盜：如何創建從圖像輸入

Question

如何創建一個功能啓v3的一個輸出功能：

1. 拍攝圖像作爲輸入。

2. 打印輸出標籤的邏輯。

爲以來v3的原始代碼是在這裏： https://github.com/tensorflow/models/tree/master/inception

在那裏他們設法計算從一個曲線圖輸出的示例代碼是在這裏。我希望模型使用檢查點而不是圖形。但是，我不知道如何執行下面的例子，但使用檢查點。

"""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 argparse 
import os.path 
import re 
import sys 
import tarfile 

import numpy as np 
from six.moves import urllib 
import tensorflow as tf 

FLAGS = None 

# 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, '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 tf.gfile.Exists(image): 
    tf.logging.fatal('File does not exist %s', image) 
    image_data = tf.gfile.FastGFile(image, 'rb').read() 

    # Creates graph from saved GraphDef. 
    create_graph() 

    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') 
    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()[-FLAGS.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.""" 
    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('Successfully 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) 


if __name__ == '__main__': 
    parser = argparse.ArgumentParser() 
    # 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. 
    parser.add_argument(
     '--model_dir', 
     type=str, 
     default='/tmp/imagenet', 
     help="""\ 
     Path to classify_image_graph_def.pb, 
     imagenet_synset_to_human_label_map.txt, and 
     imagenet_2012_challenge_label_map_proto.pbtxt.\ 
     """ 
) 
    parser.add_argument(
     '--image_file', 
     type=str, 
     default='', 
     help='Absolute path to image file.' 
) 
    parser.add_argument(
     '--num_top_predictions', 
     type=int, 
     default=5, 
     help='Display this many predictions.' 
) 
    FLAGS, unparsed = parser.parse_known_args() 
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) 

Answer 1

只要運行它像這樣python classify_image.py --image_file /path/to/file 這將需要的圖像作爲輸入，將輸出的標籤。

您可能還想嘗試添加下面的代碼行。它會識別並分析上次添加的.jpg文件到指定的文件夾。

newest = max(glob.iglob('/home/l2grp/Jetty/src/ubiserv/simple/img/*.[Jj][Pp][Gg]'), key=os.path.getctime)

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 argparse 
 
import os.path 
 
import re 
 
import sys 
 
import tarfile 
 
import glob 
 

 
import numpy as np 
 
from six.moves import urllib 
 
import tensorflow as tf 
 

 
FLAGS = None 
 

 
# pylint: disable=line-too-long 
 
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz' 
 
# pylint: enable=line-too-long 
 

 
newest = max(glob.iglob('/home/l2grp/Jetty/src/ubiserv/simple/img/*.[Jj][Pp][Gg]'), key=os.path.getctime) 
 

 
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, '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 tf.gfile.Exists(image): 
 
    tf.logging.fatal('File does not exist %s', image) 
 
    image_data = tf.gfile.FastGFile(image, 'rb').read() 
 

 
    # Creates graph from saved GraphDef. 
 
    create_graph() 
 

 
    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') 
 
    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()[-FLAGS.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 main(_): 
 
    image = newest 
 
    run_inference_on_image(image) 
 

 

 
if __name__ == '__main__': 
 
    parser = argparse.ArgumentParser() 
 
    # 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. 
 
    parser.add_argument(
 
     '--model_dir', 
 
     type=str, 
 
     default='/tmp/imagenet', 
 
     help="""\ 
 
     Path to classify_image_graph_def.pb, 
 
     imagenet_synset_to_human_label_map.txt, and 
 
     imagenet_2012_challenge_label_map_proto.pbtxt.\ 
 
     """ 
 
) 
 
    parser.add_argument(
 
     '--image_file', 
 
     type=str, 
 
     default='', 
 
     help='Absolute path to image file.' 
 
) 
 
    parser.add_argument(
 
     '--num_top_predictions', 
 
     type=int, 
 
     default=5, 
 
     help='Display this many predictions.' 
 
) 
 
    FLAGS, unparsed = parser.parse_known_args() 
 
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)