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classify_image_getvec_fromlist_1.py
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classify_image_getvec_fromlist_1.py
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Simple image classification with Inception.
Run image classification with Inception trained on ImageNet 2012 Challenge data
set.
This program creates a graph from a saved GraphDef protocol buffer,
and runs inference on an input JPEG image. It outputs human readable
strings of the top 5 predictions along with their probabilities.
Change the --image_file argument to any jpg image to compute a
classification of that image.
Please see the tutorial and website for a detailed description of how
to use this script to perform image recognition.
https://tensorflow.org/tutorials/image_recognition/
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path, glob, os
import re
import sys
import tarfile
import Image
import numpy as np
from six.moves import urllib
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
# classify_image_graph_def.pb:
# Binary representation of the GraphDef protocol buffer.
# imagenet_synset_to_human_label_map.txt:
# Map from synset ID to a human readable string.
# imagenet_2012_challenge_label_map_proto.pbtxt:
# Text representation of a protocol buffer mapping a label to synset ID.
tf.app.flags.DEFINE_string(
'model_dir', '/home/scopeserver/RaidDisk/DeepLearning/mwang/tensorflow/v3model/',
"""Path to classify_image_graph_def.pb, """
"""imagenet_synset_to_human_label_map.txt, and """
"""imagenet_2012_challenge_label_map_proto.pbtxt.""")
tf.app.flags.DEFINE_string('image_file', '',
"""Absolute path to image file.""")
tf.app.flags.DEFINE_integer('num_top_predictions', 5,
"""Display this many predictions.""")
# pylint: disable=line-too-long
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
# pylint: enable=line-too-long
class NodeLookup(object):
"""Converts integer node ID's to human readable labels."""
def __init__(self,
label_lookup_path=None,
uid_lookup_path=None):
if not label_lookup_path:
label_lookup_path = os.path.join(
FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
if not uid_lookup_path:
uid_lookup_path = os.path.join(
FLAGS.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):
"""Loads a human readable English name for each softmax node.
Args:
label_lookup_path: string UID to integer node ID.
uid_lookup_path: string UID to human-readable string.
Returns:
dict from integer node ID to human-readable string.
"""
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]
def create_graph():
"""Creates a graph from saved GraphDef file and returns a saver."""
# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile(os.path.join(
FLAGS.model_dir, 'v1.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
for node in tf.get_default_graph().as_graph_def().node:
print (node.name)
def run_inference_on_image():
"""Runs inference on an image.
Returns:
Nothing
"""
# Creates graph from saved GraphDef.
create_graph()
features=[]
files=[]
#filelist=open('/home/scopeserver/RaidDisk/DeepLearning/mwang/tensorflow/tensorflow/models/image/imagenet/list.txt','r')
with open('/home/scopeserver/RaidDisk/DeepLearning/mwang/tensorflow/tensorflow/models/image/imagenet/testv2.txt','r') as reader:
filelist = [line.rstrip() for line in reader]
#for i in range(1,346521):
for image in filelist:
#for image in sorted(os.listdir("/home/scopeserver/RaidDisk/DeepLearning/mwang/data/google_lace/")):
#image=image.replace("\n","")
#print (image)
#image=filelist[i]
if image.lower().endswith("jpg"):
#image=os.path.join("/home/scopeserver/RaidDisk/DeepLearning/mwang/data/google_lace/",image)
print("\n"+image)
#if not tf.gfile.Exists(image):
#tf.logging.fatal('File does not exist %s', image)
#image_data = tf.gfile.FastGFile(image, 'rb').read().
#with open(image, 'rb') as f:
# image_data = f.read()
#image_buffer=tf.image.decode_jpeg(image_data)
#resize_image=tf.image.resize_images(image_buffer,[224,224])
#unified_image=tf.image.per_image_whitening(resize_image)
#image_4dTensor=tf.expand_dims(unified_image, 0)
try:
with tf.Session() as sess:
with tf.device("/gpu:0"):
img=Image.open(image) #.convert('RGB')
if img.mode!='RGB':
img=img.convert('RGB')
longersize = max(img.size)
#background = Image.new('RGB', (longersize, longersize), (255,255,255))
background = Image.new('RGB', (longersize, longersize), "white")
background.paste(img, (int((longersize-img.size[0])/2), int((longersize-img.size[1])/2)))
img = background
resize_image=np.array(img.resize((224,224),Image.BICUBIC))
normalize_image=(resize_image-128.0)/128.0
image_4d=np.expand_dims(normalize_image, axis=0)
#stack_vector=np.vstack((image_4d,image_4d,image_4d,image_4d,image_4d))
#imagearray=image_4dTensor.eval()
#feature_tensor = sess.graph.get_tensor_by_name('pool_3:0') #ADDED
#feature_set = sess.run(feature_tensor,{'DecodeJpeg/contents:0': image_data}) #ADDED
feature_tensor = sess.graph.get_tensor_by_name('avgpool0/reshape:0') #ADDED
feature_set = sess.run(feature_tensor,{'input:0': image_4d}) #ADDED
feature_set = np.squeeze(feature_set) #ADDED
print(np.size(feature_set))
print(feature_set) #ADDED
#files.append(image)
#features.append(feature_set)
#os.remove()
with open("fashion_lists_1024.txt", 'a') as f:
#for s in image:
f.write(image +'\n')
with open("fashion_feature_1024.txt", 'a') as q:
#for s in feature_set:
#f.write(feature_set +'\n',fmt="%f")
np.savetxt(q,feature_set,fmt="%f")
#sess.close()
#os.remove(image)
except Exception, e:
#sess.close()
print(e)
print("error image: "+image)
#os.remove(image)
with open("fashion_error_1024.txt", 'a') as t:
t.write(image +'\n')
#with open("filelist.txt", 'w') as f:
#f.write("\n".join(files))
#with open("features.txt", 'w') as q:
#np.savetxt(q,features,fmt="%f")
#filelist.close()
def maybe_download_and_extract():
"""Download and extract model tar file."""
dest_directory = FLAGS.model_dir
if not os.path.exists(dest_directory):
os.makedirs(dest_directory)
filename = DATA_URL.split('/')[-1]
filepath = os.path.join(dest_directory, filename)
if not os.path.exists(filepath):
def _progress(count, block_size, total_size):
sys.stdout.write('\r>> Downloading %s %.1f%%' % (
filename, float(count * block_size) / float(total_size) * 100.0))
sys.stdout.flush()
filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
tarfile.open(filepath, 'r:gz').extractall(dest_directory)
def main(_):
#maybe_download_and_extract()
#image = (FLAGS.image_file if FLAGS.image_file else
# os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
#run_inference_on_image(image)
run_inference_on_image()
if __name__ == '__main__':
tf.app.run()