如果图像具有 (28,28,3) 形状，我如何将其转换为 (28.28,1)?

问题描述

我正在使用手写数字的mnist数据集，并且试图预测自己写的数字.问题是我的数字的形状是 (28,28,3)，而我的神经网络的预期形状是 (28,28,1).如何转换?

I'm using the mnist dataset of handwritten digits and I am trying to predict a digit that I wrote. The problem is that my digit is of shape (28,28,3) and the expected shape for my neural network is (28,28,1). How can I convert it?

我的代码:

import tensorflow as to
from tensorflow import keras
from keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import cv2

data = mnist.load_data()
(x_train, y_train), (x_test, y_test) = data

classes = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

x_train = x_train / 255
x_test = x_test / 255

model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape=(28,28)))
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dense(10, activation='softmax'))

model.compile(
     optimizer='adam',
     loss='sparse_categorical_crossentropy',
     metrics=['accuracy']
)

model.fit(x_train, y_train, epochs=7)
test_loss, test_acc = model.evaluate(x_test, y_test)
print('\nTest Loss:', test_loss)
print('Test accuracy:', test_acc)

img = Image.open("7.jpg").convert('L')
img_array = cv2.imread('7.jpg')
new_array = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)
new_array = cv2.resize(new_array, (28,28))
print(new_array.shape)
print(x_test[0].shape)
plt.imshow(new_array, cmap='gray')
plt.show()

predictions = model.predict(new_array)

plt.grid(False)
plt.imshow(new_array, cmap='gray')
plt.title("Prediction: " + classes[np.argmax(predictions)])
plt.show()

推荐答案

假设 img 的形状为(28，28，3)，您可以执行以下操作:

Assuming img has shape of (28, 28, 3) you can do this:

gray = cv2. cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.expand_dims(gray, 2)

这会将其转换为(28，28，1)的形状

This will convert it to a shape of (28, 28, 1)

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