36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
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import tensorflow as tf
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import os
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import numpy as np
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import cv2
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width=160
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height=120
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dir='dataE/'
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with tf.Session() as s:
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saver=tf.train.import_meta_graph('./mon_modele/modele.meta')
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saver.restore(s, tf.train.latest_checkpoint('./mon_modele/'))
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graph=tf.get_default_graph()
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images=graph.get_tensor_by_name("entree:0")
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sortie=graph.get_tensor_by_name("sortie:0")
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for file in os.listdir(dir+'CameraRGB/'):
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img=cv2.resize(cv2.imread(dir+'CameraRGB/'+file), (width, height))/255
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cv2.imshow("image", img)
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m=cv2.resize(cv2.imread(dir+'CameraSeg/'+file)[:,:,2], (width, height))
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m[m==7]=255
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m[m!=255]=0
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cv2.imshow("mask 7", m)
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m=cv2.resize(cv2.imread(dir+'CameraSeg/'+file)[:,:,2], (width, height))
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m[m==9]=255
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m[m!=255]=0
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cv2.imshow("mask 9", m)
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prediction=s.run(sortie, feed_dict={images:[img]})
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cv2.imshow("mask prediction 7", prediction[0][:,:,0])
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cv2.imshow("mask prediction 9", prediction[0][:,:,1])
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if cv2.waitKey()&0xFF==ord('q'):
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break
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