Inception-v 3既成重みによる特徴抽出(画像認識)

tensorflow公式サイトの画像認識の中国語紹介では、Tensorflowのモデルコードライブラリのclassify_image.pyで画像認識を行います.どのようにテストするか、そして最後の層の1*1*2048次元の特徴抽出方法も紹介されているので、ここで紹介します.

......

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')

.......

ソースコードはここで紹介したもので、3つのインタフェースがあり、
'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.
予測すると直接'softmax:0':'DecodeJpeg/contents:0':画像認識のテストが可能
フィーチャーを抽出するには

            fc_tensor = sess.graph.get_tensor_by_name('pool_3:0')
            pool_1 = sess.run(fc_tensor,{'DecodeJpeg/contents:0': image_data})

はい、保存するならCSVか選択できます.matファイル

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('
')

            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})

兄弟子は自分のデータセットの画像の特徴を抽出する必要があるので、このように書きました.もう一つのサイクルを加えて、データセット全体を遍歴することもできます.コンピュータの配置が限られているので、このように書きました.私が変更したソース+重み
新しい変更

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('
')

            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)

ベクトルをまっすぐにしてデータセット全体を巡回