34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
import cv2
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import numpy as np
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import tensorflow as tf
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from L42Project import ia as LPia
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mnist_test_images=np.fromfile("mnist/t10k-images-idx3-ubyte", dtype=np.uint8)[16:].reshape(-1, 28, 28, 1)/255
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mnist_test_labels=np.eye(10)[np.fromfile("mnist/t10k-labels-idx1-ubyte", dtype=np.uint8)[8:]]
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tf.reset_default_graph()
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np.set_printoptions(formatter={'float': '{:0.3f}'.format})
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with tf.Session() as s:
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saver=tf.train.import_meta_graph('./mon_vgg/modele.meta')
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saver.restore(s, tf.train.latest_checkpoint('./mon_vgg/'))
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graph=tf.get_default_graph()
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images=graph.get_tensor_by_name("images:0")
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sortie=graph.get_tensor_by_name("sortie:0")
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is_training=graph.get_tensor_by_name("is_training:0")
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while True:
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image=cv2.imread("/home/laurent/chiffre.png", cv2.IMREAD_GRAYSCALE)
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image=cv2.resize(image, (28, 28))
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image=image.reshape(28, 28, 1)/255
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test_images=[]
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test_images.append(image)
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test_images=np.asarray(test_images)
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#cv2.imshow('image', test_images[0])
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for i in mnist_test_images[0:10]:
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#for i in test_images:
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prediction=s.run(sortie, feed_dict={images: [i], is_training: False})
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print(prediction, np.argmax(prediction))
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#if cv2.waitKey()==ord('q'):
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# break
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break
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cv2.destroyAllWindows()
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