Tensorflow學習(7)用別人訓練好的模型進行影象分類
阿新 • • 發佈:2019-02-01
其中的classify_image_graph_def.pb 檔案就是訓練好的Inception-v3模型。
imagenet_synset_to_human_label_map.txt是類別檔案。
隨機找一張圖片:如
對這張圖片進行識別,看它屬於什麼類?
程式碼如下:先建立一個類NodeLookup來將softmax概率值對映到標籤上。
然後建立一個函式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轉換為人類易讀的標籤
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模型來建立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()
#建立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')
#輸入影象資料,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)