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78 lines
2.2 KiB
78 lines
2.2 KiB
from sklearn.utils import shuffle
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import numpy as np
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import os
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import cv2
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import utils
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def read_image(path):
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image = cv2.imread(path)
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try:
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image = cv2.resize(image, (utils.IMAGE_WIDTH, utils.IMAGE_HEIGHT))
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normalizer_image = image / 255.0 - 0.5
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return normalizer_image
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except:
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print(path)
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def read_flowers(training=True):
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flowers_dir = "./flowers_images/"
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if training:
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tao = flowers_dir + "training/tao/"
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mei = flowers_dir + "training/mei/"
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mudan = flowers_dir + "training/mudan/"
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qianniu = flowers_dir + "training/qianniu/"
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meigui = flowers_dir + "training/meigui/"
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moli = flowers_dir + "training/moli/"
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pugongying = flowers_dir + "training/pugongying/"
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else:
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tao = flowers_dir + "test/tao/"
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mei = flowers_dir + "test/mei/"
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mudan = flowers_dir + "test/mudan/"
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qianniu = flowers_dir + "test/qianniu/"
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meigui = flowers_dir + "test/meigui/"
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moli = flowers_dir + "test/moli/"
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pugongying = flowers_dir + "test/pugongying/"
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images = []
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labels = []
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image_name = []
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for f in os.listdir(tao):
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images.append(read_image(tao + f))
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labels.append(utils.TAO)
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image_name.append(f)
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for f in os.listdir(mei):
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images.append(read_image(mei + f))
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labels.append(utils.MEI)
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image_name.append(f)
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for f in os.listdir(mudan):
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images.append(read_image(mudan + f))
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labels.append(utils.MUDAN)
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image_name.append(f)
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for f in os.listdir(qianniu):
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images.append(read_image(qianniu + f))
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labels.append(utils.QIANNIU)
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image_name.append(f)
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for f in os.listdir(meigui):
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images.append(read_image(meigui + f))
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labels.append(utils.MEIGUI)
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image_name.append(f)
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for f in os.listdir(moli):
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images.append(read_image(moli + f))
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labels.append(utils.MOLI)
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image_name.append(f)
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for f in os.listdir(pugongying):
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images.append(read_image(pugongying + f))
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labels.append(utils.PUGONGYING)
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image_name.append(f)
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return shuffle(np.array(images), np.array(labels), np.array(image_name))
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