行的,目錄要用r'路徑'或者雙反斜槓"\"轉意) 或者斜槓"/"!不然總會出如下錯誤
tensorflow.python.framework.errors_impl.NotFoundError: NewRandomAccessFile failed to Create/Open: D: f.jpg : ϵ ͳ\udcd5Ҳ\udcbb\udcb5\udcbdָ\udcb6\udca8\udcb5\udcc4\udcceļ\udcfe\udca1\udca3
; No such file or directory
還有,我目前不知道爲什麼一把圖片弄個路徑就出錯,目前我是直接放文件夾裏才能用的。不然就是下面那個錯誤
#讀取圖片
image_data = tf.gfile.FastGFile('1.jpg', 'rb').read() #直接的'1.jpg'
tensorflow.python.framework.errors_impl.InvalidArgumentError: NewRandomAccessFile failed to Create/Open: D:\tf\1.jpg : \udcceļ\udcfe\udcc3\udcfb\udca1\udca2Ŀ¼\udcc3\udcfb\udcbb\udcf2\udcbe\udced\udcb1\udcea\udcd3\udcb2\udcbb\udcd5\udcfdȷ\udca1\udca3
; Unknown error
最後放兩個不錯的補充鏈接:
https://blog.csdn.net/juezhanangle/article/details/78725913
https://blog.csdn.net/muyiyushan/article/details/64124953