AI实战：上海垃圾分类系列（一）之快速搭建垃圾分类模型

前言

AI实战：上海垃圾分类系列（一）之快速搭建垃圾分类模型
AI实战：上海垃圾分类系列（二）之快速搭建垃圾分类模型后台服务
AI实战：上海垃圾分类系列（三）之快速搭建垃圾分类智能问答机器人

有上海网友说，如今每天去丢垃圾时，都要接受垃圾分类阿姨的灵魂拷问：“你是什么垃圾？”

Emmmm…

为了避免每天阿姨的灵魂拷问，我们最好是出门前提前对垃圾进精准分类。

下面提供一种快速搭建基于深度学习（AI）的垃圾分类模型，让垃圾分类不再难！

---

垃圾分类模型搭建

使用imagenet的1000个分类，模型网络使用inception-v3。再把1000个分类映射到垃圾的4个类别中，下面看详细步骤。

• 搭建环境
  Ubuntu16.04
  python3.5
  tensorflow==1.4.0

• 代码：
  classify_image.py：

# 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 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, 
                uid_chinese_lookup_path, 
                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)
    self.node_lookup = self.load_chinese_map(uid_chinese_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,]*')
    p = re.compile(r'(n\d*)\t(.*)')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      print(parsed_items)
      uid = parsed_items[0]
      human_string = parsed_items[1]
      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 load_chinese_map(self, uid_chinese_lookup_path):
    # Loads mapping from string UID to human-readable string
    proto_as_ascii_lines = tf.gfile.GFile(uid_chinese_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile(r'(\d*)\t(.*)')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      #print(parsed_items)
      uid = parsed_items[0][0]
      human_string = parsed_items[0][1]
      uid_to_human[int(uid)] = human_string
    
    return uid_to_human

  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 --> chinese string lookup.
    node_lookup = NodeLookup(uid_chinese_lookup_path='./data/imagenet_2012_challenge_label_chinese_map.pbtxt')

    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))
      #print('node_id: %s' %(node_id))


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)

• 下载模型
  python classify_image.py

  下载成功是这个样子的：

• 模型测试

  从网上找一张图片，保存为：./img/2.png，如下：

  
  测试方法：
  python classify_image.py --image_file ./data/2.png

  结果输出：

  cellular telephone, cellular phone, cellphone, cell, mobile phone (score = 0.70547)
  iPod (score = 0.06823)
  notebook, notebook computer (score = 0.04934)
  modem (score = 0.01472)
  hand-held computer, hand-held microcomputer (score = 0.00770)
  

  可以看到识别结果还是蛮准的，而且给出了top5.

  使用中文标签：
  测试方法：
  python classify_image.py --image_file ./data/2.png

  结果输出：

  移动电话，移动电话，手机，手机，手机 (score = 0.70547)
  iPod (score = 0.06823)
  笔记本，笔记本电脑 (score = 0.04934)
  调制解调器 (score = 0.01472)
  手持电脑，手持微电脑 (score = 0.00770)
  

  有了中文分类类别，下面就可以做垃圾分类映射了。

垃圾分类映射

上海对垃圾分干垃圾、湿垃圾、可回收物、有害垃圾四种，生活垃圾主要分干垃圾和湿垃圾。

上海生活垃圾分类标准及投放要求 【点击查看】

• 核心思想：
  1、使用4类垃圾分类数据作为标注数据，形如

    0	饮料瓶
    1	废电池
    2	绿叶菜
    3	卫生间用纸
  

  2、使用TextCNN训练分类模型

  • 实战
    1、数据标注
    标注结果见：./data/train_data.txt , ./data/vilid_data.txt

    2、核心代码：
    predict.py ：

    import tensorflow as tf
    import numpy as np
    import os, sys
    import data_input_helper as data_helpers
    import jieba
    
    # Parameters
    
    # Data Parameters
    tf.flags.DEFINE_string("w2v_file", "./data/word2vec.bin", "w2v_file path")
    
    # Eval Parameters
    tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
    tf.flags.DEFINE_string("checkpoint_dir", "./runs/checkpoints/", "Checkpoint directory from training run")
    
    # Misc Parameters
    tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
    tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
    
    FLAGS = tf.flags.FLAGS
    FLAGS._parse_flags()
    
    
    class RefuseClassification():
    
        def __init__(self):
        
            self.w2v_wr = data_helpers.w2v_wrapper(FLAGS.w2v_file)#加载词向量
            self.init_model()
            self.refuse_classification_map = {0: '可回收垃圾', 1: '有害垃圾', 2: '湿垃圾', 3: '干垃圾'}
            
            
        def deal_data(self, text, max_document_length = 10):
            
            words = jieba.cut(text)
            x_text = [' '.join(words)]
            x = data_helpers.get_text_idx(x_text, self.w2v_wr.model.vocab_hash, max_document_length)
    
            return x
    
    
        def init_model(self):
            
            checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
            graph = tf.Graph()
            with graph.as_default():
                session_conf = tf.ConfigProto(
                                  allow_soft_placement=FLAGS.allow_soft_placement, 
                                  log_device_placement=FLAGS.log_device_placement)
                self.sess = tf.Session(config=session_conf)
                self.sess.as_default()
                # Load the saved meta graph and restore variables
                saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
                saver.restore(self.sess, checkpoint_file)
    
                # Get the placeholders from the graph by name
                self.input_x = graph.get_operation_by_name("input_x").outputs[0]
              
                self.dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
    
                # Tensors we want to evaluate
                self.predictions = graph.get_operation_by_name("output/predictions").outputs[0]
                    
        
        def predict(self, text):
        
            x_test = self.deal_data(text, 5)
            predictions = self.sess.run(self.predictions, {self.input_x: x_test, self.dropout_keep_prob: 1.0})
            
            refuse_text = self.refuse_classification_map[predictions[0]]
            return refuse_text
    
    
    if __name__ == "__main__":
        if len(sys.argv) == 2:
            test = RefuseClassification()
            res = test.predict(sys.argv[1])
            print('classify:', res)
    

  3、测试
  python textcnn/predict.py '猪肉饺子'

  输出结果：

    	`classify: 湿垃圾`
  

---

整合imagenet分类模型、textcnn映射模型

• 核心代码：
  rafuse.py

import numpy as np
import os, sys
sys.path.append('textcnn')
from textcnn.predict import RefuseClassification
from classify_image import *


class RafuseRecognize():
    
    def __init__(self):
        
        self.refuse_classification = RefuseClassification()
        self.init_classify_image_model()
        self.node_lookup = NodeLookup(uid_chinese_lookup_path='./data/imagenet_2012_challenge_label_chinese_map.pbtxt', 
                                model_dir = '/tmp/imagenet')
        
        
    def init_classify_image_model(self):
        
        create_graph('/tmp/imagenet')

        self.sess = tf.Session()
        self.softmax_tensor = self.sess.graph.get_tensor_by_name('softmax:0')
        
        
    def recognize_image(self, image_data):
        
        predictions = self.sess.run(self.softmax_tensor,
                               {'DecodeJpeg/contents:0': image_data})
        predictions = np.squeeze(predictions)

        top_k = predictions.argsort()[-5:][::-1]
        result_list = []
        for node_id in top_k:
            human_string = self.node_lookup.id_to_string(node_id)
            #print(human_string)
            human_string = ''.join(list(set(human_string.replace('，', ',').split(','))))
            #print(human_string)
            classification = self.refuse_classification.predict(human_string)
            result_list.append('%s  =>  %s' % (human_string, classification))
            
        return '\n'.join(result_list)
        

if __name__ == "__main__":
    if len(sys.argv) == 2:
        test = RafuseRecognize()
        image_data = tf.gfile.FastGFile(sys.argv[1], 'rb').read()
        res = test.recognize_image(image_data)
        print('classify:\n%s' %(res))

• 垃圾分类识别

  • 识别
    python rafuse.py img/2.png

    输出结果：

       移动电话手机  =>  可回收垃圾
       iPod  =>  湿垃圾
       笔记本笔记本电脑  =>  可回收垃圾
       调制解调器  =>  湿垃圾
       手持电脑手持微电脑  =>  可回收垃圾
    

  到这里整个垃圾分类识别模型基本完成，可以看到有个别错误，由于训练数据太少了导致的，这里就不在优化了。

---

完整工程代码

完整工程：https://download.csdn.net/download/zengnlp/11290336
包含：
　1、垃圾分类映射的训练数据、测试数据
　2、完整代码

---

参考

https://github.com/tensorflow/models