树莓派3B使用tensorflow的classify

硬件：树莓派3B

软件环境：

1.python3.4

2.tensorflow-1.1.0-cp34-cp34m-linux_armv7l.whl

安装呢，其实网上特别多教程，这里我是使用安装包方式安装的，指令如下：

sudo pip install tensorflow-1.1.0-cp34-cp34m-linux_armv7l.whl

这里重点讲一下使用，网上教程都是如下图所示安装完就可以使用了，但是我安装了好几次，都没有models文件夹，也就是没有classify_image这个文件，所以郁闷了好几天，重装数次都没有，windows和树莓派下面都没有

然后就是在GitHub上面寻找一些线索吧，链接：https://github.com/tensorflow/

然后发现了梦寐以求的models，如下图

将models下载下来，真的有classify_image这个文件，哈哈，开心一会

然后将这个文件拷贝到 /usr/local/lib/python3.4/dist-packages/tensorflow

然后解压缩文件，如下图，就有了classify_image

有了这个，咱们就可以像其他小伙伴一样进行图片识别了，我们来测试一下

在home下新建文件夹 tensorflow-related，里面放入一个测试照片，如下图：

下面开始测试图片识别指令了，

进入到classify_image的目录

cd /usr/local/lib/python3.4/dist-packages/tensorflow/models-master/tutorials/image/imagenet

再输入：python3.4 classify_image.py --model_dir /home/pi/tensorflow-related/model --image_file /home/pi/tensorflow-related/panda.jpg

这样就可以识别到图片是panda了，概率0.87.就可以基本确定了，是吧

其实我们再看下 tensorflow-related 这个文件夹，如下图

咦，居然多了个文件夹啊

看下文件夹里面，下载了识别模型图库 inception-2015-12-05.tgz，并解压缩了

贴出来 classify_image.py的源码

# 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_functionimport argparse
import os.path
import re
import sys
import tarfileimport numpy as np
from six.moves import urllib
import tensorflow as tfFLAGS = None# pylint: disable=line-too-long
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
# pylint: enable=line-too-longclass 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 stringproto_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 stringnode_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] = namereturn node_id_to_namedef 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_dirif 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 elseos.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, andimagenet_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)

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