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36 lines
1.1 KiB
36 lines
1.1 KiB
from PIL import Image
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import numpy as np
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import tensorflow as tf
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import mnist_cnn3
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model_save_path = 'E:\\Python_touge\\MNIST\\venv\\checkpoint\\mnist_cnn3.ckpt'
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model = mnist_cnn3.creat_model()
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model.load_weights(model_save_path)
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# preNum = int(input("input the number of test pictures:"))
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def prediction(str):
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for i in range(1):
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image_path = str
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img = Image.open(image_path)
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img = img.resize((28, 28), Image.Resampling.LANCZOS)
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img_arr = np.array(img.convert('L'))
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#
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# for i in range(28):
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# for j in range(28):
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# if img_arr[i][j] < 200:
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# img_arr[i][j] = 255
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# else:
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# img_arr[i][j] = 0
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img_arr = img_arr / 255.0
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x_predict = img_arr[tf.newaxis, ...]
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result = model.predict(x_predict)
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pred = tf.argmax(result, axis=1)
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if result[(0, pred)] <= 0.8:
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str = "无法判断，请重新输入！"
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print(result)
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return str
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else:
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return pred.numpy()[0]
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