Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
import numpy as np
import tensorflow as tf
import os
from tensorflow.python.platform import gfile
import os.path
import re
import sys
import tarfile
from subprocess import Popen, PIPE, STDOUT
def run(cmd):
p = Popen(cmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=STDOUT, close_fds=True)
return p.stdout.read()
model_dir = '/tmp/imagenet'
image_file = ""
num_top_predictions = 5
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
IMAGES_INDEX_URL = 'http://image-net.org/imagenet_data/urls/imagenet_fall11_urls.tgz'
# The number of images to process.
image_batch_size = 3
max_content = 1000L
def read_file_index():
from six.moves import urllib
content = urllib.request.urlopen(IMAGES_INDEX_URL)
data = content.read(max_content)
tmpfile = "/tmp/imagenet.tgz"
with open(tmpfile, 'wb') as f:
f.write(data)
run("tar -xOzf %s > /tmp/imagenet.txt" % tmpfile)
with open("/tmp/imagenet.txt", 'r') as f:
lines = [l.split() for l in f]
input_data = [tuple(elts) for elts in lines if len(elts) == 2]
return [input_data[i:i+image_batch_size] for i in range(0,len(input_data), image_batch_size)]
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(
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):
"""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 gfile.Exists(uid_lookup_path):
tf.logging.fatal('File does not exist %s', uid_lookup_path)
if not 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 = 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 = 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 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='')
def run_inference_on_image(image):
"""Runs inference on an image.
Args:
image: Image file name.
Returns:
Nothing
"""
if not gfile.Exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = gfile.FastGFile(image, 'rb').read()
# Creates graph from saved GraphDef.
create_graph()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.15)
with tf.Session(config=tf.ConfigProto(log_device_placement=True, gpu_options=gpu_options)) 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')
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = NodeLookup()
top_k = predictions.argsort()[-num_top_predictions:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
def maybe_download_and_extract():
"""Download and extract model tar file."""
from six.moves import urllib
dest_directory = 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):
filepath2, _ = urllib.request.urlretrieve(DATA_URL, filepath)
print("filepath2", filepath2)
statinfo = os.stat(filepath)
print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
tarfile.open(filepath, 'r:gz').extractall(dest_directory)
else:
print('Data already downloaded:', filepath, os.stat(filepath))
maybe_download_and_extract()
batched_data = read_file_index()
print "There are %d batches" % len(batched_data)
label_lookup_path = os.path.join(model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
uid_lookup_path = os.path.join(model_dir, 'imagenet_synset_to_human_label_map.txt')
def load_lookup():
"""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 gfile.Exists(uid_lookup_path):
tf.logging.fatal('File does not exist %s', uid_lookup_path)
if not 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 = 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 = 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
node_lookup = load_lookup()
node_lookup_bc = sc.broadcast(node_lookup)
model_path = os.path.join(model_dir, 'classify_image_graph_def.pb')
with gfile.FastGFile(model_path, 'rb') as f:
model_data = f.read()
model_data_bc = sc.broadcast(model_data)
def run_image(sess, img_id, img_url, node_lookup):
from six.moves import urllib
from urllib2 import HTTPError
try:
image_data = urllib.request.urlopen(img_url, timeout=1.0).read()
except HTTPError:
return (img_id, img_url, None)
except:
return (img_id, img_url, None)
scores = []
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
try:
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
except:
return (img_id, img_url, None)
predictions = np.squeeze(predictions)
top_k = predictions.argsort()[-num_top_predictions:][::-1]
scores = []
for node_id in top_k:
if node_id not in node_lookup:
human_string = ''
else:
human_string = node_lookup[node_id]
score = predictions[node_id]
scores.append((human_string, score))
return (img_id, img_url, scores)
def apply_batch(batch):
with tf.Graph().as_default() as g:
graph_def = tf.GraphDef()
graph_def.ParseFromString(model_data_bc.value)
tf.import_graph_def(graph_def, name='')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.15)
with tf.Session(config=tf.ConfigProto(log_device_placement=True, gpu_options=gpu_options)) as sess:
labelled = [run_image(sess, img_id, img_url, node_lookup_bc.value) for (img_id, img_url) in batch]
return [tup for tup in labelled if tup[2] is not None]
urls = sc.parallelize(batched_data)
labelled_images = urls.flatMap(apply_batch)
local_labelled_images = labelled_images.collect()
print(local_labelled_images)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment