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].
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
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)
Getting started with GANs Part 2: Colorful MNIST, We apply a simple technique to map MNIST images to RGB. Example images from the MNIST dataset mixed with generated images. The real batch_resized, batch_resized], axis=3) # Convert the MNIST images to binary� Convert-own-data-to-MNIST-format. MNIST is a famous test data with deep learning on the web. This project is convert own data to MNIST format. It may help you to quickly test new DL model without modify too much code.
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
Grayscale to RGB transform - vision, Example: Let xx be some image of size 28x28, then, In : xx.shape The MNIST dataset doesn't convert the images to RGB, but to a grayscale image. Have a� Convert Images to the MNIST database format ? Do anyone have the steps that I need to follow to convert an image to the idx.ubyte format (used for MNIST database) or have any code that could help
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.
Transform Grayscale Images to RGB Using Python's Matplotlib, Data pre-processing is critical for computer vision applications, and properly converting grayscale images to the RGB format expected by� The first time you learn to train Neural Network, you try to train a Model for Image recognition using MNIST format. And later, you want to use your images to train your model, and how can you do it? With Python script, it’s very easy! Step 1: Create folder tree: