41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
from pathlib import Path
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import tensorflow as tf
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from PIL import Image
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import numpy as np
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import matplotlib.pyplot as plt
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from train import CNN
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class Predict(object):
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def __init__(self):
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latest = tf.train.latest_checkpoint('./ckpt')
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self.cnn = CNN()
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# 恢复网络权重
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self.cnn.model.load_weights(latest)
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def predict(self, image_path):
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# 以黑白方式读取图片
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img = Image.open(image_path).convert('L')
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img = np.reshape(img, (28, 28, 1)) / 255.
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x = np.array([1 - img])
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y = self.cnn.model.predict(x)
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# 因为x只传入了一张图片，取y[0]即可
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# np.argmax()取得最大值的下标，即代表的数字
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print(image_path)
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# print(y[0])
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print(' -> Predict digit', np.argmax(y[0]))
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plt.figure("Image") # 图像窗口名称
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plt.imshow(img)
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plt.axis('on') # 关掉坐标轴为 off
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plt.title(np.argmax(y[0])) # 图像题目 # 必须有这个，要不然无法显示
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plt.show()
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if __name__ == "__main__":
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app = Predict()
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images_dir = Path(__file__).resolve().parent.parent / 'test_images'
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app.predict(images_dir / '0.png')
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app.predict(images_dir / '1.png')
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app.predict(images_dir / '4.png')
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