利用Inception-v3現成權重進行特徵提取(影象識別)
阿新 • • 發佈:2018-12-22
在tensorflow官網的影象識別的中文介紹中,介紹瞭如何用Tensorflow的模型程式碼庫中的classify_image.py進行影象識別。裡面有介紹如何測試,而且還提供了最後一層的1*1*2048維的特徵提取方式,所以在這裡介紹一下。
...... with tf.Session() as sess: # Some useful tensors: # 'softmax:0': A tensor containing the normalized prediction across # 1000 labels. # 'pool_3:0': A tensor containing the next-to-last layer containing 2048 # float description of the image. # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG # encoding of the image. # Runs the softmax tensor by feeding the image_data as input to the graph. softmax_tensor = sess.graph.get_tensor_by_name('softmax:0') .......
原始碼就是在這裡進行的介紹,有三個介面,
'softmax:0': A tensor containing the normalized prediction across 1000 labels.
'pool_3:0': A tensor containing the next-to-last layer containing 2048 float description of the image.
'DecodeJpeg/contents:0': A tensor containing a string providing JPEG encoding of the image.
預測的話直接調'softmax:0':和'DecodeJpeg/contents:0':可以進行影象識別的測試
如果想要提取特徵就像這樣
fc_tensor = sess.graph.get_tensor_by_name('pool_3:0')
pool_1 = sess.run(fc_tensor,{'DecodeJpeg/contents:0': image_data})
就可以,儲存的話可以選擇CSV或者.mat檔案
import tensorflow as tf import numpy as np import os from PIL import Image import matplotlib.pyplot as plt import scipy.io as scio model_dir='F:/fqh/models-master/tutorials/image/imagenet/2015' image = 'F:/fqh/models-master/tutorials/image/imagenet/data_set/face/faces95_72_20_180-200jpgfar-close/' target_path=image+'wjhugh/' 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) proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines() uid_to_human = {} for line in proto_as_ascii_lines: line = line.strip('\n') parse_items = line.split('\t') uid = parse_items[0] human_string = parse_items[1] uid_to_human[uid] = human_string 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:'): 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] 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] 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='') create_graph() with tf.Session() as sess: softmax_tensor = sess.graph.get_tensor_by_name('softmax:0') for root, dirs, files in os.walk(target_path): for file in files: # print(file) img_path = target_path+file image_data = tf.gfile.FastGFile(img_path, 'rb').read() fc_tensor = sess.graph.get_tensor_by_name('pool_3:0') pool_1 = sess.run(fc_tensor,{'DecodeJpeg/contents:0': image_data}) # print(pool_1) img_path=img_path[:len(img_path)-4] #print(img_path) scio.savemat(img_path+'.mat', {"pool_1": pool_1})
因為師兄需要提取他自己的資料集的影象的特徵,所以寫成了這樣,也可以再加一個迴圈,遍歷整個資料集,由於電腦配置有限,就只寫成了這樣。我改的原始碼+權重
新改
import tensorflow as tf
import numpy as np
import os
from PIL import Image
import matplotlib.pyplot as plt
import scipy.io as scio
model_dir='F:/fqh/models-master/tutorials/image/imagenet/2015'
image = 'F:/fqh/models-master/tutorials/image/imagenet/data_set/face/faces96_152_20_180-200jpgview-depth/'
target_path=image+'wjhugh/'
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)
proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
uid_to_human = {}
for line in proto_as_ascii_lines:
line = line.strip('\n')
parse_items = line.split('\t')
uid = parse_items[0]
human_string = parse_items[1]
uid_to_human[uid] = human_string
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:'):
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]
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]
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='')
create_graph()
list0=[]
for root, dirs,files in os.walk(image):
list0.append(dirs)
#print(list0[0])
img_list=[]
# print(img_list)
for ii in list0[0]:
img_list.append(ii)
list_img_name=np.array(img_list)
list_img_name.sort()
# print(list_img_name[0])
with tf.Session() as sess:
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
for jj in range(0,len(list_img_name)):#len(list_img_name)
target_path=image+list_img_name[jj]+'/'
for root, dirs, files in os.walk(target_path):
for file in files:
img_path = target_path+file
image_data = tf.gfile.FastGFile(img_path, 'rb').read()
fc_tensor = sess.graph.get_tensor_by_name('pool_3:0')
pool_1 = sess.run(fc_tensor,{'DecodeJpeg/contents:0': image_data})
pool_2 = pool_1[0,0,0,:]
img_path=img_path[:len(img_path)-4]
scio.savemat(img_path+'.mat', {"pool_2": pool_2})
pi= (jj/(len(list_img_name)-1))*100
print("%4.2f %%" % pi)
將向量拉直並遍歷整個資料集