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pythonnumpytensorflowkerastensorflow-datasets
farshad1123
farshad1123

Reputation: 325

How can i convert mnist data to RGB format?

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].

Upvotes: 5

Views: 7082

Answers (3)

devforfu
devforfu

Reputation: 1612

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)

Upvotes: 0

Aqwis01
Aqwis01

Reputation: 66

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)

Upvotes: 4

DMolony
DMolony

Reputation: 643

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))

Upvotes: 0

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