30 lines
880 B
Python
30 lines
880 B
Python
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import numpy as np
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from sklearn.utils import shuffle
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import cv2
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import common
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import dataset
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tab_panneau, tab_image_panneau=common.lire_images_panneaux(common.dir_images_panneaux, common.size)
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tab_images=np.array([]).reshape(0, common.size, common.size, 3)
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tab_labels=[]
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id=0
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for image in tab_image_panneau:
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lot=dataset.create_lot_img(image, 1000)
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tab_images=np.concatenate([tab_images, lot])
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tab_labels=np.concatenate([tab_labels, np.full(len(lot), id)])
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id+=1
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tab_panneau=np.array(tab_panneau)
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tab_images=np.array(tab_images, dtype=np.float32)/255
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tab_labels=np.array(tab_labels).reshape([-1, 1])
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tab_images, tab_labels=shuffle(tab_images, tab_labels)
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for i in range(len(tab_images)):
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cv2.imshow("panneau", tab_images[i])
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print("label", tab_labels[i], "panneau", tab_panneau[int(tab_labels[i])])
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if cv2.waitKey()&0xFF==ord('q'):
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quit()
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