51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
import tensorflow as tf
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import numpy as np
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import glob
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import cv2
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import model
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import config
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tab_images=[]
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tab_labels=[]
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def complete_dataset(files, value):
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for image in glob.glob(files):
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img=cv2.imread(image)
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img=cv2.resize(img, (config.size, config.size))
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tab_images.append(img)
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tab_labels.append([value])
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img=cv2.flip(img, 1)
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tab_images.append(img)
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tab_labels.append([value])
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img=cv2.flip(img, 0)
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tab_images.append(img)
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tab_labels.append([value])
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complete_dataset(config.dir_pos+'\\*.png', 1.)
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complete_dataset(config.dir_neg+'\\*.png', 0.)
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tab_images=np.array(tab_images, dtype=np.float32)/255
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tab_labels=np.array(tab_labels, dtype=np.float32)
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index=np.random.permutation(len(tab_images))
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tab_images=tab_images[index]
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tab_labels=tab_labels[index]
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#for i in range(len(tab_images)):
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# cv2.imshow('Camera', tab_images[i])
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# print("Label", tab_labels[i])
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# if cv2.waitKey()&0xFF==ord('q'):
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# quit()
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model=model.model(config.size, 8)
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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model.fit(tab_images,
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tab_labels,
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validation_split=0.05,
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batch_size=64,
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epochs=30)
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model.save('saved_model\\my_model')
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