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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 argparse
- import os.path
- import re
- import sys
- import tarfile
- from tkinter import filedialog
- from tkinter import *
- import array
- import numpy as np
- from six.moves import urllib
- import tensorflow as tf
- import pymongo
- 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
- LABELS_PREDICTED = ''
- PERCENTAGE = ''
- 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]
- print("COUCOU")
- print(node_lookup.id_to_string(top_k[0]))
- LABELS_PREDICTED = node_lookup.id_to_string(top_k[0])
- PERCENTAGE = predictions[top_k[0]]
- 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))
- my_array = [LABELS_PREDICTED, PERCENTAGE]
- return my_array
- 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 normal_execution(image):
- maybe_download_and_extract()
- image = (FLAGS.image_file if FLAGS.image_file else os.path.join(FLAGS.model_dir, image))
- my_array = run_inference_on_image(image)
- return my_array
- def main(_):
- def callback():
- root.filename = filedialog.askopenfilename(initialdir = "/",title = "Select file",filetypes = (("jpeg files","*.jpg"),("all files","*.*")))
- print (root.filename)
- my_array = normal_execution(root.filename)
- print("COUCOU_2")
- print(my_array[0])
- print(my_array[1])
- w = Label(root, text=my_array[0])
- w.pack()
- s = Label(root, text=my_array[1])
- s.pack()
- myclient = pymongo.MongoClient("mongodb://localhost:27017/")
- mydb = myclient["ia"]
- mycol = mydb["collection_ia"]
- mydb.collection_ia.insert_one({'CheminImage': root.filename, 'Label': my_array[0], 'Score': str(my_array[1])})
- root = Tk()
- b = Button(root, text="Selectionner une image", command=callback)
- b.pack()
- mainloop()
- 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)
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