forked from s452751/AI_PRO
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
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import os
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import cv2
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import matplotlib
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import numpy as np
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import matplotlib.pyplot as plt
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path_potatoes = 'neural_network\\images\\potatoes'
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path_beetroot = 'neural_network\\images\\beetroot'
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size = 250
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#POTATOES
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training_data_potatoes = []
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for img in os.listdir(path_potatoes):
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pic = cv2.imread(os.path.join(path_potatoes,img))
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pic = cv2.cvtColor(pic,cv2.COLOR_BGR2RGB)
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pic = cv2.resize(pic,(size,size))
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training_data_potatoes.append([pic])
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np.save(os.path.join('neural_network','potatoes-dataset'),np.array(training_data_potatoes))
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saved_potatoes = np.load(os.path.join('neural_network','potatoes-dataset.npy'))
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#BEETROOT
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training_data_beetroot = []
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for img in os.listdir(path_beetroot):
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pic = cv2.imread(os.path.join(path_beetroot,img))
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pic = cv2.cvtColor(pic,cv2.COLOR_BGR2RGB)
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pic = cv2.resize(pic,(size,size))
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training_data_beetroot.append([pic])
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np.save(os.path.join('neural_network','beetroot-dataset'),np.array(training_data_beetroot))
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saved_potatoes = np.load(os.path.join('neural_network','beetroot-dataset.npy'))
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# for i in range(5):
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# plt.imshow(np.array(training_data_potatoes[i]).reshape(size,size,3))
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# plt.show()
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