forked from s452751/AI_PRO
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
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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import pandas as pd
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from matplotlib.pyplot import imshow
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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 = 100
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#POTATOES
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image_data = []
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label_data = []
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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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image_data.append([pic])
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label_data.append(1)
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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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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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image_data.append([pic])
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label_data.append(0)
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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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np.save(os.path.join('neural_network','image-dataset'),np.array(image_data))
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np.save(os.path.join('neural_network','label-dataset'),np.array(label_data))
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saved_images = np.load(os.path.join('neural_network','image-dataset.npy'))
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print(saved_images.shape)
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plt.imshow(saved_images[0].reshape(size,size,3))
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plt.imshow(np.array(image_data[0]).reshape(size,size,3))
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plt.show() |