How can i convert mnist data to RGB format?

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I am trying to convert MNIST dataset to RGB format, the actual shape of each image is (28, 28), but i need (28, 28, 3).

import numpy as np
import tensorflow as tf

mnist = tf.keras.datasets.mnist
(x_train, _), (x_test, _) = mnist.load_data()

X = np.concatenate([x_train, x_test])
X = X / 127.5 - 1

X.reshape((70000, 28, 28, 1))

tf.image.grayscale_to_rgb(
    X,
    name=None
)

But i get the following error:

ValueError: Dimension 1 in both shapes must be equal, but are 84 and 3. Shapes are [28,84] and [28,3].

You should store the reshaped 3D [28x28x1] images in an array:

X = X.reshape((70000, 28, 28, 1))

When converting, set an other array to the return value of the tf.image.grayscale_to_rgb() function :

X3 = tf.image.grayscale_to_rgb(
X,
name=None
)

Finally, to plot out one example from the resulting tensor images with matplotlib and tf.session():

import matplotlib.pyplot as plt

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    image_to_plot = sess.run(image)
    plt.figure()
    plt.imshow(image_to_plot)
    plt.grid(False)

The complete code:

import numpy as np
import tensorflow as tf

mnist = tf.keras.datasets.mnist
(x_train, _), (x_test, _) = mnist.load_data()

X = np.concatenate([x_train, x_test])
X = X / 127.5 - 1

# Set reshaped array to X 
X = X.reshape((70000, 28, 28, 1))

# Convert images and store them in X3
X3 = tf.image.grayscale_to_rgb(
    X,
    name=None
)

# Get one image from the 3D image array to var. image
image = X3[0,:,:,:]

# Plot it out with matplotlib.pyplot
import matplotlib.pyplot as plt

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    image_to_plot = sess.run(image)
    plt.figure()
    plt.imshow(image_to_plot)
    plt.grid(False)

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If you print the shape of X before tf.image.grayscale_to_rgb you will see the output dimension is (70000, 28, 28). Inputs to tf.image.grayscale must have size 1 as it's final dimension.

Expand the final dimension of X to make it compatible with the function

tf.image.grayscale_to_rgb(tf.expand_dims(X, axis=3))

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In addition to @DMolony and @Aqwis01 answers, another simple solution could be using numpy.repeat method to duplicate the last dimension of your tensor several times:

X = X.reshape((70000, 28, 28, 1))
X = X.repeat(3, -1)  # repeat the last (-1) dimension three times
X_t = tf.convert_to_tensor(X)
assert X_t.shape == (70000, 28, 28, 3)

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Arlen0615/Convert-own-data-to-MNIST-format: This project , This project is convert own data to MNIST format. It may help If your own data is RGB, then mnist2 will output [batch_size, heigh x width, channels]. In normal� rgb. Raw red, green, and blue samples. This is a color model display format that uses the three primary colors (blue, red and green) to create an image. Each color can have up to 255 gradations, allowing for color depths of up to 48 bits. RGB supports the display of 16,777,216 colors.


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