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| import tensorflow as tf
import sys
import os
print(tf.version)
# Désactiver les avertissements de compilation tensorflow
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
def analyse(imageObj):
# Lire l'image_data
image_data = tf.gfile.FastGFile(imageObj, 'rb').read()
# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line in tf.io.gfile.GFile("tf_files/retrained_labels.txt")]
# Unpersists graph from file
with tf.gfile.FastGFile("tf_files/retrained_graph.pb", 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
with tf.compat.v1.Session() as sess:
# Introduire les images_data en entrée du graphique et obtenir la première prédiction
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions = sess.run(softmax_tensor, \
{'DecodeJpeg/contents:0': image_data})
#Trier pour afficher les étiquettes de la première prédiction par ordre de confiance
top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
obj = {}
for node_id in top_k:
human_string = label_lines[node_id]
score = predictions[0][node_id]
obj[human_string] = float(score)
return obj |
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