import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
import tensorflow as tf

# # 加载Fashion-MNIST数据集
# fashion_mnist = tf.keras.datasets.fashion_mnist
# (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
#
# # 数据预处理
# X_train = X_train / 255.0  # 将像素值缩放到0-1之间
# X_test = X_test / 255.0
#
# # 加载模型
# model = load_model('fashion_mnist_model.h5')
#
# # 评估模型
# loss, accuracy = model.evaluate(X_test, y_test)
# print(f'Test Loss: {loss}')
# print(f'Test Accuracy: {accuracy}')


# 图像文件路径
img_path = '运动鞋.png'

# 加载图像并调整大小
img = image.load_img(img_path, target_size=(28, 28), color_mode='grayscale')

# 将PIL图像转换为NumPy数组
img_array = image.img_to_array(img) / 255.0

# 添加批量维度
img_array = np.expand_dims(img_array, axis=0)

# 显示图像
plt.imshow(img_array[0, :, :, 0], cmap='gray')
plt.show()

# 打印图像数组形状
print(f'Image array shape: {img_array.shape}')

# 加载训练好的模型
model = load_model('fashion_mnist_model.h5')

# 进行预测
predictions = model.predict(img_array)
predicted_class = np.argmax(predictions, axis=1)
print(f'Predicted class: {predicted_class[0]}')